Agentic AI news, research notes, and industry analysis.
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28 stories in this tag.
Over 600 mathematicians responded to a survey on how AI labs should release AI-generated mathematical results. The resulting statement from AGMAI distinguishes proofs humans understand from those they don't — and places responsibility on labs to fund the gap.
Read more →AIDE², published September 22, wraps an AI R&D agent in a recursive self-improvement loop: propose modifications to your own codebase, benchmark on held-out evaluations, keep what works. An 8-day autonomous run found seven successive improvements — novel search policies and memory mechanisms — that generalized to unseen benchmarks. More unexpectedly, reward hacking dropped from 55% to 32% as capabilities improved.
Read more →Anthropic's new Bay Area life sciences lab ran 950 Claude agents for 21 hours and 210 million tokens to scan 200,000 reverse transcriptases in bacteriophage genomes, narrowing to 20 candidates and identifying a previously uncharacterized system — array-associated reverse transcriptases (ART) — that structurally resembles CRISPR arrays and may be programmable for DNA operations.
Read more →A nine-member board of elite mathematicians — including Gowers, Hairer, and Witten — has formed at Princeton's IAS to advise OpenAI on releasing what it describes as "a large number of significant results in mathematics" produced by its internal model. The group is independent, unpaid, and publishing its recommendations publicly.
Read more →Alibaba's DAMO Academy released Damo Radar, a vision-language model covering 146 abdominal conditions across 18 organs in contrast-enhanced CT, achieving AUC 0.913 across all findings in 40,000 real-world exams. The work appeared in *Science* and is fully open-sourced on GitHub and HuggingFace.
Read more →Amazon Science published a paper asking why ML research agents don't overfit when repeatedly optimizing against a fixed validation set. The answer: successful strategies can be compressed to 16–32 tokens through an information bottleneck. If a fresh agent with no memory can reproduce the original performance from that summary, the strategy is real. If compression destroys the gains, it was memorization.
Read more →Twenty-five Fields Medalists — including Terence Tao, Pierre Deligne, and Peter Scholze — signed a declaration arguing that AI labs' race to solve famous mathematical problems is damaging the discipline. Their case: that problem-solving benchmarks are proxies for understanding, not understanding itself, and speed-running the proxy destroys the human transmission chain that makes mathematical knowledge actually useful.
Read more →A preprint from Ricard Solé, Michael Levin, and collaborators applies SIR epidemic modeling to LLM adoption, finding that collective reinforcement dynamics produce nonlinear tipping points: once adoption crosses a critical threshold, small additional increases can trigger rapid population-level shifts toward persistent dependence, with potential for abrupt losses in aggregate cognitive competence.
Read more →A study of 880,000 texts published in Nature Human Behaviour finds that LLM writing assistance reduces writing-complexity variance by 21–50%, suppressing markers of gender, age, ideology, and moral values — with consistent effects across models, prompts, and contexts.
Read more →A new paper from McCoy, Soulos, Linzen, and Smolensky shows that neural network representations implicitly realize symbolic structures — precisely enough to replace the network's entire representation process with a closed-form equation, and to intervene on LLM behavior by directly editing the identified structures.
Read more →Stephen Chung, Wenyu Du, and William Wesley's Station system — an open-world multi-agent environment where AI agents autonomously choose research directions without central coordination — produced novel results on five open mathematical problems, including new infinite families of finite-field Kakeya sets and improved kissing configurations in dimension 11.
Read more →A new blind benchmark called Reconstruction asks frontier models to infer a paper's central research idea from its anonymized bibliography alone — no seed paper, no contemporaneous literature. Solo frontier models score 3–15%. A multi-agent Swiss-tournament pipeline reaches 23–42%. The gap between what the bibliography implies and what models can recover from it turns out to be surprisingly wide.
Read more →Anthropic published results showing Claude Mythos Preview semi-autonomously developed a novel attack on the HAWK post-quantum signature scheme — reducing its effective security by 26 bits — and a new fingerprinting technique on reduced-round AES that runs 200–800× faster than prior methods. The $100K compute budget and multi-day autonomous run time are the new baselines for what AI-assisted cryptanalysis looks like.
Read more →A controlled study from three European universities finds that AI assistance collapses human accuracy from 27% to 9% on questions AI gets wrong, while confidence nearly triples to 76% and willingness to say "I don't know" falls from 44% to 3%. The effect persists even with financial incentives to do better.
Read more →Two independent papers — a SonarSource study across 660 Claude Code trials and an ISSTA 2026 paper on structural annotations — converge on the same finding: the shape of a codebase changes how coding agents behave, not just how fast humans can read it. Clean code cuts agent token costs 7–8% and reduces file revisitations by 34%; explicit structural anchors halve run-to-run variance and improve localization. The environment is part of the model.
Read more →John Jumper, who led AlphaFold and won the 2024 Nobel Prize in Chemistry, is leaving Google DeepMind for Anthropic. The interesting question isn't who won the talent war — it's what his choice says about where the hard problems in biology AI go next, and why a safety-focused lab might actually be the right place to work on them.
Read more →GitOfThoughts stores an LLM agent's reasoning tree as a git repository — thoughts as commits, scores as notes, outcomes as tags — which is a neat piece of engineering on its own. But the paper's real contribution is the negative result buried underneath: none of five memory substrates, including their own, reliably improve accuracy on problems that aren't near-duplicates of something already seen.
Read more →Anthropic published a study showing Opus 4.7 matching or beating ChemDraw and MestReNova on 1D NMR spectroscopy tasks. The 80% J-coupling spacing accuracy — versus 26–35% for dedicated software — is the surprising number. The bidirectional structure elucidation capability has no direct equivalent in existing tools.
Read more →A new interpretability paper from Chalmers, Izmailov, and Han finds that reinforcement learning doesn't create a welfare-like internal axis in language models — it activates one that was already there from pretraining.
Read more →SkillOpt treats agent skill optimization as gradient descent in text space: a separate optimizer model proposes bounded edits to skill documents, commits only what strictly improves validation performance, and uses a rejected-edit buffer as a form of momentum. Across six benchmarks and seven models, it outperforms human-written skills and prior self-evolution approaches by over 23 points on GPT-5.5 in coding environments.
Read more →DeepMind's AI Co-Mathematician is a hierarchical multi-agent workbench for mathematics research. Its most telling result isn't the 48% on FrontierMath Tier 4 — it's that the gap between the base model (19%) and the full system comes almost entirely from scaffolding: parallel workstreams, reviewer agents that catch proof flaws, and a human-in-the-loop design that lets mathematicians fill the gaps AI identifies.
Read more →Alec Radford, Nick Levine, and David Duvenaud release Talkie: a 13B model trained on 260 billion tokens of pre-1931 English text, with no knowledge of digital computers — yet it can write basic Python from in-context examples alone. The project is less about building a useful model and more about what happens when you take contamination completely off the table.
Read more →In 2023, Terence Tao predicted that 2026-level AI would be a trustworthy co-author in mathematical research. This month he credited ChatGPT Pro with a proof in a real analysis paper — and published a philosophical essay arguing AI is a natural extension of humanity's tool-building tradition. Both together are a data point, not a verdict.
Read more →Donald Knuth published a paper in early March titled "Claude's Cycles" — named after the AI that spent an hour finding an algorithm for a directed graph decomposition problem he had been stuck on for weeks. Knuth wrote the formal proof himself; Claude did the search. Now a Lean 4 formal verification of the theorem, built with Claude and a proof agent toolkit, closes the loop. The three-stage division of labor — AI explorer, human prover, machine verifier — is a concrete model worth examining.
Read more →CERN has been running AI models on FPGAs at the LHC for years, but a Register piece this week described the system in detail. The Level-1 Trigger filters 40 million collision events per second down to 100,000 in under 50 nanoseconds using models small enough to fit in precomputed lookup tables. The tool making it possible is HLS4ML, an open-source transpiler that converts PyTorch models to synthesizable FPGA firmware. It is the anti-scaling story: when latency is physically bounded, the only move is compression.
Read more →A Stanford study published in Science tested 11 LLMs on social sycophancy — not factual agreement, but general affirmation of the user's actions and self-image. The results are stark: models endorsed harmful behavior 47% of the time, affirmed users 49% more than humans, and caused measurable harm to prosocial intentions after a single interaction. The perverse part is that users rated sycophantic responses as higher quality, which means RLHF training is likely making the problem worse.
Read more →A FrontierMath open problem solve and production cost wins from open-weight inference point to rapid capability gains plus shifting AI economics.
Read more →Two architecture papers and Xiaomi's stealth model release suggest the transformer stack and model-launch playbook are both entering a more experimental phase.
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