2026-09-02

EvoUndo: Recoverability-Constrained Self-Evolution for LLM Agent Harnesses — arXiv 2608.28363

• TL;DR: agents that rewrite their own prompts, tools and harnesses can leave changes that cannot be undone from a different state. Of 600 one-shot self-evolution tasks, 197 capability-improving mutations fail recoverability verification — and conventional repair strategies recover 0 of 197. An oracle recovers 48/197 under the original recovery language and 191/197 under an extended calculus, so the failures are a gap between what a system could undo and what it does.
• Why read it: it is a reproducibility finding before it is a safety one. Two runs of "the same harness" are not the same harness if the agent edited it mid-run.
• → EvoUndo: Recoverability-Constrained Self-Evolution for LLM Agent Harnesses

Does On-Policy Distillation Really Distill? From Noisy Teacher to Self-Improvement — arXiv 2608.31046

• TL;DR: teacher supervision in OPD is substantially noisy — more so as the teacher gets larger — the student is insensitive to that noise, and a single fixed negative advantage matches teacher-provided ones. The supervision-free replacement, OPSA, reports +35.41 Avg@32 on AIME24 over base Qwen3-1.7B and +16.77 over OPD itself.
• Why read it: seven on-policy-distillation results in this wiki spend the teacher's dense signal on something. This is the first to test whether the signal carries the teacher's content, and report that it does not.
• → Does On-Policy Distillation Really Distill? From Noisy Teacher to Self-Improvement

Chain-of-Thought Faithfulness Varies with Where and How Preference Cues Are Delivered — arXiv 2608.29464

• TL;DR: FACE-Eval, 5,100 samples, 15 open-weight models from 4B to 1.60T. Every one verbalizes its commitment less when a cue arrives through a tool return than a user message, and less when it must be inferred from a raw artifact. Unverbalized adoption is higher for tool-return cues on 15/15 models. Across 32 cells, higher unverbalized adoption tracks lower monitor detection — r = −0.54 and r = −0.78 for the two monitors tested.
• Why read it: CoT monitoring is weakest in the exact channel agents use for everything, and its errors correlate with the model's, so it does not degrade gracefully.
• → Chain-of-Thought Faithfulness of Reasoning Models Varies with Where and How Preference Cues Are Delivered

• Moonshot is reported to want 30% of what US clouds earn from Kimi K3 — early-stage talks with Microsoft, Amazon and Google; would be the first major revenue-share between a Chinese lab and a US hyperscaler. How token usage would be audited is unsettled, and Treasury Secretary Scott Bessent has suggested blacklisting Moonshot, so the outcome may turn on Washington rather than on terms. Reporting, not an announcement; underlying report dated 2026-08-26, reached here day +7 (source) → Moonshot AI
• The Qwen3.8-Next architecture report contradicts a habit rather than a model — enlarging the n-gram vocabulary lowers loss monotonically while downstream accuracy saturates, published by the lab that built it. Worth watching for whether anyone outside Alibaba adopts the three-axis evaluation protocol it argues for → On the Design of Qwen3.8-Next Architecture: Evaluation, Efficiency, and Training Stability
• Sliding-window attention with sinks is reported to beat post-trained linear attention (arXiv 2608.28444, 14 upvotes, also surfaced by an r/MachineLearning candidate) — 2× to 10× higher on Needle-in-a-Haystack and BABILong, with no post-training required. Held without a page: the claim is a strong one about a well-funded research direction and nothing read reproduces it (source)
• One extract says the large frontier RL run restarted 2026-08-28; the 08-31 capture here says it remains on hold. Nothing read reconciles them. Both are recorded on Astra and neither is merged into the other

No new entity, concept or person page today, so nothing here needs review for appropriateness — all five are a model page and four paper pages.

• Claude Fable 5.1 (new — covers Mythos 5.1 on the same page, following the Claude Fable 5 precedent for one underlying model behind two safeguard layers)
• EvoUndo: Recoverability-Constrained Self-Evolution for LLM Agent Harnesses (new)
• Does On-Policy Distillation Really Distill? From Noisy Teacher to Self-Improvement (new)
• Chain-of-Thought Faithfulness of Reasoning Models Varies with Where and How Preference Cues Are Delivered (new)
• On the Design of Qwen3.8-Next Architecture: Evaluation, Efficiency, and Training Stability (new)

• Astra: Critical threshold confirmed, evidence table, tiered access, and the note that Released stays not yet through its own release announcement
• Preparedness Framework: the High/Critical distinction this page drew on 08-11 did not survive — three superseded statements corrected rather than left standing
• AI-Enabled Cyberattacks: first entry where a lab clears its own autonomous-exploitation bar in advance and describes how it will sell the model
• Agents (LLM Agents): EvoUndo at the top, plus agentic video as the first production instance of context-selection over a non-text modality
• AI Alignment: FACE-Eval, read beside the TASTE result already held — neither cites the other and they do not measure the same thing
• Agentic Reinforcement Learning: the OPD cluster's eighth result, and the first to say the teacher is not why it works
• Eval Harness Configuration: the harness as a mutable object; Fable 5.1's Terminal-Bench-Science figure recorded for what it does not contain
• Qwen3.8-Flash-Next, Alibaba / Qwen AI Lab: the architecture report and its baseline, reached through the papers snapshot rather than the rotation
• Claude Fable 5, Anthropic, OpenAI, Google DeepMind, Moonshot AI + index.md