1 citations · 1 across the 16 of their papers we have counts for
16 papers
AREAs-Lab: An Interactive Environment for AI-driven Requirement Elicitation for AI Systems
Pengshan Cai, Zihao Zhang, Ting Jin +8
Building effective AI systems increasingly depends on writing high-quality task requirements, yet users often struggle to articulate the constraints, preferences, and edge cases th…
StreamHear: Domain-Adapted Pseudo-Labeling for Semi-Supervised Streaming Speech Recognition
Zefang Liu, Chenyang Zhu, Sangwoo Cho +3
Streaming automatic speech recognition (ASR) underperforms on domain-shifted target audio, where labeled in-domain data is costly to prepare while unlabeled audio is abundant. We p…
SPAR: Semantic-Pixel Self-Alignment and Adaptive Routing for Unified Multimodal Models
Hongxiang Li, Hongxu Chen, Chenyang Zhu +5
Multimodal Large Language Models (MLLMs) have achieved remarkable success in visual understanding but remain constrained in visual generation due to the fundamental feature discrep…
Ask, Don't Judge: Binary Questions for Interpretable LLM Evaluation and Self-Improvement
Sangwoo Cho, Kushal Chawla, Pengshan Cai +4
Evaluating LLM outputs remains a major bottleneck in NLP: human evaluation is expensive and slow, lexical metrics correlate poorly with human judgments on open-ended generation, an…
DynaMOMA: Instantaneous Prediction of Grasp Poses for Mobile Manipulation of Dynamic Objects
Zhinan Yu, Junyan Xu, Jiazhao Zhang +9
Mobile manipulation is a fundamental robotics task and has advanced rapidly in recent years, enabling robots to navigate, reach, and interact with objects in complex environments.…
SAFARI: Scaling Long Horizon Agentic Fault Attribution via Active Investigation
Chenyang Zhu, Jiayu Yao, Kushal Chawla +10
As autonomous agents tackle increasingly complex multi-step, multi-agent tasks, their execution trajectories have scaled beyond the constraints of even the largest context windows.…
PAIWorld: A 3D-Consistent World Foundation Model for Robotic Manipulation
Yuhang Huang, Xuan Lv, Junyan Xu +25
World foundation models (WFMs) are powerful simulators, yet they predominantly operate in a single-view setting and lack the multi-view 3D consistency required for robotic manipula…
MoKus: Leveraging Cross-Modal Knowledge Transfer for Knowledge-Aware Concept Customization
Chenyang Zhu, Hongxiang Li, Xiu Li +1
Concept customization typically binds rare tokens to a target concept. Unfortunately, these approaches often suffer from unstable performance as the pretraining data seldom contain…
EvoGuard: An Extensible Agentic RL-based Framework for Practical and Evolving AI-Generated Image Detection
Chenyang Zhu, Maorong Wang, Jun Liu +2
The rapid proliferation of AI-Generated Images (AIGIs) poses severe misinformation risks, making AIGI detection critical yet challenging. Traditional detection paradigms mainly rel…
ReHear: Iterative Pseudo-Label Refinement for Semi-Supervised Speech Recognition via Audio Large Language Models
Zefang Liu, Chenyang Zhu, Sangwoo Cho +1
Semi-supervised learning in automatic speech recognition (ASR) typically relies on pseudo-labeling, which often suffers from confirmation bias and error accumulation due to noisy s…
Lessons from the Field: An Adaptable Lifecycle Approach to Applied Dialogue Summarization
Kushal Chawla, Chenyang Zhu, Pengshan Cai +9
Summarization of multi-party dialogues is a critical capability in industry, enhancing knowledge transfer and operational effectiveness across many domains. However, automatically…
Uncertainty as Feature Gaps: Epistemic Uncertainty Quantification of LLMs in Contextual Question-Answering
Yavuz Bakman, Sungmin Kang, Zhiqi Huang +8
Uncertainty Quantification (UQ) research has primarily focused on closed-book factual question answering (QA), while contextual QA remains unexplored, despite its importance in rea…
Confidence-Based Response Abstinence: Improving LLM Trustworthiness via Activation-Based Uncertainty Estimation
Zhiqi Huang, Vivek Datla, Chenyang Zhu +4
We propose a method for confidence estimation in retrieval-augmented generation (RAG) systems that aligns closely with the correctness of large language model (LLM) outputs. Confid…
Improving Consistency in Retrieval-Augmented Systems with Group Similarity Rewards
Faisal Hamman, Chenyang Zhu, Anoop Kumar +4
RAG systems are increasingly deployed in high-stakes domains where users expect outputs to be consistent across semantically equivalent queries. However, existing systems often exh…
RAFFLES: Reasoning-based Attribution of Faults for LLM Systems
Chenyang Zhu, Spencer Hong, Jingyu Wu +6
The advent of complex, interconnected long-horizon LLM systems has made it incredibly tricky to identify where and when these systems break down. Evaluation capabilities that curre…
Refusal Tokens: A Simple Way to Calibrate Refusals in Large Language Models
Neel Jain, Aditya Shrivastava, Chenyang Zhu +6
A key component of building safe and reliable language models is enabling the models to appropriately refuse to follow certain instructions or answer certain questions. We may want…