ACM
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Production agents accumulate context every turn. The usual fix compresses on a token threshold and throws the remainder away, so the trigger fires for reasons unrelated to what the agent is working on. Meta and CMU hand the decision to the agent instead.
Context editing as a tool: ACM equips the agent with purpose-built context editing tools, so it decides when to compress, offloads what it drops into an external memory system, and queries that store on demand when it needs the detail back.
Short-term to long-term: The design is modeled on the interaction between short-term and long-term human memory, which turns compression from a lossy truncation into a transfer between two stores.
A post-training pipeline: A post-training pipeline built on high-quality context management demonstrations yields a 27% relative gain on BrowseComp-Plus and closes much of the distance to open-source models roughly 40 times larger, with code, data, and checkpoints released.
Why it matters: Analysis shows effective context management lowers peak token pressure, lets the agent explore longer before running out of room, and produces more consistent solutions across independent trials of the same task.
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