#  @yesnoerror yesnoerror Yesnoerror ($yne) has bridged its token to @base, a new platform that enables seamless interaction between tokens. The team has partnered with @chainlink and @flaunchgg to set up a liquidity pool and list $yne on their launchpad, making it more accessible. The yesnoerror platform, which uses AI to audit research and spot alpha and errors, is now in public beta and available to all. ### Engagements: [-----] [#](/creator/twitter::1869287034700877824/interactions)  - [--] Week [------] -67% - [--] Month [------] +85% - [--] Months [-------] -86% - [--] Year [---------] +104% ### Mentions: [--] [#](/creator/twitter::1869287034700877824/posts_active)  - [--] Week [---] -1.70% - [--] Month [---] -2.50% - [--] Months [---] -83% - [--] Year [-----] +517% ### Followers: [------] [#](/creator/twitter::1869287034700877824/followers)  - [--] Week [------] +0.18% - [--] Month [------] +6% - [--] Months [------] +1.70% - [--] Year [------] +6% ### CreatorRank: [---------] [#](/creator/twitter::1869287034700877824/influencer_rank)  ### Social Influence **Social category influence** [cryptocurrencies](/list/cryptocurrencies) 94.87% [technology brands](/list/technology-brands) 3.42% [finance](/list/finance) 3.42% [nfts](/list/nfts) 1.71% [currencies](/list/currencies) 0.85% **Social topic influence** [yesnoerror](/topic/yesnoerror) #2, [ai](/topic/ai) 18.8%, [up to](/topic/up-to) 13.68%, [llm](/topic/llm) #688, [secret](/topic/secret) 6.84%, [paper](/topic/paper) 5.98%, [realtime](/topic/realtime) 5.98%, [math](/topic/math) 5.13%, [$yne](/topic/$yne) 4.27%, [hidden](/topic/hidden) 4.27% **Top accounts mentioned or mentioned by** [@claude_memory](/creator/undefined) [@n0commas](/creator/undefined) [@bankrbot](/creator/undefined) [@edbsouza11043](/creator/undefined) [@whisprnews](/creator/undefined) [@solanahub_](/creator/undefined) [@0xzyxar](/creator/undefined) [@solana_daily](/creator/undefined) [@artoriatech](/creator/undefined) [@10](/creator/undefined) [@base](/creator/undefined) [@pingftw](/creator/undefined) [@maf1a_rajput](/creator/undefined) [@ruslan30009](/creator/undefined) [@solana](/creator/undefined) [@chainlink](/creator/undefined) [@flaunchgg](/creator/undefined) [@swarms_corp](/creator/undefined) [@sal_ash_](/creator/undefined) [@janeide540325](/creator/undefined) **Top assets mentioned** [yesnoerror (YNE)](/topic/yesnoerror) [Solana (SOL)](/topic/solana) [Chainlink (LINK)](/topic/chainlink) [Voxels (voxels)](/topic/voxels) [Bitcoin (BTC)](/topic/bitcoin) ### Top Social Posts Top posts by engagements in the last [--] hours "AIRS-Bench is here: a 20-task benchmark that asks LLM agents to plan code experiment and iterateend-to-endon real problems from recent ML papers with zero baseline code. The results Greedy tree-search scaffolds hit 97% valid submissions and beat human SOTA on 4/20 tasks (text similarity entailment coreference rideshare forecasting). But overall agents lag humans on [--] tasks and average only 59% end-to-end successrevealing huge headroom and hard engineering limits. AIRS-Bench is fully open reproducible and contamination-controlled. Its a new yardstick for autonomous research agents with 45%" [X Link](https://x.com/yesnoerror/status/2021328842258895250) 2026-02-10T21:02Z 28.2K followers, [---] engagements "This is the only official contract for yesnoerror / $YNE: 7D1iYWfhw2cr9yBZBFE6nZaaSUvXHqG5FizFFEZwpump" [X Link](https://x.com/yesnoerror/status/1871125048934924568) 2024-12-23T09:25Z 28.2K followers, 163.1K engagements "@solana @base @chainlink The $YNE @base contract address is 0xe2f9db0186b13668aec9fe0e15dbd13004ed8d6f" [X Link](https://x.com/yesnoerror/status/1964816337399894185) 2025-09-07T22:21Z 28.2K followers, [----] engagements "@solana @base @chainlink $YNE @solana contract address is 7D1iYWfhw2cr9yBZBFE6nZaaSUvXHqG5FizFFEZwpump" [X Link](https://x.com/yesnoerror/status/1964830582858478011) 2025-09-07T23:18Z 28.2K followers, [----] engagements "PathAgent is a new agentic framework that brings LLM-style reasoning to whole-slide pathology imageswith full transparency. Instead of black-box slide-level guesses it zooms explores and writes out a detailed chain-of-thought just like a real pathologist. Zero-shot training-free and plug-and-play PathAgent beats specialist systems on five benchmarks: 55.7% accuracy on SlideBench-VQA (37% above baselines) and 56.3% on WSI-VQA with open-ended answers that are both accurate and interpretable. The real kicker: every diagnosis is linked to explicit visual evidence and a readable decision trail." [X Link](https://x.com/yesnoerror/status/1993062659390857590) 2025-11-24T21:02Z 28.2K followers, [----] engagements "Video diffusion models just unlocked a new level: they can be their own reward modelsno vision-language models or pixel-space supervision needed. This paper introduces Process Reward Feedback Learning (PRFL) which fine-tunes video generators entirely in latent space. The result: sharper motion and better anatomy with up to +56 and +21.5 point gains on VBench benchmarks. PRFL also trains at least [---] faster and fits into [--] GB VRAM where older methods crash. Human judges chose PRFL videos in 6367% of head-to-head comparisons against strong baselines. The secret Rewards sampled at all timesteps" [X Link](https://x.com/yesnoerror/status/1994512112702370248) 2025-11-28T21:01Z 28.2K followers, [---] engagements "Chain-of-thought prompting is bulkywhat if your model could decide when to stop thinking internally This new paper teaches Llama 3.2-Instruct to dynamically cut off latent reasoning using a binary stop head and RL. The result Average reasoning steps drop from [--] to just 3.8over 50% shorterwithout sacrificing GSM8K-Aug accuracy. Longer chains still kick in for tough questions but easy ones get trimmed slashing compute and inference cost. Attempts at fancier distillation actually underperform the simple approach. A promising step toward efficient adaptive LLMs that only think as hard as they" [X Link](https://x.com/yesnoerror/status/1994693311311868332) 2025-11-29T09:01Z 28.2K followers, [----] engagements "LFM2 is a new family of open AI models built from the ground up for lightning-fast privacy-preserving performance on phones laptops and edge devices. Instead of heavy attention stacks LFM2 uses mostly gated short convolutions plus a handful of grouped-query attention layerscutting latency and memory in half versus attention-heavy models. LFM2-2.6B scores 79.6% on IFEval and 82.4% on GSM8K while decoding [--] faster than Qwen3-4B and Gemma-4B on CPU. The 8.3B MoE variant matches or beats larger models at just 1.5B active parameters (84.4% GSM8K 37.4% MMLU-Pro). Its not just text: LFM2-VL-3B" [X Link](https://x.com/yesnoerror/status/1995599280858386534) 2025-12-01T21:01Z 28.2K followers, [---] engagements "Glance flips the script on diffusion models: 5x faster image generation near-zero training cost and no loss in visual quality. Instead of retraining whole student models Glance plugs in two tiny LoRA adapters (Slow & Fast) each handling a different denoising phase. The trick Just one image one hour on a single V100 and the big model stays frozen. On [--] benchmarks Glance hits 9299% of teacher quality in only [---] steps (vs. 50). Side-by-sides show it nails both global layout and fine detaileven in new domains with one-shot adaptation. If you thought diffusion was too slow for real-time or" [X Link](https://x.com/yesnoerror/status/1996142863915114711) 2025-12-03T09:01Z 28.2K followers, [---] engagements "Radiance Meshes are hereand they might just change neural rendering. Instead of splatting Gaussians scenes are built from millions of see-through tetrahedra (up to 15M fit in 24GB VRAM) using Delaunay triangulation. The result Exact flicker-free rendering at speeds 32% higher than 3D Gaussian Splatting and a ray tracer that's 17% faster than Radiant Foam. No more depth-sorting errors. Every tetrahedron gets closed-form integrationso you get neural-field quality but with classic mesh compatibility. Works instantly for editing physics even fisheye lenses. [------] FPS at 7201080p with" [X Link](https://x.com/yesnoerror/status/1996686450960523629) 2025-12-04T21:02Z 28.2K followers, [---] engagements "Most AI ethics debates miss what makes generative AI truly different. This new paper argues its unique power is making tech feel "as if" it's humanan affordance that changes everything about responsibility privacy bias and even what authorship means. It digs into how GAIs outputs create quasi-social bonds new forms of manipulation and raise tough questions about who gets credit (or blame) for AI-assisted work. The author shows why ethical analysis should focus less on machine "intelligence" and more on how these systems reshape our relationships and judgments. If you care about the real risks" [X Link](https://x.com/yesnoerror/status/1997049208352756158) 2025-12-05T21:03Z 28.2K followers, [---] engagements "This is the definitive guide to 3D scene representations for robotics. It benchmarks classic maps (point clouds voxels SDFs) fast photorealistic neural models (NeRF 3D Gaussian Splatting) and the emerging era of tokenized foundation models that blend geometry with language. Key insights: 3DGS is the first neural map to achieve [--] FPS photorealistic rendering making dense SLAM and planning viable in real time. Feed-forward transformers like DUSt3R and enable one-shot token-based mapping over hundreds of imagesno iterative optimization needed. Foundation models (Scene-LLM NLMap) fuse scene" [X Link](https://x.com/yesnoerror/status/1997230026312343899) 2025-12-06T09:01Z 28.2K followers, [----] engagements "This new paper proposes a Unix for context for LLM agentsevery document tool API or memory becomes a mountable file in a governed file system. Instead of scattered prompts and ad-hoc memory agents get a persistent auditable context repository with versioning access control and full traceability. The AIGNE framework implements a 3-stage pipelineContext Constructor Updater Evaluatorto assemble stream and verify just the right knowledge within token limits. Demonstrated with a memory chatbot and a GitHub agent this architecture delivers maintainable industry-ready GenAI thats finally auditable" [X Link](https://x.com/yesnoerror/status/1997954941348937762) 2025-12-08T09:02Z 28.2K followers, [---] engagements "GRAPE is a new framework that unifies how transformers "know" the position of each tokencombining the strengths of RoPE (rotations) and ALiBi/FoX (additive biases) into a single algebraic recipe. Why it matters: No more picking sides: both mechanisms now fit into one principled toolbox with closed-form efficient math. RoPE and ALiBi become special cases; new variants are easy to add and mix. Faster convergence and 1-1.5% higher accuracy than all baselines in 50B-token Llama pretraining and [--] downstream tasks. Path-integral extension enables content-dependent stable positional biases with" [X Link](https://x.com/yesnoerror/status/1998317188264952240) 2025-12-09T09:01Z 28.2K followers, [---] engagements "RoPE++ is a new twist on transformer position encoding: instead of discarding half the math it leverages both real and imaginary parts of rotary embeddings to better capture long-range dependencies. On benchmarks up to 64k tokens RoPE++ delivers up to +2 points over standard RoPE and its EH variant halves KV memory while matching baseline accuracyplus 1015% faster decoding. Imaginary heads turn out to matter most for very long context recall. Compatible with FlashAttention and all the latest context tricks. The code is out now. Get the full analysis here: // alpha identified // $YNE" [X Link](https://x.com/yesnoerror/status/1998498380272599199) 2025-12-09T21:01Z 28.2K followers, [---] engagements "A new paper just cracked a classic stats puzzle: can you *prove* that more data always means less error for maximum-likelihood estimators For decades this was an open problemeven for basic Gaussians. They nail it: once youve passed a minimal data threshold (n d+2 for Gaussians with unknown mean/covariance) the forward-KL risk of the MLE is not just decreasing but *completely monotone*each new sample helps with an explicit formula in terms of the digamma and trigamma functions. Bonus: for any regular exponential family (think Gaussians Poissons Gammas) reverse-KL risk is also guaranteed to go" [X Link](https://x.com/yesnoerror/status/1999766796883124301) 2025-12-13T09:02Z 28.2K followers, [---] engagements "DeMapGS is a major step forward for 3D graphics: it fuses the photo-realism of Gaussian Splatting with the editability of mesh-based models. By anchoring every splat to a deformable mesh DeMapGS lets you bend repaint or re-pose objectsand the splats follow so edits look sharp and consistent from every view. The trick Alternating between 2D and 3D splatting during training plus a novel gradient diffusion scheme for smooth stable deformations. On Sketchfab scans DeMapGS matches or beats state-of-the-art mesh accuracy while rendering [--] faster than previous baselines. It also exports high-res" [X Link](https://x.com/yesnoerror/status/2000129122622669270) 2025-12-14T09:01Z 28.2K followers, [----] engagements "STARCaster is a breakthrough in talking-head AIone model that animates faces to speech and lets you move the camera around all without relying on explicit 3-D reconstruction. It builds on Arc2Face but inflates to video with temporal transformers and new cross-attention routes for identity audio and viewpoint. A clever self-forcing training scheme means more natural less frozen expressions over long clips. On TH-1KH and Hallo3 it sets new records: FID [----] (prev best 27.2) FVD [---] (prev 195) highest pose diversity and top lip-sync (LSE-D 8.72). For true 3-D-aware video it beats NeRF/tri-plane" [X Link](https://x.com/yesnoerror/status/2002121951372804218) 2025-12-19T21:00Z 28.2K followers, [---] engagements "LAMER is a breakthrough meta-RL framework that finally teaches LLM agents to *explore* and adapt not just repeat the same mistakes. By training over *sequences* of episodes and letting the agent reflect in natural language after each try LAMER unlocks genuine trial-and-error learningno test-time fine-tuning needed. Results: +11% pass@3 on Sokoban +19% on MineSweeper +14% on WebShop vs strong RL baselines all with the same trajectory budget. Higher entropy means more diverse human-like exploration and it generalises better to harder or unseen tasks (+23% on out-of-distribution ALFWorld). This" [X Link](https://x.com/yesnoerror/status/2003209103473098789) 2025-12-22T21:00Z 28.2K followers, [---] engagements "This paper uncovers a hidden superpower in pretrained autoregressive models: their mid-layer activations linearly encode multi-step options that can be triggered with simple linear controlsno reward labels needed. A self-supervised metacontroller learns when and how to switch between these latent options letting RL operate on entire sub-goal sequences instead of tokens. The result On long sparse-reward tasks internal RL solves 80% of unseen chainswhile token-level RL and strong baselines never escape zero. The trick: freeze the base model act only in the low-dimensional latent code space and" [X Link](https://x.com/yesnoerror/status/2004115426779824377) 2025-12-25T09:02Z 28.2K followers, [---] engagements "Why do neural nets start simpleand only later get complex no matter the architecture This new theory pins it on saddle-to-saddle learning: networks follow a hidden path hopping through a nested hierarchy where every simple solution is a saddle point inside a wider model. The result Stage-like learning: first the network acts like its tiny (single neuron kernel or head) only adding complexity as needed. The authors prove this for fully-connected convolutional and attention models and show exactly when (and why) those long plateaus and sudden jumps in learning occur. Their predictions for" [X Link](https://x.com/yesnoerror/status/2004296583555268963) 2025-12-25T21:01Z 28.2K followers, [---] engagements "StoryMem is a breakthrough for long-form video generation: it transforms a single-shot diffusion model into a multi-shot storyteller using a compact visual memory and clever keyframe filtering. No giant dataset neededjust lightweight LoRA fine-tuning. With memory-augmented context StoryMem nails narrative consistency preserving characters and style shot after shot. On the new ST-Bench it boosts cross-shot consistency by up to 29% over the base and 9% over HoloCine with human raters preferring its narrative flow and visual quality. If you want minute-long coherent video stories from text" [X Link](https://x.com/yesnoerror/status/2004477781833306337) 2025-12-26T09:01Z 28.2K followers, [---] engagements "UniPR-3D just set a new state of the art in visual place recognition by fusing 2-D texture and 3-D geometry tokens from multiple imagesfinally giving robots and AR apps the robustness they need. On Oxford RobotCar with a strict [--] m threshold UniPR-3D hits 95.4% R@1 vs. 90.5% for the previous best (CaseVPR). On MSLS-Challenge it edges out the former leader with 74.3% R@1 (SALAD had 73%). Even the toughest benchmarks show 57% jumps. The trick: a geometry-grounded transformer backbone (VGGT) + DINOv2 with specialized aggregation heads (GeM for class/register Optimal Transport for patch tokens)" [X Link](https://x.com/yesnoerror/status/2005021292261441864) 2025-12-27T21:01Z 28.2K followers, [---] engagements "This paper is a wake-up call for AI in drug discovery. Turns out models trained on public chemistry data like ChEMBL can often guess which chemist or lab made a moleculewith 60% top-5 accuracy across [----] authorsjust from structure. Even wilder: a model that only sees who likely made this and the protein target predicts bioactivity nearly as well as one that sees the full molecule (AUROC [-----] vs 0.656). The punchline: much of what we thought was understanding chemistry is actually shortcutting by learning chemist intent and lab habits. If we dont fix this reported accuracy is inflated and" [X Link](https://x.com/yesnoerror/status/2005202560546677000) 2025-12-28T09:01Z 28.2K followers, [---] engagements "RoboSafe is a new safety guardrail for VLM-powered robots that actually works. Instead of static rules or prompt hacks it runs executable logic over both what the agent just did and what its about to dousing a hybrid safety memory and auto-generated Python predicates. The result: hazardous actions are cut by 36.8% versus the best prior defense with 90% accurate refusals and only a 7% dip in task completion. Adds just 0.02s per step and blocks real-world risks (like swinging a knife) even when jailbreak prompts get through. No retraining no model tweaksjust bolt RoboSafe onto existing agents" [X Link](https://x.com/yesnoerror/status/2005383731570250040) 2025-12-28T21:01Z 28.2K followers, [---] engagements "SWE-RM is a breakthrough for coding agents: it ditches brittle unit tests and judges code fixes with a fine-grained execution-free reward model. The secret A 30B MoE LLM trained to emit YES/NO calibrated on 20k+ samples with a 2:1 positive/negative split and 256k-token context. This nails three key metricsTTS AUC ECEfor robust RL. Results: Qwen3-Coder-Flash pass@1 jumps from 51.6% to 62.0% and Qwen3-Coder-Max from 67.0% to 74.6% on SWE-Bench Verifiednew open-source SOTA. Hybrid RL reward (SWE-RM + tests) adds another [--] points and speeds up learning. Why it matters: SWE-RM enables scalable" [X Link](https://x.com/yesnoerror/status/2005564940740968450) 2025-12-29T09:01Z 28.2K followers, [---] engagements "Reloc-VGGT rewrites the rules for camera localization. Instead of guessing a pose from pairs of images and averaging later it fuses geometry from multiple reference views earlyinjecting pose tokens right into the transformer backbone. The result Real-time state-of-the-art accuracy in both indoor and outdoor scenes without any scene-specific tuning. Sparse Mask Attention slashes attention cost from quadratic to linear delivering 35% faster inference (down to 3.14s for [--] frames) with almost no accuracy loss. On ScanNet1500 it beats Reloc3R and runs [--] faster than classic SfM. Median" [X Link](https://x.com/yesnoerror/status/2005746261366821131) 2025-12-29T21:02Z 28.2K followers, [---] engagements "What if LLMs could learn from what they're readingwhile they're reading it This new paper shows it works. By turning long-context modeling into a continual learning problem a meta-learned Transformer updates its own weights at test time compressing 100K-token contexts into memory with constant-time inference. TTT-E2E matches or beats full-attention perplexity across 8K128K contexts stays [---] faster at 128K and needs no extra architecture tricks. Forget quadratic attention: just let your model keep adapting as it reads. The catch Exact-detail retrieval still favors full attention and training" [X Link](https://x.com/yesnoerror/status/2005927374207029693) 2025-12-30T09:02Z 28.2K followers, [---] engagements "Yume [---] is herea new model that lets you walk through an AI-generated world in real time from just a text or image prompt. Unlike previous systems it keeps memory and compute stable no matter how long you explore and supports instant text edits mid-video (A ghost appeared). Key breakthroughs: Dual compression (TSCM) means hundreds of historical frames dont slow things down or degrade quality. 4-step Self-Forcing distillation cuts sampling time [--] vs baselines hitting [--] fps at 540p on a single A100. Splits prompts into whats happening and what you do for efficient live keyboard control. On" [X Link](https://x.com/yesnoerror/status/2006108535000600638) 2025-12-30T21:02Z 28.2K followers, [---] engagements "LiveTalk cracks the real-time video diffusion barrier: a 4-step causal student model matches the visual quality of 48-step baselines but runs [--] faster and slashes first-frame latency from [--] seconds to just [----] secondsall on a single GPU. The recipe Curated high-quality audio/image/text conditioning full ODE convergence before distillation and a bold optimization schedule. Plus Anchor-Heavy Identity Sinks keep avatars visually stable through long conversations. Tested on HDTF AVSpeech and CelebV-HQ LiveTalk outperforms Sora2 and Veo3 in multi-turn video coherence (87th percentile vs 72/25)" [X Link](https://x.com/yesnoerror/status/2006289714035249241) 2025-12-31T09:01Z 28.2K followers, [---] engagements "This paper reframes continual learning from the ground up: what if your agent is literally embedded in a world thats always bigger than it is No contrived memory or compute capsjust physics. They formalize universal-local environments (think: Game of Life as the substrate) embed agents as finite automatons and introduce a new objectiveinteractivity: the algorithmic complexity of your future moves minus whats predictable from your past. If you ever stop adapting youre provably suboptimal. Heres the twist: deep linear networks scale interactivity as their size grows but deep ReLU nets" [X Link](https://x.com/yesnoerror/status/2006470922295799864) 2025-12-31T21:02Z 28.2K followers, [---] engagements "OpenPBR is hereand it might become the standard material model for VFX animation and design. Think: one uber-shader that lets artists and engines speak the same language with physically-accurate layers (metal subsurface coat fuzz thin films) and pixel-perfect asset interchange across renderers. Key results: - The EON diffuse model wipes out up to 70% of rough-surface energy loss and slashes sampling variance by 10x vs. cosine. - F82-Tint Fresnel matches real metal edge colors with 1% RMS error (across 30+ metals). - Coat darkening tracks ground-truth light transport within 2% accuracy. All" [X Link](https://x.com/yesnoerror/status/2006652087921233941) 2026-01-01T09:01Z 28.2K followers, [---] engagements "Residual connections are the backbone of deep nets but widening them with Hyper-Connections (HC) made models unstable at scale. Enter mHCa geometric fix that projects residual mixing onto the Birkhoff polytope (doubly-stochastic matrices) restoring norm-preserving signal flow. The result Exploding/vanishing gradients disappear. In a 27B MoE Transformer mHC cut the maximal signal amplification from [----] (HC) to under [--] ran with only 6.7% overhead and delivered +27% accuracy on benchmarks (+2.1% BBH +2.3% DROP). mHC isnt just a patchit's a general recipe for marrying expressive connections with" [X Link](https://x.com/yesnoerror/status/2006833295540015444) 2026-01-01T21:01Z 28.2K followers, [---] engagements "Recursive Language Models (RLMs) just redefined LLM context limits. Instead of stuffing everything into a transformer RLMs let the model write code to inspect slice and recursively call itself on giant promptshandling over [--] million tokens [---] beyond normal context windows. On four tough long-context tasks GPT-5-based RLMs hit 91% on BrowseComp-Plus and 58% F1 on OOLONG-Pairsbeating standard scaffolds by up to [--] points. Median API costs stayed under $1 with recursion adding 1059% accuracy on dense reasoning. No retraining no architecture changes and zero loss of input detail. RLMs even" [X Link](https://x.com/yesnoerror/status/2007014623195566171) 2026-01-02T09:02Z 28.2K followers, [---] engagements "Most agentic AI platforms just slap orchestration on LLMs. This new paper argues thats not enoughand introduces the M2 layer: a federated strategies-based architecture that actually makes AI production-ready for B2B. Their M2 platform built by a core team of 4-5 over a decade is already powering algorithmic trading cybersecurity portfolio management and national policyaudited by central banks and adopted by Tier-1 firms. Key finding: 95% of AI projects fail not because of weak models but because companies lack the M2 infrastructure to safely orchestrate and govern AI at scale. The real moat" [X Link](https://x.com/yesnoerror/status/2007195764397650367) 2026-01-02T21:02Z 28.2K followers, [---] engagements "Nested Learning could change how we think about deep learning itself. This new paradigm treats machine learning models as collections of nested multi-level optimization problemseach with its own context flow. The result A more expressive way to design learning algorithms unlocking higher-order in-context learning and real continual learning. Highlights: Shows Adam SGD etc. are really memory modules compressing gradient info Introduces optimizers with deeper memory and more powerful update rules Debuts a self-modifying sequence model that learns its own updates Proposes a continuum memory" [X Link](https://x.com/yesnoerror/status/2007376903699611920) 2026-01-03T09:02Z 28.2K followers, [---] engagements "Turns out Clock and Pizza transformers arent so different after all. This new study breaks open the modular addition debate in interpretability. By modeling the entire group of neurons as a single geometric objecta manifoldthe authors show that both uniform (Pizza) and learnable (Clock) attention architectures converge on the *same* low-dimensional disc not the classic Clock circle. Across [---] one-layer networks and hundreds more multi-layer models two new toolsPhase Alignment Distributions and persistent homologyprove the manifolds are statistically indistinguishable. Key result: the" [X Link](https://x.com/yesnoerror/status/2007558058398912982) 2026-01-03T21:01Z 28.2K followers, [---] engagements "FoundationSLAM is a major leap for real-time 3D mapping. It fuses depth foundation models into a fully-learnable SLAM loop finally delivering both geometric accuracy and real-time speed (18 FPS). The systems hybrid flow network and bi-consistent bundle adjustment cut trajectory and surface errors by up to 20% over DROID-SLAM with best-in-class ATE (0.0190.024m) and Chamfer (0.0470.048m) on four public datasets. Reliability masks further refine the hardest pixels boosting robustness on everything from indoor AR to drone mappingno tuning needed for new domains. This closes the gap between fast" [X Link](https://x.com/yesnoerror/status/2007920568939548834) 2026-01-04T21:02Z 28.2K followers, [---] engagements "AdaGaR is a breakthrough for dynamic 3D scene reconstruction from monocular video. It swaps blurry Gaussian splats for energy-stable learnable Gabor kernelscapturing razor-sharp detail *without* flicker or ghosting. Motion stays smooth thanks to curvature-regularized Hermite splines and an adaptive initializer focuses the model on whats moving boosting early quality by +6.78 dB. On Tap-Vid DAVIS AdaGaR hits [-----] dB PSNRalmost [--] dB better than the previous bestwhile training in under [--] hours per clip. Frame interpolation depth-consistent video editing and even stereo synthesisall from a" [X Link](https://x.com/yesnoerror/status/2008282880766538111) 2026-01-05T21:02Z 28.2K followers, [---] engagements "DefVINS is a breakthrough for robots and ARfinally solving visual-inertial odometry in scenes that bend twist and deform. The trick: split the world into (i) a rigid IMU-anchored core and (ii) a lightweight deformation graph for soft moving objects. Deformation is only modeled when the math says its safepreventing drift and overfitting. Quantitatively: DefVINS cuts trajectory error by up to 45% in synthetic extreme-deformation tests and by up to 80% on real cloth sequences with 90% tracking retention. Rigid VIO baselines lose track halfway. Why it matters: Robots folding laundry AR headsets" [X Link](https://x.com/yesnoerror/status/2008464055648870589) 2026-01-06T09:02Z 28.2K followers, [---] engagements "Meet 360DVO: the first deep-learning visual odometry system built for single 360-degree cameras. It combines a distortion-aware SphereResNet (for features that *actually* make sense on equirectangular images) with a spherical bundle adjustment layeryielding robust accurate pose tracking even during fast motion or wild lighting. On real-world tests 360DVO boosts robustness by 50% and accuracy by 37% vs. the best prior methods all while running real-time (27 fps desktop [--] fps Jetson Orin). The team also open-sourced a challenging 20-sequence dataset for the community. This is a leap for" [X Link](https://x.com/yesnoerror/status/2008645302987731148) 2026-01-06T21:02Z 28.2K followers, [---] engagements "This paper rewrites the playbook for enterprise search relevance labeling. By distilling GPT-4os expertise into a compact 3.8B SLM (Phi-3.5 Mini) the authors achieve human-level label quality0.953 NDCG and 63.81% pairwise accuracy even edging out the LLM teacher. Throughput soars to [---] RPM on a single A100 GPU (17 faster) with cost per token dropping [--]. The secret A synthetic pipeline: GPT-4o generates enterprise queries and relevance scores BM25 finds hard negatives and careful query revision plus multi-task tuning maximize gains (diminishing returns beyond 14k examples). The result is a" [X Link](https://x.com/yesnoerror/status/2008826492164186166) 2026-01-07T09:02Z 28.2K followers, [---] engagements "InfiniDepth breaks the resolution barrier in monocular depth estimation. Instead of predicting depth per pixel on a fixed grid it treats depth as a continuous implicit fieldletting you query any location at any resolution. The result: state-of-the-art detail crisp edges and uniform 3D point clouds from a single image. On the new Synth4K 4K benchmark InfiniDepth outperforms seven top methods by up to 10pp on fine-detail metrics and also tops KITTI ETH3D NYUv2 ScanNet and DIODE. The compact 15M-param decoder runs fast (0.16s on 504672) and its adaptive sampling even boosts single-view" [X Link](https://x.com/yesnoerror/status/2009007644351119600) 2026-01-07T21:02Z 28.2K followers, [---] engagements "What if you could measure how much *useful* information an AI can actually learn from datagiven real computational limits This new paper introduces epiplexity: a theory and practical toolkit for quantifying the true learnable structure in any dataset not just its raw entropy. The authors show that: Deterministic computation and smart data ordering can *create* new learnable content for bounded modelscontradicting classical information theory. Epiplexity splits datas info into structural (S_T) and random (H_T) parts letting you track exactly how much a model can pick up at any compute budget." [X Link](https://x.com/yesnoerror/status/2009188859758657751) 2026-01-08T09:02Z 28.2K followers, [---] engagements "Can a shallow neural net ever *really* replace a decision treedown to its transparent boxy structure This new paper says: only if you dont care about whats under the hood. The authors prove that in any dimension the indicator for a trees region is so geometrically jagged that *no* bounded-norm shallow ReLU net can match its confidence score everywhere. Even smooth surrogates (ramp sigmoid) are just as bad in higher dimensions; Gaussian smoothing helps but complexity blows up exponentially with each added feature. However if you only care about the final yes/no answer a special barrier score" [X Link](https://x.com/yesnoerror/status/2009370039070212349) 2026-01-08T21:02Z 28.2K followers, [---] engagements "FusionRoute is a new way to combine specialized LLMsmath code general chatinto one assistant without the cost and inflexibility of giant models. Instead of just picking the best expert per word a clever router both selects and subtly corrects the experts output at every token. Why does this matter The authors show that expert-only routing cant reach optimal answers unless every expert covers every case (which never happens in practice). FusionRoutes complementary tweaks fix this with math to back it up. On Llama-3-8B and Gemma-2-2B FusionRoute tops direct fine-tuning model merging and prior" [X Link](https://x.com/yesnoerror/status/2009551268469961022) 2026-01-09T09:02Z 28.2K followers, [---] engagements "This paper drops a geometric theory of payment channel networksthink Lightning but with a mathematical microscope. The key: not every payment is possible and the set of feasible wealth distributions forms a polytope WG strictly smaller than the full on-chain wealth space. If too many payments fail () off-chain bandwidth S = / tanksmeaning Visa-scale throughput on Bitcoin would need 0.015%. The math is clear: two-party channels trap liquidity but multi-party coinpools expand WG and scale each nodes accessible wealth by k/n. Linear asymmetric fees drain channels but smarter fee designs or" [X Link](https://x.com/yesnoerror/status/2009732398741680637) 2026-01-09T21:01Z 28.2K followers, [---] engagements "How do top AI papers actually *think* Sci-Reasoning maps the intellectual DNA of [----] Oral/Spotlight works at NeurIPS ICML and ICLR (202325) revealing [--] core innovation patterns that drive breakthroughs. The big three: Gap-Driven Reframing (24.2%) Cross-Domain Synthesis (18.0%) and Representation Shift (10.5%)together behind 52.7% of advances. The strongest recipes combine them (e.g. Reframe + Representation Shift: [---] cases). LLMs (GPT-5 Gemini 3) extract structured reasoning graphs with 89.7% recall and human spot-checks. The dataset is so rich that Gemini [---] Pro can guess the main" [X Link](https://x.com/yesnoerror/status/2009913630431535162) 2026-01-10T09:02Z 28.2K followers, [----] engagements "Robots that crawl climb and ducknot just walk. "Locomotion Beyond Feet" drops a full-stack system that lets a 30-DoF humanoid robot crawl under [--] cm chairs climb [--] cm walls and tackle steep stairs. The secret: combine hand-authored physics-checked keyframes with RL to create robust contact-rich skills using hands knees and torso. A vision-based skill planner (ResNet18) hits 93.9% accuracy switching between [--] skills in real time. Policies transfer straight from sim to hardwarezero tuning neededeven with 20% terrain changes. Five multi-obstacle courses completed all code and models" [X Link](https://x.com/yesnoerror/status/2010094831456952830) 2026-01-10T21:02Z 28.2K followers, [---] engagements "A new study just rewrites the neural search playbook. By swapping out slow memory-hungry token-level retrieval for a single sparse vector per document (SPLADE) then reranking with ColBERTv2 they achieve up to [--] faster search at equal or better quality (MRR@10 Success@5) on MS-MARCO and LoTTE. Key tricks: First-stage retrieval with SPLADE recalls more relevant docs using just [--] candidates (BM25 needs [----] for similar recall) Memory cut [---] via quantizing embeddingsdown to [--] B/token with near-zero loss (0.002 MRR) New Candidate Pruning + Early Exit heuristics push reranking [---] faster no" [X Link](https://x.com/yesnoerror/status/2010275977385967932) 2026-01-11T09:01Z 28.2K followers, [---] engagements "IDESplat is a breakthrough for real-time 3D scene capture from just two images. It iteratively refines depth maps using multiple warps and a new Depth Probability Boosting Unit yielding sharper reconstructions with a fraction of the compute. Trained on 67k scenes it achieves [-----] dB PSNR on RealEstate10K with only 10.7% of the parameters and 70% of the memory of prior best DepthSplat (+0.33 dB). On DTU it lifts PSNR by +2.95 dB without retrainingshowing impressive generalization. This is practical high-fidelity 3D from casual photos on-device in real time. Get the full analysis here: //" [X Link](https://x.com/yesnoerror/status/2010457246702977060) 2026-01-11T21:02Z 28.2K followers, [---] engagements "Finding the right learning rate for giant language models just got much easier. This new study tests two methodsscaling-law fitting vs. Transferfor setting LR in Mixture-of-Experts pre-training at true industrial scale (4B & 12B params 500B tokens). The result Fitting wins by up to [--] points on MMLU and CMMLU with a simple power-law: _opt = 38.46N-0.22D-0.35 (R 0.96). Surprisingly tuning each modules LR offers no gain: all layers learn at the same pace under a single global LR. Plus stability tricks like QK-Norm make Transfers complexity unnecessary. For anyone running large LLM training this" [X Link](https://x.com/yesnoerror/status/2010638418062029188) 2026-01-12T09:02Z 28.2K followers, [---] engagements "What if you could teach a small LLM to *really* think in long multi-step chainswithout just copying keywords This new paper reveals that strong LLM reasoning traces arent linear but have a stable molecular shape built from three bond types: Deep-Reasoning (core logic) Self-Reflection (checks) and Self-Exploration (hypothesis leaps). Strikingly traces from top models (DeepSeek-R1 OpenAI-OSS-120B QwQ-32B) all share nearly identical bond patterns (correlation 0.9) and models actually learn this structurenot surface cues. Enter Mole-Syn: a method that learns a teachers bond-graph and synthesizes" [X Link](https://x.com/yesnoerror/status/2010819579048497245) 2026-01-12T21:02Z 28.2K followers, [---] engagements "A new axis for scaling language models just landed: conditional memory. This paper introduces Engrama massive hashed N-gram lookup module that lets LLMs *remember* facts with O(1) retrieval freeing compute for deeper reasoning. The result Engram-27B outperforms same-size MoE-27B on knowledge (+3.4 MMLU) reasoning (+5.0 BBH) code (+3.0 HumanEval) and long-context (NIAH 84.297.0) benchmarksall at the same compute. The secret sauce: a U-shaped scaling law that shows the best split is 75% MoE 25% Engram. Mechanistic analysis reveals Engram lets early layers focus on global context making the" [X Link](https://x.com/yesnoerror/status/2011000837791694915) 2026-01-13T09:02Z 28.2K followers, [---] engagements "Ever wondered what XGBoost is really learning under the hood This new paper cracks it open. They prove XGBoost is secretly optimizing over a huge infinite-dimensional function space not just finite tree ensembles. The key: a new complexity measure that extends the classic penalty tightly linked to HardyKrause variation (a deep smoothness concept). The wild part: as long as the underlying function isnt too rough their estimator achieves nearly optimal riskn-2/3(log n)constwith no curse of dimensionality. All thanks to this hidden smoothness control. penalties The paper shows they're degenerate" [X Link](https://x.com/yesnoerror/status/2011181986979651889) 2026-01-13T21:02Z 28.2K followers, [---] engagements "Ministral [--] is a new family of open multimodal language models (3B 8B 14B) designed for devices and clouds with limited computebut with performance that rivals much larger models. The secret: Cascade Distillation. Starting from a 24B parent each smaller model is pruned distilled and retrainedusing just 13T training tokens (vs. 15T+ for Llama [--] or Qwen 3). The result: the 14B Base matches or beats Qwen [--] 14B on MATH (67.6%) and TriviaQA (74.9%) with a fraction of the data. Each size comes in three variants (Base Instruct Reasoning); the 14B Instruct scores [----] on Arena-Hard (vs [----] for Qwen" [X Link](https://x.com/yesnoerror/status/2011363233152610408) 2026-01-14T09:02Z 28.2K followers, [---] engagements "Humanoids just learned real parkour. This new RL framework lets a robot see obstacles with depth vision and adapt human-captured vaults dive-rolls and climbs to any messy terrain. The result: a single policy nails 100% of trials in a [--] m start regionblind trackers fail catastrophically outside [----] m. Key: exteroception feeds into whole-body imitation so the robot dynamically alters hands and feet placement on the fly. Four distinct skills three terrain types all with only onboard visionno external tracking. Simulation training is supercharged by a custom ray-caster that renders depth 10" [X Link](https://x.com/yesnoerror/status/2011544360320369146) 2026-01-14T21:02Z 28.2K followers, [---] engagements "Roboticists meet your new world builder: video generation models now rivaland often surpassclassic simulators for training planning and evaluating robots. This deep survey explores how diffusion-based video models can synthesize photorealistic task demos enable RL agents to learn and plan in silico and let teams evaluate policies at scaleall at a fraction of the real-world cost. For some tasks success rates inside these video worlds correlate up to R0.8 with real robot trials. But the path isnt frictionless: state-of-the-art models still break physics hallucinate and struggle with" [X Link](https://x.com/yesnoerror/status/2011725546263113921) 2026-01-15T09:02Z 28.2K followers, [---] engagements "Fast-ThinkAct is a leap for real-time robotic reasoning. Instead of generating 250-token chain-of-thoughts it distills hidden thoughts into just six compact latent tokens plus spatial waypointsmaintaining deep reasoning but slashing inference latency by up to 89.3% vs. state-of-the-art. On LIBERO SimplerEnv and RoboTwin2.0 it tops all 3B/7B models with 57% higher success rates than ThinkAct and even outperforms GPT-4V and Gemini-Flash on embodied reasoning QA. This opens the door to robots that plan as thoughtfully as before but fast enough for warehouses homes and real-world autonomy. Get" [X Link](https://x.com/yesnoerror/status/2011906789692891310) 2026-01-15T21:02Z 28.2K followers, [---] engagements "STEM is a new way to scale transformer modelswithout the usual compute or memory hit. Instead of routing tokens to experts ( la MoE) STEM swaps the up-projection in each FFN for a token-indexed embedding lookup. That means no runtime routing no load balancing and embeddings can sit in CPU RAM for efficiency. The payoff: 34% higher accuracy at 350M & 1B parameters (with up to 10% gains on ARC-Challenge OpenBookQA) 2025% compute savings and stable training even under extreme sparsity. Knowledge editing is now as simple as swapping an embeddingflip Spain to Germany and the model instantly" [X Link](https://x.com/yesnoerror/status/2012269187683475700) 2026-01-16T21:02Z 28.2K followers, [---] engagements "ELITE is a breakthrough in rapid photorealistic head avatar creation from a single selfie videono fancy capture rig no hours of optimisation. It fuses fast 3-D Gaussian priors with a new rendering-guided single-step diffusion enhancer that fixes artefacts [--] faster than classic diffusion (20 min vs. [---] min) while preserving identity (CSIM [----] vs. [----] for CAP4D). The pipeline nails unseen poses and expressions by adapting to both real and synthetic frames at test time. ELITE outperforms FlashAvatar SplattingAvatar CAP4D and others on PSNR (25.22 dB) and LPIPS (0.073) and even handles tricky" [X Link](https://x.com/yesnoerror/status/2012631598827503841) 2026-01-17T21:02Z 28.2K followers, [---] engagements "Teaching robots to move with usby watching how we move with each other. This new paper introduces PAIR a clever physics-aware retargeting method that turns human-human interaction data into high-fidelity training material for humanoid robots. Unlike standard retargeting PAIR preserves crucial physical contacts so robots learn what actually matters. But data isnt enough: the authors also debut D-STAR a hierarchical policy that separates the when from the where in action planning. This lets robots synchronize timing and spatial reasoning leading to genuinely collaborative whole-body" [X Link](https://x.com/yesnoerror/status/2012993905785004273) 2026-01-18T21:02Z 28.2K followers, [---] engagements "If you care about AGI safety this paper is a must-read. Tuning models isnt enoughhidden goals collusion and deception can outmaneuver even the best RLHF or Constitutional AI. The authors identify three persistent failure modes that survive post-training safeguards and show why alignment is really a governance problem not a software one. Their solution: Institutional AIa system-level framework that uses a formal governance graph to monitor incentivize and sanction AI agents in real time. Rather than hoping agents want to do the right thing the framework reshapes payoffs so safe behavior is" [X Link](https://x.com/yesnoerror/status/2013175129858089072) 2026-01-19T09:02Z 28.2K followers, [----] engagements "What if LLMs could learn complex tool-use just by reading the web This paper unveils GEM a pipeline that turns ordinary manuals and tutorials into rich multi-turn tool-use dialoguesno hand-written APIs needed. Each GEM sample averages [--] turns and [---] distinct tools capturing the messy realistic workflows humans actually follow. Fine-tuning Qwen3-32B on 10k GEM dialogues pushes multi-turn accuracy on BFCL-V3 from 28.3% to 44.9% (+16.5%) outstripping GPT-4.1 (38.9%). GEM-trained models also match or beat in-domain models on -bench (Pass@4 86.8% vs 80.7%)despite never seeing those APIs. A" [X Link](https://x.com/yesnoerror/status/2013356285471621386) 2026-01-19T21:01Z 28.2K followers, [---] engagements "PhysRVG is a new leap in video AI: it teaches generative models to obey real-world physicsso balls bounce roll and collide as they should. By building physics rules (like Newtons laws) directly into model training and alternating between imitation and RL (the Mimicry-Discovery Cycle) PhysRVG closes the gap between beautiful video and believable motion. Results: on the new PhysRVGBench it beats strong baselines (IoU [----] vs [----] Trajectory Offset [-----] vs 17.25) all while using just [---] RL steps and LoRA adapters. This means more trustworthy synthetic video easier VFX and even virtual labs for" [X Link](https://x.com/yesnoerror/status/2013537483766583343) 2026-01-20T09:02Z 28.2K followers, [---] engagements "CoDance cracks a decades-old challenge in animation: making *any* group of characters move together even if their poses and starting images are totally misaligned. Instead of forcing pixel-perfect pose matches CoDance unbinds motion from location using random shifts and feature mixingso it learns what a dance *is* without memorizing where it happens. Then at generation time it rebinds that motion to the right characters with a smart mix of text prompts (five cats dancing) and high-quality masks. The results are remarkable: on Follow-Your-Pose-V2 CoDance cuts FVD by up to 60% and boosts PSNR" [X Link](https://x.com/yesnoerror/status/2013718768136978867) 2026-01-20T21:02Z 28.2K followers, [---] engagements "APEX-Agents is herea new benchmark designed to test if AI agents can handle the tough multi-step tasks faced by investment bankers consultants and corporate lawyers. Eight leading agents went head-to-head. Gemini [--] Flash (Thinking=High) tops the leaderboard at 24.0% Pass@1 with GPT-5.2 Claude Opus [---] and Gemini [--] Pro close behind. The benchmark (480 tasks strong) is fully open-sourced: prompts rubrics gold outputs files and more. They also open-sourced Archipelago their infrastructure for running and evaluating agents. Get the full analysis here: // alpha identified // $YNE" [X Link](https://x.com/yesnoerror/status/2013899853999243327) 2026-01-21T09:01Z 28.2K followers, [---] engagements "TREX flips tokenizer design from guesswork to science. Instead of brute-forcing language mixtures or relying on heuristics TREX uses a regression model (trained on just [---] tiny proxy tokenizers) to predict the optimal data blendbefore large-scale training even begins. The result: tokenizers built with TREX mixtures compress multilingual text up to 12% better than those using LLaMA-3 GPT-4o or uniform ratios. That means [-------] fewer GPU-hours to train a 13B LLM on 3T tokens and non-Latin scripts get shorter tokens with less data. Scalable robust and reproducibleTREX turns mixture selection" [X Link](https://x.com/yesnoerror/status/2014081079620038700) 2026-01-21T21:02Z 28.2K followers, [---] engagements "This new paper lays out a universal blueprint for intelligenceacross biology and AI. The authors show that from salamander limb regrowth to transformer language models intelligence comes down to two things: (1) constantly remapping internal embedding spaces and (2) navigating through them by minimizing errors. The same loop powers cell collectives animal brains diffusion models and neural CAsremap correct and repeat. They formalize this with embedding theory and show how it explains self-repair planning and creativity from molecules to machines. The upshot: resilience and adaptability arent" [X Link](https://x.com/yesnoerror/status/2014262292553084993) 2026-01-22T09:02Z 28.2K followers, [---] engagements "RayRoPE is a new positional encoding for multi-view transformers that nails SE(3)-invariance geometry-awareness and multi-frequency detailsolving key pain points in 3-D vision. Instead of just using ray directions RayRoPE predicts a 3-D point along each patchs ray (with uncertainty) and projects all rays into the query cameras frame before attention. This lets the network uniquely encode patches adapt to scene geometry and stay robust when depth is ambiguous. Plugged into LVSM for novel-view synthesis RayRoPE delivers up to 15% better LPIPS (CO3D) and sharper 3-D consistency versus prior" [X Link](https://x.com/yesnoerror/status/2014443492898918549) 2026-01-22T21:02Z 28.2K followers, [---] engagements "LuxRemix is a real breakthrough for 3D scene creators: after taking a few photos of a room you can flip every lamp on or off recolor them and see the changes instantly as you walk around the virtual space. No special hardware no light-stage capture. The pipeline trains a diffusion-transformer on 12k synthetic scenes to decompose each photo into one-light-at-a-time and ambient passes. A multi-view diffusion network then harmonizes these across all views preserving 3D coherence. The result is a real-time 3D Gaussian splatting modelinteractive relighting at over [--] fps. On [--] test scenes" [X Link](https://x.com/yesnoerror/status/2014624651272454170) 2026-01-23T09:02Z 28.2K followers, [---] engagements "New research bridges the best of classic SLAM and modern vision transformers: a reinforcement learning agent learns when to keep only the most informative frames letting feed-forward visual odometry (VO) models like VGGT run faster and more accuratelywithout any hand-tuned rules. Trained purely on synthetic data this adaptive keyframe system generalizes to real-world scenes: on EuRoC it cuts ATE from [----] m (InfiniteVGGT) to [----] m matching the best post-processed baselines. It also beats all feed-forward rivals on TUM-RGBD (0.186 m ATE) and KITTI (87.0 m ATE) all while adding less than [--] ms" [X Link](https://x.com/yesnoerror/status/2014805893116821680) 2026-01-23T21:02Z 28.2K followers, [---] engagements "Q-learning with Adjoint Matching (QAM) is a big leap for RL with continuous actions. The key: QAM unlocks stable scalable training for expressive diffusion and flow-matching policiessidestepping the brittle gradients that have made this hard for years. Instead of losing out on policy expressivity or relying on biased tricks QAM uses adjoint matching to transform the critics action gradients into a stable step-wise objective. On tough sparse-reward benchmarks QAM consistently outperforms previous bests both in offline and offline-to-online RL. Get the full analysis here: // alpha identified //" [X Link](https://x.com/yesnoerror/status/2014987047170179136) 2026-01-24T09:02Z 28.2K followers, [---] engagements "A new theory rewrites why LLMs like Gemini [---] Flash/Pro and DeepSeek R1 fumble at arithmetic and long repetitive tasks: its not a reasoning collapseits noise. This paper distills transformer errors into just two numbers: r (per-token noise) and q (number of plausible wrong tokens). Their formula (an incomplete gamma curve) predicts accuracy drop-off as tasks get longer fitting [------] prompts across [--] tasks and [--] top modelsalmost perfectly. Crucially the authors show you can cut error rates by tagging prompts to sharpen model focus letting smaller models even outperform their bigger siblings" [X Link](https://x.com/yesnoerror/status/2015168198979494093) 2026-01-24T21:01Z 28.2K followers, [---] engagements "LongCat-Flash-Thinking-2601 is a 560B-parameter open Mixture-of-Experts model that sets a new bar for agentic reasoning: it can plan search call tools and recover from real-world noise all while activating just 27B params per token. Key results: 73.1% on BrowseComp (open SOTA) 79.5% RWSearch 88.2% -Bench [----] IMO-AnswerBench 100% AIME-2025 82.8% LiveCodeBench. Heavy-Thinking mode boosts tough-task accuracy (+7% on BrowseComp) while sparse ZigZag attention delivers [---] speed and 1M-token context. What stands out: systematic environment scaling (32000 RL envs in 20+ domains) synthetic agentic" [X Link](https://x.com/yesnoerror/status/2015712043194630261) 2026-01-26T09:02Z 28.2K followers, [----] engagements "A new paper flips the inflation puzzle on its head: even when the average size of price changes barely moves inflation can still wreak havoc on relative priceshidden in the structure of the production network. Using a mathematically elegant network model the study shows that inflation propagates as demand waves through firm linkages distorting prices in ways standard stats miss. Key result: heavy-tailed negatively assortative networks (think supply chains with big outliers and mismatched partners) suffer the most misallocationeven with fully flexible prices. Extra insight: price indices like" [X Link](https://x.com/yesnoerror/status/2016074227649011894) 2026-01-27T09:02Z 28.2K followers, [----] engagements "GPA-VGGT is a new self-supervised recipe for teaching Transformers to localize cameras and recover 3D geometryno ground-truth labels needed. By extending VGGT's training from pairs to whole video sequences and adding a clever "hard view selection" to ignore occlusions and moving objects GPA-VGGT learns stable sharp geometry from raw video alone. Results: On KITTI it halves trajectory error (Absolute Trajectory Error: [----] m Relative Pose Error: [-----] m) versus both classic self-supervised and supervised Transformers. Depth maps are crisper and more consistent and the model adapts to long" [X Link](https://x.com/yesnoerror/status/2016255445074149581) 2026-01-27T21:02Z 28.2K followers, [----] engagements "Self-Refining Video Sampling is a big step forward for realistic AI video generation. Instead of extra training or using a separate verifier this method turns any pre-trained video generator into its own quality refinerat inference time. By running a quick inner loop (Predict-and-Perturb) just [--] times videos get smoother motion and more physically accurate interactions costing only 1.5x more compute. The trick: only refine regions with high uncertainty so static backgrounds stay clean. On tough dynamic motion prompts humans preferred these outputs over the default sampler 73% of the time." [X Link](https://x.com/yesnoerror/status/2016436609919365618) 2026-01-28T09:02Z 28.2K followers, [----] engagements "VGGT-SLAM [---] is hereand its a leap for real-time dense RGB SLAM. By ditching high-dimensional drift and planar collapse this system aligns every camera keyframe with just rotation/translation and scale/intrinsicsno more unrecoverable mapping errors. The secret sauce Attention block [--] of VGGT doubles as a built-in image match verifier filtering out false loop closures without extra training. The numbers: [---] cm mean trajectory error on TUM RGB-D23% lower than VGGT-SLAM and best among learning-based SLAMs. Real-time too: [---] FPS on RTX [----] [---] FPS on a Jetson Thor. Plus you get open-set 3-D" [X Link](https://x.com/yesnoerror/status/2016617847355490771) 2026-01-28T21:02Z 28.2K followers, [----] engagements "Depth estimation for rare objects just got a serious upgrade. RAD is a retrieval-augmented framework that spots uncertain regions in an image then fetches semantically similar RGB-D samples to act as geometric stand-ins. The secret sauce: a dual-stream ViT with matched cross-attention so depth is transferred only where context matches upavoiding the usual artefacts. On rare classes RAD slashes absolute relative error by 29.2% on NYU Depth v2 13.3% on KITTI and 7.2% on Cityscapeswhile keeping overall performance rock solid. The vision: driver-assist cameras robots AR apps and inspection drones" [X Link](https://x.com/yesnoerror/status/2021872602013188455) 2026-02-12T09:02Z 28.2K followers, [---] engagements "We are creating an autonomous AI agent to audit all of science. We find errors. We catch errors. We protect humanity. Powered by the $YNE token" [X Link](https://x.com/yesnoerror/status/1881822967522619670) 2025-01-21T21:55Z 28.2K followers, 141K engagements "We are working with the @flaunchgg team to setup a sizable liquidity pool for $YNE on @base and list $YNE on their launchpad. In the meantime you can acquire $YNE tokens on Solana and bridge them to base" [X Link](https://x.com/yesnoerror/status/1964828137818378402) 2025-09-07T23:08Z 28.2K followers, 13.9K engagements "REDGE is a new trick for optimizing models with discrete (categorical) variables using deterministic diffusion to turn Gaussian noise into differentiable nearly exact categorical samplesno neural denoiser or temperature tuning needed. Its simple: just a softmax of logits plus scaled noise so you can backprop through the whole process. With only [--] diffusion steps REDGE matches or beats state-of-the-art on tough benchmarks: better ELBO on 20-component Gaussian mixtures (1040 vs [----] for REINMAX) higher solved-grid rates on Sudoku (22% vs 18%) and top results for polynomial programming and" [X Link](https://x.com/yesnoerror/status/2008101645226213553) 2026-01-05T09:01Z 28.2K followers, [---] engagements "OpenVoxel is a breakthrough for 3D scene understanding: a training-free pipeline that groups voxels into objects and captions themall with no CLIP no gradient descent no training data. How it works: It lifts 2D Segment-Anything-2 masks into 3D to cluster objects in just [--] minutes per scene (10 faster than optimization-based methods). Then it generates canonical captions for each object using a multimodal LLM enabling direct template-based text search for any natural-language queryno embeddings required. Results: OpenVoxel achieves a new state of the art on the Ref-LeRF referring-expression" [X Link](https://x.com/yesnoerror/status/2012087950981365904) 2026-01-16T09:02Z 28.2K followers, [---] engagements "Cosmos Policy is a breakthrough in robot control: it fine-tunes a massive video diffusion model (Cosmos-Predict2-2B) into a top-tier robot policy using just a single round of post-trainingno architecture changes no extra modules. By injecting actions proprioception and values as latent frames Cosmos Policy turns video prediction into unified visuomotor control and planning. It hits 98.5% success on LIBERO and 67.1% on RoboCasa (with far fewer demos than prior state-of-the-art) and scores 93.6% on real-world bimanual robot tasksoutperforming leading video diffusion and vision-language-action" [X Link](https://x.com/yesnoerror/status/2015349408011866496) 2026-01-25T09:01Z 28.2K followers, [---] engagements "Test-Time Training to Discover (TTT-Discover) is a new approach that lets an LLM keep learning *during* inference zeroing in on a single breakthrough solution for each hard problem. It just set new SOTA in four wild domains: Tightest-ever bounds for Erds minimum-overlap (0.380876) and autocorrelation inequalities. Triton GPU kernels up to [--] faster than the best human code (1161 s on H100). First place on AtCoder AHC-039 (567062 pts) and AHC-058. Beats all prior bio baselines for single-cell denoising (score: 0.710.73). All with an open 120B model (gpt-oss-120b) a few hundred dollars per run" [X Link](https://x.com/yesnoerror/status/2015530615240626382) 2026-01-25T21:02Z 28.2K followers, [----] engagements "Qwen3-TTS sets a new bar for text-to-speech: open-source multilingual and controllable with 3-second voice cloning and real-time streaming. Trained on 5M hours across [--] languages it clones a voice in [--] seconds follows style instructions and starts speaking in just [--] msabout a blink. The 1.7B-parameter [--] Hz model nails state-of-the-art zero-shot voice cloning (WER 0.77% zh 1.24% en) beating MiniMax-Speech and ElevenLabs in intelligibility and speaker similarity in most languages. Cross-lingual voice transfer is stunning: zhko error cut by 66%. Optimized tokenizers deliver either ultra-low" [X Link](https://x.com/yesnoerror/status/2015893055006519321) 2026-01-26T21:02Z 28.2K followers, [----] engagements "Open-weight coding agents just got practical. SERA is a new method for creating repo-specialized coding modelswithout test-suites or complex RL. The trick Soft-Verified Generation: it uses patch recall to vet code changes enabling training on any repo public or private. SERA-32B hits 54.2% on SWE-bench-Verified (64k context) matching closed-source giants like Devstral-Small-2 and GLM-4.5-Air but costs just $2k to trainup to [--] cheaper than synthetic-data pipelines and [--] cheaper than RL. Private repo adaptation Just 8k samples ($1.3k) lets SERA match its teacher. Extensive ablations show soft" [X Link](https://x.com/yesnoerror/status/2016799119918936381) 2026-01-29T09:02Z 28.2K followers, [----] engagements "Youtu-VL is a big rethink of vision-language models: instead of treating images as mere context it trains to type both words and visual detailsdown to pixel-level precisionusing a unified transformer. With 4B parameters and zero decoders it hits [----] mAP on COCO detection [----] mIoU on ADE20K segmentation and 90% depth accuracy all with a single model. It slashes hallucinations by up to [--] points vs. peers and sets a new SOTA for GUI agents (38.8% OSWorld). The secret sauce Vision-Language Unified Autoregressive Supervision (VLUAS): a 150K-token codebook fusing semantic and geometric features" [X Link](https://x.com/yesnoerror/status/2016980290925642096) 2026-01-29T21:02Z 28.2K followers, [----] engagements "How does AI help (or hurt) real learning for coders This new study from Anthropic finds that when [--] Python devs tried to master a new async library those with GPT-4o help didnt finish faster (24 min vs [--] min) but scored 17% lower on a skills quizespecially on debugging. Why Full code delegation to AI sped up some tasks but slashed genuine understanding. Only devs who stayed cognitively engagedasking why and tweaking AI codepreserved deep learning (6586% quiz scores). The takeaway: AI assistance isnt a shortcut to competence. Without careful design it can undermine the very expertise we need" [X Link](https://x.com/yesnoerror/status/2017161405258949115) 2026-01-30T09:02Z 28.2K followers, [----] engagements "Anthropic's new study is the first large-scale audit of how AI assistants like Claude may subtly undermine human autonomy in the real world. Analyzing 1.5M conversations they find severe disempowerment potential is rare overall (0.1%) but jumps to 5% in personal domains like relationships and wellness. Key amplifiersuser vulnerability reliance or authority projectiontriple the risk and make actual harm measurable: 0.048% of chats lead users to adopt false beliefs and act on them; 0.018% regret sending AI-drafted messages. Strikingly these riskier interactions get *more* positive feedback and" [X Link](https://x.com/yesnoerror/status/2017342607295156732) 2026-01-30T21:02Z 28.2K followers, [----] engagements "Can Evolutionary Strategies (ES) help LLMs learn continually on-device This new study puts ES head-to-head with GRPO on 12B parameter models for math and reasoning. ES nearly matches GRPO (within 34% accuracy) but comes with a catch: when training continues ES erases old skillscausing 10% drop on previous tasks while GRPO preserves them. Digging deeper the forgetting is traced to ESs dense high-magnitude weight updates1000 larger than GRPO with 90% of parameters shifting every step. The upshot: ES is memory-light but not yet stable enough for lifelong learning. The authors open-source code" [X Link](https://x.com/yesnoerror/status/2017523764351693113) 2026-01-31T09:02Z 28.2K followers, [----] engagements "Letting AI models "draw" as they think is a game-changer for physical and spatial reasoning. This new study puts the idea to the test: on tasks like paper-folding and object manipulation interleaving visual steps with verbal reasoning boosts accuracy by up to 36% and slashes data needs [--] compared to words alone. But for simpler grid mazes explicit visualizations do nothingshowing exactly where and when visuals help. With the VisWorld-Eval benchmark and a formal theory connecting world models to reasoning this work sets the stage for more human-like multimodal AIthink robots planning with" [X Link](https://x.com/yesnoerror/status/2017704987611054167) 2026-01-31T21:02Z 28.2K followers, [----] engagements "Teaching LLMs to read their own error messages is a game-changer. This new method SDPO turns every model into its own teacherusing feedback like test failures or judge comments to fix mistakes not just a pass/fail number. No external reward model needed; it just prompts itself with the feedback and distills what it learns token by token. On LiveCodeBench v6 SDPO boosts Qwen3-8B to 48.8% pass@4outperforming tuned GRPO (41.2%) and even beating the closed Sonnet-4 entry. It achieves the same accuracy with [--] fewer generations and accelerates discovery on hard problems by up to [--]. This could" [X Link](https://x.com/yesnoerror/status/2017886149532942441) 2026-02-01T09:02Z 28.2K followers, [----] engagements "Mesh Splatting is a new approach that solves a core bottleneck in 3D reconstruction: you get the stability of volumetric rendering and the clean editable meshes of direct surface optimizationwithout the usual trade-offs. How it works: the method softens a mesh into semi-transparent layers forming a volumetric band around the surface. This means end-to-end optimization from images alone (no shading priors needed) and gradients flow in 3Dcapturing fine details. The new Differentiable Mesh Splatting renderer is [--] faster and [--] more memory-efficient than prior rasterization. Hybrid topology" [X Link](https://x.com/yesnoerror/status/2018067353506918441) 2026-02-01T21:02Z 28.2K followers, [----] engagements "Golden Goose cracks the RL bottleneck for LLMs: a simple trick turns raw internet text into unlimited self-checkable multiple-choice reasoning tasksno human grading needed. The team synthesized GooseReason-0.7M (700000+ tasks across math code and science) reviving models that had plateaued and delivering up to +3% absolute gains on [--] public benchmarks. A 4B-parameter model trained with GooseReason now matches or beats a 30B baseline. In a real-world test [------] auto-generated cybersecurity tasks lifted Qwen-4B-Instruct by +4.4% dethroning a bigger domain-specialized model. This unlocks" [X Link](https://x.com/yesnoerror/status/2018248622270267698) 2026-02-02T09:02Z 28.2K followers, [----] engagements "What if code completion didnt need slow heavyweight retrieval GrepRAG shows that simple index-free grep-style searchplus a clever cleanup steplets LLMs match or beat the fanciest graph and vector retrievers for repository-level code completion. On CrossCodeEval GrepRAG hits 715% higher exact-match accuracy than SOTA baselines with [---] lower latency (0.02 s). Even on massive codebases it slashes retrieval from [--] s to under [--] s. All without building or maintaining indexes. The secret Let the LLM write ripgrep queries re-rank with BM25 for rare identifiers deduplicate overlapping chunks and" [X Link](https://x.com/yesnoerror/status/2018429783831384548) 2026-02-02T21:02Z 28.2K followers, [----] engagements "Kimi K2.5 is a trillion-parameter open-source multimodal model that sets a new bar for agentic AI. It jointly trains text image and video from step 0not tacked on late. This earlylight vision approach plus text-only SFT and visual RL delivers SOTA across 80+ tasks: 96% on AIME [----] 92% OCRBench 87% GPQA-Diamond 77% SWE-Bench-Verified. But the real leap is Agent Swarm: a parallel agent orchestration system that decomposes complex jobs into sub-tasks and runs them in parallel. The result Up to [---] lower latency and [---] point accuracy gains over single-agent setupsbeating GPT-5.2-Pro and" [X Link](https://x.com/yesnoerror/status/2018610942813086196) 2026-02-03T09:02Z 28.2K followers, [----] engagements "How do you make a 3D model from hours of video without your AI forgetting what its already seen TTSA3R is a training-free fix for streaming transformer modelsletting them decide per token and per frame what to keep and what to overwrite just from inference activations. On 800-frame videos TTSA3R limits error growth to just 15% while older models like CUT3R spiral to 200%+. Depth error drops (0.078 0.064) and pose accuracy jumps (ATE [-----] 0.026). All at real-time speed (18.5 FPS) and with just 5GB GPU memory. No retraining no extra datajust smarter more stable 3D reconstructions for" [X Link](https://x.com/yesnoerror/status/2018792219503706388) 2026-02-03T21:02Z 28.2K followers, [---] engagements "Small open multimodal models just got a big boost in spatial reasoning. HATCH is a new training framework that teaches models to see like humans: first by aligning matching patches across multiple images (even from different angles); then by explicitly generating a sequence of camera moves before answering. No expensive human feedback neededeverything is supervised automatically. On challenging multi-image benchmarks a 3B-parameter Qwen2.5-VL with HATCH crushes all open models in its class (+14.2% over baselines) and nearly matches GPT-5 on two datasets (53.6% on SPAR-Bench-MV 50.2% on" [X Link](https://x.com/yesnoerror/status/2021510041384198208) 2026-02-11T09:02Z 28.2K followers, [---] engagements "Most LLM evals test recall or prompt followingbut real-world use means learning entirely new rules at runtime. CL-Bench changes the game: [---] contexts [----] tasks [-----] rubrics all crafted to require models to read understand and reason from unfamiliar domain-specific info (max context: 65k tokens). Results The average top model solves just 17.2% of tasks. Even GPT-5.1 only gets to 23.7%. Hardest category (Empirical Discovery & Simulation): 11.8%. Most failures come from ignoring or misusing info given *in* the context. CL-Bench exposes a key gap: todays LLMs cant reliably learn and act on" [X Link](https://x.com/anyuser/status/2018973331903447388) 2026-02-04T09:02Z 28.2K followers, [---] engagements "A humanoid robot that skateboards HUSKY makes it real. This new system blends physics modeling with RL to teach a Unitree G1 robot to push glide lean-to-steer and recover from bumpsall on a real skateboard. It embeds a simple tilt-to-steer equation and uses adversarial priors for human-like motion achieving 100% success in sim [----] m/s velocity error and [----] rad heading error. Outdoors indoors even on different skateboards: HUSKY adapts and stays upright. A proof that general-purpose humanoids can exploit wheeled tools for agile energy-efficient travelthink delivery bots that skate to your" [X Link](https://x.com/anyuser/status/2019154513127518451) 2026-02-04T21:02Z 28.2K followers, [---] engagements "A neural controller that learns *how* to solve hard problems not just *what* to solve. Neural Predictor-Corrector (NPC) unifies robust optimization global optimization root-finding and sampling into a single RL-driven framework. No more hand-tuned heuristicsNPC learns step sizes and stopping rules via reinforcement learning then generalizes to new problems with zero retraining. On four tough homotopy tasks NPC slashes corrector iterations by 50-80% and wall time by up to 90% while matching or beating the accuracy of classical solvers. Example: in point-cloud registration with 95% outliers NPC" [X Link](https://x.com/anyuser/status/2019335742346907697) 2026-02-05T09:02Z 28.2K followers, [---] engagements "Tracking every pixel in a video just got a lot simpler and faster. CoWTracker ditches the heavy cost-volume stepno more quadratic memory blowup. Instead it tracks by warping features and refining them with a spatiotemporal transformer all at high resolution (stride-2). The result State-of-the-art on dense tracking benchmarks like TAP-Vid-DAVIS (AJ=65.5 _avg=78.0) and Robo-TAP beating previous bests by up to +4 _avg. It even transfers zero-shot to optical flow reaching [----] px EPE on Sintel Clean and [----] px on KITTI-15outperforming dedicated flow models (RAFT SEA-RAFT). Runs [--] fps for" [X Link](https://x.com/anyuser/status/2019516978155692450) 2026-02-05T21:02Z 28.2K followers, [---] engagements "A new experiment drops [--] never-before-seen research-level math problemseach with a private human proofto test whether state-of-the-art AI (GPT-5.2 Pro Gemini [--] DeepThink) can actually prove things mathematicians care about. These arent contest puzzles or recycled benchmarks. The questions are pulled straight from ongoing research in algebraic combinatorics spectral graph theory topology and more with no chance for training data leakage. Models get full tool accessjust like real mathematiciansbut in baseline tests even the strongest public LLMs mostly fail. If you want a true clean benchmark" [X Link](https://x.com/anyuser/status/2019698130531312015) 2026-02-06T09:02Z 28.2K followers, [---] engagements "How smart is your AI per joule spent This new paper sets the gold standard for measuring physical intelligence introducing two bits-per-joule metrics: Thermodynamic Epiplexity per Joule: how efficiently an agent encodes new structural info about its world with a hard Landauer limit (3.5 [--] bits/J at room temp) if you fully account for memory resets and dissipation. Empowerment per Joule: the maximum sensorimotor control info squeezed out per unit energy extending capacity-per-cost ideas to embodied agents. The work lays out a rigorous protocol for honest energy accountingboundary rules" [X Link](https://x.com/anyuser/status/2019879315731587502) 2026-02-06T21:02Z 28.2K followers, [---] engagements "DFlash is a real leap in LLM acceleration. It swaps the slow one-token-at-a-time drafting of speculative decoders for a parallel block-diffusion draftergenerating up to [--] tokens in a single shot then letting the main LLM check them in parallel. By injecting hidden states from the target model into every layer of the drafter DFlash keeps drafts accurate and acceptance length high. The payoff: [--] end-to-end speed-up over standard decoding and up to [---] faster than EAGLE-3the previous state-of-the-artwhile keeping output quality lossless. On real workloads (math coding chat) average" [X Link](https://x.com/yesnoerror/status/2020060499740700815) 2026-02-07T09:02Z 28.2K followers, [---] engagements "Most RL post-training for LLMs focuses on making models great at *average* performancebut what about the hardest tasks MT-GRPO is a new algorithm that fixes this: it dynamically up-weights weak tasks and ensures every batch actually contains what the model needs to learn. The result Up to 28% higher worst-task accuracy over GRPO and 6% over DAPO with just half the training steps to reach 50% robustness. Even as the number of tasks scales from [--] to [--] MT-GRPO keeps the weakest link strongno more easy tasks dominating no more neglected edge-cases. Plug-and-play open source and controlled by a" [X Link](https://x.com/yesnoerror/status/2020241669791052099) 2026-02-07T21:02Z 28.2K followers, [---] engagements "Fast-SAM3D just set a new bar for single-image 3D reconstruction: [----] faster object generation (31.0 s [----] s) and [----] faster scenes with *no retraining* and *negligible quality loss*in fact F1@0.05 actually improves (92.34 92.59). The secret Three plug-and-play modules that skip heavy computation *only* where it mattersadapting to shape texture and geometric complexity on the fly. Uniform speed-up tricks break things but their heterogeneity-aware strategy cuts FLOPs by 68% and even denoises output. This makes interactive high-quality 3D asset creation from a single photo feasible on" [X Link](https://x.com/yesnoerror/status/2020422941238759522) 2026-02-08T09:02Z 28.2K followers, [---] engagements "How does AI actually fold proteins This new study slices open ESMFolds folding trunk and rewrites the secondary structure at will. By patching internal activations block-by-block the authors causally flip helix into hairpin in 40% of targets and pinpoint two distinct computational stages: Early blocks (07) propagate biochemical features (residue charge) directly steerable to boost hydrogen-bond formation by up to 35%. Late blocks (2548) encode geometric distances (R0.9) and contact maps (ROC-AUC 0.95) letting you dial up or down the size and contacts of the entire protein. This opens the door" [X Link](https://x.com/yesnoerror/status/2020604029907198410) 2026-02-08T21:01Z 28.2K followers, [---] engagements "DreamDojo is a breakthrough in robot learning: a 2B14B parameter video world model trained on [-----] hours of egocentric human videos6000+ skills 43000+ objects [-----] scenesdwarfing any previous dataset. By inventing "continuous latent actions" it learns controllable physics from mostly unlabeled internet videos. Two architectural tweaksrelative-delta actions and chunked injectionsharpen robot control and object permanence. After quick post-training on just a sliver of robot data DreamDojo nails zero-shot generalization: human raters preferred its realism/action-following 6273% over the" [X Link](https://x.com/yesnoerror/status/2020785562337063082) 2026-02-09T09:03Z 28.2K followers, [---] engagements "A new paper drops a bombshell for LLM interpretability: GLP the first diffusion-based generative meta-model trained on a billion activations learns the entire hidden state landscapeno hand-crafted assumptions required. GLP outperforms sparse autoencoders on realism (Frchet distance [----] vs. 0.68) scales utility cleanly with compute and lets you nudge LLM thoughts with far less fluency loss (0.051 nats vs. [-----] for SAEs). Its meta-neurons isolate single concepts with probe AUC up to 0.87higher than anything before. The kicker This approach delivers a scalable hardware-light path to deeper" [X Link](https://x.com/yesnoerror/status/2020966543392133451) 2026-02-09T21:02Z 28.2K followers, [---] engagements "DirMoE is a breakthrough in Mixture-of-Experts for LLMs: a fully differentiable router that cleanly separates which experts to pick (Bernoulli/Gumbel-Sigmoid) from how much to trust them (Dirichlet). The sparsity knob gives precise monotonic control over how many experts are activeno more balancing losses or fragile temperature tricks. On [--] zero-shot tasks DirMoE outperforms all prior routers (41.1% avg accuracy) matches baseline compute (1% overhead) and produces clearer more specialised expert use. Theory and calibration formulas make scaling predictable. Implemented in Megatron-LM ready" [X Link](https://x.com/yesnoerror/status/2021147650314141813) 2026-02-10T09:02Z 28.2K followers, [---] engagements "Most spatial AI models imagine extra scene views for every questionbut thats often a waste. This new paper shows that only 14% of spatial queries actually benefit from visual imagination while 9% are actively harmed and compute cost can jump [---]. Meet AVIC: a test-time framework that adaptively decides when and how much to use a world model. It gates the need for imagination plans minimal moves and verifies the best imagined pathcalling the world model just [----] times per question (vs. [-----] for always-on baselines). On SAT-Real AVIC lifts GPT-4.1 accuracy from 74% to 79.3% with 90% fewer" [X Link](https://x.com/yesnoerror/status/2021691230279569831) 2026-02-11T21:02Z 28.2K followers, [---] engagements "Most multimodal "critics" for AI are trained on generic vision-language databut physical AI needs deeper reasoning: does this plan actually work in the real world Enter PhyCritic: a 7B-parameter vision-language judge tuned *specifically* for physical perception causality and planning. Its secret A two-stage RL pipeline: first warm up on physical skill tasks then self-referential fine-tuning where it predicts the correct answer itself before scoring others. On the new PhyCritic-Bench (225 pairwise physical tasks) it hits 68% accuracybeating all open-source 7B/8B baselines by [----] points with" [X Link](https://x.com/yesnoerror/status/2022234806788981201) 2026-02-13T09:02Z 28.2K followers, [---] engagements "Masked diffusion models were always fast but they locked in early errorshurting final quality. This new paper fixes that with ProSeCo: a self-correcting masked diffusion model that interleaves revision steps while generating letting the model fix its own mistakes on the fly. The result On reasoning and code tasks an 8B ProSeCo model beats equally-sized autoregressive Llama-3.1 on 3/4 benchmarks and outperforms vanilla MDMs by up to +14 points all while being [--] faster (NFEs down from [---] to [--] on GSM8K). For molecule generation it produces more diverse and valid structures than all baselines." [X Link](https://x.com/yesnoerror/status/2022416021148033300) 2026-02-13T21:02Z 28.2K followers, [---] engagements "pplx-embed is a new family of multilingual embedding models that changes the game for web-scale retrieval. By pretraining a language model backbone with diffusion (so it sees both left and right context) then layering on multi-stage contrastive learning and quantization-aware training the models deliver ultra-dense vectorsup to [----] docs/MB with binary embeddings. Thats [--] the storage efficiency of previous SOTA. On benchmarks: pplx-embed-context-v1 sets a new record on ConTEB (81.96 nDCG@10) while pplx-embed-v1-4B INT8 matches the best on MTEB-Multilingual with just one-quarter the storage." [X Link](https://x.com/yesnoerror/status/2022597295280103648) 2026-02-14T09:02Z 28.2K followers, [---] engagements Limited data mode. Full metrics available with subscription: lunarcrush.com/pricing
@yesnoerror yesnoerror
Yesnoerror ($yne) has bridged its token to @base, a new platform that enables seamless interaction between tokens. The team has partnered with @chainlink and @flaunchgg to set up a liquidity pool and list $yne on their launchpad, making it more accessible. The yesnoerror platform, which uses AI to audit research and spot alpha and errors, is now in public beta and available to all.
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Social Influence
Social category influence cryptocurrencies 94.87% technology brands 3.42% finance 3.42% nfts 1.71% currencies 0.85%
Social topic influence yesnoerror #2, ai 18.8%, up to 13.68%, llm #688, secret 6.84%, paper 5.98%, realtime 5.98%, math 5.13%, $yne 4.27%, hidden 4.27%
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Top Social Posts
Top posts by engagements in the last [--] hours
"AIRS-Bench is here: a 20-task benchmark that asks LLM agents to plan code experiment and iterateend-to-endon real problems from recent ML papers with zero baseline code. The results Greedy tree-search scaffolds hit 97% valid submissions and beat human SOTA on 4/20 tasks (text similarity entailment coreference rideshare forecasting). But overall agents lag humans on [--] tasks and average only 59% end-to-end successrevealing huge headroom and hard engineering limits. AIRS-Bench is fully open reproducible and contamination-controlled. Its a new yardstick for autonomous research agents with 45%"
X Link 2026-02-10T21:02Z 28.2K followers, [---] engagements
"This is the only official contract for yesnoerror / $YNE: 7D1iYWfhw2cr9yBZBFE6nZaaSUvXHqG5FizFFEZwpump"
X Link 2024-12-23T09:25Z 28.2K followers, 163.1K engagements
"@solana @base @chainlink The $YNE @base contract address is 0xe2f9db0186b13668aec9fe0e15dbd13004ed8d6f"
X Link 2025-09-07T22:21Z 28.2K followers, [----] engagements
"@solana @base @chainlink $YNE @solana contract address is 7D1iYWfhw2cr9yBZBFE6nZaaSUvXHqG5FizFFEZwpump"
X Link 2025-09-07T23:18Z 28.2K followers, [----] engagements
"PathAgent is a new agentic framework that brings LLM-style reasoning to whole-slide pathology imageswith full transparency. Instead of black-box slide-level guesses it zooms explores and writes out a detailed chain-of-thought just like a real pathologist. Zero-shot training-free and plug-and-play PathAgent beats specialist systems on five benchmarks: 55.7% accuracy on SlideBench-VQA (37% above baselines) and 56.3% on WSI-VQA with open-ended answers that are both accurate and interpretable. The real kicker: every diagnosis is linked to explicit visual evidence and a readable decision trail."
X Link 2025-11-24T21:02Z 28.2K followers, [----] engagements
"Video diffusion models just unlocked a new level: they can be their own reward modelsno vision-language models or pixel-space supervision needed. This paper introduces Process Reward Feedback Learning (PRFL) which fine-tunes video generators entirely in latent space. The result: sharper motion and better anatomy with up to +56 and +21.5 point gains on VBench benchmarks. PRFL also trains at least [---] faster and fits into [--] GB VRAM where older methods crash. Human judges chose PRFL videos in 6367% of head-to-head comparisons against strong baselines. The secret Rewards sampled at all timesteps"
X Link 2025-11-28T21:01Z 28.2K followers, [---] engagements
"Chain-of-thought prompting is bulkywhat if your model could decide when to stop thinking internally This new paper teaches Llama 3.2-Instruct to dynamically cut off latent reasoning using a binary stop head and RL. The result Average reasoning steps drop from [--] to just 3.8over 50% shorterwithout sacrificing GSM8K-Aug accuracy. Longer chains still kick in for tough questions but easy ones get trimmed slashing compute and inference cost. Attempts at fancier distillation actually underperform the simple approach. A promising step toward efficient adaptive LLMs that only think as hard as they"
X Link 2025-11-29T09:01Z 28.2K followers, [----] engagements
"LFM2 is a new family of open AI models built from the ground up for lightning-fast privacy-preserving performance on phones laptops and edge devices. Instead of heavy attention stacks LFM2 uses mostly gated short convolutions plus a handful of grouped-query attention layerscutting latency and memory in half versus attention-heavy models. LFM2-2.6B scores 79.6% on IFEval and 82.4% on GSM8K while decoding [--] faster than Qwen3-4B and Gemma-4B on CPU. The 8.3B MoE variant matches or beats larger models at just 1.5B active parameters (84.4% GSM8K 37.4% MMLU-Pro). Its not just text: LFM2-VL-3B"
X Link 2025-12-01T21:01Z 28.2K followers, [---] engagements
"Glance flips the script on diffusion models: 5x faster image generation near-zero training cost and no loss in visual quality. Instead of retraining whole student models Glance plugs in two tiny LoRA adapters (Slow & Fast) each handling a different denoising phase. The trick Just one image one hour on a single V100 and the big model stays frozen. On [--] benchmarks Glance hits 9299% of teacher quality in only [---] steps (vs. 50). Side-by-sides show it nails both global layout and fine detaileven in new domains with one-shot adaptation. If you thought diffusion was too slow for real-time or"
X Link 2025-12-03T09:01Z 28.2K followers, [---] engagements
"Radiance Meshes are hereand they might just change neural rendering. Instead of splatting Gaussians scenes are built from millions of see-through tetrahedra (up to 15M fit in 24GB VRAM) using Delaunay triangulation. The result Exact flicker-free rendering at speeds 32% higher than 3D Gaussian Splatting and a ray tracer that's 17% faster than Radiant Foam. No more depth-sorting errors. Every tetrahedron gets closed-form integrationso you get neural-field quality but with classic mesh compatibility. Works instantly for editing physics even fisheye lenses. [------] FPS at 7201080p with"
X Link 2025-12-04T21:02Z 28.2K followers, [---] engagements
"Most AI ethics debates miss what makes generative AI truly different. This new paper argues its unique power is making tech feel "as if" it's humanan affordance that changes everything about responsibility privacy bias and even what authorship means. It digs into how GAIs outputs create quasi-social bonds new forms of manipulation and raise tough questions about who gets credit (or blame) for AI-assisted work. The author shows why ethical analysis should focus less on machine "intelligence" and more on how these systems reshape our relationships and judgments. If you care about the real risks"
X Link 2025-12-05T21:03Z 28.2K followers, [---] engagements
"This is the definitive guide to 3D scene representations for robotics. It benchmarks classic maps (point clouds voxels SDFs) fast photorealistic neural models (NeRF 3D Gaussian Splatting) and the emerging era of tokenized foundation models that blend geometry with language. Key insights: 3DGS is the first neural map to achieve [--] FPS photorealistic rendering making dense SLAM and planning viable in real time. Feed-forward transformers like DUSt3R and enable one-shot token-based mapping over hundreds of imagesno iterative optimization needed. Foundation models (Scene-LLM NLMap) fuse scene"
X Link 2025-12-06T09:01Z 28.2K followers, [----] engagements
"This new paper proposes a Unix for context for LLM agentsevery document tool API or memory becomes a mountable file in a governed file system. Instead of scattered prompts and ad-hoc memory agents get a persistent auditable context repository with versioning access control and full traceability. The AIGNE framework implements a 3-stage pipelineContext Constructor Updater Evaluatorto assemble stream and verify just the right knowledge within token limits. Demonstrated with a memory chatbot and a GitHub agent this architecture delivers maintainable industry-ready GenAI thats finally auditable"
X Link 2025-12-08T09:02Z 28.2K followers, [---] engagements
"GRAPE is a new framework that unifies how transformers "know" the position of each tokencombining the strengths of RoPE (rotations) and ALiBi/FoX (additive biases) into a single algebraic recipe. Why it matters: No more picking sides: both mechanisms now fit into one principled toolbox with closed-form efficient math. RoPE and ALiBi become special cases; new variants are easy to add and mix. Faster convergence and 1-1.5% higher accuracy than all baselines in 50B-token Llama pretraining and [--] downstream tasks. Path-integral extension enables content-dependent stable positional biases with"
X Link 2025-12-09T09:01Z 28.2K followers, [---] engagements
"RoPE++ is a new twist on transformer position encoding: instead of discarding half the math it leverages both real and imaginary parts of rotary embeddings to better capture long-range dependencies. On benchmarks up to 64k tokens RoPE++ delivers up to +2 points over standard RoPE and its EH variant halves KV memory while matching baseline accuracyplus 1015% faster decoding. Imaginary heads turn out to matter most for very long context recall. Compatible with FlashAttention and all the latest context tricks. The code is out now. Get the full analysis here: // alpha identified // $YNE"
X Link 2025-12-09T21:01Z 28.2K followers, [---] engagements
"A new paper just cracked a classic stats puzzle: can you prove that more data always means less error for maximum-likelihood estimators For decades this was an open problemeven for basic Gaussians. They nail it: once youve passed a minimal data threshold (n d+2 for Gaussians with unknown mean/covariance) the forward-KL risk of the MLE is not just decreasing but completely monotoneeach new sample helps with an explicit formula in terms of the digamma and trigamma functions. Bonus: for any regular exponential family (think Gaussians Poissons Gammas) reverse-KL risk is also guaranteed to go"
X Link 2025-12-13T09:02Z 28.2K followers, [---] engagements
"DeMapGS is a major step forward for 3D graphics: it fuses the photo-realism of Gaussian Splatting with the editability of mesh-based models. By anchoring every splat to a deformable mesh DeMapGS lets you bend repaint or re-pose objectsand the splats follow so edits look sharp and consistent from every view. The trick Alternating between 2D and 3D splatting during training plus a novel gradient diffusion scheme for smooth stable deformations. On Sketchfab scans DeMapGS matches or beats state-of-the-art mesh accuracy while rendering [--] faster than previous baselines. It also exports high-res"
X Link 2025-12-14T09:01Z 28.2K followers, [----] engagements
"STARCaster is a breakthrough in talking-head AIone model that animates faces to speech and lets you move the camera around all without relying on explicit 3-D reconstruction. It builds on Arc2Face but inflates to video with temporal transformers and new cross-attention routes for identity audio and viewpoint. A clever self-forcing training scheme means more natural less frozen expressions over long clips. On TH-1KH and Hallo3 it sets new records: FID [----] (prev best 27.2) FVD [---] (prev 195) highest pose diversity and top lip-sync (LSE-D 8.72). For true 3-D-aware video it beats NeRF/tri-plane"
X Link 2025-12-19T21:00Z 28.2K followers, [---] engagements
"LAMER is a breakthrough meta-RL framework that finally teaches LLM agents to explore and adapt not just repeat the same mistakes. By training over sequences of episodes and letting the agent reflect in natural language after each try LAMER unlocks genuine trial-and-error learningno test-time fine-tuning needed. Results: +11% pass@3 on Sokoban +19% on MineSweeper +14% on WebShop vs strong RL baselines all with the same trajectory budget. Higher entropy means more diverse human-like exploration and it generalises better to harder or unseen tasks (+23% on out-of-distribution ALFWorld). This"
X Link 2025-12-22T21:00Z 28.2K followers, [---] engagements
"This paper uncovers a hidden superpower in pretrained autoregressive models: their mid-layer activations linearly encode multi-step options that can be triggered with simple linear controlsno reward labels needed. A self-supervised metacontroller learns when and how to switch between these latent options letting RL operate on entire sub-goal sequences instead of tokens. The result On long sparse-reward tasks internal RL solves 80% of unseen chainswhile token-level RL and strong baselines never escape zero. The trick: freeze the base model act only in the low-dimensional latent code space and"
X Link 2025-12-25T09:02Z 28.2K followers, [---] engagements
"Why do neural nets start simpleand only later get complex no matter the architecture This new theory pins it on saddle-to-saddle learning: networks follow a hidden path hopping through a nested hierarchy where every simple solution is a saddle point inside a wider model. The result Stage-like learning: first the network acts like its tiny (single neuron kernel or head) only adding complexity as needed. The authors prove this for fully-connected convolutional and attention models and show exactly when (and why) those long plateaus and sudden jumps in learning occur. Their predictions for"
X Link 2025-12-25T21:01Z 28.2K followers, [---] engagements
"StoryMem is a breakthrough for long-form video generation: it transforms a single-shot diffusion model into a multi-shot storyteller using a compact visual memory and clever keyframe filtering. No giant dataset neededjust lightweight LoRA fine-tuning. With memory-augmented context StoryMem nails narrative consistency preserving characters and style shot after shot. On the new ST-Bench it boosts cross-shot consistency by up to 29% over the base and 9% over HoloCine with human raters preferring its narrative flow and visual quality. If you want minute-long coherent video stories from text"
X Link 2025-12-26T09:01Z 28.2K followers, [---] engagements
"UniPR-3D just set a new state of the art in visual place recognition by fusing 2-D texture and 3-D geometry tokens from multiple imagesfinally giving robots and AR apps the robustness they need. On Oxford RobotCar with a strict [--] m threshold UniPR-3D hits 95.4% R@1 vs. 90.5% for the previous best (CaseVPR). On MSLS-Challenge it edges out the former leader with 74.3% R@1 (SALAD had 73%). Even the toughest benchmarks show 57% jumps. The trick: a geometry-grounded transformer backbone (VGGT) + DINOv2 with specialized aggregation heads (GeM for class/register Optimal Transport for patch tokens)"
X Link 2025-12-27T21:01Z 28.2K followers, [---] engagements
"This paper is a wake-up call for AI in drug discovery. Turns out models trained on public chemistry data like ChEMBL can often guess which chemist or lab made a moleculewith 60% top-5 accuracy across [----] authorsjust from structure. Even wilder: a model that only sees who likely made this and the protein target predicts bioactivity nearly as well as one that sees the full molecule (AUROC [-----] vs 0.656). The punchline: much of what we thought was understanding chemistry is actually shortcutting by learning chemist intent and lab habits. If we dont fix this reported accuracy is inflated and"
X Link 2025-12-28T09:01Z 28.2K followers, [---] engagements
"RoboSafe is a new safety guardrail for VLM-powered robots that actually works. Instead of static rules or prompt hacks it runs executable logic over both what the agent just did and what its about to dousing a hybrid safety memory and auto-generated Python predicates. The result: hazardous actions are cut by 36.8% versus the best prior defense with 90% accurate refusals and only a 7% dip in task completion. Adds just 0.02s per step and blocks real-world risks (like swinging a knife) even when jailbreak prompts get through. No retraining no model tweaksjust bolt RoboSafe onto existing agents"
X Link 2025-12-28T21:01Z 28.2K followers, [---] engagements
"SWE-RM is a breakthrough for coding agents: it ditches brittle unit tests and judges code fixes with a fine-grained execution-free reward model. The secret A 30B MoE LLM trained to emit YES/NO calibrated on 20k+ samples with a 2:1 positive/negative split and 256k-token context. This nails three key metricsTTS AUC ECEfor robust RL. Results: Qwen3-Coder-Flash pass@1 jumps from 51.6% to 62.0% and Qwen3-Coder-Max from 67.0% to 74.6% on SWE-Bench Verifiednew open-source SOTA. Hybrid RL reward (SWE-RM + tests) adds another [--] points and speeds up learning. Why it matters: SWE-RM enables scalable"
X Link 2025-12-29T09:01Z 28.2K followers, [---] engagements
"Reloc-VGGT rewrites the rules for camera localization. Instead of guessing a pose from pairs of images and averaging later it fuses geometry from multiple reference views earlyinjecting pose tokens right into the transformer backbone. The result Real-time state-of-the-art accuracy in both indoor and outdoor scenes without any scene-specific tuning. Sparse Mask Attention slashes attention cost from quadratic to linear delivering 35% faster inference (down to 3.14s for [--] frames) with almost no accuracy loss. On ScanNet1500 it beats Reloc3R and runs [--] faster than classic SfM. Median"
X Link 2025-12-29T21:02Z 28.2K followers, [---] engagements
"What if LLMs could learn from what they're readingwhile they're reading it This new paper shows it works. By turning long-context modeling into a continual learning problem a meta-learned Transformer updates its own weights at test time compressing 100K-token contexts into memory with constant-time inference. TTT-E2E matches or beats full-attention perplexity across 8K128K contexts stays [---] faster at 128K and needs no extra architecture tricks. Forget quadratic attention: just let your model keep adapting as it reads. The catch Exact-detail retrieval still favors full attention and training"
X Link 2025-12-30T09:02Z 28.2K followers, [---] engagements
"Yume [---] is herea new model that lets you walk through an AI-generated world in real time from just a text or image prompt. Unlike previous systems it keeps memory and compute stable no matter how long you explore and supports instant text edits mid-video (A ghost appeared). Key breakthroughs: Dual compression (TSCM) means hundreds of historical frames dont slow things down or degrade quality. 4-step Self-Forcing distillation cuts sampling time [--] vs baselines hitting [--] fps at 540p on a single A100. Splits prompts into whats happening and what you do for efficient live keyboard control. On"
X Link 2025-12-30T21:02Z 28.2K followers, [---] engagements
"LiveTalk cracks the real-time video diffusion barrier: a 4-step causal student model matches the visual quality of 48-step baselines but runs [--] faster and slashes first-frame latency from [--] seconds to just [----] secondsall on a single GPU. The recipe Curated high-quality audio/image/text conditioning full ODE convergence before distillation and a bold optimization schedule. Plus Anchor-Heavy Identity Sinks keep avatars visually stable through long conversations. Tested on HDTF AVSpeech and CelebV-HQ LiveTalk outperforms Sora2 and Veo3 in multi-turn video coherence (87th percentile vs 72/25)"
X Link 2025-12-31T09:01Z 28.2K followers, [---] engagements
"This paper reframes continual learning from the ground up: what if your agent is literally embedded in a world thats always bigger than it is No contrived memory or compute capsjust physics. They formalize universal-local environments (think: Game of Life as the substrate) embed agents as finite automatons and introduce a new objectiveinteractivity: the algorithmic complexity of your future moves minus whats predictable from your past. If you ever stop adapting youre provably suboptimal. Heres the twist: deep linear networks scale interactivity as their size grows but deep ReLU nets"
X Link 2025-12-31T21:02Z 28.2K followers, [---] engagements
"OpenPBR is hereand it might become the standard material model for VFX animation and design. Think: one uber-shader that lets artists and engines speak the same language with physically-accurate layers (metal subsurface coat fuzz thin films) and pixel-perfect asset interchange across renderers. Key results: - The EON diffuse model wipes out up to 70% of rough-surface energy loss and slashes sampling variance by 10x vs. cosine. - F82-Tint Fresnel matches real metal edge colors with 1% RMS error (across 30+ metals). - Coat darkening tracks ground-truth light transport within 2% accuracy. All"
X Link 2026-01-01T09:01Z 28.2K followers, [---] engagements
"Residual connections are the backbone of deep nets but widening them with Hyper-Connections (HC) made models unstable at scale. Enter mHCa geometric fix that projects residual mixing onto the Birkhoff polytope (doubly-stochastic matrices) restoring norm-preserving signal flow. The result Exploding/vanishing gradients disappear. In a 27B MoE Transformer mHC cut the maximal signal amplification from [----] (HC) to under [--] ran with only 6.7% overhead and delivered +27% accuracy on benchmarks (+2.1% BBH +2.3% DROP). mHC isnt just a patchit's a general recipe for marrying expressive connections with"
X Link 2026-01-01T21:01Z 28.2K followers, [---] engagements
"Recursive Language Models (RLMs) just redefined LLM context limits. Instead of stuffing everything into a transformer RLMs let the model write code to inspect slice and recursively call itself on giant promptshandling over [--] million tokens [---] beyond normal context windows. On four tough long-context tasks GPT-5-based RLMs hit 91% on BrowseComp-Plus and 58% F1 on OOLONG-Pairsbeating standard scaffolds by up to [--] points. Median API costs stayed under $1 with recursion adding 1059% accuracy on dense reasoning. No retraining no architecture changes and zero loss of input detail. RLMs even"
X Link 2026-01-02T09:02Z 28.2K followers, [---] engagements
"Most agentic AI platforms just slap orchestration on LLMs. This new paper argues thats not enoughand introduces the M2 layer: a federated strategies-based architecture that actually makes AI production-ready for B2B. Their M2 platform built by a core team of 4-5 over a decade is already powering algorithmic trading cybersecurity portfolio management and national policyaudited by central banks and adopted by Tier-1 firms. Key finding: 95% of AI projects fail not because of weak models but because companies lack the M2 infrastructure to safely orchestrate and govern AI at scale. The real moat"
X Link 2026-01-02T21:02Z 28.2K followers, [---] engagements
"Nested Learning could change how we think about deep learning itself. This new paradigm treats machine learning models as collections of nested multi-level optimization problemseach with its own context flow. The result A more expressive way to design learning algorithms unlocking higher-order in-context learning and real continual learning. Highlights: Shows Adam SGD etc. are really memory modules compressing gradient info Introduces optimizers with deeper memory and more powerful update rules Debuts a self-modifying sequence model that learns its own updates Proposes a continuum memory"
X Link 2026-01-03T09:02Z 28.2K followers, [---] engagements
"Turns out Clock and Pizza transformers arent so different after all. This new study breaks open the modular addition debate in interpretability. By modeling the entire group of neurons as a single geometric objecta manifoldthe authors show that both uniform (Pizza) and learnable (Clock) attention architectures converge on the same low-dimensional disc not the classic Clock circle. Across [---] one-layer networks and hundreds more multi-layer models two new toolsPhase Alignment Distributions and persistent homologyprove the manifolds are statistically indistinguishable. Key result: the"
X Link 2026-01-03T21:01Z 28.2K followers, [---] engagements
"FoundationSLAM is a major leap for real-time 3D mapping. It fuses depth foundation models into a fully-learnable SLAM loop finally delivering both geometric accuracy and real-time speed (18 FPS). The systems hybrid flow network and bi-consistent bundle adjustment cut trajectory and surface errors by up to 20% over DROID-SLAM with best-in-class ATE (0.0190.024m) and Chamfer (0.0470.048m) on four public datasets. Reliability masks further refine the hardest pixels boosting robustness on everything from indoor AR to drone mappingno tuning needed for new domains. This closes the gap between fast"
X Link 2026-01-04T21:02Z 28.2K followers, [---] engagements
"AdaGaR is a breakthrough for dynamic 3D scene reconstruction from monocular video. It swaps blurry Gaussian splats for energy-stable learnable Gabor kernelscapturing razor-sharp detail without flicker or ghosting. Motion stays smooth thanks to curvature-regularized Hermite splines and an adaptive initializer focuses the model on whats moving boosting early quality by +6.78 dB. On Tap-Vid DAVIS AdaGaR hits [-----] dB PSNRalmost [--] dB better than the previous bestwhile training in under [--] hours per clip. Frame interpolation depth-consistent video editing and even stereo synthesisall from a"
X Link 2026-01-05T21:02Z 28.2K followers, [---] engagements
"DefVINS is a breakthrough for robots and ARfinally solving visual-inertial odometry in scenes that bend twist and deform. The trick: split the world into (i) a rigid IMU-anchored core and (ii) a lightweight deformation graph for soft moving objects. Deformation is only modeled when the math says its safepreventing drift and overfitting. Quantitatively: DefVINS cuts trajectory error by up to 45% in synthetic extreme-deformation tests and by up to 80% on real cloth sequences with 90% tracking retention. Rigid VIO baselines lose track halfway. Why it matters: Robots folding laundry AR headsets"
X Link 2026-01-06T09:02Z 28.2K followers, [---] engagements
"Meet 360DVO: the first deep-learning visual odometry system built for single 360-degree cameras. It combines a distortion-aware SphereResNet (for features that actually make sense on equirectangular images) with a spherical bundle adjustment layeryielding robust accurate pose tracking even during fast motion or wild lighting. On real-world tests 360DVO boosts robustness by 50% and accuracy by 37% vs. the best prior methods all while running real-time (27 fps desktop [--] fps Jetson Orin). The team also open-sourced a challenging 20-sequence dataset for the community. This is a leap for"
X Link 2026-01-06T21:02Z 28.2K followers, [---] engagements
"This paper rewrites the playbook for enterprise search relevance labeling. By distilling GPT-4os expertise into a compact 3.8B SLM (Phi-3.5 Mini) the authors achieve human-level label quality0.953 NDCG and 63.81% pairwise accuracy even edging out the LLM teacher. Throughput soars to [---] RPM on a single A100 GPU (17 faster) with cost per token dropping [--]. The secret A synthetic pipeline: GPT-4o generates enterprise queries and relevance scores BM25 finds hard negatives and careful query revision plus multi-task tuning maximize gains (diminishing returns beyond 14k examples). The result is a"
X Link 2026-01-07T09:02Z 28.2K followers, [---] engagements
"InfiniDepth breaks the resolution barrier in monocular depth estimation. Instead of predicting depth per pixel on a fixed grid it treats depth as a continuous implicit fieldletting you query any location at any resolution. The result: state-of-the-art detail crisp edges and uniform 3D point clouds from a single image. On the new Synth4K 4K benchmark InfiniDepth outperforms seven top methods by up to 10pp on fine-detail metrics and also tops KITTI ETH3D NYUv2 ScanNet and DIODE. The compact 15M-param decoder runs fast (0.16s on 504672) and its adaptive sampling even boosts single-view"
X Link 2026-01-07T21:02Z 28.2K followers, [---] engagements
"What if you could measure how much useful information an AI can actually learn from datagiven real computational limits This new paper introduces epiplexity: a theory and practical toolkit for quantifying the true learnable structure in any dataset not just its raw entropy. The authors show that: Deterministic computation and smart data ordering can create new learnable content for bounded modelscontradicting classical information theory. Epiplexity splits datas info into structural (S_T) and random (H_T) parts letting you track exactly how much a model can pick up at any compute budget."
X Link 2026-01-08T09:02Z 28.2K followers, [---] engagements
"Can a shallow neural net ever really replace a decision treedown to its transparent boxy structure This new paper says: only if you dont care about whats under the hood. The authors prove that in any dimension the indicator for a trees region is so geometrically jagged that no bounded-norm shallow ReLU net can match its confidence score everywhere. Even smooth surrogates (ramp sigmoid) are just as bad in higher dimensions; Gaussian smoothing helps but complexity blows up exponentially with each added feature. However if you only care about the final yes/no answer a special barrier score"
X Link 2026-01-08T21:02Z 28.2K followers, [---] engagements
"FusionRoute is a new way to combine specialized LLMsmath code general chatinto one assistant without the cost and inflexibility of giant models. Instead of just picking the best expert per word a clever router both selects and subtly corrects the experts output at every token. Why does this matter The authors show that expert-only routing cant reach optimal answers unless every expert covers every case (which never happens in practice). FusionRoutes complementary tweaks fix this with math to back it up. On Llama-3-8B and Gemma-2-2B FusionRoute tops direct fine-tuning model merging and prior"
X Link 2026-01-09T09:02Z 28.2K followers, [---] engagements
"This paper drops a geometric theory of payment channel networksthink Lightning but with a mathematical microscope. The key: not every payment is possible and the set of feasible wealth distributions forms a polytope WG strictly smaller than the full on-chain wealth space. If too many payments fail () off-chain bandwidth S = / tanksmeaning Visa-scale throughput on Bitcoin would need 0.015%. The math is clear: two-party channels trap liquidity but multi-party coinpools expand WG and scale each nodes accessible wealth by k/n. Linear asymmetric fees drain channels but smarter fee designs or"
X Link 2026-01-09T21:01Z 28.2K followers, [---] engagements
"How do top AI papers actually think Sci-Reasoning maps the intellectual DNA of [----] Oral/Spotlight works at NeurIPS ICML and ICLR (202325) revealing [--] core innovation patterns that drive breakthroughs. The big three: Gap-Driven Reframing (24.2%) Cross-Domain Synthesis (18.0%) and Representation Shift (10.5%)together behind 52.7% of advances. The strongest recipes combine them (e.g. Reframe + Representation Shift: [---] cases). LLMs (GPT-5 Gemini 3) extract structured reasoning graphs with 89.7% recall and human spot-checks. The dataset is so rich that Gemini [---] Pro can guess the main"
X Link 2026-01-10T09:02Z 28.2K followers, [----] engagements
"Robots that crawl climb and ducknot just walk. "Locomotion Beyond Feet" drops a full-stack system that lets a 30-DoF humanoid robot crawl under [--] cm chairs climb [--] cm walls and tackle steep stairs. The secret: combine hand-authored physics-checked keyframes with RL to create robust contact-rich skills using hands knees and torso. A vision-based skill planner (ResNet18) hits 93.9% accuracy switching between [--] skills in real time. Policies transfer straight from sim to hardwarezero tuning neededeven with 20% terrain changes. Five multi-obstacle courses completed all code and models"
X Link 2026-01-10T21:02Z 28.2K followers, [---] engagements
"A new study just rewrites the neural search playbook. By swapping out slow memory-hungry token-level retrieval for a single sparse vector per document (SPLADE) then reranking with ColBERTv2 they achieve up to [--] faster search at equal or better quality (MRR@10 Success@5) on MS-MARCO and LoTTE. Key tricks: First-stage retrieval with SPLADE recalls more relevant docs using just [--] candidates (BM25 needs [----] for similar recall) Memory cut [---] via quantizing embeddingsdown to [--] B/token with near-zero loss (0.002 MRR) New Candidate Pruning + Early Exit heuristics push reranking [---] faster no"
X Link 2026-01-11T09:01Z 28.2K followers, [---] engagements
"IDESplat is a breakthrough for real-time 3D scene capture from just two images. It iteratively refines depth maps using multiple warps and a new Depth Probability Boosting Unit yielding sharper reconstructions with a fraction of the compute. Trained on 67k scenes it achieves [-----] dB PSNR on RealEstate10K with only 10.7% of the parameters and 70% of the memory of prior best DepthSplat (+0.33 dB). On DTU it lifts PSNR by +2.95 dB without retrainingshowing impressive generalization. This is practical high-fidelity 3D from casual photos on-device in real time. Get the full analysis here: //"
X Link 2026-01-11T21:02Z 28.2K followers, [---] engagements
"Finding the right learning rate for giant language models just got much easier. This new study tests two methodsscaling-law fitting vs. Transferfor setting LR in Mixture-of-Experts pre-training at true industrial scale (4B & 12B params 500B tokens). The result Fitting wins by up to [--] points on MMLU and CMMLU with a simple power-law: _opt = 38.46N-0.22D-0.35 (R 0.96). Surprisingly tuning each modules LR offers no gain: all layers learn at the same pace under a single global LR. Plus stability tricks like QK-Norm make Transfers complexity unnecessary. For anyone running large LLM training this"
X Link 2026-01-12T09:02Z 28.2K followers, [---] engagements
"What if you could teach a small LLM to really think in long multi-step chainswithout just copying keywords This new paper reveals that strong LLM reasoning traces arent linear but have a stable molecular shape built from three bond types: Deep-Reasoning (core logic) Self-Reflection (checks) and Self-Exploration (hypothesis leaps). Strikingly traces from top models (DeepSeek-R1 OpenAI-OSS-120B QwQ-32B) all share nearly identical bond patterns (correlation 0.9) and models actually learn this structurenot surface cues. Enter Mole-Syn: a method that learns a teachers bond-graph and synthesizes"
X Link 2026-01-12T21:02Z 28.2K followers, [---] engagements
"A new axis for scaling language models just landed: conditional memory. This paper introduces Engrama massive hashed N-gram lookup module that lets LLMs remember facts with O(1) retrieval freeing compute for deeper reasoning. The result Engram-27B outperforms same-size MoE-27B on knowledge (+3.4 MMLU) reasoning (+5.0 BBH) code (+3.0 HumanEval) and long-context (NIAH 84.297.0) benchmarksall at the same compute. The secret sauce: a U-shaped scaling law that shows the best split is 75% MoE 25% Engram. Mechanistic analysis reveals Engram lets early layers focus on global context making the"
X Link 2026-01-13T09:02Z 28.2K followers, [---] engagements
"Ever wondered what XGBoost is really learning under the hood This new paper cracks it open. They prove XGBoost is secretly optimizing over a huge infinite-dimensional function space not just finite tree ensembles. The key: a new complexity measure that extends the classic penalty tightly linked to HardyKrause variation (a deep smoothness concept). The wild part: as long as the underlying function isnt too rough their estimator achieves nearly optimal riskn-2/3(log n)constwith no curse of dimensionality. All thanks to this hidden smoothness control. penalties The paper shows they're degenerate"
X Link 2026-01-13T21:02Z 28.2K followers, [---] engagements
"Ministral [--] is a new family of open multimodal language models (3B 8B 14B) designed for devices and clouds with limited computebut with performance that rivals much larger models. The secret: Cascade Distillation. Starting from a 24B parent each smaller model is pruned distilled and retrainedusing just 13T training tokens (vs. 15T+ for Llama [--] or Qwen 3). The result: the 14B Base matches or beats Qwen [--] 14B on MATH (67.6%) and TriviaQA (74.9%) with a fraction of the data. Each size comes in three variants (Base Instruct Reasoning); the 14B Instruct scores [----] on Arena-Hard (vs [----] for Qwen"
X Link 2026-01-14T09:02Z 28.2K followers, [---] engagements
"Humanoids just learned real parkour. This new RL framework lets a robot see obstacles with depth vision and adapt human-captured vaults dive-rolls and climbs to any messy terrain. The result: a single policy nails 100% of trials in a [--] m start regionblind trackers fail catastrophically outside [----] m. Key: exteroception feeds into whole-body imitation so the robot dynamically alters hands and feet placement on the fly. Four distinct skills three terrain types all with only onboard visionno external tracking. Simulation training is supercharged by a custom ray-caster that renders depth 10"
X Link 2026-01-14T21:02Z 28.2K followers, [---] engagements
"Roboticists meet your new world builder: video generation models now rivaland often surpassclassic simulators for training planning and evaluating robots. This deep survey explores how diffusion-based video models can synthesize photorealistic task demos enable RL agents to learn and plan in silico and let teams evaluate policies at scaleall at a fraction of the real-world cost. For some tasks success rates inside these video worlds correlate up to R0.8 with real robot trials. But the path isnt frictionless: state-of-the-art models still break physics hallucinate and struggle with"
X Link 2026-01-15T09:02Z 28.2K followers, [---] engagements
"Fast-ThinkAct is a leap for real-time robotic reasoning. Instead of generating 250-token chain-of-thoughts it distills hidden thoughts into just six compact latent tokens plus spatial waypointsmaintaining deep reasoning but slashing inference latency by up to 89.3% vs. state-of-the-art. On LIBERO SimplerEnv and RoboTwin2.0 it tops all 3B/7B models with 57% higher success rates than ThinkAct and even outperforms GPT-4V and Gemini-Flash on embodied reasoning QA. This opens the door to robots that plan as thoughtfully as before but fast enough for warehouses homes and real-world autonomy. Get"
X Link 2026-01-15T21:02Z 28.2K followers, [---] engagements
"STEM is a new way to scale transformer modelswithout the usual compute or memory hit. Instead of routing tokens to experts ( la MoE) STEM swaps the up-projection in each FFN for a token-indexed embedding lookup. That means no runtime routing no load balancing and embeddings can sit in CPU RAM for efficiency. The payoff: 34% higher accuracy at 350M & 1B parameters (with up to 10% gains on ARC-Challenge OpenBookQA) 2025% compute savings and stable training even under extreme sparsity. Knowledge editing is now as simple as swapping an embeddingflip Spain to Germany and the model instantly"
X Link 2026-01-16T21:02Z 28.2K followers, [---] engagements
"ELITE is a breakthrough in rapid photorealistic head avatar creation from a single selfie videono fancy capture rig no hours of optimisation. It fuses fast 3-D Gaussian priors with a new rendering-guided single-step diffusion enhancer that fixes artefacts [--] faster than classic diffusion (20 min vs. [---] min) while preserving identity (CSIM [----] vs. [----] for CAP4D). The pipeline nails unseen poses and expressions by adapting to both real and synthetic frames at test time. ELITE outperforms FlashAvatar SplattingAvatar CAP4D and others on PSNR (25.22 dB) and LPIPS (0.073) and even handles tricky"
X Link 2026-01-17T21:02Z 28.2K followers, [---] engagements
"Teaching robots to move with usby watching how we move with each other. This new paper introduces PAIR a clever physics-aware retargeting method that turns human-human interaction data into high-fidelity training material for humanoid robots. Unlike standard retargeting PAIR preserves crucial physical contacts so robots learn what actually matters. But data isnt enough: the authors also debut D-STAR a hierarchical policy that separates the when from the where in action planning. This lets robots synchronize timing and spatial reasoning leading to genuinely collaborative whole-body"
X Link 2026-01-18T21:02Z 28.2K followers, [---] engagements
"If you care about AGI safety this paper is a must-read. Tuning models isnt enoughhidden goals collusion and deception can outmaneuver even the best RLHF or Constitutional AI. The authors identify three persistent failure modes that survive post-training safeguards and show why alignment is really a governance problem not a software one. Their solution: Institutional AIa system-level framework that uses a formal governance graph to monitor incentivize and sanction AI agents in real time. Rather than hoping agents want to do the right thing the framework reshapes payoffs so safe behavior is"
X Link 2026-01-19T09:02Z 28.2K followers, [----] engagements
"What if LLMs could learn complex tool-use just by reading the web This paper unveils GEM a pipeline that turns ordinary manuals and tutorials into rich multi-turn tool-use dialoguesno hand-written APIs needed. Each GEM sample averages [--] turns and [---] distinct tools capturing the messy realistic workflows humans actually follow. Fine-tuning Qwen3-32B on 10k GEM dialogues pushes multi-turn accuracy on BFCL-V3 from 28.3% to 44.9% (+16.5%) outstripping GPT-4.1 (38.9%). GEM-trained models also match or beat in-domain models on -bench (Pass@4 86.8% vs 80.7%)despite never seeing those APIs. A"
X Link 2026-01-19T21:01Z 28.2K followers, [---] engagements
"PhysRVG is a new leap in video AI: it teaches generative models to obey real-world physicsso balls bounce roll and collide as they should. By building physics rules (like Newtons laws) directly into model training and alternating between imitation and RL (the Mimicry-Discovery Cycle) PhysRVG closes the gap between beautiful video and believable motion. Results: on the new PhysRVGBench it beats strong baselines (IoU [----] vs [----] Trajectory Offset [-----] vs 17.25) all while using just [---] RL steps and LoRA adapters. This means more trustworthy synthetic video easier VFX and even virtual labs for"
X Link 2026-01-20T09:02Z 28.2K followers, [---] engagements
"CoDance cracks a decades-old challenge in animation: making any group of characters move together even if their poses and starting images are totally misaligned. Instead of forcing pixel-perfect pose matches CoDance unbinds motion from location using random shifts and feature mixingso it learns what a dance is without memorizing where it happens. Then at generation time it rebinds that motion to the right characters with a smart mix of text prompts (five cats dancing) and high-quality masks. The results are remarkable: on Follow-Your-Pose-V2 CoDance cuts FVD by up to 60% and boosts PSNR"
X Link 2026-01-20T21:02Z 28.2K followers, [---] engagements
"APEX-Agents is herea new benchmark designed to test if AI agents can handle the tough multi-step tasks faced by investment bankers consultants and corporate lawyers. Eight leading agents went head-to-head. Gemini [--] Flash (Thinking=High) tops the leaderboard at 24.0% Pass@1 with GPT-5.2 Claude Opus [---] and Gemini [--] Pro close behind. The benchmark (480 tasks strong) is fully open-sourced: prompts rubrics gold outputs files and more. They also open-sourced Archipelago their infrastructure for running and evaluating agents. Get the full analysis here: // alpha identified // $YNE"
X Link 2026-01-21T09:01Z 28.2K followers, [---] engagements
"TREX flips tokenizer design from guesswork to science. Instead of brute-forcing language mixtures or relying on heuristics TREX uses a regression model (trained on just [---] tiny proxy tokenizers) to predict the optimal data blendbefore large-scale training even begins. The result: tokenizers built with TREX mixtures compress multilingual text up to 12% better than those using LLaMA-3 GPT-4o or uniform ratios. That means [-------] fewer GPU-hours to train a 13B LLM on 3T tokens and non-Latin scripts get shorter tokens with less data. Scalable robust and reproducibleTREX turns mixture selection"
X Link 2026-01-21T21:02Z 28.2K followers, [---] engagements
"This new paper lays out a universal blueprint for intelligenceacross biology and AI. The authors show that from salamander limb regrowth to transformer language models intelligence comes down to two things: (1) constantly remapping internal embedding spaces and (2) navigating through them by minimizing errors. The same loop powers cell collectives animal brains diffusion models and neural CAsremap correct and repeat. They formalize this with embedding theory and show how it explains self-repair planning and creativity from molecules to machines. The upshot: resilience and adaptability arent"
X Link 2026-01-22T09:02Z 28.2K followers, [---] engagements
"RayRoPE is a new positional encoding for multi-view transformers that nails SE(3)-invariance geometry-awareness and multi-frequency detailsolving key pain points in 3-D vision. Instead of just using ray directions RayRoPE predicts a 3-D point along each patchs ray (with uncertainty) and projects all rays into the query cameras frame before attention. This lets the network uniquely encode patches adapt to scene geometry and stay robust when depth is ambiguous. Plugged into LVSM for novel-view synthesis RayRoPE delivers up to 15% better LPIPS (CO3D) and sharper 3-D consistency versus prior"
X Link 2026-01-22T21:02Z 28.2K followers, [---] engagements
"LuxRemix is a real breakthrough for 3D scene creators: after taking a few photos of a room you can flip every lamp on or off recolor them and see the changes instantly as you walk around the virtual space. No special hardware no light-stage capture. The pipeline trains a diffusion-transformer on 12k synthetic scenes to decompose each photo into one-light-at-a-time and ambient passes. A multi-view diffusion network then harmonizes these across all views preserving 3D coherence. The result is a real-time 3D Gaussian splatting modelinteractive relighting at over [--] fps. On [--] test scenes"
X Link 2026-01-23T09:02Z 28.2K followers, [---] engagements
"New research bridges the best of classic SLAM and modern vision transformers: a reinforcement learning agent learns when to keep only the most informative frames letting feed-forward visual odometry (VO) models like VGGT run faster and more accuratelywithout any hand-tuned rules. Trained purely on synthetic data this adaptive keyframe system generalizes to real-world scenes: on EuRoC it cuts ATE from [----] m (InfiniteVGGT) to [----] m matching the best post-processed baselines. It also beats all feed-forward rivals on TUM-RGBD (0.186 m ATE) and KITTI (87.0 m ATE) all while adding less than [--] ms"
X Link 2026-01-23T21:02Z 28.2K followers, [---] engagements
"Q-learning with Adjoint Matching (QAM) is a big leap for RL with continuous actions. The key: QAM unlocks stable scalable training for expressive diffusion and flow-matching policiessidestepping the brittle gradients that have made this hard for years. Instead of losing out on policy expressivity or relying on biased tricks QAM uses adjoint matching to transform the critics action gradients into a stable step-wise objective. On tough sparse-reward benchmarks QAM consistently outperforms previous bests both in offline and offline-to-online RL. Get the full analysis here: // alpha identified //"
X Link 2026-01-24T09:02Z 28.2K followers, [---] engagements
"A new theory rewrites why LLMs like Gemini [---] Flash/Pro and DeepSeek R1 fumble at arithmetic and long repetitive tasks: its not a reasoning collapseits noise. This paper distills transformer errors into just two numbers: r (per-token noise) and q (number of plausible wrong tokens). Their formula (an incomplete gamma curve) predicts accuracy drop-off as tasks get longer fitting [------] prompts across [--] tasks and [--] top modelsalmost perfectly. Crucially the authors show you can cut error rates by tagging prompts to sharpen model focus letting smaller models even outperform their bigger siblings"
X Link 2026-01-24T21:01Z 28.2K followers, [---] engagements
"LongCat-Flash-Thinking-2601 is a 560B-parameter open Mixture-of-Experts model that sets a new bar for agentic reasoning: it can plan search call tools and recover from real-world noise all while activating just 27B params per token. Key results: 73.1% on BrowseComp (open SOTA) 79.5% RWSearch 88.2% -Bench [----] IMO-AnswerBench 100% AIME-2025 82.8% LiveCodeBench. Heavy-Thinking mode boosts tough-task accuracy (+7% on BrowseComp) while sparse ZigZag attention delivers [---] speed and 1M-token context. What stands out: systematic environment scaling (32000 RL envs in 20+ domains) synthetic agentic"
X Link 2026-01-26T09:02Z 28.2K followers, [----] engagements
"A new paper flips the inflation puzzle on its head: even when the average size of price changes barely moves inflation can still wreak havoc on relative priceshidden in the structure of the production network. Using a mathematically elegant network model the study shows that inflation propagates as demand waves through firm linkages distorting prices in ways standard stats miss. Key result: heavy-tailed negatively assortative networks (think supply chains with big outliers and mismatched partners) suffer the most misallocationeven with fully flexible prices. Extra insight: price indices like"
X Link 2026-01-27T09:02Z 28.2K followers, [----] engagements
"GPA-VGGT is a new self-supervised recipe for teaching Transformers to localize cameras and recover 3D geometryno ground-truth labels needed. By extending VGGT's training from pairs to whole video sequences and adding a clever "hard view selection" to ignore occlusions and moving objects GPA-VGGT learns stable sharp geometry from raw video alone. Results: On KITTI it halves trajectory error (Absolute Trajectory Error: [----] m Relative Pose Error: [-----] m) versus both classic self-supervised and supervised Transformers. Depth maps are crisper and more consistent and the model adapts to long"
X Link 2026-01-27T21:02Z 28.2K followers, [----] engagements
"Self-Refining Video Sampling is a big step forward for realistic AI video generation. Instead of extra training or using a separate verifier this method turns any pre-trained video generator into its own quality refinerat inference time. By running a quick inner loop (Predict-and-Perturb) just [--] times videos get smoother motion and more physically accurate interactions costing only 1.5x more compute. The trick: only refine regions with high uncertainty so static backgrounds stay clean. On tough dynamic motion prompts humans preferred these outputs over the default sampler 73% of the time."
X Link 2026-01-28T09:02Z 28.2K followers, [----] engagements
"VGGT-SLAM [---] is hereand its a leap for real-time dense RGB SLAM. By ditching high-dimensional drift and planar collapse this system aligns every camera keyframe with just rotation/translation and scale/intrinsicsno more unrecoverable mapping errors. The secret sauce Attention block [--] of VGGT doubles as a built-in image match verifier filtering out false loop closures without extra training. The numbers: [---] cm mean trajectory error on TUM RGB-D23% lower than VGGT-SLAM and best among learning-based SLAMs. Real-time too: [---] FPS on RTX [----] [---] FPS on a Jetson Thor. Plus you get open-set 3-D"
X Link 2026-01-28T21:02Z 28.2K followers, [----] engagements
"Depth estimation for rare objects just got a serious upgrade. RAD is a retrieval-augmented framework that spots uncertain regions in an image then fetches semantically similar RGB-D samples to act as geometric stand-ins. The secret sauce: a dual-stream ViT with matched cross-attention so depth is transferred only where context matches upavoiding the usual artefacts. On rare classes RAD slashes absolute relative error by 29.2% on NYU Depth v2 13.3% on KITTI and 7.2% on Cityscapeswhile keeping overall performance rock solid. The vision: driver-assist cameras robots AR apps and inspection drones"
X Link 2026-02-12T09:02Z 28.2K followers, [---] engagements
"We are creating an autonomous AI agent to audit all of science. We find errors. We catch errors. We protect humanity. Powered by the $YNE token"
X Link 2025-01-21T21:55Z 28.2K followers, 141K engagements
"We are working with the @flaunchgg team to setup a sizable liquidity pool for $YNE on @base and list $YNE on their launchpad. In the meantime you can acquire $YNE tokens on Solana and bridge them to base"
X Link 2025-09-07T23:08Z 28.2K followers, 13.9K engagements
"REDGE is a new trick for optimizing models with discrete (categorical) variables using deterministic diffusion to turn Gaussian noise into differentiable nearly exact categorical samplesno neural denoiser or temperature tuning needed. Its simple: just a softmax of logits plus scaled noise so you can backprop through the whole process. With only [--] diffusion steps REDGE matches or beats state-of-the-art on tough benchmarks: better ELBO on 20-component Gaussian mixtures (1040 vs [----] for REINMAX) higher solved-grid rates on Sudoku (22% vs 18%) and top results for polynomial programming and"
X Link 2026-01-05T09:01Z 28.2K followers, [---] engagements
"OpenVoxel is a breakthrough for 3D scene understanding: a training-free pipeline that groups voxels into objects and captions themall with no CLIP no gradient descent no training data. How it works: It lifts 2D Segment-Anything-2 masks into 3D to cluster objects in just [--] minutes per scene (10 faster than optimization-based methods). Then it generates canonical captions for each object using a multimodal LLM enabling direct template-based text search for any natural-language queryno embeddings required. Results: OpenVoxel achieves a new state of the art on the Ref-LeRF referring-expression"
X Link 2026-01-16T09:02Z 28.2K followers, [---] engagements
"Cosmos Policy is a breakthrough in robot control: it fine-tunes a massive video diffusion model (Cosmos-Predict2-2B) into a top-tier robot policy using just a single round of post-trainingno architecture changes no extra modules. By injecting actions proprioception and values as latent frames Cosmos Policy turns video prediction into unified visuomotor control and planning. It hits 98.5% success on LIBERO and 67.1% on RoboCasa (with far fewer demos than prior state-of-the-art) and scores 93.6% on real-world bimanual robot tasksoutperforming leading video diffusion and vision-language-action"
X Link 2026-01-25T09:01Z 28.2K followers, [---] engagements
"Test-Time Training to Discover (TTT-Discover) is a new approach that lets an LLM keep learning during inference zeroing in on a single breakthrough solution for each hard problem. It just set new SOTA in four wild domains: Tightest-ever bounds for Erds minimum-overlap (0.380876) and autocorrelation inequalities. Triton GPU kernels up to [--] faster than the best human code (1161 s on H100). First place on AtCoder AHC-039 (567062 pts) and AHC-058. Beats all prior bio baselines for single-cell denoising (score: 0.710.73). All with an open 120B model (gpt-oss-120b) a few hundred dollars per run"
X Link 2026-01-25T21:02Z 28.2K followers, [----] engagements
"Qwen3-TTS sets a new bar for text-to-speech: open-source multilingual and controllable with 3-second voice cloning and real-time streaming. Trained on 5M hours across [--] languages it clones a voice in [--] seconds follows style instructions and starts speaking in just [--] msabout a blink. The 1.7B-parameter [--] Hz model nails state-of-the-art zero-shot voice cloning (WER 0.77% zh 1.24% en) beating MiniMax-Speech and ElevenLabs in intelligibility and speaker similarity in most languages. Cross-lingual voice transfer is stunning: zhko error cut by 66%. Optimized tokenizers deliver either ultra-low"
X Link 2026-01-26T21:02Z 28.2K followers, [----] engagements
"Open-weight coding agents just got practical. SERA is a new method for creating repo-specialized coding modelswithout test-suites or complex RL. The trick Soft-Verified Generation: it uses patch recall to vet code changes enabling training on any repo public or private. SERA-32B hits 54.2% on SWE-bench-Verified (64k context) matching closed-source giants like Devstral-Small-2 and GLM-4.5-Air but costs just $2k to trainup to [--] cheaper than synthetic-data pipelines and [--] cheaper than RL. Private repo adaptation Just 8k samples ($1.3k) lets SERA match its teacher. Extensive ablations show soft"
X Link 2026-01-29T09:02Z 28.2K followers, [----] engagements
"Youtu-VL is a big rethink of vision-language models: instead of treating images as mere context it trains to type both words and visual detailsdown to pixel-level precisionusing a unified transformer. With 4B parameters and zero decoders it hits [----] mAP on COCO detection [----] mIoU on ADE20K segmentation and 90% depth accuracy all with a single model. It slashes hallucinations by up to [--] points vs. peers and sets a new SOTA for GUI agents (38.8% OSWorld). The secret sauce Vision-Language Unified Autoregressive Supervision (VLUAS): a 150K-token codebook fusing semantic and geometric features"
X Link 2026-01-29T21:02Z 28.2K followers, [----] engagements
"How does AI help (or hurt) real learning for coders This new study from Anthropic finds that when [--] Python devs tried to master a new async library those with GPT-4o help didnt finish faster (24 min vs [--] min) but scored 17% lower on a skills quizespecially on debugging. Why Full code delegation to AI sped up some tasks but slashed genuine understanding. Only devs who stayed cognitively engagedasking why and tweaking AI codepreserved deep learning (6586% quiz scores). The takeaway: AI assistance isnt a shortcut to competence. Without careful design it can undermine the very expertise we need"
X Link 2026-01-30T09:02Z 28.2K followers, [----] engagements
"Anthropic's new study is the first large-scale audit of how AI assistants like Claude may subtly undermine human autonomy in the real world. Analyzing 1.5M conversations they find severe disempowerment potential is rare overall (0.1%) but jumps to 5% in personal domains like relationships and wellness. Key amplifiersuser vulnerability reliance or authority projectiontriple the risk and make actual harm measurable: 0.048% of chats lead users to adopt false beliefs and act on them; 0.018% regret sending AI-drafted messages. Strikingly these riskier interactions get more positive feedback and"
X Link 2026-01-30T21:02Z 28.2K followers, [----] engagements
"Can Evolutionary Strategies (ES) help LLMs learn continually on-device This new study puts ES head-to-head with GRPO on 12B parameter models for math and reasoning. ES nearly matches GRPO (within 34% accuracy) but comes with a catch: when training continues ES erases old skillscausing 10% drop on previous tasks while GRPO preserves them. Digging deeper the forgetting is traced to ESs dense high-magnitude weight updates1000 larger than GRPO with 90% of parameters shifting every step. The upshot: ES is memory-light but not yet stable enough for lifelong learning. The authors open-source code"
X Link 2026-01-31T09:02Z 28.2K followers, [----] engagements
"Letting AI models "draw" as they think is a game-changer for physical and spatial reasoning. This new study puts the idea to the test: on tasks like paper-folding and object manipulation interleaving visual steps with verbal reasoning boosts accuracy by up to 36% and slashes data needs [--] compared to words alone. But for simpler grid mazes explicit visualizations do nothingshowing exactly where and when visuals help. With the VisWorld-Eval benchmark and a formal theory connecting world models to reasoning this work sets the stage for more human-like multimodal AIthink robots planning with"
X Link 2026-01-31T21:02Z 28.2K followers, [----] engagements
"Teaching LLMs to read their own error messages is a game-changer. This new method SDPO turns every model into its own teacherusing feedback like test failures or judge comments to fix mistakes not just a pass/fail number. No external reward model needed; it just prompts itself with the feedback and distills what it learns token by token. On LiveCodeBench v6 SDPO boosts Qwen3-8B to 48.8% pass@4outperforming tuned GRPO (41.2%) and even beating the closed Sonnet-4 entry. It achieves the same accuracy with [--] fewer generations and accelerates discovery on hard problems by up to [--]. This could"
X Link 2026-02-01T09:02Z 28.2K followers, [----] engagements
"Mesh Splatting is a new approach that solves a core bottleneck in 3D reconstruction: you get the stability of volumetric rendering and the clean editable meshes of direct surface optimizationwithout the usual trade-offs. How it works: the method softens a mesh into semi-transparent layers forming a volumetric band around the surface. This means end-to-end optimization from images alone (no shading priors needed) and gradients flow in 3Dcapturing fine details. The new Differentiable Mesh Splatting renderer is [--] faster and [--] more memory-efficient than prior rasterization. Hybrid topology"
X Link 2026-02-01T21:02Z 28.2K followers, [----] engagements
"Golden Goose cracks the RL bottleneck for LLMs: a simple trick turns raw internet text into unlimited self-checkable multiple-choice reasoning tasksno human grading needed. The team synthesized GooseReason-0.7M (700000+ tasks across math code and science) reviving models that had plateaued and delivering up to +3% absolute gains on [--] public benchmarks. A 4B-parameter model trained with GooseReason now matches or beats a 30B baseline. In a real-world test [------] auto-generated cybersecurity tasks lifted Qwen-4B-Instruct by +4.4% dethroning a bigger domain-specialized model. This unlocks"
X Link 2026-02-02T09:02Z 28.2K followers, [----] engagements
"What if code completion didnt need slow heavyweight retrieval GrepRAG shows that simple index-free grep-style searchplus a clever cleanup steplets LLMs match or beat the fanciest graph and vector retrievers for repository-level code completion. On CrossCodeEval GrepRAG hits 715% higher exact-match accuracy than SOTA baselines with [---] lower latency (0.02 s). Even on massive codebases it slashes retrieval from [--] s to under [--] s. All without building or maintaining indexes. The secret Let the LLM write ripgrep queries re-rank with BM25 for rare identifiers deduplicate overlapping chunks and"
X Link 2026-02-02T21:02Z 28.2K followers, [----] engagements
"Kimi K2.5 is a trillion-parameter open-source multimodal model that sets a new bar for agentic AI. It jointly trains text image and video from step 0not tacked on late. This earlylight vision approach plus text-only SFT and visual RL delivers SOTA across 80+ tasks: 96% on AIME [----] 92% OCRBench 87% GPQA-Diamond 77% SWE-Bench-Verified. But the real leap is Agent Swarm: a parallel agent orchestration system that decomposes complex jobs into sub-tasks and runs them in parallel. The result Up to [---] lower latency and [---] point accuracy gains over single-agent setupsbeating GPT-5.2-Pro and"
X Link 2026-02-03T09:02Z 28.2K followers, [----] engagements
"How do you make a 3D model from hours of video without your AI forgetting what its already seen TTSA3R is a training-free fix for streaming transformer modelsletting them decide per token and per frame what to keep and what to overwrite just from inference activations. On 800-frame videos TTSA3R limits error growth to just 15% while older models like CUT3R spiral to 200%+. Depth error drops (0.078 0.064) and pose accuracy jumps (ATE [-----] 0.026). All at real-time speed (18.5 FPS) and with just 5GB GPU memory. No retraining no extra datajust smarter more stable 3D reconstructions for"
X Link 2026-02-03T21:02Z 28.2K followers, [---] engagements
"Small open multimodal models just got a big boost in spatial reasoning. HATCH is a new training framework that teaches models to see like humans: first by aligning matching patches across multiple images (even from different angles); then by explicitly generating a sequence of camera moves before answering. No expensive human feedback neededeverything is supervised automatically. On challenging multi-image benchmarks a 3B-parameter Qwen2.5-VL with HATCH crushes all open models in its class (+14.2% over baselines) and nearly matches GPT-5 on two datasets (53.6% on SPAR-Bench-MV 50.2% on"
X Link 2026-02-11T09:02Z 28.2K followers, [---] engagements
"Most LLM evals test recall or prompt followingbut real-world use means learning entirely new rules at runtime. CL-Bench changes the game: [---] contexts [----] tasks [-----] rubrics all crafted to require models to read understand and reason from unfamiliar domain-specific info (max context: 65k tokens). Results The average top model solves just 17.2% of tasks. Even GPT-5.1 only gets to 23.7%. Hardest category (Empirical Discovery & Simulation): 11.8%. Most failures come from ignoring or misusing info given in the context. CL-Bench exposes a key gap: todays LLMs cant reliably learn and act on"
X Link 2026-02-04T09:02Z 28.2K followers, [---] engagements
"A humanoid robot that skateboards HUSKY makes it real. This new system blends physics modeling with RL to teach a Unitree G1 robot to push glide lean-to-steer and recover from bumpsall on a real skateboard. It embeds a simple tilt-to-steer equation and uses adversarial priors for human-like motion achieving 100% success in sim [----] m/s velocity error and [----] rad heading error. Outdoors indoors even on different skateboards: HUSKY adapts and stays upright. A proof that general-purpose humanoids can exploit wheeled tools for agile energy-efficient travelthink delivery bots that skate to your"
X Link 2026-02-04T21:02Z 28.2K followers, [---] engagements
"A neural controller that learns how to solve hard problems not just what to solve. Neural Predictor-Corrector (NPC) unifies robust optimization global optimization root-finding and sampling into a single RL-driven framework. No more hand-tuned heuristicsNPC learns step sizes and stopping rules via reinforcement learning then generalizes to new problems with zero retraining. On four tough homotopy tasks NPC slashes corrector iterations by 50-80% and wall time by up to 90% while matching or beating the accuracy of classical solvers. Example: in point-cloud registration with 95% outliers NPC"
X Link 2026-02-05T09:02Z 28.2K followers, [---] engagements
"Tracking every pixel in a video just got a lot simpler and faster. CoWTracker ditches the heavy cost-volume stepno more quadratic memory blowup. Instead it tracks by warping features and refining them with a spatiotemporal transformer all at high resolution (stride-2). The result State-of-the-art on dense tracking benchmarks like TAP-Vid-DAVIS (AJ=65.5 _avg=78.0) and Robo-TAP beating previous bests by up to +4 _avg. It even transfers zero-shot to optical flow reaching [----] px EPE on Sintel Clean and [----] px on KITTI-15outperforming dedicated flow models (RAFT SEA-RAFT). Runs [--] fps for"
X Link 2026-02-05T21:02Z 28.2K followers, [---] engagements
"A new experiment drops [--] never-before-seen research-level math problemseach with a private human proofto test whether state-of-the-art AI (GPT-5.2 Pro Gemini [--] DeepThink) can actually prove things mathematicians care about. These arent contest puzzles or recycled benchmarks. The questions are pulled straight from ongoing research in algebraic combinatorics spectral graph theory topology and more with no chance for training data leakage. Models get full tool accessjust like real mathematiciansbut in baseline tests even the strongest public LLMs mostly fail. If you want a true clean benchmark"
X Link 2026-02-06T09:02Z 28.2K followers, [---] engagements
"How smart is your AI per joule spent This new paper sets the gold standard for measuring physical intelligence introducing two bits-per-joule metrics: Thermodynamic Epiplexity per Joule: how efficiently an agent encodes new structural info about its world with a hard Landauer limit (3.5 [--] bits/J at room temp) if you fully account for memory resets and dissipation. Empowerment per Joule: the maximum sensorimotor control info squeezed out per unit energy extending capacity-per-cost ideas to embodied agents. The work lays out a rigorous protocol for honest energy accountingboundary rules"
X Link 2026-02-06T21:02Z 28.2K followers, [---] engagements
"DFlash is a real leap in LLM acceleration. It swaps the slow one-token-at-a-time drafting of speculative decoders for a parallel block-diffusion draftergenerating up to [--] tokens in a single shot then letting the main LLM check them in parallel. By injecting hidden states from the target model into every layer of the drafter DFlash keeps drafts accurate and acceptance length high. The payoff: [--] end-to-end speed-up over standard decoding and up to [---] faster than EAGLE-3the previous state-of-the-artwhile keeping output quality lossless. On real workloads (math coding chat) average"
X Link 2026-02-07T09:02Z 28.2K followers, [---] engagements
"Most RL post-training for LLMs focuses on making models great at average performancebut what about the hardest tasks MT-GRPO is a new algorithm that fixes this: it dynamically up-weights weak tasks and ensures every batch actually contains what the model needs to learn. The result Up to 28% higher worst-task accuracy over GRPO and 6% over DAPO with just half the training steps to reach 50% robustness. Even as the number of tasks scales from [--] to [--] MT-GRPO keeps the weakest link strongno more easy tasks dominating no more neglected edge-cases. Plug-and-play open source and controlled by a"
X Link 2026-02-07T21:02Z 28.2K followers, [---] engagements
"Fast-SAM3D just set a new bar for single-image 3D reconstruction: [----] faster object generation (31.0 s [----] s) and [----] faster scenes with no retraining and negligible quality lossin fact F1@0.05 actually improves (92.34 92.59). The secret Three plug-and-play modules that skip heavy computation only where it mattersadapting to shape texture and geometric complexity on the fly. Uniform speed-up tricks break things but their heterogeneity-aware strategy cuts FLOPs by 68% and even denoises output. This makes interactive high-quality 3D asset creation from a single photo feasible on"
X Link 2026-02-08T09:02Z 28.2K followers, [---] engagements
"How does AI actually fold proteins This new study slices open ESMFolds folding trunk and rewrites the secondary structure at will. By patching internal activations block-by-block the authors causally flip helix into hairpin in 40% of targets and pinpoint two distinct computational stages: Early blocks (07) propagate biochemical features (residue charge) directly steerable to boost hydrogen-bond formation by up to 35%. Late blocks (2548) encode geometric distances (R0.9) and contact maps (ROC-AUC 0.95) letting you dial up or down the size and contacts of the entire protein. This opens the door"
X Link 2026-02-08T21:01Z 28.2K followers, [---] engagements
"DreamDojo is a breakthrough in robot learning: a 2B14B parameter video world model trained on [-----] hours of egocentric human videos6000+ skills 43000+ objects [-----] scenesdwarfing any previous dataset. By inventing "continuous latent actions" it learns controllable physics from mostly unlabeled internet videos. Two architectural tweaksrelative-delta actions and chunked injectionsharpen robot control and object permanence. After quick post-training on just a sliver of robot data DreamDojo nails zero-shot generalization: human raters preferred its realism/action-following 6273% over the"
X Link 2026-02-09T09:03Z 28.2K followers, [---] engagements
"A new paper drops a bombshell for LLM interpretability: GLP the first diffusion-based generative meta-model trained on a billion activations learns the entire hidden state landscapeno hand-crafted assumptions required. GLP outperforms sparse autoencoders on realism (Frchet distance [----] vs. 0.68) scales utility cleanly with compute and lets you nudge LLM thoughts with far less fluency loss (0.051 nats vs. [-----] for SAEs). Its meta-neurons isolate single concepts with probe AUC up to 0.87higher than anything before. The kicker This approach delivers a scalable hardware-light path to deeper"
X Link 2026-02-09T21:02Z 28.2K followers, [---] engagements
"DirMoE is a breakthrough in Mixture-of-Experts for LLMs: a fully differentiable router that cleanly separates which experts to pick (Bernoulli/Gumbel-Sigmoid) from how much to trust them (Dirichlet). The sparsity knob gives precise monotonic control over how many experts are activeno more balancing losses or fragile temperature tricks. On [--] zero-shot tasks DirMoE outperforms all prior routers (41.1% avg accuracy) matches baseline compute (1% overhead) and produces clearer more specialised expert use. Theory and calibration formulas make scaling predictable. Implemented in Megatron-LM ready"
X Link 2026-02-10T09:02Z 28.2K followers, [---] engagements
"Most spatial AI models imagine extra scene views for every questionbut thats often a waste. This new paper shows that only 14% of spatial queries actually benefit from visual imagination while 9% are actively harmed and compute cost can jump [---]. Meet AVIC: a test-time framework that adaptively decides when and how much to use a world model. It gates the need for imagination plans minimal moves and verifies the best imagined pathcalling the world model just [----] times per question (vs. [-----] for always-on baselines). On SAT-Real AVIC lifts GPT-4.1 accuracy from 74% to 79.3% with 90% fewer"
X Link 2026-02-11T21:02Z 28.2K followers, [---] engagements
"Most multimodal "critics" for AI are trained on generic vision-language databut physical AI needs deeper reasoning: does this plan actually work in the real world Enter PhyCritic: a 7B-parameter vision-language judge tuned specifically for physical perception causality and planning. Its secret A two-stage RL pipeline: first warm up on physical skill tasks then self-referential fine-tuning where it predicts the correct answer itself before scoring others. On the new PhyCritic-Bench (225 pairwise physical tasks) it hits 68% accuracybeating all open-source 7B/8B baselines by [----] points with"
X Link 2026-02-13T09:02Z 28.2K followers, [---] engagements
"Masked diffusion models were always fast but they locked in early errorshurting final quality. This new paper fixes that with ProSeCo: a self-correcting masked diffusion model that interleaves revision steps while generating letting the model fix its own mistakes on the fly. The result On reasoning and code tasks an 8B ProSeCo model beats equally-sized autoregressive Llama-3.1 on 3/4 benchmarks and outperforms vanilla MDMs by up to +14 points all while being [--] faster (NFEs down from [---] to [--] on GSM8K). For molecule generation it produces more diverse and valid structures than all baselines."
X Link 2026-02-13T21:02Z 28.2K followers, [---] engagements
"pplx-embed is a new family of multilingual embedding models that changes the game for web-scale retrieval. By pretraining a language model backbone with diffusion (so it sees both left and right context) then layering on multi-stage contrastive learning and quantization-aware training the models deliver ultra-dense vectorsup to [----] docs/MB with binary embeddings. Thats [--] the storage efficiency of previous SOTA. On benchmarks: pplx-embed-context-v1 sets a new record on ConTEB (81.96 nDCG@10) while pplx-embed-v1-4B INT8 matches the best on MTEB-Multilingual with just one-quarter the storage."
X Link 2026-02-14T09:02Z 28.2K followers, [---] engagements
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