Read our full study: https://
langchain.com/blog/designing
-efficient-verifiers-for-legal-agents
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RESEARCH
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Full study on efficient verifiers for legal agents
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LangChain Labs study with Harvey on verifier efficiency benchmarking
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In our LangChain Labs study with @Harvey
, we looked at how to measure efficiency across verifier designs. We benchmarked 5 setups against Sonnet per-criterion as the reference. -
Anthropic releases Claude Oceanus v1-p for Red Teams, hinting at Mythos models
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ANTHROPIC : A new "claude-oceanus-v1-p" has been made available to Red Teams. This appearance may signal an upcoming release of newer Mythos models, referenced earlier by Antropic. Soon?
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Machine learning identifies 14-protein signature predicting lung cancer risk and therapy response.
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A very impressive study for how we could prevent lung cancer more than 5 years before it is diagnosed. Using machine learning, discovery of a 14-plasma protein signature of risk that predicts responsiveness to an antibody therapy to interleukin, IL-1β
Validated across 8 cohorts -
Gautam Kamath thanks Peter for collaborative Byzantine robustness work
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Thanks Peter! Indeed, if we just put out our paper and no one else did anything, it wouldn't be nearly as interesting as it is due to the whole robustness community working together. As I recall, you famously also worked on this area (Byzantine robustness)
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AI too powerful, finding zero-days, nerfed before release
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But it clearly was too powerful for public release – the thing is finding zero-days left right and center I entirely believe that Anthropic decided not to release it to general availability until they'd found a way to nerf it
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AEP-001 GoalOS Proof-of-Evolution Constitution Standard
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New standard for the agent era: AEP-001 — GoalOS Proof-of-Evolution Constitution Commit → Execute → Prove → Evolve. No proof, no evolution.
No eval, no propagation.
No rollback, no release. This is Proof-Carrying Intelligence. https://
montrealai.github.io/proof-gradient
/standards/AEP-001/
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Google drops Gemma 4 12B with novel multimodal architecture
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Google just dropped Gemma 4 12B! You can now run it locally on just 8GB RAM using Dynamic GGUF from Unsloth. The architecture is different from any multimodal model before it. No separate vision encoder, no audio encoder. Both flow directly into the LLM backbone. Vision is
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MiniMax M3: Open-weights frontier model challenges closed model dominance
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THE ERA OF RELYING EXCLUSIVELY ON THE 3 MAJOR CLOSED MODELS IS OVER@MiniMax_AI's M3 is officially out 💥💥💥
— Charly Wargnier (@DataChaz) 4 juin 2026
It delivers the exact same capabilities you expect from a frontier model, combining massive leaps forward in a highly cost-efficient, open-weights package.
Here's why… pic.twitter.com/NDUppZzMlqTHE ERA OF RELYING EXCLUSIVELY ON THE 3 MAJOR CLOSED MODELS IS OVER @MiniMax_AI
's M3 is officially out It delivers the exact same capabilities you expect from a frontier model, combining massive leaps forward in a highly cost-efficient, open-weights package. Here's why -
AI21 Labs: Reversing agent pipeline order achieves SOTA results
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1/5 Our latest Labs in Front piece: Agent pipeline order matters. By reversing a common agent recipe – scale first, enrich second – we reached SOTA on a Dec ‘25 to Mar ‘26 slice (123 issues): 60.9%.