1 citations · 1 across the 14 of their papers we have counts for
17 papers
PreciseCoder: Faithful and Generalizable Debugging Beyond Unit-Test Rewards
Yunkai Zhan, Miaosen Chai, Shangshang Wang +5
Reinforcement learning (RL) from unit tests is the common recipe for post-training code models, but a unit test only asks whether the final program passes. We analyze what this uni…
DSV-Mem: Evaluating Multimodal Memory in Professional Workflows for MLLM Agents
Jike Zhong, Ritwick Chaudhry, Xuanbai Chen +4
Conversational MLLM agents are increasingly expected to assist in professional workflows, from AI research and engineering design to product management and business operations. Yet…
When the Right Answer Is Missing: An Arithmetic-Dependent Rejection Bottleneck in Jev
Jike Zhong, Ming Li, Yuxiang Lai
Typed decision models such as Jev offer an efficient alternative to generative LLMs in decision-making workflows by selecting directly from predefined options. When candidate sets…
JigShape: Evaluating Visual-Geometric Reasoning in VLMs through Jigsaw Puzzles
Shawn Li, Wei Yang, Jike Zhong +11
Jigsaw puzzle solving requires jointly reasoning about visual content and geometric constraints, yet existing benchmarks use rectangular cuts that create ambiguous ground truth in…
Agentic Context Learning with Self-Discovered Specification
Jike Zhong, Ming Li, Yuxiang Lai +8
Context learning is an emerging inference-time task where LLMs must learn and apply novel, task-specific knowledge from intricate contexts absent from pre-training; even frontier m…
From Shortcuts to Reasoning: Robust Post-Training of Theory of Mind with Reinforcement Learning
Jike Zhong, Yuxiang Lai, Ming Li +5
Theory of Mind (ToM) is a must-acquire skill for modern foundation model systems to operate effectively and safely in the real world. Recent works have explored honing ToM via post…
ClawForge: Generating Executable Interactive Benchmarks for Command-Line Agents
Yuxiang Lai, Peng Xia, Haonian Ji +8
Interactive agent benchmarks face a tension between scalable construction and realistic workflow evaluation. Hand-authored tasks are expensive to extend and revise, while static pr…
VRIQ: Benchmarking and Diagnosing the Visual-Reasoning IQ of Vision-Language Models
Tina Khezresmaeilzadeh, Jike Zhong, Konstantinos Psounis
Vision-language model (VLM) errors on visual reasoning may come from missing visual structure, from mistakes after that structure is extracted, or from both. We introduce VRIQ, a b…
EchoVLM: Measurement-Grounded Multimodal Learning for Echocardiography
Yuheng Li, Yue Zhang, Abdoul Aziz Amadou +5
Echocardiography is the most widely used imaging modality in cardiology, yet its interpretation remains labor-intensive and inherently multimodal, requiring view recognition, quant…
TIR-Bench: A Comprehensive Benchmark for Agentic Thinking-with-Images Reasoning
Ming Li, Jike Zhong, Shitian Zhao +6
The frontier of visual reasoning is shifting toward models like OpenAI o3, which can intelligently create and operate tools to transform images for problem-solving, also known as t…
Are Video Models Emerging as Zero-Shot Learners and Reasoners in Medical Imaging?
Yuxiang Lai, Jike Zhong, Ming Li +2
Recent advances in large generative models have shown that simple autoregressive formulations, when scaled appropriately, can exhibit strong zero-shot generalization across domains…
MedVista3D: Vision-Language Modeling for Reducing Diagnostic Errors in 3D CT Disease Detection, Understanding and Reporting
Yuheng Li, Yenho Chen, Yuxiang Lai +3
Radiologic diagnostic errors-under-reading errors, inattentional blindness, and communication failures-remain prevalent in clinical practice. These issues often stem from missed lo…
Patient-Specific Autoregressive Models for Organ Motion Prediction in Radiotherapy
Yuxiang Lai, Jike Zhong, Vanessa Su +1
Radiotherapy often involves a prolonged treatment period. During this time, patients may experience organ motion due to breathing and other physiological factors. Predicting and mo…
Think or Not Think: A Study of Explicit Thinking in Rule-Based Visual Reinforcement Fine-Tuning
Ming Li, Jike Zhong, Shitian Zhao +4
This paper investigates the role of explicit thinking process in rule-based reinforcement fine-tuning (RFT) for MLLMs. We first propose CLS-RL for MLLM image classification, using…
Med-R1: Reinforcement Learning for Generalizable Medical Reasoning in Vision-Language Models
Yuxiang Lai, Jike Zhong, Ming Li +4
Vision-language models (VLMs) have achieved impressive progress in natural image reasoning, yet their potential in medical imaging remains underexplored. Medical vision-language ta…
EEE-Bench: A Comprehensive Multimodal Electrical And Electronics Engineering Benchmark
Ming Li, Jike Zhong, Tianle Chen +2
Recent studies on large language models (LLMs) and large multimodal models (LMMs) have demonstrated promising skills in various domains including science and mathematics. However,…
Vision-Language Model Fine-Tuning via Simple Parameter-Efficient Modification
Ming Li, Jike Zhong, Chenxin Li +3
Recent advances in fine-tuning Vision-Language Models (VLMs) have witnessed the success of prompt tuning and adapter tuning, while the classic model fine-tuning on inherent paramet…