11 citations · 16 across the 16 of their papers we have counts for
11 papers · 1 filter
Beyond Correctness: Compute-Responsive Data Selection for Reasoning Model Post-Training
Liang He, Xiangyu Li, Yuxin Wang +4
Reasoning language models increasingly rely on test-time compute scaling, yet most existing work studies how to allocate inference computation while treating the model itself as fi…
SE-ADD: Self-Evolving Audio Deepfake Detection with Mistake-Driven Supervision
Rong Wan, Wei Xie, Jiaxi Li +4
Audio deepfake detection (ADD) must remain effective when new spoofing attacks emerge after deployment. Emerging audio language model (ALM)-based ADD methods are built on predefine…
Grounded Checklist Partial Credit for Agent Skill Trajectories
Suliu Qin, Lu Yin, Xilu Wang
Language-model agents increasingly tackle long-horizon tasks in interactive environments, yet their evaluation commonly relies on task-level success rates by reducing an entire exe…
Not All Visual Tokens Are Equally Safe to Remove:Consequence-Sensitive Visual Token Compression
Jingbo Wen, Liang He, Mingyu Cao +4
Visual token compression for vision--language models (VLMs) has largely relied on criteria such as attention, redundancy, and uncertainty to maximize average accuracy under a fixed…
From Relevance to Execution Utility: Reward-Aware Dynamic Execution Gating for Skill-Based LLM Agents
Liang He, Jingbo Wen, Hongyu Gu +5
Agent skills are increasingly used to equip large language model (LLM) agents with reusable procedural knowledge. Although recent work has substantially improved skill retrieval du…
CRIP: Channel Level Representation Injection for Personalized One-Shot Federated Learning
Zijian Jiang, Chaoli Sun, Handing Wang +1
One-shot federated learning (OSFL) has emerged as a promising collaborative model learning framework with only a single round of communication, offering significant advantages in c…
BudgetDraft: Acceptance-Aware Multi-View Training for Sparse-KV Speculative Decoding
Liang He, Jingbo Wen, Qishi Zhan +4
Speculative decoding speeds up autoregressive decoding by using a drafter to propose multiple tokens that a verifier validates in parallel. In resource-constrained deployments, the…
ELAS: Efficient Pre-Training of Low-Rank Large Language Models via 2:4 Activation Sparsity
Jiaxi Li, Lu Yin, Li Shen +5
Large Language Models (LLMs) have achieved remarkable capabilities, but their immense computational demands during training remain a critical bottleneck for widespread adoption. Lo…
Self-Supervised Federated Learning under Data Heterogeneity for Label-Scarce Diatom Classification
Mingkun Tan, Xilu Wang, Michael Kloster +1
Label-scarce visual classification under decentralized and heterogeneous data is a fundamental challenge in pattern recognition, especially when sites exhibit partially overlapping…
Long Chain-of-Thought Compression via Fine-Grained Group Policy Optimization
Xinchen Han, Hossam Afifi, Michel Marot +2
Large Language Models (LLMs) often generate unnecessarily verbose Chain-of-Thought (CoT) reasoning that increases computational costs and latency without proportional performance g…
TSegAgent: Zero-Shot Tooth Segmentation via Geometry-Aware Vision-Language Agents
Shaojie Zhuang, Lu Yin, Guangshun Wei +3
Automatic tooth segmentation and identification from intra-oral scanned 3D models are fundamental problems in digital dentistry, yet most existing approaches rely on task-specific…