1 citations · 2 across the 14 of their papers we have counts for
15 papers
EvalMem: An Operation-Level Diagnostic Framework for Long-Term Memory Systems
Zeyu Liu, Jian Zhong, Rongduo Han +12
Long-horizon interactions with LLM-based assistants require memory systems that preserve and update user states, preferences, and interaction histories. Existing evaluations report…
Claim-Level Reliability Assessment for Efficient Test-Time Reasoning
Sen Xu, Wei Wang, Shixi Liu +5
We propose claim-level falsification as a principle for test-time scaling and instantiate it through Claim-Level Reliability Assessment (CLR), a training-free framework that reallo…
FinanceHarness: Autonomous Financial Deep Research Framework
Yijia Xiao, Rujun Han, Yanfei Chen +8
Powered by advances in LLMs and autonomous agents, deep research has become one of the most widely adopted agentic products. However, most deep research systems write general-purpo…
Beyond Transformers: Linear Attention Policy for Open-Vocabulary Object Goal Navigation
Jiahong Zhang, Yifan Lin, Yandong Zhang +6
Open-Vocabulary Object Goal Navigation (OVON) requires agents to operate under partial observability, making effective internal state updates critical for navigation performance. T…
Kairos: A Regret-Aware Native World-Action Model Stack for Physical AI
Kairos Team, Fei Wang, Shan You +21
We introduce \textbf{Kairos}, a regret-aware native world-action model stack for Physical AI. Kairos is motivated by the view that a physical world model should not aim to fully si…
AI Supply Chain Galaxy: 3D Visual Analytics for License Compliance
Weiru Han, Xuetao Shi, Wenyi He +3
The rapid proliferation of machine learning model reuse has transformed the AI ecosystem into a highly interconnected supply chain. Traditional compliance tools and static reports…
VibeThinker-3B: Exploring the Frontier of Verifiable Reasoning in Small Language Models
Sen Xu, Shixi Liu, Wei Wang +6
This technical report introduces VibeThinker-3B, a compact dense model with 3B parameters developed to investigate how far verifiable reasoning can be pushed within a strictly smal…
Thinking with Visual Grounding
Junkai Zhang, Yihe Deng, Kai-Wei Chang +1
Visual thinking should not only sound right; it should show its evidence. While recent vision-language models (VLMs) can produce natural-language reasoning traces, these traces oft…
Evaluating Gemma4 Models as AI Teaching Assistants for Introductory Parallel Programming: A DataRaceBench Study
Sabbir Hussain Meraj, Riham Chowdhury, Shimul Debnath +1
Debugging data races is a major challenge for students learning parallel programming due to the non-deterministic nature of concurrent execution and the complexity of shared-memory…
EmbodiSkill: Skill-Aware Reflection for Self-Evolving Embodied Agents
Ruofei Ju, Xinrui Wang, Xin Ding +12
Embodied agents can benefit from skills that guide object search, action execution, and state changes across diverse environments. Since embodied environments vary across layouts,…
Supervised Reinforcement Learning: From Expert Trajectories to Step-wise Reasoning
Yihe Deng, I-Hung Hsu, Jun Yan +7
Large Language Models (LLMs) often struggle with problems that require multi-step reasoning. For small-scale open-source models, Reinforcement Learning with Verifiable Rewards (RLV…
OpenVLThinker: Complex Vision-Language Reasoning via Iterative SFT-RL Cycles
Yihe Deng, Hritik Bansal, Fan Yin +3
We introduce OpenVLThinker, one of the first open-source large vision-language models (LVLMs) to exhibit sophisticated chain-of-thought reasoning, achieving notable performance gai…
Enhancing LLM Safety Through a Theoretical Minimax Game Lens
Yihe Deng, Yu Yang, Junkai Zhang +2
The rapid advancement of large language models (LLMs) necessitates effective mechanisms to ensure their responsible deployment by accurately distinguishing unsafe content from beni…
MIRAI: Evaluating LLM Agents for Event Forecasting
Chenchen Ye, Ziniu Hu, Yihe Deng +4
Recent advancements in Large Language Models (LLMs) have empowered LLM agents to autonomously collect world information, over which to conduct reasoning to solve complex problems.…
Enhancing Large Vision Language Models with Self-Training on Image Comprehension
Yihe Deng, Pan Lu, Fan Yin +6
Large vision language models (LVLMs) integrate large language models (LLMs) with pre-trained vision encoders, thereby activating the perception capability of the model to understan…