Evaluating Memory Condensation Strategies for Coding Agents in Data-Driven Scientific Discovery
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Computer Science > Machine Learning
Title:Evaluating Memory Condensation Strategies for Coding Agents in Data-Driven Scientific Discovery
Abstract:Coding agents accumulate extensive context during long-running tasks, yet fixed context windows force practitioners to choose between truncation and task failure. While numerous memory condensation strategies have been proposed, from simple sliding windows to LLM-generated summaries, no systematic comparison exists to guide strategy selection, especially in scientific discovery tasks. We evaluate eight memory condensation strategies using GPT-4o on sixty DiscoveryBench tasks spanning six scientific domains (480 total evaluations). We find that no condenser significantly alters hypothesis quality, while LLM-based condensers increase token costs by 24-94 percent, and masking tool-call outputs achieves an 8.6 percent net savings. We also observe that the optimal condenser for data-driven scientific discovery varies by scientific domain and task length.
| Subjects: | Machine Learning (cs.LG) |
| Cite as: | arXiv:2605.18854 [cs.LG] |
| (or arXiv:2605.18854v1 [cs.LG] for this version) | |
| https://doi.org/10.48550/arXiv.2605.18854
arXiv-issued DOI via DataCite
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Submission history
From: Renuka Chintalapati [view email][v1] Wed, 13 May 2026 13:10:41 UTC (1,879 KB)
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