Compact-Memory LLM Agents via Online Max-Member Clustering and Atom-Aware Packing
Read the original on arXiv AI →The paper introduces RSM‑full, an online clustered‑memory pipeline that uses a cosine‑gated max‑member merge rule and an atom‑aware grouped context packer to improve compact‑memory performance for large language model deployments. On the AMA‑Bench benchmark, RSM‑full achieves 83 % of full‑context quality while using only 32 % of the token cost at a 4 k budget, outperforming streaming‑clustered baselines by 3.5–6.0 pp. Similar gains are observed on the RealMem persona‑memory benchmark, where RSM‑full surpasses Budget‑RAG, matches BM25‑RAG, and outperforms Streaming‑Proto and A‑MEM. whyItMatters":"RSM‑full demonstrates that careful design of memory merging and context packing can substantially reduce token costs while maintaining high quality, addressing practical constraints of latency, cost, and context limits in long‑horizon LLM deployments."
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