1 citations · 2 across the 9 of their papers we have counts for
9 papers
RENDER: Controlling Reader-Facing Evidence in LLM Memory Evaluation
Yuan Si, Simeng Han, Daming Li +1
Memory and RAG evaluations often treat the answering model's input as an implementation detail, even though systems may render the same history as a memory entry, summary, typed re…
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…
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,…
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…
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.…
Scheherazade: Evaluating Chain-of-Thought Math Reasoning in LLMs with Chain-of-Problems
Stephen Miner, Yoshiki Takashima, Simeng Han +4
Benchmarks are critical for measuring Large Language Model (LLM) reasoning capabilities. Some benchmarks have even become the de facto indicator of such capabilities. However, as L…
Optimizing Language Model's Reasoning Abilities with Weak Supervision
Yongqi Tong, Sizhe Wang, Dawei Li +6
While Large Language Models (LLMs) have demonstrated proficiency in handling complex queries, much of the past work has depended on extensively annotated datasets by human experts.…
Eliminating Reasoning via Inferring with Planning: A New Framework to Guide LLMs' Non-linear Thinking
Yongqi Tong, Yifan Wang, Dawei Li +4
Chain-of-Thought(CoT) prompting and its variants explore equipping large language models (LLMs) with high-level reasoning abilities by emulating human-like linear cognition and log…