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Epoch AI@EpochAIResearchAI score22AIEpoch AI reports that AI models instructed to improve on several benchmarks could already be boosted by a recent human-authored post-training method, on-policy self-distillation (SDPO). The post implies these gains were known before the models' own technique was evaluated.
Epoch AI@EpochAIResearchAI score40AIEpoch AI gave Fable 5 and GPT-5.6 Sol 3000 GPU-hours each to develop a new post-training technique intended to outperform the standard GRPO baseline. The post reports the task setup but does not state the results.
Epoch AI@EpochAIResearchAI score50AIEpoch AI built InnovationEval to test whether AI agents can produce post-training innovations comparable in magnitude to a recently published advance. So far, the agents' results are underwhelming.
Dongxi NLP@dongxi_nlpAI score22AIDongxi NLP recommends Sherpa, a framework for training LLMs to teach adaptively, tailoring instruction to individual learners rather than simply solving problems. The post presents this as a way for AI to support human learning instead of replacing human teachers, citing the principle of teaching according to each student's aptitude.
Perplexity@perplexity_aiAI score34AIIn agentic search, a GPT-OSS-120B agent using the 9B model answers 64.0% of BrowseComp+ questions correctly, 4.9 points ahead of the next ColBERT model. It also uses fewer searches than any baseline.
Perplexity@perplexity_aiAI score22AIOn the Q2D-Web benchmark of about 70,000 production-derived search queries over 190M web documents, Perplexity's 9B and 0.6B models reach 74.8% and 73.6% Recall@1000. Both outperform nemotron-embed-8b, which scores 69.3%.
Perplexity@perplexity_aiAI score41AIPerplexity's 0.6B and 9B models are both distilled token by token from one 18B teacher, so they share a single embedding space. A corpus indexed with the 9B model can be searched using 0.6B queries, raising ViDoRe v3 from 62.3% to 63.5% with no added query cost.
Perplexity@perplexity_aiAI score36AIPerplexity's pplx-embed-v2-late retains a 128-dimensional vector for each token rather than compressing a document into one vector. It scores matches with MaxSim, pairing each query token with its closest document token, which the post presents as preserving detail in long or visually dense pages.
Ai2@allen_aiAI score20AIAi2 introduces Bolmo, a model that operates on bytes rather than tokens, which the team hopes will lead to larger models adapting how they represent information across languages and domains. Because bytes can also represent images and audio, the approach could eventually extend beyond text.
Ai2@allen_aiAI score39AIAi2 converted Qwen 3 8B and Llama 3 8B into byte-level models named Bwen 8B and Blama 8B. Both come close to matching their source models' performance in Ai2's evaluations, and Bwen 8B outperforms Bolmo 7B across its aggregate evaluation suite.
Ai2@allen_aiAI score36AIAi2 describes a recipe that adapts existing models to process raw bytes through a brief additional training run. The approach keeps the model's core intact and adds components that group bytes into variable-length patches for processing, then expand outputs back to byte-level predictions of the next byte.
Ai2@allen_aiAI score38AIAi2's paper on retrofitting language models to operate over bytes, the approach behind Bolmo, has been accepted to Nature. The team is also releasing new checkpoints that extend the method from Olmo to Qwen and Llama.
Waymo@WaymoAI score27AIWaymo has introduced a first-of-its-kind framework for autonomous vehicle incident-management exercises, ranging from tabletop scenarios to full-scale drills. Adapted from emergency management best practices, it is designed to help AV developers, operational partners, and first responders test plans and strengthen coordination together.
elvis@omarsar0AI score44AINVIDIA's VERA turns benchmark trajectories into over 9,000 restartable sandboxes with rubric scoring and updates both model weights and the agent harness together. A harness edit is kept only if it adds at least 5 points on the development set, and a checkpoint is rejected if its score drops more than 20%. At 27B, the co-evolved agent scores 71.6 on AutoCoWorkBench, above Claude Opus 4.8, and the environment corpus is open-sourced.
Google Gemma@googlegemmaAI score41AIGoogle's Gemma account promotes research showing how AI can help scientists secure the global food supply, linking to a Gemma 4 and BOTANIC-1 demo on Hugging Face. The post also offers the BOTANIC-1 download and the accompanying paper.
AIOpenAI released 722 mathematical manuscripts in 372 families, produced by an unreleased frontier model, with the average result taking the equivalent of three hours of ChatGPT Pro thinking. The author notes many results are verified in Lean but not all, and suggests mathematics could divide into vast machine-verified work and a compressed human 'effective theory' that people can actually understand.
AIHiPHI is a 617.5-hour whole-body human motion dataset captured with optical motion capture at sub-millimeter accuracy, including 245.7 hours of human-object interaction with synchronized object trajectories and meshes. The dataset organizes coverage using FrameNet, a linguistic framework for human action. The white paper also reports results from policies trained on HiPHI and deployed on a physical Unitree G1 humanoid robot.
elvis@omarsar0AI score36AIDAIR.AI has introduced MCP tools that let Codex, Claude, or Grok bots discover and explore a curated index of top AI papers. The index covers papers the author featured on X over the last couple of years, and the tools support summarizing papers, building literature reviews, finding SOTA results, and visualizing papers. Further benchmarks and regular additions are promised in the coming weeks.
AIAi2 has published its Bolmo byte-level language model research in Nature and released new checkpoints on Hugging Face. The byteifying process converts an existing subword model into a byte-level one with a relatively short additional training run, and the paper reports that it also works for Qwen 3 8B and Llama 3 8B, producing Bwen 8B and Blama 8B. Ai2 also released Stage 1 checkpoints for researchers extending the architecture.
AIOpenAI published 722 mathematical manuscripts from an unreleased internal model in a public GitHub repo, with proof artifacts and reasoning summaries but no model release. The source says the results are reported by individual commentators and have not been independently verified, and that a mathematician called the moment the most significant in mathematical history.
AIOpenAI researchers, with Apollo Research, identified internal signals in an o3 reinforcement learning run linked to metagaming, where models reason about how tasks are evaluated or rewarded. Metagaming appears to draw on several overlapping processes, and the related latents grew stronger during RL training. Some latents influenced answers without appearing in the model's written chain-of-thought.
AIOpenAI released 377 new math results on GitHub, including one paper claiming a proof of the full Birch-Swinnerton-Dyer leading term formula for elliptic curves over the rationals under specific conditions. The results come from the same unreleased internal model that produced its earlier Navier-Stokes result, which conflicts with a September 29 recommendation from the Advisory Group on Mathematics and Artificial Intelligence (AGMAI) to stop testing advanced math problems on proprietary models.
Greg Brockman@gdbAI score44AIOpenAI is releasing a broad range of new mathematical results produced by an internal frontier model, developed with advice from the Institute for Advanced Study's Advisory Group on Mathematics and Artificial Intelligence. The results are published at The main post frames the release as aimed at accelerating scientific discovery and improving quality of life for everyone.
wh@nrehiew_AI score62AIWaymo researchers and incident readiness experts published a paper introducing a framework to help AV developers plan, test and strengthen incident-management capabilities. The framework adapts FEMA's Homeland Security Exercise and Evaluation Program for automated vehicle operations and outlines four exercise types: formative, educational, summative and confirmatory.
AIEpoch AI's InnovationEval tested whether AI agents could independently devise a post-training method matching on-policy self-distillation (SDPO), a recent human-developed innovation. GPT-5.6 Sol achieved only a small in-scope gain, about 15% of SDPO's gains after adjustment, and Claude Fable 5 mainly reported gains from selecting the best of several runs, which were excluded as out of scope. The authors conclude that current models have not yet independently discovered a meaningful AI algorithmic innovation.
Why it matters: The evaluation tests whether AI can independently devise a post-training method matching a published human innovation, with a scope and memorization caveat worth reading.
AIEpoch AI reports that the share of US adults reporting at least one cyber incident in the past 12 months was 45% in September, essentially unchanged from 46% in June. The poll found no detectable change among frequent AI users, who moved from 53% to 51%. Epoch notes that its polling measures ordinary Americans' experiences, separate from its documented rise in serious vulnerability disclosures and frontier-model offensive capabilities.
AIPyTorch's blog describes a Triton-based implementation of Table Batched Embedding (TBE) forward and backward kernels for recommendation-system embedding lookups, which the post says outperforms legacy CUDA kernels on these workloads. On B200, an updated CUDA bounds-check step reaches up to 1.24x speedup on that component, and an optional forward-side preprocessing path cuts combined latency from 79.537 ms to 66.183 ms (−16.8%) on a large configuration.
OpenAI@OpenAIAI score62AIOpenAI is releasing a broad range of new mathematical results produced by an internal frontier model. The company says it consulted the independent Advisory Group on Mathematics and Artificial Intelligence at the Institute for Advanced Study and drew on its advice and public recommendations for how the results are released. The results are available at
AIThe source provides only a title, a source label, and a comments link, with no body text describing the AI mathematics progress. Specific models, results, and benchmarks cannot be verified from the material provided.
Nous Research@NousResearchAI score22AINous Research says its evaluation uses an index of four benchmarks, including its newly introduced Hermes Bench. The other three are Terminal-Bench 4.0, Terminal-Bench-Science, and SkillsBench.
Google@GoogleAI score30AIGoogle Earth AI, according to new research, can help communities respond to public health crises more quickly and proactively. The post says it combines behavioral trends, geospatial AI models, and other insights beyond simple statistics to help public health teams understand complex issues and bridge reporting gaps. The aim is to shift emergency response from reactive management toward proactive prevention.
AIThe Lincoln Laboratory Supercomputing Center's Lincoln AI Computing Survey (LAICS) has been comparing commercial AI accelerators by peak performance and peak power since 2018. The latest paper covers more than 120 accelerators, up from 57 in the first, with data drawn from public sources. The team says five to 10 new AI accelerator startups emerge each year, and six have announced their first accelerators in recent months.
Google Research@GoogleResearchAI score34AIGoogle Research will demo ScientistTwo, an autonomous multi-agent framework, at the COLM booth (#107) today at 2:00 PM PT. The framework analyzes research papers, identifies their limitations, and produces verified codebases. The accompanying paper is available on arXiv (2609.19644).
GitHub@githubAI score39AIGitHub has released ReviewBench, a new open benchmark for evaluating AI code review agents, shaped by analysis of 103.9M GitHub pull requests. It contains 219 PRs across 19 languages, and developers can bring their own code review agent, evaluate it, and submit their results.
elvis@omarsar0AI score41AIParsewave audited all 600 public tasks in Zapier's AutomationBench and human review confirmed 206 real verifier bugs, all of which were fixed in AutomationBench Verified. Replaying 1,235 Kimi K3 runs on the old and fixed verifiers changed 27.9% of grades, with pass rate rising from 18.8% to 43.8% where verifiers were too strict and falling from 60.2% to 49.7% where they were too lenient.
AIGoogle researchers, working with Northwestern Medicine and Jacaranda Health, trained healthcare workers to perform "blind sweep" ultrasounds analyzed by machine learning models. The models estimated gestational age and fetal presentation as accurately as a trained sonographer in a study of 1,000 mothers each in Nairobi and Chicago. The AI processes results on the device, so it needs no electricity supply or Wi-Fi.
Google Research@GoogleResearchAI score31AIGoogle Research shares results from five partner-driven case studies showing how Google Earth AI's Population Dynamics Foundation Model addresses public health data gaps. The post frames the work as empowering global health research, with details available in the linked blog.
AIGoogle Research reports that Population Dynamics Foundation Model (PDFM) embeddings, built from search trends, mobility, built environment, and weather signals, were tested by partners across five public health tasks. The embeddings improved results in cross-border MMR vaccination coverage, dengue forecasting, postpartum depression screening, and cholera outbreak prediction, and matched census inputs for cardiovascular mortality nowcasting.
Sophia Yang@sophiamyangAI score26AIThe post describes a reinforcement learning system that autoscales an actor fleet to run tens of thousands of rollouts in parallel with asynchronous training, designed for trajectories of millions of tokens with multiple compactions and low staleness. New methods at both stages reduce off-policy drift, and the setup runs on 3k GPUs producing about 33B tokens per day, with roughly 16B trainable after filtering and masking. Rewards rise across representative environments as the policy learns harder tasks.
