activity
20242026
most citedTest-Time Learning for Large Language Models

1 citations · 1 across the 8 of their papers we have counts for

collaborators

11 papers

cs.AI2026

Beyond Fast and Slow: Cognitive-Inspired Elastic Reasoning for Large Language Models

Jinwu Hu, Dongjin Yang, Langyu Bian +6

Large language models (LLMs) have demonstrated impressive performance across various language tasks. However, existing LLM reasoning strategies mainly rely on the LLM itself with f…

cs.AI2026

Beyond Model Scaling: Test-Time Intervention for Efficient Deep Reasoning

Qianyue Wang, Jinwu Hu, Yufeng Wang +5

Large Reasoning Models (LRMs) excel at multi-step reasoning but often suffer from inefficient reasoning processes like overthinking and overshoot, where excessive or misdirected re…

cs.MA2026

EvidFuse: Writing-Time Evidence Learning for Consistent Text-Chart Data Reporting

Huanxiang Lin, Qianyue Wang, Jinwu Hu +3

Data-driven reports communicate decision-relevant insights by tightly interleaving narrative text with charts grounded in underlying tables. However, current LLM-based systems typi…

cs.AI2025

Continual Knowledge Adaptation for Reinforcement Learning

Jinwu Hu, Zihao Lian, Zhiquan Wen +5

Reinforcement Learning enables agents to learn optimal behaviors through interactions with environments. However, real-world environments are typically non-stationary, requiring ag…

cs.CL20251 cited

Test-Time Learning for Large Language Models

Jinwu Hu, Zhitian Zhang, Guohao Chen +6

While Large Language Models (LLMs) have exhibited remarkable emergent capabilities through extensive pre-training, they still face critical limitations in generalizing to specializ…

cs.AI2025

Enhancing User-Oriented Proactivity in Open-Domain Dialogues with Critic Guidance

Yufeng Wang, Jinwu Hu, Ziteng Huang +10

Open-domain dialogue systems aim to generate natural and engaging conversations, providing significant practical value in real applications such as social robotics and personal ass…