LLM Digest

Story

arxiv_llm_reliability · Nov 9, 2026 · paper

Source brief

MaxIM: Maximally Informative Incremental Summarization via Reinforcement Learning

arxiv.orgNov 9, 2026
original source linked

In brief

AI systems must often maintain bounded memory of unbounded interaction histories. However, optimizing the memory representation, and its updates, for downstream task utility is difficult for standard reinforcement lea...

Feed lens

evaluation

Read the original at arxiv.org →Open in live feed Read that day’s brief

MaxIM: Maximally Informative Incremental Summarization via Reinforcement Learning

Earlier in this thread 4 items

MAGIC: Mixed-Granularity Agent Graphs via Incremental Construction with Dense-Reward Reinforcement Learning

Compaction, decay and reinforcement in a two-tier conversational memory: a paired evaluation against retrieval-augmented generation

CausalXRL: Explainable Reinforcement Learning through Causal Graph Reasoning

RobustRLlib: A Unified Library and Benchmark for Robust Reinforcement Learning Algorithms