Search papers, labs, and topics across Lattice.
Looping language models isn't just for single agents anymore: Recursive Multi-Agent Systems (RecursiveMAS) show that agent collaboration itself can be scaled through recursion, yielding faster and more efficient problem-solving.
Language models can now learn to forget strategically, achieving 2-3x memory efficiency without sacrificing reasoning accuracy.
A lightweight, RL-trained context curator can match GPT-4o's context management abilities, slashing token consumption by 8x and opening the door to efficient long-horizon LLM agents.
LLMs can decide when they need more "thinking time" – and boost their accuracy while slashing compute costs by up to 65% – simply by checking if they agree with themselves.
LLMs' chain-of-thought reasoning often falls apart due to factual incompleteness, with errors compounding across multiple hops, as revealed by a new multi-hop QA dataset.
An AI agent can triage remote patient monitoring data with higher sensitivity than individual clinicians, suggesting a path to scalable and cost-effective patient monitoring.
LLMs struggle to explore multiple valid reasoning paths, often committing to a single route and missing alternative solutions, especially in complex, multi-step logical problems.
LLMs may grasp the broad strokes of causal strategies, but struggle with the devilish details of research design, as revealed by a new benchmark separating causal identification from estimation.
