World Wire

Anthropic has released Claude Fable 5.1 and Claude Mythos 5.1, three months after the Fable 5 line shipped in June 2026. The two are the same underlying model behind different safeguard layers. Fable 5.1 is generally available as claude-fable-5-1 ; Mythos 5.1 stays restricted to vetted organizations. Both carry a 1M token context window and 128K max output tokens, with adaptive thinking always on. The headline capability number is 52.6% on Terminal-Bench-Science 0.1, against 24.7% for Fable 5 and 29.0% for Opus 5. The headline commercial number is a 75% cut to cache reads, from $1.00 to $0.25 per million tokens, which Anthropic measures as roughly 25% lower cost on typical workloads and up to 45% on agentic ones. Base input and output pricing is unchanged at $10 and $50 per million. Is it deployable? Yes, Claude Fable 5.1 is generally available as claude-fable-5-1 on the Claude API, Amazon Bedrock, Claude Platform on AWS, Google Cloud, and Microsoft Foundry. Claude Mythos 5....

Quantitative research agents that write their own experiments can corrupt the evidence they later learn from. A leaky feature that scores well gets stored as a successful precedent and propagated through later iterations. Prompt-level instructions and reviewer agents do not close this, because author and reviewer share the same blind spots. A team of researchers from Princeton University, Ant Group and Stanford University propose AQuA . AQuA is a pair of language-model-driven research systems that improve their own research process across iterations while the thing judging them stays frozen. One discovers symbolic alpha factors on crypto; the other develops time-series models on US equities. They share no agents, memories, candidate spaces or research state. The failure mode AQuA is built around Quantitative research breaks on small methodological errors that produce convincing but non-reproducible backtests, documented since Bailey et al. . An agent writing its own experime...

How do you benchmark a web search API when the thing being tested can read the answer key? A search agent has a fetch tool. If the gold labels sit in a public dataset, the agent can download them mid-evaluation and skip retrieval entirely. A similar problem arises when the answers are already encoded in the model’s parametric memory: a correct response no longer demonstrates that web search worked. Keenable’s answer is NEEDLE , a live open-source benchmark that rebuilds its query set from fresh public sources rather than freezing one. News queries are regenerated hourly from RSS feeds and Google Trends; finance, scholar, legal, and rare-entity queries are regenerated daily from SEC XBRL, arXiv, Europe PMC, CourtListener, and public agent logs. Fifteen search APIs run against the same query text under one protocol, and every score is read against ultimate , a pooled oracle engine that marks what the whole field managed to find. Is it reproducible? Yes, as an open source ev...