4 citations · 6 across the 11 of their papers we have counts for
12 papers
ProRetrieval: Learning to Orchestrate Hybrid Search via Executable Program Synthesis
Chengsong You, Zhen Sun, Yunhai Hu +17
Real-world retrieval often composes structured constraints with semantic intents over text and images through arbitrary Boolean logic. Existing hybrid pipelines such as reciprocal…
OpenPM: Auditable Point-in-Time Evaluation for LLM Portfolio-Management Agents
Xinying Cai, Minghao Guo, Jiahe Liu +7
Large language models are increasingly used to read markets, assess risk, and allocate capital. However, reported results for LLM trading agents can be inflated by look-ahead leaka…
Bayesian Data Reweighting Improves Multimodal Retrieval for Knowledge-Based Visual Question Answering
Jingchen Sun, Shaobo Han, Ruiyi Zhang +5
Multimodal retrievers are essential for knowledge-based visual question answering, where they retrieve external evidence for image-question pairs. However, existing contrastive tra…
DSL-R1: From SQL to DSL for Training Retrieval Agents across Structured and Unstructured Data with Reinforcement Learning
Yunhai Hu, Junwei Zhou, Yumo Cao +8
Effective retrieval in complex domains requires bridging the gap between structured metadata and unstructured content. Existing systems typically isolate these capabilities, relyin…
PuzzlePlex: Benchmarking Foundation Models on Reasoning and Planning with Puzzles
Yitao Long, Yuru Jiang, Hongjun Liu +6
This work investigates the reasoning and planning capabilities of foundation models and their scalability in complex, dynamic environments. We introduce PuzzlePlex, a benchmark des…
FinLFQA: Evaluating Attributed Text Generation of LLMs in Financial Long-Form Question Answering
Yitao Long, Tiansheng Hu, Yilun Zhao +2
Large Language Models (LLMs) frequently hallucinate to long-form questions, producing plausible yet factually incorrect answers. A common mitigation strategy is to provide attribut…
MedMMV: A Controllable Multimodal Multi-Agent Framework for Reliable and Verifiable Clinical Reasoning
Hongjun Liu, Yinghao Zhu, Yuhui Wang +4
Recent progress in multimodal large language models (MLLMs) has demonstrated promising performance on medical benchmarks and in preliminary trials as clinical assistants. Yet, our…
MMVU: Measuring Expert-Level Multi-Discipline Video Understanding
Yilun Zhao, Lujing Xie, Haowei Zhang +16
We introduce MMVU, a comprehensive expert-level, multi-discipline benchmark for evaluating foundation models in video understanding. MMVU includes 3,000 expert-annotated questions…
FinDVer: Explainable Claim Verification over Long and Hybrid-Content Financial Documents
Yilun Zhao, Yitao Long, Yuru Jiang +7
We introduce FinDVer, a comprehensive benchmark specifically designed to evaluate the explainable claim verification capabilities of LLMs in the context of understanding and analyz…
Open-FinLLMs: Open Multimodal Large Language Models for Financial Applications
Jimin Huang, Mengxi Xiao, Dong Li +41
Financial LLMs hold promise for advancing financial tasks and domain-specific applications. However, they are limited by scarce corpora, weak multimodal capabilities, and narrow ev…
DocMath-Eval: Evaluating Math Reasoning Capabilities of LLMs in Understanding Long and Specialized Documents
Yilun Zhao, Yitao Long, Hongjun Liu +7
Recent LLMs have demonstrated remarkable performance in solving exam-like math word problems. However, the degree to which these numerical reasoning skills are effective in real-wo…
FinanceMath: Knowledge-Intensive Math Reasoning in Finance Domains
Yilun Zhao, Hongjun Liu, Yitao Long +3
We introduce FinanceMath, a novel benchmark designed to evaluate LLMs' capabilities in solving knowledge-intensive math reasoning problems. Compared to prior works, this study feat…