#Expert opinion

AIArtificial Analysis and Harvey released LAB-AA v1.1, which credits a legal task only when deliverables pass every rubric criterion with no material hallucinations. Grok 4.7 (xhigh) leads at 9.4%, ahead of Muse Spark 1.3 (max) at 8.9% and GPT-6 Astra (max) at 8.6%, while over 60% of otherwise passing results contained a material hallucination. The sharper reordering appears in the hallucination counts, where GPT-6 Astra averages 0.03 material hallucinations per task against 13.96 for Gemini 3.8 Flash (high).

AIArtificial Analysis reports that Kimi K3 (max) achieves a 93.0% Criterion Pass Rate but averages 2.09 material hallucinations per task. Muse Spark 1.3 (max) scores higher at 96.0% while averaging 1.68 material hallucinations per task, showing that completing criteria and avoiding hallucinations are distinct skills.

AIEpoch AI introduced Epoch Automation Reports, which evaluate frontier models on realistic, open-ended tasks drawn from its own work. Claude Fable 5.1 and GPT-6 Astra lead the results, but neither comes close to fully automating Epoch's work.

AIThe post argues that counting all research as safety work because it widens the safety-usefulness Pareto frontier is misleading. Safety research typically creates new safety options without boosting usefulness, while capabilities research typically raises usefulness at safety's expense, so developers tend to choose less safe points.

AIModels using Kimi Delta Attention show a recency bias, as the mechanism controlling how much earlier information is kept shapes memory. As new information mixes into that memory, nearby words share more information, which gives the model clues about how far apart words are.

AIEpoch AI estimates that the cheapest cost of reaching a given benchmark score has fallen about 13× per year over the past five years, faster than DNA sequencing, compute, lithium batteries, or electricity. Its example: a 75% GPQA Diamond score that cost about 30 cents per question with o3 in January 2025 cost $0.0004 per question with GPT-5.6 Luna under 18 months later. The authors caution that benchmarks are imperfect proxies for market prices, and the decline rate slows over time.

Why it matters: The source compares AI price declines with other transformative technologies using benchmark-based cost estimates, giving readers a measured sense of how fast cost per capability is falling.

AIA blog post by @Reveur_7 asks whether GEPA-style evolutionary search can improve NVIDIA's Graph-as-Policies, which generates robot controllers. Matei Zaharia praises the research as a cool approach to automatically generating state-of-the-art robot controllers.

AIAnthropic reports that Claude solved a nine-loop scattering amplitude problem in planar N=4 super-Yang-Mills, surpassing the previous eight-loop record set by SLAC's Lance Dixon and collaborators. Working largely unsupervised for days from a single prompt, at a total cost of a few thousand dollars, Claude used methods developed by Dixon's group, and Dixon independently verified the result.

Why it matters: The post shows Claude solving a nine-loop physics calculation beyond the previous eight-loop record, verified independently, which bears on AI use in theoretical physics research.

AIOn FrontierCode, Opus 5.5 at xHigh reasoning performed worse than at lower effort, a problem also seen with Opus 5. The author attributes this to scope creep, since FrontierCode penalizes unnecessary changes and models consistently score lower at higher reasoning efforts.

AIMETR's preliminary evaluation concludes that Claude Opus 5.5 likely gives slightly higher AI R&D productivity uplift than Fable 5.1 but is unlikely to fully automate AI R&D. The evaluation used five capability tasks over 10 business days of API access, and METR says Anthropic reviewed and edited the summary before sign-off.

Why it matters: The report separates two claims about AI R&D acceleration and discloses that Anthropic reviewed the summary, which helps readers weigh its independence and evidence.

AIOpenAI's GPT-6 Astra scores 99.9% on ARC-AGI-3, versus 7.8% for GPT-5.6 Sol, and leads Raschka's coding and math tests. Its strongest showing is in graphics and computer-use tasks, such as redrawing an image in a browser-based Paint app. The author notes that Artificial Analysis shows Astra at the frontier but not pulling far ahead on its Coding Agent Index.

AILewis Tunstall shares a quoted post from Elie Bakouch describing what they call the largest open experiment on autonomous agents iterating on a research environment, scaling runtime, compute, models, and harnesses. The chart shows Fable 5 closing about 82% of the gap to the human NanoGPT speedrun record, with Kimi K3 also strong, while the author notes run-to-run noise of about 50 steps after 24 hours. Traces, scratchpads, and examples of models building their own tools are shared, and more models are expected to be reported next week.

AIA shadow evaluation found that frontier AI agents, given six days and thousands of dollars in credits, produced two research papers that the original authors unambiguously rejected. The authors' log analysis cited poor judgment, underused budgets, weak responses to feedback, and failure to backtrack or follow instructions as main causes.

AIThe post argues that the system surrounding a base model, called the harness, increasingly determines how well AI agents deploy and improve. It reviews research where harness components such as workflows, context, and code are optimized automatically through evolutionary search and meta-agent loops. The author concludes that evaluators, memory management, and human oversight remain open bottlenecks.