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ARXIV:2601.19568 · SOFTWARE DEVELOPMENT AI · SUBMITTED 02 APR · 02:30 UTC · FRESHNESS STALE
ARXIV:2601.19568SOFTWARE DEVELOPMENT AISUBMITTED 02 APR · 02:30 UTCFRESHNESS STALEarXiv
FuseSearch optimizes code localization with adaptive parallel strategies for efficient software development.
Opportunity summary
Pain FuseSearch optimizes code localization with adaptive parallel strategies for efficient software development.
Evidence 0 refs | 0 sources | 17% coverage
Blocker Evidence unverified
FuseSearch optimizes code localization with adaptive parallel strategies for efficient software development. While concurrent tool execution can enhance discovery speed, current agents demonstrate a 34.9\% redundant invocation rate, which negates parallelism benefits.
Code localization constitutes a key bottleneck in automated software development pipelines. While concurrent tool execution can enhance discovery speed, current agents demonstrate a 34.9\% redundant invocation rate, which negates parallelism benefits.
ScienceToStartup currently rates this 7.0/10 on the public viability pass. While concurrent tool execution can enhance discovery speed, current agents demonstrate a 34.9\% redundant invocation rate, which negates parallelism benefits.
Software Development AI moved forward this cycle; last verified April 2026. Public score 7.0/10.
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Score7.0Public score shown from the verified overall while the stale axis breakdown refreshesAnalysis summary
FuseSearch optimizes code localization with adaptive parallel strategies for efficient software development.
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Paper Pack
10.48550/arXiv.2601.19568FuseSearch optimizes code localization with adaptive parallel strategies for efficient software development.
Abstract
Code localization constitutes a key bottleneck in automated software development pipelines. While concurrent tool execution can enhance discovery speed, current agents demonstrate a 34.9\% redundant invocation rate, which negates parallelism benefits. We propose \textbf{FuseSearch}, reformulating parallel code localization as a \textbf{joint quality-efficiency optimization} task. Through defining \textbf{tool efficiency} -- the ratio of unique information gain to invocation count -- we utilize a two-phase SFT and RL training approach for learning adaptive parallel strategies. Different from fixed-breadth approaches, FuseSearch dynamically modulates search breadth according to task context, evolving from exploration phases to refinement stages. Evaluated on SWE-bench Verified, FuseSearch-4B achieves SOTA-level performance (84.7\% file-level and 56.4\% function-level $F_1$ scores) with 93.6\% speedup, utilizing 67.7\% fewer turns and 68.9\% fewer tokens. Results indicate that efficiency-aware training naturally improves quality through eliminating noisy redundant signals, enabling high-performance cost-effective localization agents.
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Proof status
unverified0 refs; 0 sources; 17% coverage.
What was readable
Derived fallback: Estimated from adjacent evidence; not verified from source.
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Dimensions overall score 7.0
PROBLEM
FuseSearch optimizes code localization with adaptive parallel strategies for efficient software development. While concurrent tool execution can enhance discovery speed, current agents demonstrate a 34.9\% redundant invocation rate, which negates parallelism benefits.
METHOD
Code localization constitutes a key bottleneck in automated software development pipelines. While concurrent tool execution can enhance discovery speed, current agents demonstrate a 34.9\% redundant invocation rate, which negates parallelism benefits.
RESULT
ScienceToStartup currently rates this 7.0/10 on the public viability pass. While concurrent tool execution can enhance discovery speed, current agents demonstrate a 34.9\% redundant invocation rate, which negates parallelism benefits.
WHY NOW
Software Development AI moved forward this cycle; last verified April 2026. Public score 7.0/10.
Abstract-backed public claims while anchored extraction refreshes.
FuseSearch optimizes code localization with adaptive parallel strategies for efficient software development. While concurrent tool execution can enhance discovery speed, current agents demonstrate a 34.9\% redundant invocation rate, which negates parallelism benefits.
Abstract-backed fallback claim; anchored extraction has not materialized a public claim row yet.
partial
Code localization constitutes a key bottleneck in automated software development pipelines. While concurrent tool execution can enhance discovery speed, current agents demonstrate a 34.9\% redundant invocation rate, which negates parallelism benefits.
Abstract-backed fallback claim; anchored extraction has not materialized a public claim row yet.
partial
ScienceToStartup currently rates this 7.0/10 on the public viability pass. While concurrent tool execution can enhance discovery speed, current agents demonstrate a 34.9\% redundant invocation rate, which negates parallelism benefits.
Abstract-backed fallback claim; anchored extraction has not materialized a public claim row yet.
partial
Software Development AI moved forward this cycle; last verified April 2026. Public score 7.0/10.
Abstract-backed fallback claim; anchored extraction has not materialized a public claim row yet.
partial
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FuseSearch optimizes code localization with adaptive parallel strategies for efficient software development.
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Software Development AI
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7.0/10 public viability
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reason
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proof status
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partial
Current read
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Write integration checklist from prototype path and target workflow.
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DEFENSIBILITY
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