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Jie Yang

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#computer vision Preprint Sep 2026

VehicleArena: A Realistic Urban Environment for Multi-Agent Driving

Real-world embodied agents often pursue independent objectives within a shared physical environment, where their actions can alter the conditions faced by others. Existing benchmarks, however, typically assume shared goals or explicitly prescribed interaction protocols, leaving such emergent physical coupling underexpl...

Jie Yang, Jia-Jun Chen, Jia-Zheng Zhou et al. · 0 citations
#natural language process... Preprint May 2026

Beyond Math and Code: Lightweight Corpus-Grounded Process Rewards for Factual Question Answering

CorVer (Corpus Verify) is a lightweight training-time approach to process supervision that derives sentence-level rewards from corpus co-occurrence statistics and outperforms the unmodified models in all standard factual-QA settings.

Shi-Chen Fan, Hao-Chang Hao, De-Hai Min et al. · 5 citations
#artificial intelligence Preprint Sep 2026

TraceDance: An Automated System for Building Agent Behavior Benchmarks from Real-World Agent Deployment Traces

An agent can complete a task while exhibiting undesirable behavior during execution. Developers need tests for the specific behaviors encountered in deployment, beyond fixed benchmark suites. We present TraceDance, an agent system that constructs targeted benchmarks from deployment traces for user-specified undesirable...

De-Hai Min, Dao-An Zhang, Yiming Zeng et al. · 0 citations
#artificial intelligence Preprint Sep 2026

TimeEvo: Failure-Driven Self-Evolution of a Time Series Agent

TimeEvo is proposed, which clusters an agent's diagnosed failures into capability gaps, plans a measurement for each, synthesizes evidence-only tools that fill them, and admits the candidate library only through a paired admission gate.

Jie Yang, Yan Zheng, Jia-Rui Sun et al. · 0 citations
Preprint Aug 2026

ContextWeave: A Real-World Workflow Benchmark

ContextWeave is introduced, a longitudinal benchmark that evaluates whether recalled experience improves downstream agent performance in realistic office-work streams and motivates memory systems that optimize not only retrieval relevance but also reliable use during execution.

Bo Wang, Yu Yao, Enxi Wang et al. · 0 citations

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