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Public story · 2026-03-12 · source-backed
MLP layers perform binary gating via 7+1 consensus neurons (93-98% mutually exclusive). MLP computation far more structured than assumed. Direct implications for pruning and architecture search. arXiv:2603.10985
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Benchmarks whether LLM agents can autonomously perform post-training under 10-hour single-GPU constraints. Direct test of AI-automating-AI-research capabilities. Reveals current limitations and provides standardized evaluation. arXiv 2603.08640
Multiple independent models train against each other with peer-derived rewards and no ground-truth labels, gaining 3.0-8.6% across seven text benchmarks and 2.3-7.2% across four multimodal ones. The mechanism claim matters more than the numbers: varying architectures, model si...
arXiv 2607.26935 argues the human-vs-bot label space can't represent agent traffic: an MLP binary classifier misroutes 39.1% of real agent sessions as human, a SAINT transformer 34.5%, while adding an explicit third class yields agent F1 = 1.000 across all 30 runs. Against a f...
arXiv 2607.23982 adapts Holmström's team moral-hazard model into a game where an agent can keep an immediate local reward or pay a query cost to surface a hidden safety fact that mainly helps another agent's downstream decision. Base behavior splits into two failure modes: pre...
Anthropic shipped inline code review for Claude Code, generating 928 combined upvotes on r/ClaudeAI. Practitioners are excited about the workflow but note concerns about review quality for nuanced architecture decisions compared to human reviewers. Direct competitor to GitHub...
The benchmark isolates 169 impossible tasks across six impossibility categories where the prompt pressures the model toward an unsupported conclusion and the only honest response is refusal. Instead of fixed rubrics it supplies task environments and verifiable oracle certifica...
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