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cs.AI2026

AgentDebugX: An Open-Source Toolkit for Failure Observability, Attribution, and Recovery in LLM Agents

Kunlun Zhu, Xuyan Ye, Zhiguang Han +9

LLM agent failures are difficult to debug because the step where an error surfaces is often not the one that caused it. Existing observability tools replay execution traces but pro…

cs.AI2026

BioInsight: Multi-Agent Orchestration for Interactive Biomedical Knowledge Discovery

Jieyi Wang, Bingxuan Li, Nanyi Jiang +9

Biomedical deep-research systems increasingly retrieve and synthesize scientific evidence, but their outputs typically collapse heterogeneous evidence into static text, making prov…

cs.AI2026

Brick-Composer: Using MLLMs for Assembly with Diverse Bricks

Jiateng Liu, Bingxuan Li, Zhenhailong Wang +8

We dream of AI agents that can read arbitrary designs and construct real-world objects from reusable building blocks. As a first step toward this vision, we study whether multimoda…

cs.AI2026

Advancing Creative Physical Intelligence in Large Multimodal Models

Cheng Qian, Hyeonjeong Ha, Jiayu Liu +10

Large multimodal models (LMMs) have rapidly advanced in perception and reasoning; however, it remains unclear whether these capabilities generalize to discovering visually grounded…

cs.AI2026

Useful Memories Become Faulty When Continuously Updated by LLMs

Dylan Zhang, Yanshan Lin, Zhengkun Wu +4

Learning from past experience benefits from two complementary forms of memory: episodic traces -- raw trajectories of what happened -- and consolidated abstractions distilled acros…

cs.AI2026

CreativityBench: Evaluating Agent Creative Reasoning via Affordance-Based Tool Repurposing

Cheng Qian, Hyeonjeong Ha, Jiayu Liu +10

Recent advances in large language models have led to strong performance on reasoning and environment-interaction tasks, yet their ability for creative problem-solving remains under…