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cs.CL2026

Measure, Don't Optimize: Forecasting Recovery in LLM Unlearning

Zirui Song, Huaxing Liu, Xiang Wang +8

Prior white-box studies show that large language models can retain latent traces of target knowledge after unlearning, even when the knowledge is no longer expressed in their outpu…

cs.CL2026

CAVE: Competence-Aware Visual Boundary Evidence Alignment for Video Temporal Grounding

Wei Jia, Zhicong Lu, Yu Chen +6

Large vision-language models (LVLMs) have achieved substantial performance gains in Video Temporal Grounding (VTG) through reinforcement learning (RL). However, existing methods pr…

cs.CL2026

QUEST: Training Frontier Deep Research Agents with Fully Synthetic Tasks

Jian Xie, Tianhe Lin, Zilu Wang +16

Deep research agents extend the role of search engines from retrieving keyword-matched pages to synthesizing knowledge, fundamentally changing how humans interact with information.…

cs.CL2026

Faithful-MR1: Faithful Multimodal Reasoning via Anchoring and Reinforcing Visual Attention

Changyuan Tian, Zhicong Lu, Huaxing Liu +7

Reinforcement learning with verifiable rewards (RLVR) has emerged as a promising paradigm for advancing complex reasoning in large language models, and recent work extends RLVR to…

cs.CL2025

AlphaEdit: Null-Space Constrained Knowledge Editing for Language Models

Junfeng Fang, Houcheng Jiang, Kun Wang +5

Large language models (LLMs) often exhibit hallucinations due to incorrect or outdated knowledge. Hence, model editing methods have emerged to enable targeted knowledge updates. To…