2026-09-28

From sources/papers-daily/hf-daily-2026-09-28.md — 25 entries, 24 carried over from yesterday's file; only 2609.28603 is newly listed, and 2609.29444 dropped off. Upvote counts are that community's popularity signal and nothing more. All three picks manufacture a verifier where the field had none, which is not a theme anyone chose.

Coding Agents for Generalized Task and Motion Planning Problems — arXiv 2609.30233 (1.65 — the day's top paper)

• TL;DR: Claude Code (Opus 5) and Codex on GPT-5.6 Sol and GPT-6 Astra are given a task description and a simulator and asked to write a planner, not to plan. The program is frozen and run on unseen instances: 980 programs × 100 held-out instances = 98,000 episodes over 28 KinDER/PDDLStream environments. All three beat hand-engineered planners — 56%–95% mean success against 47% on the 16 where a planner exists, widen the margin as object counts grow, and use an order of magnitude less computation per instance
• Why read it: the deliverable outlives the agent, so generalisation is testable on instances larger than the benchmark was built for and the agent's inference cost never enters the runtime number. A one-time synthesis budget buys a permanent per-instance saving — the opposite of the economics every agentic-inference result here reports. The one-shot baseline is the same pipeline with the simulator removed, and it loses
• The band is the spread across the three agents, not across environments, and the paper does not say which is at either end. A 39-point gap between three frontier coding agents is either the most interesting number in it or an artefact of budget
• → Coding Agents for Generalized Task and Motion Planning Problems

Learning to Discover Interesting Mathematics — arXiv 2609.28603 (1.66)

• TL;DR: FAIR @ Meta with CERMICS/ENPC and NYU (Patel, Rammal, Hayat, Munos, Kempe) define a theorem's intrinsic interestingness as proof length ÷ statement length, show it correlates strongly with an extrinsic measure of downstream utility, and reduce both to one primitive — proof difficulty conditioned on a premise set — which a 27B model predicts more accurately than frontier general-purpose models. Optimising for the metric cuts substantial-or-full Mathlib overlap from 91.9% to 30.6%
• Why read it: every AI-for-mathematics result this wiki holds is scored against a problem somebody set. This is the first that optimises which problem to work on, and it is the direct answer to the 25 Fields Medallists' objection on AI for Mathematics that problem-solving-as-benchmark is misaligned with what mathematics needs. The overlap figure says a generator without the metric spends most of its output re-deriving the library it was handed
• The correlation is the entire defence of the metric and no coefficient is given. A proof-to-statement ratio rewards a deep theorem and an obfuscated one identically; the correlation with utility is the only thing separating them, and it is asserted as "strong". The whole method also presupposes decidable truth and an enumerable premise set — it works inside a formal library and claims nothing outside one
• → Learning to Discover Interesting Mathematics

RewardVerse: Rubric-Guided Policy Optimization for Video Reward Modeling — arXiv 2609.22947 (1.49)

• TL;DR: names scalar drift — a video reward model's scale collapsing or shifting across prompts, so the same number means different things in different places — and answers it by generating a per-query rubric first and scoring against it, trained by RGPO: warm up the scorer on self-evolving seed rubrics, then jointly optimise the rubric generator while realigning the scorer to human ratings. SOTA on the 16-dimensional EvalVerse, pointwise and pairwise
• Why read it: scalar drift is not a video problem. It is what happens whenever one number must span heterogeneous prompts, and video generation is simply the domain with no verifier to fall back on. A rubric per query narrows the reward model's question without narrowing its domain
• No figure on this page is quotable as a score. The snapshot carries no table and no percentages — including for the drift reduction the architecture exists to deliver — so the central claim is recorded qualitatively. And the rubric generator is itself trained, so the drift has been moved rather than removed, with human ratings as the anchor of last resort and their coverage unstated
• → RewardVerse: Rubric-Guided Policy Optimization for Video Reward Modeling

• A count is not a dedup key, and yesterday's run proved it. Yesterday's log recorded "8 of the 17 new arXiv ids below threshold" naming none of them, so nothing in this repository says which papers were read and dropped. Today's run could identify them only by diffing two snapshot files. They are re-scored and named individually this time, and it is carried to the W40 lint
• A 42× speedup that is not a 42× speedup. 42x Faster Prompt Lookup Drafting in llama.cpp — four changes to llama.cpp's n-gram caches making drafting up to 41.6× faster per drafted token, static-cache load up to 23.5× faster, and peak memory up to 2.65× lower. The blog is github.io and blocked from this run, so this is one outlet plus search summaries. The figure is per drafted token in the drafting step, not end-to-end, and the same summaries note a widely cited benchmark finding no net speedup on general chat workloads. Recorded here rather than on a page for exactly that reason
• Noam Brown on what a leaderboard is not telling you — interest weight 1.3, and not adopted onto any page. Two passes agree that he criticised CAIS for evaluating every model at "reasoning high" when "high" is not comparable across models, and asked for dollar cost per evaluation to be published instead. The date does not survive — one pass gives 2026-09-23, the other gives none — and no primary post was retrievable, so it stays here
• sources/evals/lmarena-2026-09-27.md is still absent, and the last LMArena capture remains lmarena-2026-09-20.md. Leaderboards are Sunday-only and were not due today; a missing Sunday file does not become present on a Monday. Carried to W40

For review. No entity, model, concept or person page today — three papers, and two deliberate refusals.

• Coding Agents for Generalized Task and Motion Planning Problems (new — paper)
• Learning to Discover Interesting Mathematics (new — paper)
• RewardVerse: Rubric-Guided Policy Optimization for Video Reward Modeling (new — paper)
• Pages deliberately not created: Swift 1.5 (Story 2 — Released bounded, not stated; context window unread; huggingface.co blocked) and UkisAI as an entity (one artefact, no first-party read). The reasons are on Open-Weights Policy Fight rather than in a stub

• Anthropic: the 2026-08-31 post added at `
• Eval Environment Containment: a new dated section for the first structured remediation on a page that had only held failures — the classifier, the four external-partner requirements, the February rollback and the April freeze, and the headcount
• Frontier Pacing: a 2026-08-31 section revising when the coordinated-pacing ask was first made, and by whom, without displacing the 09-12 entry
• AI for Mathematics: a 2026-09-28 State of the Art for the interestingness metric, plus a Key Papers line
• Agents (LLM Agents), Agentic Reinforcement Learning: the TAMP and RewardVerse results, each read against what its page already assumes about verifiers
• Embodied Agents: three papers as one-off mentions, no pages — 2609.28256 MemBodied (7.81× a stateless policy and 2.98× vanilla recurrent memory on five memory-requiring RMBench tasks, 1.3× the strongest memory-augmented baseline at 10× fewer added parameters, 90.6% and +5.4% over stateless π₀ on LIBERO-Long — the week's third memory paper and the first about a body, landing on fixed-size compression where JitMem defers and SpeakerMem-R1 writes eagerly), 2609.23038 Spatial-Interactor (three-level curriculum over LSI-108K; no absolute number in the abstract), 2609.23784 PackLab (beats heuristics, RL and general MLLMs "on average", which is the whole result text)
• Open-Weights Policy Fight: Story 2's licence finding
• index.md: three paper lines