5 papers
Agent Skill Evaluation and Evolution: Frameworks and Benchmarks
Kexin Ding, Yang Zhou, Can Jin +3
The growth of agent skills has transformed how agentic systems are built, evaluated, and deployed. As skill libraries continue to scale, rigorous evaluation becomes critical to ens…
AIRL-S: Unifying Reinforcement Learning and Search-Based Test-Time Scaling via Adversarial Inverse Reinforcement Learning
Can Jin, Yang Zhou, Qixin Zhang +8
Test-time scaling strategies for Large Language Models predominantly rely on either reinforcement learning with sparse outcome rewards or search-based methods guided by static Proc…
M^3-Bench: Multi-Modal, Multi-Hop, Multi-Threaded Tool-Using MLLM Agent Benchmark
Yang Zhou, Mingyu Zhao, Zhenting Wang +6
We present M^3-Bench, the first benchmark for evaluating multimodal tool use under the Model Context Protocol. The benchmark targets realistic, multi-hop and multi-threaded workflo…
MHB: Multimodal Handshape-aware Boundary Detection for Continuous Sign Language Recognition
Mingyu Zhao, Zhanfu Yang, Yang Zhou +4
This paper employs a multimodal approach for continuous sign recognition by first using ML for detecting the start and end frames of signs in videos of American Sign Language (ASL)…
LED: LLM Enhanced Open-Vocabulary Object Detection without Human Curated Data Generation
Yang Zhou, Shiyu Zhao, Yuxiao Chen +3
Large foundation models trained on large-scale vision-language data can boost Open-Vocabulary Object Detection (OVD) via synthetic training data, yet the hand-crafted pipelines oft…