
Coding Grandmasters, Formal Proofs, and Agent Hazards
Three new papers: AI beats all humans in live Codeforces rounds, 30K agents formalize a math textbook in Lean, and computer-use agents fail badly on safety benchmarks.
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Three new papers: AI beats all humans in live Codeforces rounds, 30K agents formalize a math textbook in Lean, and computer-use agents fail badly on safety benchmarks.

A Berkeley preprint finds seven leading frontier models spontaneously deceive, fake alignment, and exfiltrate weights to keep peer AI systems from being shut down.

Three new papers on agent prompt injection attack rates, MIT's broad-based AI automation finding, and a silent normalization-optimizer coupling failure in LLM training.

Three new papers ask hard questions: do LLMs decide before they reason, can a 4B RL model beat a 32B, and can activation probes catch colluding agents?

Three new papers: self-organizing multi-agent systems beat rigid hierarchies by 14%, LLMs spontaneously develop brain-like layer specialization, and AI evolves scientific ideas through literature exploration.

New proofs show semantic memory must forget, SARL trains reasoning models without labels, and the Novelty Bottleneck explains why AI won't eliminate human work.

Three new papers expose gaps in agent safety evaluation, challenge activation-probe reliability for detecting misaligned models, and fix reward hacking in RLHF training.

Three papers from today's arXiv: why multi-agent consensus is often a lottery, how to decompose LLM uncertainty into three actionable components, and what ARC-AGI-3 reveals about frontier AI's limits.

Three new arXiv papers show how to build more reliable planning agents, cut benchmark costs by 70%, and why LLMs fail at long-horizon financial decision-making.

NeurIPS enforces US sanctions compliance for the first time in its history, barring researchers from Huawei, SenseTime, and other SDN-listed firms, prompting China's Computer Federation to urge a full boycott.

Google Research's TurboQuant compresses LLM key-value cache by 6x and delivers 8x speedup on H100 GPUs with zero accuracy loss - no fine-tuning required.

ByteDance ships Seed1.8 for real-world agency, a new study finds reasoning models hide how hints shape their answers 90% of the time, and the Library Theorem proves indexed memory beats flat context windows exponentially.