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GLaQ: Grounding Latent Queries in Visual Evidence for Multimodal Reasoning
Zesheng Yang, Lingling Zhang, Xinyu Zhang +4
Chain-of-thought reasoning has substantially improved the problem-solving capabilities of multimodal large language models. Fine-grained visual evidence, however, remains difficult…
QUMem: Personalized Memory for Query-Conditioned User-State Inference in LLM Agents
Heng Wang, Yifei Li, Lingling Zhang +4
Large language model (LLM) agents increasingly use external memory systems to support personalization by drawing on long and evolving interaction histories, in which user preferenc…
Beyond Retrieval: Query-Conditioned Reuse of Long-Horizon Agent Trajectories
Yifei Li, Heng Wang, Lingling Zhang +5
Retrieval can identify a past trajectory that may matter, yet it does not specify how an acting agent should use that trajectory after users, entities, constraints, or environment…
Dual-Cluster Memory Agent: Resolving Multi-Paradigm Ambiguity in Optimization Problem Solving
Xinyu Zhang, Yuchen Wan, Boxuan Zhang +4
Large Language Models (LLMs) often struggle with structural ambiguity in optimization problems, where a single problem admits multiple related but conflicting modeling paradigms, h…
OptiVerse: A Comprehensive Benchmark towards Optimization Problem Solving
Xinyu Zhang, Boxuan Zhang, Yuchen Wan +5
While Large Language Models (LLMs) demonstrate remarkable reasoning, complex optimization tasks remain challenging, requiring domain knowledge and robust implementation. However, e…
LogicGraph : Benchmarking Multi-Path Logical Reasoning via Neuro-Symbolic Generation and Verification
Yanrui Wu, Lingling Zhang, Xinyu Zhang +5
Evaluations of large language models (LLMs) primarily emphasize convergent logical reasoning, where success is defined by producing a single correct proof. However, many real-world…