1 citations · 1 across the 12 of their papers we have counts for
12 papers
T2J-BENCH: Benchmarking Coding Agents on Execution-Grounded PyTorch-to-JAX Repository Migration
Tao Feng, Linxin Song, Jiefeng Chen +9
Migrating a machine-learning codebase across frameworks--for example from PyTorch to JAX--is a routine but costly engineering task that is fundamentally cross-paradigm: eager, stat…
Memory Retrieval for Changing Preferences
Yuehan Qin, Li Li, Linxin Song +4
Long-context dialogue systems must decide both when to access memory and which parts of the interaction history are relevant. Existing approaches typically rely on heuristic retrie…
Mitigating Bias in Locally Constrained Decoding via Tractable Proposals
Meihua Dang, Linxin Song, Honghua Zhang +3
Generations from large language models often fail to conform to desired constraints such as JSON schema. Existing locally constrained decoding (LCD) approaches enforce constraints…
When the Strongest Teacher Is Not the Best Teacher: Student-Centric Answer Selection
Zhengyu Hu, Zheyuan Xiao, Linxin Song +10
LLM training increasingly relies on teacher-generated supervision, from synthetic responses to reasoning traces and tool-use demonstrations. Current practice often chooses the high…
Video-Based Reward Modeling for Computer-Use Agents
Linxin Song, Jieyu Zhang, Huanxin Sheng +6
Computer-using agents (CUAs) are becoming increasingly capable; however, it remains difficult to scale evaluation of whether a trajectory truly fulfills a user instruction. In this…
Experiential Reinforcement Learning
Taiwei Shi, Sihao Chen, Bowen Jiang +3
Reinforcement learning has become the central approach for language models (LMs) to learn from environmental reward or feedback. In practice, the environmental feedback is usually…
Video-Thinker: Sparking "Thinking with Videos" via Reinforcement Learning
Shijian Wang, Jiarui Jin, Xingjian Wang +6
Recent advances in image reasoning methods, particularly "Thinking with Images", have demonstrated remarkable success in Multimodal Large Language Models (MLLMs); however, this dyn…
A Personalized Conversational Benchmark: Towards Simulating Personalized Conversations
Li Li, Peilin Cai, Ryan A. Rossi +21
We present PersonaConvBench, a large-scale benchmark for evaluating personalized reasoning and generation in multi-turn conversations with large language models (LLMs). Unlike exis…
ProVision: Programmatically Scaling Vision-centric Instruction Data for Multimodal Language Models
Jieyu Zhang, Le Xue, Linxin Song +11
With the rise of multimodal applications, instruction data has become critical for training multimodal language models capable of understanding complex image-based queries. Existin…
Investigating the Scaling Effect of Instruction Templates for Training Multimodal Language Model
Shijian Wang, Linxin Song, Jieyu Zhang +9
Current multimodal language model (MLM) training approaches overlook the influence of instruction templates. Previous research deals with this problem by leveraging hand-crafted or…
Adaptive In-conversation Team Building for Language Model Agents
Linxin Song, Jiale Liu, Jieyu Zhang +5
Leveraging multiple large language model (LLM) agents has shown to be a promising approach for tackling complex tasks, while the effective design of multiple agents for a particula…
Better Explain Transformers by Illuminating Important Information
Linxin Song, Yan Cui, Ao Luo +2
Transformer-based models excel in various natural language processing (NLP) tasks, attracting countless efforts to explain their inner workings. Prior methods explain Transformers…