Xiaocheng Feng

Focus Like a Human: Efficient GUI Grounding via Coarse-to-Fine Visual Attention and Parallel Verification

Building upon powerful Large Visual Language Models, recent GUI agents have revolutionized autonomous GUI interaction. Given the high information density and structural complexity of GUI layouts, a critical challenge lies in accurately identifying where to focus, i.e., precise GUI grounding. To ensure grounding accura...

What Should SFT Learn? Conditional Information Gain for Generalizable Post-Training

Supervised fine-tuning (SFT) remains a widely used approach for post-training large language models, because fixed demonstrations can directly introduce desired reasoning strategies and behavioral patterns. Yet forcing a model to imitate externally provided trajectories may also interfere with behaviors already support...

Unlocking Multilingual Reasoning Capabilities of LLMs and LVLMs via Representation Engineering

Adaptive Mutual Distillation for Balanced Multi-Task Post-Training of Large Language Models

Multi-task post-training of large language models (LLMs) aims to improve performance across tasks with unequal amounts of training data. Existing methods focus primarily on balancing task contributions during single-model training. Different task-balancing strategies can produce models with complementary strengths, cre...

CRISP: Cultural Reward Modeling for Implicit Situated Propriety

A culturally situated reward model that assigns rewards according to cultural appropriateness in open-ended social scenarios, and a collaborative multi-agent framework that instantiates implicit cultural norms into diverse social scenarios to construct culturally situated data are introduced.

Rethinking Thinking Steps in Overthinking: Towards More Effective Reasoning

This work proposes a training-free, interpretable framework that selects thinking words via attention heads to guide LRMs toward more effective reasoning, and consistently outperforms the strong baseline DEER.

ACTD: Anchor-Based Cross-Tokenizer Distillation with Residual Regularization

An Anchor-Based Cross-Tokenizer Distillation with Residual Regularization with Residual Regularization (ACTD) bridges structural heterogeneity through vocabulary and sequence alignment, while mitigating alignment noise via a novel anchor loss with residual regularization.

H2SD: Hybrid Hindsight Self-Distillation

Experiments on challenging reasoning benchmarks show that H$^2$SD achieves the strongest overall performance among representative RLVR and self-distillation baselines, with stable optimization and a favorable accuracy-efficiency trade-off.