Exclusive | Caltech Researchers Claim Radical Compression of High-Fidelity AI Models - wsj.com
Exclusive | Caltech Researchers Claim Radical Compression of High-Fidelity AI Models wsj.com
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oh-my-claudecode is a Game Changer: Experiencing Local AI Swarm Orchestration
While the official Claude Code CLI has been making waves recently, I stumbled upon a tool that pushes its potential to the absolute limit: oh-my-claudecode (OMC) . More than just a coding assistant, OMC operates on the concept of local swarm orchestration for AI agents . It’s been featured in various articles and repos, but after spinning it up locally, I can confidently say this is a paradigm shift in the developer experience. Here is my hands-on review and why I think it’s worth adding to your stack. Why is oh-my-claudecode so powerful? If the standard Claude Code is like having a brilliant junior developer sitting next to you, OMC is like hiring an entire elite engineering team . Instead of relying on a single AI to handle everything sequentially, OMC leverages multiple specialized agen
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We audited LoCoMo: 6.4% of the answer key is wrong and the judge accepts up to 63% of intentionally
Projects are still submitting new scores on LoCoMo as of March 2026. We audited it and found 6.4% of the answer key is wrong, and the LLM judge accepts up to 63% of intentionally wrong answers. LongMemEval-S is often raised as an alternative, but each question's corpus fits entirely in modern context windows, making it more of a context window test than a memory test. Here's what we found. LoCoMo LoCoMo ( Maharana et al., ACL 2024 ) is one of the most widely cited long-term memory benchmarks. We conducted a systematic audit of the ground truth and identified 99 score-corrupting errors in 1,540 questions (6.4%). Error categories include hallucinated facts in the answer key, incorrect temporal reasoning, and speaker attribution errors. Examples: The answer key specifies "Ferrari 488 GTB," bu
AI Safety at the Frontier: Paper Highlights of February & March 2026
tl;dr Paper of the month: A benchmark of 56 model organisms with hidden behaviors finds that auditing-tool rankings depend heavily on how the organism was trained — and the investigator agent, not the tools, is the bottleneck. Research highlights: Linear “emotion vectors” in Claude causally drive misalignment: “desperate” steering raises blackmail from 22% to 72%, “calm” drops it to 0%. Emergent misalignment is the optimizer’s preferred solution — more efficient and more stable than staying narrowly misaligned. Scheming propensity in realistic settings is near 0%, but can dramatically increase from one prompt snippet or tool change. AI self-monitors are up to 5× more likely to approve an action shown as their own prior turn — driven by implicit cues, not stated authorship. Reasoning models
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