When Tomorrow Becomes Today: Self-Evolving Policies for Agentic Time-Series Forecasting
为解决时间序列预测中模型随时间变化的有效性问题，本文提出TimEvolve方法，通过将每个实际结果转换为专家信任度、路径选择和干预强度的持久更新来系统地利用延迟反馈。
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BabelArena: A Large-Scale Multilingual Benchmark for LLM Agents
为了解决多语言环境下大语言模型代理评估不足的问题，通过BabelFlow方法构建了BabelArena基准，涵盖23种语言，用于评估模型在多语言任务中的表现。
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BENCHCOMPASS: From Scores to Signals for Training and Harness Decisions in Payment-Domain LLMs
为了解决支付领域大语言模型的应用问题，本文提出了BENCHCOMPASS基准测试，通过构建基于场景的任务和质量检查来评估模型处理支付规则、证据利用及鲁棒性。
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Beyond Numerical Time Series: A Unified Benchmark for Multimodal Forecasting with Heterogeneous Context
本文提出MUSE-Bench，一个用于多模态时间序列预测的统一基准，包含多种类型上下文，评估不同预测方法的效果。
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Grounded Skill Synthesis from Code at Scale for Agentic Intelligence
Reusable skills give agents transferable procedural knowledge, making scalable acquisition essential for extending agents beyond prior experience. Existing methods face two limitations: trajectory-based synthesis requires interactions with specific environments, while document-derived skills may lack executable evidence and verification. Source code offers a complementary path: it requires no prior agent experience yet provides executable evidence for grounding abstractions. We present Code2Skill, a fully automated pipeline that transforms selected code units into implementation-anchored records of atomic operations, composite workflows, and recurring patterns, then verifies each record through source-body-blind reconstruction and source-aware comparison. Applied to 19,769 popular, actively maintained GitHub repositories, Code2Skill produces CodeSkillBank, a grounded bank of 1,006,822 accepted records with workflow, boundary, provenance, and source-evidence metadata. Across 72 protocol-matched evaluations covering nine model settings and eight benchmarks, models augmented with retrieved CodeSkillBank skills improve by 11.7% on average over matched baselines and outperform them in 57 cases. Under a unified downstream interface, Code2Skill also outperforms trajectory-derived skill banks on all seven shared benchmarks, showing that repository-derived skills can provide useful procedural knowledge before agents accumulate sufficient interaction experience. Skills synthesized from tested AI-generated code achieve a 93.50% pass rate, compared with 93.00% for human-written code, providing initial evidence that the pipeline can expand with the growing volume of AI-generated software. Overall, Code2Skill transforms procedural knowledge embedded in repositories into grounded, verifiable, and transferable agent skills.
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When Tomorrow Becomes Today: Self-Evolving Policies for Agentic Time-Series Forecasting
为解决时间序列预测中模型随时间变化的有效性问题，本文提出TimEvolve方法，通过将每个实际结果转换为专家信任度、路径选择和干预强度的持久更新来系统地利用延迟反馈。
0 citationsRead paper
BabelArena: A Large-Scale Multilingual Benchmark for LLM Agents
为了解决多语言环境下大语言模型代理评估不足的问题，通过BabelFlow方法构建了BabelArena基准，涵盖23种语言，用于评估模型在多语言任务中的表现。
0 citationsRead paper
BENCHCOMPASS: From Scores to Signals for Training and Harness Decisions in Payment-Domain LLMs
为了解决支付领域大语言模型的应用问题，本文提出了BENCHCOMPASS基准测试，通过构建基于场景的任务和质量检查来评估模型处理支付规则、证据利用及鲁棒性。
0 citationsRead paper
Beyond Numerical Time Series: A Unified Benchmark for Multimodal Forecasting with Heterogeneous Context
本文提出MUSE-Bench，一个用于多模态时间序列预测的统一基准，包含多种类型上下文，评估不同预测方法的效果。
0 citationsRead paper
Grounded Skill Synthesis from Code at Scale for Agentic Intelligence
Reusable skills give agents transferable procedural knowledge, making scalable acquisition essential for extending agents beyond prior experience. Existing methods face two limitations: trajectory-based synthesis requires interactions with specific environments, while document-derived skills may lack executable evidence and verification. Source code offers a complementary path: it requires no prior agent experience yet provides executable evidence for grounding abstractions. We present Code2Skill, a fully automated pipeline that transforms selected code units into implementation-anchored records of atomic operations, composite workflows, and recurring patterns, then verifies each record through source-body-blind reconstruction and source-aware comparison. Applied to 19,769 popular, actively maintained GitHub repositories, Code2Skill produces CodeSkillBank, a grounded bank of 1,006,822 accepted records with workflow, boundary, provenance, and source-evidence metadata. Across 72 protocol-matched evaluations covering nine model settings and eight benchmarks, models augmented with retrieved CodeSkillBank skills improve by 11.7% on average over matched baselines and outperform them in 57 cases. Under a unified downstream interface, Code2Skill also outperforms trajectory-derived skill banks on all seven shared benchmarks, showing that repository-derived skills can provide useful procedural knowledge before agents accumulate sufficient interaction experience. Skills synthesized from tested AI-generated code achieve a 93.50% pass rate, compared with 93.00% for human-written code, providing initial evidence that the pipeline can expand with the growing volume of AI-generated software. Overall, Code2Skill transforms procedural knowledge embedded in repositories into grounded, verifiable, and transferable agent skills.
0 citationsRead paper