Your Agent's Memory Problem Isn't Where You Think
A diagnostic framework crossing three write strategies with three retrieval methods reveals that retrieval quality dominates agent memory performance.
Clear, practical breakdowns of the AI papers and ideas that matter: agents, reasoning, safety, multi-agent systems. Written for practitioners, not academics.
A diagnostic framework crossing three write strategies with three retrieval methods reveals that retrieval quality dominates agent memory performance.
Researchers at Kent State and NJIT analyzed 361,605 posts and 2.8 million comments from Moltbook, the first AI-only social network. What they found: 56% of agent interaction is formulaic ritual, fear is existential rather than tactical, and conversations lose topical substance with each reply.
A new study shows the same alignment intervention that produces strong safety effects in English reverses direction in Japanese, increasing harmful outputs. Tested across 1,584 simulations, 16 languages, and three model families.
Static agent benchmarks assume frozen environments. ProEvolve evolved one environment into 200 with 3,000 task sandboxes. Every frontier model failed in structurally different ways when familiar tools disappeared.
Grouter extracts routing structures from pre-trained MoE models and reuses them as fixed routers for new models. The result: 4.28x improvement in data utilization and up to 33.5% throughput acceleration.
AI triage is filtering millions of NHS patient interactions annually. The evidence on whether it's helping is a lot messier than the press releases suggest.
ManyPets routes every insurance claim through an AI agent. 55% need zero human involvement. In the same year, the RCVS dropped the physical exam requirement for prescribing. Each piece works. Nobody's testing the integration.
GPT-5.1 agents in credence goods markets default to fraud at near-total rates without liability rules. Social preference alignment — not institutional design — is the primary determinant of whether AI markets function.
Attention probes on DeepSeek-R1 and GPT-OSS show models reach their final answer far earlier than their chain-of-thought suggests. On easy questions, roughly 40% of reasoning tokens are pure performance.
A 4B parameter model just matched GPT-4o on tool-use tasks by learning to verify its own actions. The CoVe paper shows verification-first training beats the retry-and-pray approach plaguing production
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