4 citations · 4 across the 18 of their papers we have counts for
14 papers · 1 filter
DaGRPO: Rectifying Gradient Conflict in Reasoning via Distinctiveness-Aware Group Relative Policy Optimization
Xuan Xie, Xuan Wang, Wenjie Wang +2
The evolution of Large Language Models (LLMs) has catalyzed a paradigm shift from superficial instruction following to rigorous long-horizon reasoning. While Group Relative Policy…
DeepEvidence: Empowering Biomedical Discovery with Deep Knowledge Graph Research
Zifeng Wang, Zheng Chen, Ziwei Yang +5
Biomedical knowledge graphs (KGs) encode vast, heterogeneous information spanning literature, genes, pathways, drugs, diseases, and clinical trials, but leveraging them collectivel…
BioArc: Discovering Optimal Neural Architectures for Biological Foundation Models
Yi Fang, Haoran Xu, Jiaxin Han +4
Foundation models have revolutionized various fields such as natural language processing (NLP) and computer vision (CV). While efforts have been made to transfer the success of the…
Scaling Agentic Reinforcement Learning for Tool-Integrated Reasoning in VLMs
Meng Lu, Ran Xu, Yi Fang +14
While recent vision-language models (VLMs) demonstrate strong image understanding, their ability to "think with images", i.e., to reason through multi-step visual interactions, rem…
LLM4Cell: A Survey of Large Language and Agentic Models for Single-Cell Biology
Sajib Acharjee Dip, Adrika Zafor, Bikash Kumar Paul +4
Large language models (LLMs) and emerging agentic frameworks are beginning to transform single-cell biology by enabling natural-language reasoning, generative annotation, and multi…
JudgeBoard: Benchmarking and Enhancing Small Language Models for Reasoning Evaluation
Zhenyu Bi, Gaurav Srivastava, Yang Li +4
While small language models (SLMs) have shown promise on various reasoning tasks, their ability to judge the correctness of answers remains unclear compared to large language model…
AMO-Bench: Large Language Models Still Struggle in High School Math Competitions
Shengnan An, Xunliang Cai, Xuezhi Cao +8
We present AMO-Bench, an Advanced Mathematical reasoning benchmark with Olympiad level or even higher difficulty, comprising 50 human-crafted problems. Existing benchmarks have wid…
Sequential Comics for Jailbreaking Multimodal Large Language Models via Structured Visual Storytelling
Deyue Zhang, Dongdong Yang, Junjie Mu +6
Multimodal large language models (MLLMs) exhibit remarkable capabilities but remain susceptible to jailbreak attacks exploiting cross-modal vulnerabilities. In this work, we introd…
Agentic Reasoning and Refinement through Semantic Interaction
Xuxin Tang, Rehema Abulikemu, Eric Krokos +3
Sensemaking report writing often requires multiple refinements in the iterative process. While Large Language Models (LLMs) have shown promise in generating initial reports based o…
EvolProver: Advancing Automated Theorem Proving by Evolving Formalized Problems via Symmetry and Difficulty
Yuchen Tian, Ruiyuan Huang, Xuanwu Wang +6
Large Language Models (LLMs) for formal theorem proving have shown significant promise, yet they often lack generalizability and are fragile to even minor transformations of proble…
Do LLMs Overthink Basic Math Reasoning? Benchmarking the Accuracy-Efficiency Tradeoff in Language Models
Gaurav Srivastava, Aafiya Hussain, Sriram Srinivasan +1
Large language models (LLMs) achieve impressive performance on complex mathematical benchmarks yet sometimes fail on basic math reasoning while generating unnecessarily verbose res…
DEBATE, TRAIN, EVOLVE: Self Evolution of Language Model Reasoning
Gaurav Srivastava, Zhenyu Bi, Meng Lu +1
Large language models (LLMs) have improved significantly in their reasoning through extensive training on massive datasets. However, relying solely on additional data for improveme…
MILLION: Mastering Long-Context LLM Inference Via Outlier-Immunized KV Product Quantization
Zongwu Wang, Peng Xu, Fangxin Liu +7
Large language models (LLMs) are increasingly utilized for complex tasks requiring longer context lengths, with some models supporting up to 128K or 1M tokens. This trend, however,…
Towards Reasoning Ability of Small Language Models
Gaurav Srivastava, Shuxiang Cao, Xuan Wang
Reasoning has long been viewed as an emergent property of large language models (LLMs). However, recent studies challenge this assumption, showing that small language models (SLMs)…