6 papers
MCTS-Judge: Test-Time Scaling in LLM-as-a-Judge for Code Correctness Evaluation
Yutong Wang, Pengliang Ji, Chaoqun Yang +4
The LLM-as-a-Judge paradigm shows promise for evaluating generative content but lacks reliability in reasoning-intensive scenarios, such as programming. Inspired by recent advances…
Beyond Policy Optimization: A Data Curation Flywheel for Sparse-Reward Long-Horizon Planning
Yutong Wang, Pengliang Ji, Kaixin Li +3
Large Language Reasoning Models have demonstrated remarkable success on static tasks, yet their application to multi-round agentic planning in interactive environments faces two fu…
LLMCBench: Benchmarking Large Language Model Compression for Efficient Deployment
Ge Yang, Changyi He, Jinyang Guo +6
Although large language models (LLMs) have demonstrated their strong intelligence ability, the high demand for computation and storage hinders their practical application. To this…
Beyond Uncertainty: Evidential Deep Learning for Robust Video Temporal Grounding
Kaijing Ma, Haojian Huang, Jin Chen +8
Existing Video Temporal Grounding (VTG) models excel in accuracy but often overlook open-world challenges posed by open-vocabulary queries and untrimmed videos. This leads to unrel…
NeuroBind: Towards Unified Multimodal Representations for Neural Signals
Fengyu Yang, Chao Feng, Daniel Wang +8
Understanding neural activity and information representation is crucial for advancing knowledge of brain function and cognition. Neural activity, measured through techniques like e…
Decoding by Contrasting Knowledge: Enhancing LLMs' Confidence on Edited Facts
Baolong Bi, Shenghua Liu, Lingrui Mei +3
The knowledge within large language models (LLMs) may become outdated quickly. While in-context editing (ICE) is currently the most effective method for knowledge editing (KE), it…