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From the 1 of 41 linked papers with an AI index.

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20242026
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cs.CL2026

Hi-TTRL: Regulating Consensus with Hints for Test-Time Reinforcement Learning

Kunbin Xu, Xingzuo Li, Xuefeng Bai +1

Test-time reinforcement learning (TTRL) improves the reasoning capabilities of large language models without labeled data by updating the policy with pseudo-labels constructed thro…

cs.CL2026

DualAnchor: Preserving Language Priors and Improving Lexical Fidelity in Gloss-Free Sign Language Translation

Hongbin Zhang, Junhao Liu, Xuefeng Bai +3

The paper introduces DualAnchor, a training framework for gloss-free sign language translation that preserves large language model priors with token-level prior anchoring and enhan…

cs.CL2026

Agentic Tool Use in Large Language Models

Jinchao Hu, Meizhi Zhong, Kehai Chen +2

Large language models are increasingly being deployed as autonomous agents yet their real world effectiveness depends on reliable tools for information retrieval, computation and e…

cs.CL2026

Instruction Anchor: Dissecting the Mechanistic Dynamics of Modality Arbitration

Yu Zhang, Mufan Xu, Xuefeng Bai +4

Modality following is the ability to selectively leverage multimodal contexts based on user instructions. It is fundamental to the safety and reliability of multimodal large langua…

cs.CL2026

Mitigating Translationese Bias in Multilingual LLM-as-a-Judge via Disentangled Information Bottleneck

Hongbin Zhang, Kehai Chen, Xuefen Bai +4

Large language models (LLMs) have become a standard for multilingual evaluation, yet they exhibit a severe systematic translationese bias. In this paper, translationese bias is cha…

cs.CL2026

Beyond Token-Level Policy Gradients for Complex Reasoning with Large Language Models

Mufan Xu, Kehai Chen, Xuefeng Bai +4

Existing policy-gradient methods for auto-regressive language models typically select subsequent tokens one at a time as actions in the policy. While effective for many generation…