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ToolVision: Learning When and How to Use Visual Tools with Capability-Aligned Supervision
Delin Mao, Chenghao Sun, Jingwei Song +2
Thinking with images allows a multimodal model to compensate for limited perception by invoking visual tools through code. Yet the prevailing SFT-then-RL recipe creates a different…
Look Ahead Before You Distill: Future Trajectory Validation of Teacher Guidance for Agentic On-Policy Distillation
Chishui Chen, Yaoyou Fan, Te Sun +11
On-policy distillation (OPD) provides teacher supervision on states visited by the student, reducing the distribution gap between training and inference. However, in multi-turn age…
GameCraft-Bench: Can Agents Build Playable Games End-to-End in a Real Game Engine?
Tongxu Luo, Rongsheng Wang, Jiaxi Bi +22
Game generation is an emerging application of coding agents, requiring models to transform natural-language specifications into playable interactive systems. Unlike traditional cod…
ICBCBench: An Industry Consortium Benchmark for Financial Deep Research
Weiya Li, Zhiwei Tang, Yizhou He +10
With the rapid advancement of Deep Research Agents in knowledge-intensive domains such as finance, establishing reliable and domain-aligned evaluation standards remains a critical…
Socratic-SWE: Self-Evolving Coding Agents via Trace-Derived Agent Skills
Chuan Xiao, Zhengbo Jiao, Shaobo Wang +5
LLM-driven software engineering agents have become a central testbed for real-world language-model capability, yet their training remains limited by the availability of high-qualit…
IndustryCode: A Benchmark for Industry Code Generation
Puyu Zeng, Zhaoxi Wang, Zhixu Duan +7
Code generation and comprehension by Large Language Models (LLMs) have emerged as core drivers of industrial intelligence and decision optimization, finding widespread application…
Bridging Visual Representation and Reinforcement Learning from Verifiable Rewards in Large Vision-Language Models
Yuhang Han, Yuyang Wu, Zhengbo Jiao +6
Reinforcement Learning from Verifiable Rewards (RLVR) has substantially enhanced the reasoning capabilities of large language models in abstract reasoning tasks. However, its appli…
Towards Principled Dataset Distillation: A Spectral Distribution Perspective
Ruixi Wu, Shaobo Wang, Jiahuan Chen +9
Dataset distillation (DD) aims to compress large-scale datasets into compact synthetic counterparts for efficient model training. However, existing DD methods exhibit substantial p…
Credit Where It is Due: Cross-Modality Connectivity Drives Precise Reinforcement Learning for MLLM Reasoning
Zhengbo Jiao, Shaobo Wang, Zifan Zhang +4
Reinforcement Learning with Verifiable Rewards (RLVR) has significantly advanced the reasoning capabilities of Multimodal Large Language Models (MLLMs), yet how visual evidence is…
Socratic-Geo: Synthetic Data Generation and Geometric Reasoning via Multi-Agent Interaction
Zhengbo Jiao, Shaobo Wang, Zifan Zhang +4
Multimodal Large Language Models (MLLMs) have significantly advanced vision-language understanding. However, even state-of-the-art models struggle with geometric reasoning, reveali…
Agentic Proposing: Enhancing Large Language Model Reasoning via Compositional Skill Synthesis
Zhengbo Jiao, Shaobo Wang, Zifan Zhang +5
Advancing complex reasoning in large language models relies on high-quality, verifiable datasets, yet human annotation remains cost-prohibitive and difficult to scale. Current synt…
Grounding and Enhancing Informativeness and Utility in Dataset Distillation
Shaobo Wang, Yantai Yang, Guo Chen +5
Dataset Distillation (DD) seeks to create a compact dataset from a large, real-world dataset. While recent methods often rely on heuristic approaches to balance efficiency and qual…