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From the 2 of 130 linked papers with an AI index.

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20242026
most citedMindCube: Spatial Mental Modeling from Limited Views

1 citations · 1 across the 46 of their papers we have counts for

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cs.CL2026

Structural Rationale Distillation via Reasoning Space Compression

Jialin Yang, Jiankun Wang, Jiajun Wu +3

When distilling reasoning from large language models (LLMs) into smaller ones, teacher rationales for similar problems often vary wildly in structure and strategy. Like a chef who…

cs.CL2025

Close the Loop: Synthesizing Infinite Tool-Use Data via Multi-Agent Role-Playing

Yuwen Li, Wei Zhang, Zelong Huang +8

Enabling Large Language Models (LLMs) to reliably invoke external tools remains a critical bottleneck for autonomous agents. Existing approaches suffer from three fundamental chall…

cs.CL2025

UCoder: Unsupervised Code Generation by Internal Probing of Large Language Models

Jiajun Wu, Jian Yang, Wei Zhang +6

Large language models (LLMs) have demonstrated remarkable capabilities in code generation tasks. However, their effectiveness heavily relies on supervised training with extensive l…

cs.CL2025

Why Is Spatial Reasoning Hard for VLMs? An Attention Mechanism Perspective on Focus Areas

Shiqi Chen, Tongyao Zhu, Ruochen Zhou +7

Large Vision Language Models (VLMs) have long struggled with spatial reasoning tasks. Surprisingly, even simple spatial reasoning tasks, such as recognizing "under" or "behind" rel…

cs.CL2025

Dual-stage and Lightweight Patient Chart Summarization for Emergency Physicians

Jiajun Wu, Swaleh Zaidi, Braden Teitge +4

Electronic health records (EHRs) contain extensive unstructured clinical data that can overwhelm emergency physicians trying to identify critical information. We present a two-stag…

cs.CL2025

Small Language Models for Emergency Departments Decision Support: A Benchmark Study

Zirui Wang, Jiajun Wu, Braden Teitge +2

Large language models (LLMs) have become increasingly popular in medical domains to assist physicians with a variety of clinical and operational tasks. Given the fast-paced and hig…