Slipstream: Trajectory-Grounded Compaction Validation for Long-Horizon Agents
Researchers have introduced Slipstream, a novel system designed to enhance the performance of long-horizon Large Language Model (LLM) agents by addressing critical issues in context compaction. Traditional methods synchronously compress agent trajectories, often leading to accuracy degradation because the compactor lacks awareness of future information needs. Slipstream employs an asynchronous compaction strategy, running the compactor in parallel with continued agent execution on the original context. This approach generates candidate summaries and subsequent agent steps independently, allowing a judge module to validate the summary against the agent's ongoing reasoning. The system ensures that key facts, constraints, and forward intent are preserved. Experimental results on long-horizon coding tasks (SWE-bench Verified) and web-browsing workloads (BrowseComp) demonstrate significant improvements. Slipstream increases task accuracy by up to 8.8 percentage points and reduces end-to-end latency by up to 39.7%, offering a robust solution for managing large contexts in complex AI agent workflows without silent error propagation.
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Slipstream: Trajectory-Grounded Compaction Validation for Long-Horizon Agents
Researchers have introduced Slipstream, a novel system designed to enhance the performance of long-horizon Large Language Model (LLM) agents by addressing critical issues in context compaction. Traditional methods synchronously compress agent trajectories, often leading to accuracy degradation because the compactor lacks awareness of future information needs. Slipstream employs an asynchronous compaction strategy, running the compactor in parallel with continued agent execution on the original context. This approach generates candidate summaries and subsequent agent steps independently, allowing a judge module to validate the summary against the agent's ongoing reasoning. The system ensures that key facts, constraints, and forward intent are preserved. Experimental results on long-horizon coding tasks (SWE-bench Verified) and web-browsing workloads (BrowseComp) demonstrate significant improvements. Slipstream increases task accuracy by up to 8.8 percentage points and reduces end-to-end latency by up to 39.7%, offering a robust solution for managing large contexts in complex AI agent workflows without silent error propagation.
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