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cs.LG2026

VisAudit: Evaluating Multimodal Agents for Visual Diagnosis and Repair

Shicheng Liu, Adam Kahirov, Qi Zhang +5

Multimodal agents are increasingly used for data visualization tasks but remain limited in autonomous review. Unlike humans, they may fail to recognize when a visualization is inco…

cs.LG2026

How Much Reasoning Do Retrieval-Augmented Models Add beyond LLMs? A Benchmarking Framework for Multi-Hop Inference over Hybrid Knowledge

Junhong Lin, Bing Zhang, Song Wang +4

Large language models (LLMs) continue to struggle with knowledge-intensive questions that require up-to-date information and multi-hop reasoning. Augmenting LLMs with hybrid extern…

cs.LG2025

Adaptive Overclocking: Dynamic Control of Thinking Path Length via Real-Time Reasoning Signals

Shuhao Jiang, Songbo Wang, Yang Qiao +7

Large Reasoning Models (LRMs) often suffer from computational inefficiency due to overthinking, where a fixed reasoning budget fails to match the varying complexity of tasks. To ad…

cs.LG2025

Temporal Reasoning with Large Language Models Augmented by Evolving Knowledge Graphs

Junhong Lin, Song Wang, Xiaojie Guo +2

Large language models (LLMs) excel at many language understanding tasks but struggle to reason over knowledge that evolves. To address this, recent work has explored augmenting LLM…

cs.LG2025

Plan and Budget: Effective and Efficient Test-Time Scaling on Reasoning Large Language Models

Junhong Lin, Xinyue Zeng, Jie Zhu +4

Large Language Models (LLMs) have achieved remarkable success in complex reasoning tasks, but their inference remains computationally inefficient. We observe a common failure mode…