Sakana AI published the TMLR paper Beyond Imitation, introducing an LLM-assisted peer review system called Multi-Layered Review that aims to catch inserted mistakes rather than mimic human reviews. It reported catching 73.43% of core-claim errors in its Contradiction Benchmark using 4 reviews, versus 14.81% for the best baseline. The release changes peer review evaluation by providing a planted-error benchmark plus a multi-agent, 3-pass workflow that improves error detection but still struggles with exact matches and can be affected by hidden prompt injection.
CoreWeave Forge was introduced to connect production traces, curated data, improvements, and evaluations so AI agent answers can be measured and made better before release. The announcement says Agent Lens improved failure detection by 20% while fixing issues at half the cost. Teams can run a single end-to-end loop (run, observe, curate, improve, evaluate) with traceability, reducing handoff gaps and keeping evaluation evidence attached to each change.
Arena closed a $200 million Series B to value the AI evaluation platform at $3.1 billion.
In the round, its valuation nearly doubled from $1.7 billion in January.
Arena plans to expand its service into ratings for AI agents, using an alignment index based on about 90,000 real sessions.
Google DeepMind’s Pushmeet Kohli and Biohub’s Sal Candido discussed why AlphaFold’s protein-structure success hasn’t solved protein folding or broader biology modeling. They pointed to a goal of 10x–100x acceleration in drug discovery as part of what future AI-driven biology would need to deliver. The focus shifts from scaling compute and data alone toward finding the right biological scaling laws, improving data and inductive biases, and modeling dynamics and whole-cell systems with more trustworthy uncertainty handling.
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