6 papers
TIGER: Traceable Inference with Graph-Based Evidence Routing for Mitigating Hallucinations in Multimodal Generation
Kaixiang Zhao, Tianrun Yu, Shawn Huang +3
We study fact-level repair for multimodal generation, where a fluent output may contain specific facts that are not supported by the input. Existing inference-time repair methods o…
LARK: Learnability-Grounded Trajectory Selection for Efficient Reasoning Distillation
Tianrun Yu, Kaixiang Zhao, Chih-Chun Chen +5
We study trajectory selection for reasoning distillation, where teacher-generated reasoning trajectories are selectively used as supervision for a student model. Existing methods r…
FRAME: Forensic Routing and Adaptive Multi-path Evidence Fusion for Image Manipulation Detection
Kaixiang Zhao, Tianrun Yu, Aoxu Zhang +3
The proliferation of sophisticated image editing tools and generative artificial intelligence models has made verifying the authenticity of digital images increasingly challenging,…
Provable and Practical In-Context Policy Optimization for Self-Improvement
Tianrun Yu, Yuxiao Yang, Zhaoyang Wang +6
We study test-time scaling, where a model improves its answer through multi-round self-reflection at inference. We introduce In-Context Policy Optimization (ICPO), in which an agen…
Assessing the Probabilistic Fit of Neural Regressors via Conditional Congruence
Spencer Young, Riley Sinema, Cole Edgren +3
While significant progress has been made in specifying neural networks capable of representing uncertainty, deep networks still often suffer from overconfidence and misaligned pred…
Fully Heteroscedastic Count Regression with Deep Double Poisson Networks
Spencer Young, Porter Jenkins, Longchao Da +2
Neural networks capable of accurate, input-conditional uncertainty representation are essential for real-world AI systems. Deep ensembles of Gaussian networks have proven highly ef…