7 papers
Do Recommendation Algorithms Work When Users Are LLM Agents? A Case Study on Moltbook
Daming Li, Simeng Han, Jialu Zhang
Large language model (LLM) agents are increasingly populating web platforms, raising a fundamental question for recommender systems: do algorithms designed for human users still wo…
Attraction, Not Adaptation: How AI Agent Communities Develop Distinct Linguistic Identities
Daming Li, Simeng Han, Can Meng +2
When tens of thousands of autonomous AI agents interact in topical online forums, do they develop distinct community-specific linguistic identities? We study this question on Moltb…
Meta-Reasoner: Dynamic Guidance for Optimized Inference-time Reasoning in Large Language Models
Yuan Sui, Yufei He, Tri Cao +3
Large Language Models (LLMs) often struggle with computational efficiency and error propagation in multi-step reasoning tasks. While recent advancements on prompting and post-train…
TeamPath: Building MultiModal Pathology Experts with Reasoning AI Copilots
Tianyu Liu, Weihao Xuan, Hao Wu +15
Advances in AI have introduced several strong models in computational pathology to usher it into the era of multi-modal diagnosis, analysis, and interpretation. However, the curren…
ScratchEval : A Multimodal Evaluation Framework for LLMs in Block-Based Programming
Yuan Si, Simeng Han, Daming Li +2
LLMs have achieved strong performance on text-based programming tasks, yet they remain unreliable for block-based languages such as Scratch. Scratch programs exhibit deeply nested,…
GraphIC: A Graph-Based In-Context Example Retrieval Model for Multi-Step Reasoning
Jiale Fu, Yaqing Wang, Simeng Han +2
In-context learning (ICL) enhances large language models (LLMs) by incorporating demonstration examples, yet its effectiveness heavily depends on the quality of selected examples.…