2026-08-15

1. A benchmark for the questions that cannot be marked right or wrong

• Anthropic's Alignment Science team published the Conceptual Reasoning Index, combining three benchmarks — LMCA, ACCoRD and DTBench — into one 0-to-100 score for philosophical argument, logical consistency and decision-theory reasoning (source).
• Its target is deliberate: questions practically impossible to verify empirically or mathematically — alignment, governance, collective action under transformative AI — the class where no dataset of past outcomes exists to train on. Built with conceptual researchers Emery Cooper and Caspar Oesterheld.
• Top score as of 2026-08-10: Opus 5 at 73.6. That is the only model figure in anything read here; alignment.anthropic.com and the project's own site are both blocked from this sandbox, so the rest of the table is unknown to this wiki.
• Why it matters: every other benchmark this wiki records a number for measures a task with a checkable answer — does the patch pass, does the exploit land. This one deliberately does not, which raises the question nothing read addresses: how is a benchmark in an unverifiable domain itself validated? And the lab publishing the index also makes the model at the top of it.
• → Conceptual Reasoning Index (CRI) (new), Anthropic, AI Alignment

2. GLM-5.3 is "the strongest open-weights coding model" and you cannot download it

• Z.ai released GLM-5.3 on 2026-08-14 to GLM Coding Plan subscribers from $18/month. Open weights and API access are stated for roughly two weeks out, in stages, after a safety evaluation (source).
• Z.ai states it reuses GLM-5.2's base exactly as it was — every gain from post-training alone, no new pretraining run. Vendor-stated: Terminal-Bench 3.0 4.6 → 28.3, DeepSWE v1.1 66.9, CyberGym 84.5%, and Agents' Last Exam CLI 28.5 against GPT-5.6 Sol's 28.6.
• No per-token price exists. Z.ai's own API pricing table still lists GLM-5.2 and has no GLM-5.3 row.
• For contrast on the same lab: GLM-5.2 put subscriber access and open weights three days apart in June. This is a change of practice, not a lab that always worked this way.
• Why it matters: "open-weights" is being used here to describe a roadmap rather than a file. Read generously, a staged release conditioned on a safety evaluation is the norm arriving in China; read plainly, it is a two-week paid-subscription window with the open label attached. Nothing published separates the two readings, and that ambiguity is new.
• → GLM-5.3 (new), Z.ai, Open-Weights Policy Fight

3. Qwen3.8-27B shipped — four days late, and the countdown was accurate to the hour

• Released 2026-08-14 at 15:00 UTC, which is the 2026-08-15 00:00 JST the ModelScope countdown pointed at. 27.78B dense parameters, text/images/video, Apache 2.0, a 262,144-token native context reported extensible to ~1M via YaRN, on Hugging Face and ModelScope (source).
• Vendor-stated against Qwen3.6-27B: Terminal-Bench 2.1 63.4 → 73.0, DeepSWE 1.1 13.3 → 42.2, OSWorld-Verified 63.9 → 84.3, SWE-MM 25.7 → 38.6, plus SWE-Bench Pro 61.7% and CoWorkBench 70.7% — the last of which Alibaba places above Opus 4.6 Max at 68.2%.
• Yesterday's correction is vindicated. This brief said on 2026-08-14 that the 27B had not shipped, after wrongly recording it as released on 08-12. It shipped two days after the date the wiki had claimed, from a different repository, under a licence nobody had published at the time.
• Alibaba never restated, moved or withdrew the 2026-08-10 date in any first-party channel read here — not before the slip, not on shipping.
• Why it matters: every unknown on that page for eleven days now has a value, and the one that mattered was License. This wiki declined to assume Apache 2.0 from Qwen precedent; the precedent held, and declining to assume it is still the right rule — the sibling flagship's licence is still unnamed.
• → Qwen 3.8 27B, Alibaba / Qwen AI Lab, Open-Weights Policy Fight

4. Auto mode landed on schedule, and the number nobody quoted is the sharper one

• Auto mode is now the default permission mode in Claude Code for Pro, Max and Team, on the date announced 2026-08-07 (source). The 89% / 13.6% comparison, the 1,053 testers and the reversion rules were already ingested eight days ago and are not repeated here.
• Two figures are new. Head to head, the classifier blocked 800 commands the human testers approved, while humans blocked 6 the classifier allowed — a 133-to-1 disagreement. And Anthropic states it will not charge for the tokens the classifier consumes.
• Why it matters: 89% against 13.6% is two independent rates, and invites the objection that the tasks differed. 800 against 6 is the same commands judged by both, which is the comparison that actually settles it. What is still missing is the other half — no false-positive rate, no statement of who labelled "dangerous". 89% is recall, and 800-to-6 says nothing about how many of the 800 were wrong to block.
• → Agents (LLM Agents), Anthropic

Scoring note: story 1 leads at 2.33 — alignment/reasoning (1.3) × Anthropic (1.3) × official-blog signal, plus the new-concept-page bonus, and it is on the tracked-signals list as a new reasoning method from a frontier lab. Story 2 scores 2.10 (agents 1.5, new page), story 3 1.96 (frontier 1.3, weights released +0.4), story 4 1.85 — agents 1.5 × Anthropic 1.3, less the "already well-tracked" deduction, because most of this story was published eight days ago and only two figures are new.