9 citations · 20 across the 17 of their papers we have counts for
18 papers
ToolSciVer: Multimodal Scientific Claim Verification with Visual Tool Augmented Reinforcement Learning
Binglin Zhou, Peng Shi, Ryo Kamoi +2
Multimodal Scientific Claim Verification (MSCV) requires models to verify scientific claims using visually grounded evidence from papers, including figures, tables, charts, and tex…
SIMMER: Benchmarking Latent Failures in LLM Executable Planning with a World Model
Xiaoxin Lu, Ranran Haoran Zhang, Rui Zhang
Large language models (LLMs) are increasingly deployed as planners for autonomous agents in household environments. While existing benchmarks evaluate whether LLM-generated plans e…
From Correctness to Preference: A Framework for Personalized Agentic Reinforcement Learning
Ranxu zhang, zeyang li, Jiacheng Huang +5
Agentic reinforcement learning (Agentic RL) has achieved strong progress in tasks with clear success signals. However, many real-world agent applications require user-conditioned b…
Nexus : An Agentic Framework for Context-Aware Time Series Forecasting
Sarkar Snigdha Sarathi Das, Palash Goyal, Mihir Parmar +6
Language models have emerged as highly capable reasoners, yet their performance in time series forecasting remains uneven across domains and levels of contextual grounding. This is…
Evaluating LLM-Simulated Conversations in Modeling Inconsistent and Uncollaborative Behaviors in Human Social Interaction
Ryo Kamoi, Ameya Godbole, Binglin Zhou +5
Simulating human conversations using large language models (LLMs) has emerged as a scalable methodology for modeling human social interaction. This paper reconsiders the evaluation…
Synapse: Adaptive Arbitration of Complementary Expertise in Time Series Foundational Models
Sarkar Snigdha Sarathi Das, Palash Goyal, Mihir Parmar +7
Pre-trained Time Series Foundational Models (TSFMs) represent a significant advance, capable of forecasting diverse time series with complex characteristics, including varied seaso…
Enhance Multimodal Consistency and Coherence for Text-Image Plan Generation
Xiaoxin Lu, Ranran Haoran Zhang, Yusen Zhang +1
People get informed of a daily task plan through diverse media involving both texts and images. However, most prior research only focuses on LLM's capability of textual plan genera…
Efficient PRM Training Data Synthesis via Formal Verification
Ryo Kamoi, Yusen Zhang, Nan Zhang +4
Process Reward Models (PRMs) have emerged as a promising approach for improving LLM reasoning capabilities by providing process supervision over reasoning traces. However, existing…
GREATERPROMPT: A Unified, Customizable, and High-Performing Open-Source Toolkit for Prompt Optimization
Wenliang Zheng, Sarkar Snigdha Sarathi Das, Yusen Zhang +1
LLMs have gained immense popularity among researchers and the general public for its impressive capabilities on a variety of tasks. Notably, the efficacy of LLMs remains significan…
Verbosity Veracity: Demystify Verbosity Compensation Behavior of Large Language Models
Yusen Zhang, Sarkar Snigdha Sarathi Das, Rui Zhang
Although Large Language Models (LLMs) have demonstrated their strong capabilities in various tasks, recent work has revealed LLMs also exhibit undesirable behaviors, such as halluc…
GReaTer: Gradients over Reasoning Makes Smaller Language Models Strong Prompt Optimizers
Sarkar Snigdha Sarathi Das, Ryo Kamoi, Bo Pang +3
The effectiveness of large language models (LLMs) is closely tied to the design of prompts, making prompt optimization essential for enhancing their performance across a wide range…
Coverage-based Fairness in Multi-document Summarization
Haoyuan Li, Yusen Zhang, Rui Zhang +1
Fairness in multi-document summarization (MDS) measures whether a system can generate a summary fairly representing information from documents with different social attribute value…
VisOnlyQA: Large Vision Language Models Still Struggle with Visual Perception of Geometric Information
Ryo Kamoi, Yusen Zhang, Sarkar Snigdha Sarathi Das +2
Large Vision Language Models (LVLMs) have achieved remarkable performance in various vision-language tasks. However, it is still unclear how accurately LVLMs can perceive visual in…
From Lazy to Prolific: Tackling Missing Labels in Open Vocabulary Extreme Classification by Positive-Unlabeled Sequence Learning
Ranran Haoran Zhang, Bensu Uçar, Soumik Dey +3
Open-vocabulary Extreme Multi-label Classification (OXMC) extends traditional XMC by allowing prediction beyond an extremely large, predefined label set (typically to $10^{1…
Chain of Agents: Large Language Models Collaborating on Long-Context Tasks
Yusen Zhang, Ruoxi Sun, Yanfei Chen +3
Addressing the challenge of effectively processing long contexts has become a critical issue for Large Language Models (LLMs). Two common strategies have emerged: 1) reducing the i…
Unified Low-Resource Sequence Labeling by Sample-Aware Dynamic Sparse Finetuning
Sarkar Snigdha Sarathi Das, Ranran Haoran Zhang, Peng Shi +2
Unified Sequence Labeling that articulates different sequence labeling problems such as Named Entity Recognition, Relation Extraction, Semantic Role Labeling, etc. in a generalized…
Exploring the Potential of Large Language Models in Generating Code-Tracing Questions for Introductory Programming Courses
Aysa Xuemo Fan, Ranran Haoran Zhang, Luc Paquette +1
In this paper, we explore the application of large language models (LLMs) for generating code-tracing questions in introductory programming courses. We designed targeted prompts fo…
Hermes: Unlocking Security Analysis of Cellular Network Protocols by Synthesizing Finite State Machines from Natural Language Specifications
Abdullah Al Ishtiaq, Sarkar Snigdha Sarathi Das, Syed Md Mukit Rashid +8
In this paper, we present Hermes, an end-to-end framework to automatically generate formal representations from natural language cellular specifications. We first develop a neural…