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20242026
most citedAlphaEdit: Null-Space Constrained Knowledge Editing for Language Models

2 citations · 2 across the 15 of their papers we have counts for

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cs.CL2026

Distill What You Trust: Reliability-Aware Multi-Teacher On-Policy Distillation

Jie Sun, Mao Zheng, Mingyang Song +8

Multi-teacher on-policy distillation allows a student to learn from complementary specialists on its own trajectories. Domain-routed approaches, however, select one teacher per exa…

cs.AI2026

RePolicy: Reinforcement Learning for Safety-Policy Invocation in Agent Safeguards

Houcheng Jiang, Boxuan Zhang, Qiyong Zhong +3

Safeguarding language model agents requires assessing complete execution trajectories under context-dependent safety policies. Existing policy-aware safeguards mainly rely on promp…

cs.CL2026

PolicyAlign: Direct Policy-Based Safety Alignment for Large Language Models

Chang Wu, Junfeng Fang, Houcheng Jiang +5

Safety alignment of large language models (LLMs) typically depends on high-quality supervision data, such as safe demonstrations or preference pairs. However, in real-world deploym…

cs.CV2026

UniVLR: Unifying Text and Vision in Visual Latent Reasoning for Multimodal LLMs

Houcheng Jiang, Jiajun Fu, Junfeng Fang +4

Multimodal large language models are increasingly expected to perform thinking with images, yet existing visual latent reasoning methods still rely on explicit textual chain-of-tho…

cs.LG2026

Rubric-based On-policy Distillation

Junfeng Fang, Zhepei Hong, Mao Zheng +7

On-policy distillation (OPD) is a powerful paradigm for model alignment, yet its reliance on teacher logits restricts its application to white-box scenarios. We contend that struct…

cs.CL2026

SOD: Step-wise On-policy Distillation for Small Language Model Agents

Qiyong Zhong, Mao Zheng, Mingyang Song +5

Tool-integrated reasoning (TIR) is difficult to scale to small language models due to instability in long-horizon tool interactions and limited model capacity. While reinforcement…

cs.LG2026

NExT-Guard: Training-Free Streaming Safeguard without Token-Level Labels

Junfeng Fang, Nachuan Chen, Houcheng Jiang +5

Large language models are increasingly deployed in streaming scenarios, rendering conventional post-hoc safeguards ineffective as they fail to interdict unsafe content in real-time…