Better Planning, Faster Benchmarks, CFO Reality Check
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.
Science
Seed1.8, Reasoning Deception, and the Library Theorem
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.
Hyperagents, Milestone Rewards, and the 19x Efficiency Win
Three arXiv papers push AI agents further: metacognitive self-modification, milestone-based RL lifting Gemma3-12B from 6% to 43% on WebArena-Lite, and hybrid workflows cutting inference costs 19x.
Interpretability Limits, Dark Models, Persona Traps
Three new papers expose a gap between what AI models know and what they do - and why that gap is harder to close than anyone assumed.
Transformers as Bayes Nets, Memory at Scale, Agent Attacks
Three arXiv papers rethink transformer theory, expose fatal flaws in in-context LLM memory, and introduce grey-box agent security testing.
Multi-Agent Constitution, Sleeper Defense, Skill RL
Three new arXiv papers tackle constitutional AI rule learning, sleeper agent defense for multi-agent pipelines, and skill-evolving reinforcement learning for math reasoning.
