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    Context management reportedly matters most when coding-agent budgets are tight

    HuggingPapers describes a study that isolates planning, the actions an agent can take and context management across 176 settings.

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    TLDR

    HuggingPapers highlights two findings from a study of coding-agent setups: context management matters most when budgets are tight, and planning shifts from accuracy to efficiency as models improve. The study, as summarized by the account, isolates planning, available actions and context management across 176 settings.

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    3 Sources, first seen 20d ago

    Combined views

    18.4K

    3 Sources, first seen 20d ago

    187 likes
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    20d ago
    first seen 20d ago
    187 likes
    24 comments
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    24 comments
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    28 reposts

    3 Sources

    DailyPapers@HuggingPapersCoding agent harnesses, taken apart A new study isolates planning, action space, and context management across 176 settings. Key finding: context management matters most when budgets are tight, and planning flips from accuracy to efficiency as models improve.20d
    AK@_akhaliqRT @HuggingPapers: Coding agent harnesses, taken apart A new study isolates planning, action space, and context management across 176 sett…20d
    DAIR.AI@dair_aiSuper interesting work from Zoom and colleagues. If you maintain a hand-built coding harness, there are some great insights here. (bookmark it) They held the execution loop of a coding harness fixed and varied planning, the action space, and context management one at a time. They did across 176 matched settings, four models, SWE-Bench Verified and Terminal-Bench 2.1. Context management pays off more as the context window tightens. Most of its benefit comes from preventing overflow failures rather than from better reasoning. Staging rule-based elision before LLM summarization gave the best accuracy to cost ratio of the five strategies tested. Making elided content recoverable added machinery the models rarely used and produced no accuracy gain. Planning changed role with model strength. For the weakest model it raised the success rate. For stronger models accuracy barely moved and the gain showed up as lower cost, because planning shortened post-edit verification. On the action space, predefined tools helped models with weak bash proficiency, while bash-capable models ran a bash-only interface at substantially lower cost on command-line tasks. Paper: https://academy.dair.ai/papers/an-empirical-study-of-harness-design-for-coding-agents-2609.2080419d

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    3 Sources

    DailyPapers@HuggingPapersCoding agent harnesses, taken apart A new study isolates planning, action space, and context management across 176 settings. Key finding: context management matters most when budgets are tight, and planning flips from accuracy to efficiency as models improve.20d
    AK@_akhaliqRT @HuggingPapers: Coding agent harnesses, taken apart A new study isolates planning, action space, and context management across 176 sett…20d
    DAIR.AI@dair_aiSuper interesting work from Zoom and colleagues. If you maintain a hand-built coding harness, there are some great insights here. (bookmark it) They held the execution loop of a coding harness fixed and varied planning, the action space, and context management one at a time. They did across 176 matched settings, four models, SWE-Bench Verified and Terminal-Bench 2.1. Context management pays off more as the context window tightens. Most of its benefit comes from preventing overflow failures rather than from better reasoning. Staging rule-based elision before LLM summarization gave the best accuracy to cost ratio of the five strategies tested. Making elided content recoverable added machinery the models rarely used and produced no accuracy gain. Planning changed role with model strength. For the weakest model it raised the success rate. For stronger models accuracy barely moved and the gain showed up as lower cost, because planning shortened post-edit verification. On the action space, predefined tools helped models with weak bash proficiency, while bash-capable models ran a bash-only interface at substantially lower cost on command-line tasks. Paper: https://academy.dair.ai/papers/an-empirical-study-of-harness-design-for-coding-agents-2609.2080419d