Author

Lei Jiang

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Preprint Sep 2026

OmniStepBench: Deployment-Aware Agent Evaluation across Modalities and Task Complexity

As large language models evolve from question-answering systems into general-purpose agents, evaluation must move beyond static answer correctness to assess multimodal perception, multi-step execution, tool use, and artifact delivery. However, existing benchmarks are often tied to specific task types, execution environ...

Yu Liu, Zhi-Lin Liu, Zhi-Wei Yang et al. · 0 citations

#artificial intelligence Preprint Sep 2026

DAREBench: Deployment-Aware and Reliable Evaluation of Models as Agents

DAREBench (Deployment-Aware and Reliable Evaluation of Models as Agents), a benchmark designed to capture workload variation and support reliable agent evaluation, is introduced, suggesting that agent deployment and model selection should consider workload profiles, deployment mode, and accuracy--cost trade-offs rather...

Yu Liu, Zhi-Lin Liu, Zhi-Wei Yang et al. · 0 citations

#artificial intelligence Preprint Sep 2026

Making Every Tool Call Count: Necessary Tool-Evidence Path Rewards for Agentic Vision-Language Models

This work proposes NTEP-R (NTEP Reward), a supervision mechanism ensuring that each tool invocation strictly advances the reasoning process toward the final solution, and introduces a non-repeated-goal regularizer to penalize redundant calls that revisit satisfied NTEP goals.

Xing-Ming Long, Yu Liu, Zhi-Wei Yang et al. · 0 citations

#machine learning Preprint Aug 2026

LM-X: Explainable Vision--Language--Action Modeling via Progress, Event, and Uncertainty Prediction

LM-X is introduced, which organizes prediction across task, event, and motor scales without claiming anatomical correspondence and shows that explicit multi-timescale predictive state can strengthen control while exposing interpretable internal estimates.

Jin Lou, Zhi Jing, Xu-Peng Wang et al. · 0 citations