9 citations · 9 across the 20 of their papers we have counts for
21 papers
S1CogBench: What System One Decision Models Can and Cannot Do on Cognitive Experiments
Dengzhe Hou, Kazunori D Yamada
Language models are no longer used only to generate text. Software also asks them for decisions, in routing, guardrails, and automated gates. System One decision models, also calle…
Control-Diverse Reinforcement Fine-Tuning: Decoupling the Shared Control Bottleneck of RL Post-Training
Binwen Tan, Jingchao Wang, Dengzhe Hou +6
Reinforcement learning post-training unlocks complex reasoning in LLMs. Yet benchmark scores reveal only whether a model improved, not what changed inside it, nor how it splits fin…
CogEEGAgent: Toward Autonomous Cognitive EEG Analysis with Grounded Execution and Selection-Aware Verification
Dengzhe Hou, Lingyu Jiang, Fangzhou Lin +1
Electroencephalography (EEG) analysis in cognitive studies requires specialized expertise and involves many defensible choices over contrasts, channels, time windows, and statistic…
CogBattery: Human-Referenced Evaluation of Cognitive Task Behavior and Structure in Language Models
Dengzhe Hou, Lingyu Jiang, Fangzhou Lin +1
Human cognitive tasks let us compare language models with humans under controlled conditions. Yet a model can reproduce the direction of a human effect while making different error…
CV-Arena: An Open Benchmark for Instructional Computer Vision Problem Solving with Human-AI Collaborative Preferences
Fangzhou Lin, Peiran Li, Lingyu Xu +12
Instruction-guided image editing is becoming a general interface for visual work, yet existing benchmarks still focus largely on narrow appearance edits and do not fully capture th…
PathCal: State-Aware Reflection-Marker Calibration for Efficient Reasoning
Lingyu Jiang, Zirui Li, Shuo Xing +7
Large Reasoning Language Models (LRMs) tackle complex tasks through test-time scaling, generating long-form Chain-of-Thought (CoT) trajectories during inference. These trajectories…
CAPS: Cascaded Adaptive Pairwise Selection for Efficient Parallel Reasoning
Fangzhou Lin, Shuo Xing, Peiran Li +6
Parallel reasoning, where a generator samples many candidate solutions and an aggregator selects the best, is one of the most effective forms of test-time scaling in large language…
Same Brain, Different Prediction: How Preprocessing Choices Undermine EEG Decoding Reliability
Dengzhe Hou, Zihao Wu, Lingyu Jiang +3
Electroencephalography (EEG) is a cornerstone of brain-computer interfaces and clinical neuroscience, yet deep learning models are typically trained and evaluated under a single, u…
Physics-Aware Video Instance Removal Benchmark
Zirui Li, Xinghao Chen, Lingyu Jiang +5
Video Instance Removal (VIR) requires removing target objects while maintaining background integrity and physical consistency, such as specular reflections and illumination interac…
AdaptFuse: Training-Free Sequential Preference Inference via Externalized Bayesian Computation
Fangzhou Lin, Zilin Dai, Peiran Li +8
Large language models (LLMs) struggle to accumulate evidence across multiple rounds of user interactions, failing to update their beliefs in a manner consistent with Bayesian infer…
WMIP: A Working-Memory-Inspired Programmable Probe of Cumulative State Tracking in Large Language Models
Dengzhe Hou, Lingyu Jiang, Deng Li +3
Human working memory lets people maintain and update information. Large language models (LLMs) face a related demand when tracking updates within a single prompt without writing ou…
Position: Human-Centric AI Requires a Minimum Viable Level of Human Understanding
Fangzhou Lin, Qianwen Ge, Lingyu Xu +7
AI systems increasingly produce fluent, correct, end-to-end outcomes. Over time, this erodes users' ability to explain, verify, or intervene. We define this divergence as the Capab…
NexusFlow: Unifying Disparate Tasks under Partial Supervision via Invertible Flow Networks
Fangzhou Lin, Yuping Wang, Yuliang Guo +7
Partially Supervised Multi-Task Learning (PS-MTL) aims to leverage knowledge across tasks when annotations are incomplete. Existing approaches, however, have largely focused on the…
TimePre: Bridging Accuracy, Efficiency, and Stability in Probabilistic Time-Series Forecasting
Lingyu Jiang, Lingyu Xu, Peiran Li +14
We propose TimePre, a simple framework that unifies the efficiency of Multilayer Perceptron (MLP)-based models with the distributional flexibility of Multiple Choice Learning (MCL)…
KANMixer: a minimal KAN-centered mixer for long-term time series forecasting
Lingyu Jiang, Dengzhe Hou, Yuping Wang +9
Long-term time series forecasting (LTSF) underpins critical applications from energy management to weather prediction, yet achieving reliable multi-step-ahead accuracy remains chal…
Constitutive Components for Human-Like Autonomous Artificial Intelligence
Kazunori D Yamada
This study is the first to clearly identify the functions required to construct artificial entities capable of behaving autonomously like humans, and organizes them into a three-la…
A Strong View-Free Baseline Approach for Single-View Image Guided Point Cloud Completion
Fangzhou Lin, Zilin Dai, Rigved Sanku +4
The single-view image guided point cloud completion (SVIPC) task aims to reconstruct a complete point cloud from a partial input with the help of a single-view image. While previou…
A Language Anchor-Guided Method for Robust Noisy Domain Generalization
Zilin Dai, Lehong Wang, Fangzhou Lin +5
Real-world machine learning applications often struggle with two major challenges: distribution shift and label noise. Models tend to overfit by focusing on redundant and uninforma…
Hyperbolic Chamfer Distance for Point Cloud Completion and Beyond
Fangzhou Lin, Songlin Hou, Haotian Liu +4
Chamfer Distance (CD) is widely used as a metric to quantify difference between two point clouds. In point cloud completion, Chamfer Distance (CD) is typically used as a loss funct…
Loss Distillation via Gradient Matching for Point Cloud Completion with Weighted Chamfer Distance
Fangzhou Lin, Haotian Liu, Haoying Zhou +6
3D point clouds enhanced the robot's ability to perceive the geometrical information of the environments, making it possible for many downstream tasks such as grasp pose detection…
Procedural Content Generation via Generative Artificial Intelligence
Xinyu Mao, Wanli Yu, Yuya Okawara +3
The attempt to utilize machine learning in procedural content generation (PCG) has been made in the past. In this survey paper, we investigate how generative artificial intelligenc…