Three New Papers on LLM Memory and Reasoning: ChatHealthAI, Traj-Evolve, and DELTAMEM
Three New Papers on LLM Memory and Reasoning: ChatHealthAI, Traj-Evolve, and DELTAMEM Three recent arXiv papers look at a shared limitation in today’s LLM systems: they are often strong at language generation, but weaker when they must work over long histories, structured records, or repeated experience. ChatHealthAI focuses on aligning structured electronic health record representations with LLM-based clinical reasoning, Traj-Evolve studies patient trajectory modeling through a self-evolving multi-agent system for lung cancer early detection, and DELTAMEM proposes a residual-tree approach for managing experience memory in LLM agents. Taken together, these papers frame medical data and agent memory as related problems of structure, continuity, and retrieval. [S1][S2][S7] [S1] [S2] [S7] Intro: paper names and release context All three works are newly announced papers on arXiv. ChatHealthAI is presented as "Aligning Electronic Health Record Representations with Large Language Mod...