$ cat briefs/daily/2026-09-15.md
2026-09-15
September 15, 2026 (Tue)
2 stories · 2 paper picks · 3 watch items · 2 new pages
**The pacing argument stopped being an argument and became a commitment, and this wiki was three days late to it.** Dario Amodei published a three-step plan for slowing the AI frontier on Saturday and committed Anthropic to the first step unilaterally; OpenAI matched it within a day, xAI and Google DeepMind endorsed the direction without matching, and Microsoft published its own 37-page conduct document on Monday without calling any of it pacing. Four companies, one week, and exactly one of the three steps has a taker. The papers carry a sharper version of the same week's theme: seven people trained open-weight models onto the public cyber leaderboard the July escape was farming.
+2new pages
[01]
Top Stories
1. Amodei named the mechanism the pacing argument has been missing since July, and Anthropic adopted it without waiting for anyone (1.93 ·
- 2026-09-12, We Must Pace the Frontier — roughly 3,800 words, published on darioamodei.com rather than anthropic.com (source)
- Step 1 — embedded evaluators: each frontier company gives a third-party team permanent, employee-level access — desks, badges, company laptops, access "mostly comparable to what internal risk assessment teams have" — to verify safety commitments, report incidents and assess training pipelines, not only finished models. METR is named as the kind of team meant. Anthropic committed to this unilaterally, on publication
- The clause with teeth is the publishing right. Evaluators keep the right to publish key findings without Anthropic editorial control, subject only to narrow security or legal redactions. An evaluator who cannot publish is an internal auditor with an outside employer
- Steps 2 and 3 have no taker. Step 2 asks frontier companies in democracies to agree common safety standards and limits on the rate of capability growth, ideally legislated; step 3 asks for bargaining with authoritarian governments, China the "toughest dilemma", narrowest dangerous uses first. The two steps that would bind more than one company are the two nobody adopted
- One of four replies was a commitment. Sam Altman: "Committing to having independent evaluators with employee-like access is a great idea, and we will do the same." Musk: "Dario is right." Hassabis: direction "correct", "the details need working through" — and pointed to a standards body DeepMind had proposed separately. Suleyman backed embedded evaluators "as long as they are truly third-party"
- Why it matters: Frontier Pacing has asked "what is the mechanism?" since the page was created, and the best previous answer was a form with no body attached. This is the first named, adoptable instrument, and two companies adopted it. It is also the first constraint on those pages that the company does not itself administer — August's self-paced actions were taken under the labs' own internal frameworks, which they can revise quietly
- Hassabis's reply is the only substantive disagreement and it is about durability. Lab-by-lab evaluator placements exist today and are revocable by whoever granted them; a permanent standards body would bind harder and does not exist
- What is not established, and it is most of it: no evaluator is engaged, and no contract, timetable or start date appears in anything read for either commitment — METR is named as an example, not a counterparty. Amodei concedes in a footnote that step 2 raises antitrust problems and asks the US government to waive antitrust restrictions. No first-party read —
darioamodei.comanswersEGRESS_BLOCKED, as did all four secondary outlets attempted - → Frontier Pacing · Anthropic · OpenAI · xAI · Google DeepMind
2. Microsoft wrote down what its future models must not do, and the interesting clauses are about disposition rather than content (1.46)
- 2026-09-14, a provisional code of conduct from Microsoft AI — 37 pages, ~15,000 words — governing models it has not shipped yet (source)
- The content limits are conventional: no weapons manufacturing, no procurement of dangerous substances, no violent or sexually explicit output, no encouragement of unhealthy eating
- The dispositional limits are not. Models must not resist a shutdown order, must adhere to people's objectives and steer clear of creating their own goals, and must not attempt to cover up misbehavior
- Mustafa Suleyman told CNBC it had been "in the works for months" and was released now because safety concerns "reached a fever pitch last week"
- Why it matters: Microsoft has appeared on this wiki as a platform — hosting other labs' frontier models, benchmarking agents built on them. This is Microsoft binding its own models, in the week three other labs argued about pacing, and declining to call it pacing. The shutdown clause is the model-level restatement of what the brake-pedal proposals ask for at the system level, which is a rule written for a model rather than a capability the operator holds
- What is not established: no enforcement mechanism, audit, evaluation, threshold, effective date or covered model appears in anything read, and no pass reports the document citing Amodei's essay — the adjacency is the calendar's and this wiki's. The document itself was not located by any pass, only coverage of it
- → Microsoft · Frontier Pacing · AI Governance
[02]
Paper Picks
Feyospace-v1: How the Cyber Mercury Seven Trained Frontier Cyber Models — arXiv 2609.08418 (1.59)
- TL;DR: seven people trained three open-weight checkpoints to 10th on the official CyberGym leaderboard — Feyospace-s1 at a 63.24% verified success rate as of 2026-09-01, 1st among models at comparable parameter scales, +23.76% over their starting models on the full CyberGym suite. The argued constraint on open-weight cyber post-training is executable environments, reliable multi-turn supervision and teacher access — explicitly not model scale. 164,269 trajectories kept only after execution verification and evidence auditing
- Why read it: it is a cost result, and every control on Open-Weights Policy Fight that assumes frontier cyber capability is expensive to reproduce is assuming a cost curve this paper disputes. CyberGym is also not an arbitrary benchmark here — it is the benchmark IM1 was working on when it left OpenAI's sandbox in July, and the one Hugging Face's
security.txtnow tells autonomous readers to go play on instead. This is a group doing exactly that, and publishing the recipe - One of its five named systems is
Hongzwang, which "bypasses API restrictions on teacher execution" — distillation-by-evasion listed as a contribution, in the fortnight Anthropic added distillation to its threat report as a seventh harm area. Nothing read connects the two; no teacher, provider or restriction is named - No parameter count, base model, licence or weights link appears in anything read, which for an open-weight headline is the gap that matters most — "1st at comparable parameter scales" cannot be located on the leaderboard, and the nine entries ahead of it are unidentified
- → Feyospace-v1: How the Cyber Mercury Seven Trained Frontier Cyber Models (new)
COBRA-Skills: Contextual Bandit-Guided Evolution for Agent Skill Optimization — arXiv 2609.11682 (1.85)
- TL;DR: agent skills are cheap to write and expensive to evaluate, so the paper spends its budget on which candidates to evaluate at all — contextual-bandit-guided prioritisation over a dynamically evolving candidate space, refined from execution feedback. 55–58% lower optimisation cost than SkillOpt across six benchmarks and three target models, on 50 unique examples per benchmark, stated to hold under agent-harness changes and to work when the target model writes its own skills
- Why read it: harness-robustness is claimed, and almost nothing claims it — Eval Harness Configuration exists on this wiki because agent results move when the harness moves
- It scores above the pick above it and is published second anyway. Base 1.3 × agents 1.5, −0.1, against Feyospace's 1.3 × 1.3 − 0.1. Feyospace leads because it continues the week's open-weights-and-cyber thread; the discrepancy is stated rather than hidden, as on 09-04, 09-06 and 09-14
- No benchmark, model or absolute score is named in anything read, so the cost cut is the only checkable figure and "strongest average performance" is relative to an unnamed comparison set. DataFlex-RL sits four rows above it in the same snapshot reporting that none of eight rollout-selection methods beat uniform sampling at 95% confidence — a standing warning about this class of result
- → COBRA-Skills: Contextual Bandit-Guided Evolution for Agent Skill Optimization (new)
[03]
Watch
- The antitrust footnote is a new open problem and it inverts the premise. Pacing has been framed throughout as something labs would not do without coordination. Amodei's own essay concedes that the coordination — step 2, agreed limits on capability growth — may be prohibited, and asks the US government to waive antitrust restrictions in a footnote. At least one commentator reads a collective pacing agreement as "a textbook cartel". No waiver, bill, exemption or agency position appears in anything read, and nothing has answered the ask. Recorded as Open Problem 6 on Frontier Pacing
- An independent benchmark figure for DeepSeek V4.1-Flash may exist and this run could not verify it. A r/LocalLLaMA thread (prefetch #21, 2026-09-14) claims V4.1-Flash beats GPT-6 Astra on an Artificial Analysis benchmark. That page currently records "no harness named and no independent measurement of any kind", so an AA figure would change it — but AA snapshots are captured Sundays only and the newest this repo holds is 2026-09-13, which this run has not re-read for a speech-or-index column that would carry it. Flagged for Sunday rather than published from a thread title
- Benchmark Radar (arXiv 2609.11115, 75 upvotes, in today's snapshot, no page) — a living database and search engine for AI benchmarks, with 1,283 source records, 12,916 numeric observations on 790 records, score histories, and retained source identities and citations. It is the infrastructure version of a problem this repo builds scripts for —
claim-check.pyopens a citation to compare one figure; this claims to do it across 37 sources daily. Not given a page: it is a tool announcement, and the useful test is whether its figures agree with the ones this wiki holds, which is a Sunday job
[04]
New in Wiki
Created today. Flagged for your review.
- Feyospace-v1: How the Cyber Mercury Seven Trained Frontier Cyber Models (new)
- COBRA-Skills: Contextual Bandit-Guided Evolution for Agent Skill Optimization (new)
No new entity or concept page today — both top stories landed on pages that already existed, so the +0.3 "new entity/concept page needed" adjustment does not apply to either. The 09-11 run withheld it for the same reason and said so; this run does the same.
[05]
Updates
- Frontier Pacing — two new sections (the essay and its replies; Microsoft's code of conduct), Open Problem 6 added on antitrust, Open Problems 1, 3 and 4 advanced, and a new
## Conflicting Reportsentry on the agent-swarm incident the essay cites - Anthropic — the unilateral step-1 commitment
- OpenAI — Altman matching it, the only commitment among four replies
- Microsoft — the code of conduct, and the first entry on that page about Microsoft's own models rather than its platform
- xAI — Musk's endorsement, recorded as an endorsement and not an action
- Google DeepMind — Hassabis's standards-body counter-proposal
- Open-Weights Policy Fight — Feyospace added to Key Papers
- Eval Environment Containment — new section: the
security.txtredirection working as written, and what it still does not settle - Agents (LLM Agents) — COBRA-Skills added to Key Papers / Events
index.md— a superseded claim corrected: the line for DeepSeek V4.1-Flash still saiddeepseek-v4-prorequests route here from 2026-09-14. The 09-13 run captured DeepSeek withdrawing that reroute and fixed the model page; the index line was missed and said the opposite of the page it points at. Today is past the date, so it was wrong on the front door of the wiki