12 citations · 13 across the 10 of their papers we have counts for
12 papers
Second-Order Problem Solving for Recursive Self-Improvement in Formal Verification
Yuxuan Jiang, Aditya Vempaty, Ashish Jagmohan
Recursive self-improvement (RSI) enables agents to iteratively optimize their workflows via execution feedback. However, standard RSI typically operates as a first-order optimizer:…
New Evidence, Same Choice: Testing Physical Experiment Selection in Vision Language Models
Sourajit Saha, Shubhashis Roy Dipta, Nobin Sarwar +4
A model first sees an image from one physical measurement experiment, such as how far a block coasted, and must answer a question about a new trial, such as whether the block will…
BEFORE THE FLIP: Measuring Hidden Score Shifts In Quantized Vision Language Models Before The Answer Changes for Visual Question Answering
Sourajit Saha, Shubhashis Roy Dipta, Shaswati Saha +2
Quantization makes vision language models (VLMs) cheaper to store and run by using fewer bits to represent their weights. While unchanged answers on visual question answering (VQA)…
Cornerstones or Stumbling Blocks? Deciphering the Rock Tokens in On-Policy Distillation
Yuxuan Jiang, Runchao Li, Shubhashis Roy Dipta +2
While recent work in Reinforcement Learning with Verifiable Rewards (RLVR) has shown that a small subset of critical tokens disproportionately drives reasoning gains, an analogous…
Bridging Reasoning Trajectories in On-Policy Distillation via Near-Future Guidance
Yuxuan Jiang, Francis Ferraro
On-Policy Distillation (OPD) improves large language model reasoning by training a student model on trajectories sampled from its own policy under teacher supervision. Although OPD…
From Recognition to Reasoning: Benchmarking and Enhancing MLLMs on Real-World Receipt Document Understanding
Yandi Wang, Libin Zhan, Ziwei Huang +5
Extracting structured information from visual documents (Visual Information Extraction, VIE) is a cornerstone of business automation. While recent Multimodal Large Language Models…
SCRIBE: Structured Mid-Level Supervision for Tool-Using Language Models
Yuxuan Jiang, Francis Ferraro
Training reliable tool-augmented agents remains a significant challenge, largely due to the difficulty of credit assignment in multi-step reasoning. While process-level reward mode…
Find Your Optimal Teacher: Personalized Data Synthesis via Router-Guided Multi-Teacher Distillation
Hengyuan Zhang, Shiping Yang, Xiao Liang +8
Training student models on synthetic data generated by strong teacher models is a promising way to distilling the capabilities of teachers. However, recent studies show that strong…
Learning How to Use Tools, Not Just When: Pattern-Aware Tool-Integrated Reasoning
Ningning Xu, Yuxuan Jiang, Shubhashis Roy Dipta +1
Tool-integrated reasoning (TIR) has become a key approach for improving large reasoning models (LRMs) on complex problems. Prior work has mainly studied when to invoke tools, while…
DRP: Distilled Reasoning Pruning with Skill-aware Step Decomposition for Efficient Large Reasoning Models
Yuxuan Jiang, Dawei Li, Francis Ferraro
While Large Reasoning Models (LRMs) have demonstrated success in complex reasoning tasks through long chain-of-thought (CoT) reasoning, their inference often involves excessively v…
Beyond Math: Stories as a Testbed for Memorization-Constrained Reasoning in LLMs
Yuxuan Jiang, Francis Ferraro
Recently, Large Language Models (LLMs) have shown impressive performance in character understanding tasks, such as analyzing the roles, personalities, and relationships of fictiona…
From Generation to Judgment: Opportunities and Challenges of LLM-as-a-judge
Dawei Li, Bohan Jiang, Liangjie Huang +10
Assessment and evaluation have long been critical challenges in artificial intelligence (AI) and natural language processing (NLP). Traditional methods, usually matching-based or s…