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cs.AI2026

Evaluating the Hidden Costs of Personalization in Large Language Models

Yumeng Wang, Yuchen Wu, Cheng Qian +6

While Large language models (LLMs) incorporate user personalization signals to improve usability and helpfulness, they increasingly shift from providing balanced, informative respo…

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

PlanBench-XL: Evaluating Long-Horizon Planning of LLM Tool-Use Agents in Large-Scale Tool Ecosystems

Jiayu Liu, Qihan Lin, Cheng Qian +8

LLM agents increasingly operate in large tool ecosystems, where real-world tasks require discovering relevant tools, inferring implicit sub-goals, and adapting to dynamic environme…

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

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…