activity
20242026
collaborators

14 papers

cs.LG2026

When Low Prediction Error Misleads Planning: Diagnosing Representation, Dynamics, and Decision Failures in Latent World Models

Rui Min, Xianyao Li, Fang Xu +1

The component that dominates a latent world model's prediction error need not be the one whose repair most improves action selection. We show this by comparing action sequences fro…

cs.LG2026

Minimal Recurrent Behavioral Memory for Imitation under Partial Observability

Xianyao Li, Fang Xu, Rui Min +2

What is the least recurrent memory needed to reproduce a specified expert under partial observability? The instantaneous requirement is the conditional entropy of the expert's beha…

cs.RO2026

Same World, Different Knowledge: When Isolated Audits Misjudge World-Model Repairs

Rui Min, Xianyao Li, Fang Xu +2

A repair favored under an isolated input fault can be inferior when deployed modules share the faulty information. We introduce an information-interface audit for world models, dis…

cs.RO2026

DreamLedger: Where to Refuse World-Model Imagination Using Execution-Settled Credit

Xianyao Li, Ruitong Tian, Rui Min +2

World-model predictions inform robot actions, yet instantaneous reliability signals do not retain the outcomes of comparable past predictions. DreamLedger registers consumed predic…

cs.AI2026

Some Large Language Models Exhibit Consistent Risk Attitudes

Bowen Sun, Rui Min, Yuxi Wang +3

As artificial intelligence systems are deployed in open-ended, high-stakes settings, a critical dimension remains unmeasured: how perceived risk is translated into action. We test…

cs.CL2026

Scalable Token-Level Hallucination Detection in Large Language Models

Rui Min, Tianyu Pang, Chao Du +2

Large language models (LLMs) have demonstrated remarkable capabilities, but they still frequently produce hallucinations. These hallucinations are difficult to detect in reasoning-…

cs.CV2026

RemoteZero: Geospatial Reasoning with Zero Labels

Liang Yao, Fan Liu, Shengxiang Xu +4

Geospatial reasoning requires models to identify image regions that satisfy complex and often implicit user intents. Recent reinforcement learning approaches improve reasoning with…

cs.CV2026

RemoteAgent: Bridging Vague Human Intents and Earth Observation with RL-based Agentic MLLMs

Liang Yao, Shengxiang Xu, Fan Liu +7

Earth Observation (EO) systems are essentially designed to support domain experts who often express their requirements through vague natural language rather than precise, machine-f…

cs.CL2026

GeoBrowse: A Geolocation Benchmark for Agentic Tool Use with Expert-Annotated Reasoning Traces

Xinyu Geng, Yanjing Xiao, Yuyang Zhang +5

Deep research agents integrate fragmented evidence through multi-step tool use. BrowseComp offers a text-only testbed for such agents, but existing multimodal benchmarks rarely req…

cs.AI2025

EcomBench: Towards Holistic Evaluation of Foundation Agents in E-commerce

Rui Min, Zile Qiao, Ze Xu +18

Foundation agents have rapidly advanced in their ability to reason and interact with real environments, making the evaluation of their core capabilities increasingly important. Whi…

cs.CL2025

Dripper: Token-Efficient Main HTML Extraction with a Lightweight LM

Mengjie Liu, Jiahui Peng, Wenchang Ning +14

High-quality main content extraction from web pages is a critical prerequisite for constructing large-scale training corpora. While traditional heuristic extractors are efficient,…

cs.CL2025

Tongyi DeepResearch Technical Report

Tongyi DeepResearch Team, Baixuan Li, Bo Zhang +54

We present Tongyi DeepResearch, an agentic large language model, which is specifically designed for long-horizon, deep information-seeking research tasks. To incentivize autonomous…

cs.CL2025

WebResearcher: Unleashing unbounded reasoning capability in Long-Horizon Agents

Zile Qiao, Guoxin Chen, Xuanzhong Chen +13

Recent advances in deep-research systems have demonstrated the potential for AI agents to autonomously discover and synthesize knowledge from external sources. In this paper, we in…

cs.IR2024

Hardware Acceleration for Knowledge Graph Processing: Challenges & Recent Developments

Maciej Besta, Robert Gerstenberger, Patrick Iff +9

Knowledge graphs (KGs) have achieved significant attention in recent years, particularly in the area of the Semantic Web as well as gaining popularity in other application domains…