Agentic AI news, research notes, and industry analysis.
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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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