Good work on improving memory for long-horizon agents. They separate two things that agent memory papers usually collapse into one. How memories get merged when they are written, and how retrieved content gets assembled into the prompt. The setting is a tight prompt budget of 2k to 5k tokens, where full-context prompting is off the table because of latency, cost and context limits. RSM-full combines a cosine-gated max-member merge rule on the write side with an atom-aware grouped packer on the read side. At a 4k budget it reaches 83% of full-context quality at 32% of the token cost. The ablations attribute the gain to both halves separately. The merge rule is worth 5.7 points over online k-means and matched DP-means. The grouped packer is worth 5.0 points over flat concatenation. It reproduces on RealMem, beating Budget-RAG, Streaming-Proto and the A-MEM agentic memory baseline, and landing level with BM25-RAG rather than above it. The authors state that higher-token baselines stay stronger outside this budget range. Paper: academy.dair.ai/papers/compa…