LongWoF-Bench: Evaluating EvoMap Genes for Verifiable Long-Workflow Tasks
Summary
The paper presents LongWoF-Bench, a benchmark for evaluating long-workflow tasks, and demonstrates that EvoMap Gene enhances task completion efficiency and reduces token costs by reusing verified execution experience.
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Paper page - LongWoF-Bench: Evaluating EvoMap Genes for Verifiable Long-Workflow Tasks
Source: https://huggingface.co/papers/2608.23200
Abstract
EvoMap externalizes verified execution experience into reusable structured Gene, improving long-workflow task completion and reducing token costs across diverse models.
Large language models are increasingly expected to execute complex workflows whose success depends on maintaining interdependent constraints and producing artifacts that satisfy strict end-to-end verification. Yet successful execution experience is typically lost after a single run, forcing subsequent models to rediscover strategies and failure modes from scratch. We study whether such experience can instead be externalized and reused throughEvoMap, whereverifier-confirmed execution trajectoriesare consolidated into structuredGene. To evaluate this setting, we introduce the Long-Workflow Benchmark (LongWoF-Bench), comprising 778 machine-verifiable tasks across codegeneration, agent-environment synthesis, mathematical reasoning, and rule following. On the 252 tasks with verifier-confirmed Opus trajectories, evolvedEvoMapGeneoutperformSkillacross all seven evaluated models by 8.7-15.5 percentage points, with the gains extending to consumer models from different model families. In contrast,reference-distilled Genedo not exhibit the same advantage, indicating that compact representation alone is insufficient and thatGeneutility is closely associated withverified experience provenance. For Claude Opus,Genereuse also completes 39 more tasks thanSkillwhile reducing solve-time token consumption by 9.9%. Together, these results show that verified execution experience can be retained and shared as a reusable external resource, enabling models to improve long-workflow completion without repeatedly paying the full cost of experience discovery.
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