1 citations · 1 across the 10 of their papers we have counts for
11 papers
Facts Don't Speak Louder than Words: The Behavioral Essence of Long-CoT Distillation
Yongcan Yu, Jian Liang, Lingxiao He +5
While long chain-of-thought (CoT) distillation significantly improves the reasoning abilities of language models, its underlying mechanism remains poorly understood. Current pipeli…
DualStake: Dual-Path Confidence Calibration in Deep Research Agents
Yinuo Xu, Yuwei Liang, Jianjie Cheng +4
Deep Research agents tackle knowledge-intensive tasks through multi-round retrieval and decision-oriented generation. However, these agents suffer from severe overconfidence, makin…
Topological collapse of higher-order interactions bottlenecks collective intelligence in AI agent societies
Shuo Lu, Weicheng Meng, Aijing Yu +4
Current paradigms in artificial intelligence concentrate on scaling the capabilities of individual models, yet the collective behaviour of interacting agents is shaped by the topol…
HarnessX: A Composable, Adaptive, and Evolvable Agent Harness Foundry
Tingyang Chen, Shuo Lu, Kang Zhao +11
AI agent performance depends critically on the runtime harness, comprising the prompts, tools, memory, and control flow that mediate how a model observes, reasons, and acts. Yet to…
Mi-Memory: A Lifecycle Memory Framework for Personal AI
Xule Liu, Hanlin Teng, Chao Li +15
Personal AI is moving beyond chat-only interaction toward continuous services that span phones, cars, homes, wearables, cameras, and tools. In this setting, memory cannot remain a…
KAT-Coder-V2.5 Technical Report
Bo Huang, Fengxiang Li, Hao Xu +50
We present KAT-Coder-V2.5, a coding-focused agentic model trained to act autonomously inside real, executable repositories rather than as a single-turn code generator. Its capabili…
How to Train Your Deep Research Agent? Prompt, Reward, and Policy Optimization in Search-R1
Yinuo Xu, Shuo Lu, Jianjie Cheng +5
Deep Research agents tackle knowledge-intensive tasks through multi-round retrieval and decision-oriented generation. While reinforcement learning (RL) has been shown to improve pe…
One Size, Many Fits: Aligning Diverse Group-Wise Click Preferences in Large-Scale Advertising Image Generation
Shuo Lu, Haohan Wang, Wei Feng +14
Advertising image generation has increasingly focused on online metrics like Click-Through Rate (CTR), yet existing approaches adopt a ``one-size-fits-all" strategy that optimizes…
Do MLLMs Really Understand Space? A Mathematical Reasoning Evaluation
Shuo Lu, Jianjie Cheng, Yinuo Xu +16
Multimodal large language models (MLLMs) have achieved strong performance on perception-oriented tasks, yet their ability to perform mathematical spatial reasoning, defined as the…
DeepResearch-Slice: Bridging the Retrieval-Utilization Gap via Explicit Text Slicing
Shuo Lu, Yinuo Xu, Jianjie Cheng +3
Deep Research agents predominantly optimize search policies to maximize retrieval probability. However, we identify a critical bottleneck: the retrieval-utilization gap, where mode…
Reassessing the Role of Supervised Fine-Tuning: An Empirical Study in VLM Reasoning
Yongcan Yu, Lingxiao He, Shuo Lu +10
Recent advances in vision-language models (VLMs) reasoning have been largely attributed to the rise of reinforcement Learning (RL), which has shifted the community's focus away fro…