arXiv AI
ENCP: Episode-Normalized Conformal Prediction for Vision-and-Language Navigation
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Hugging Face Trending Papers
Sep 15
ENCP: Episode-Normalized Conformal Prediction for Vision-and-Language Navigation
Uncertainty estimation for Vision-Language-Navigation (VLN) models is a critical task since it can help identify ambiguous and unreliable predictions, enabling agents to make safer navigation decision...
arXiv Computation and Language
Sep 2
IntroConformal: Conformal Factuality Guarantees for Large Vision-Language Models via Introspective Signals
IntroConformal introduces a training‑free Conformal Risk Control framework that offers finite‑sample, distribution‑free factuality guarantees for Large Vision‑Language Models. It uses introspective signals—layer‑wise semantic stability and verification probability derived from the model’s own hidden states—to assess claim factuality. Experiments across multiple LVLM architectures show that IntroConformal meets the conformal risk guarantee while reducing abstention and matching or surpassing external verifier baselines in claim‑level discrimination.
arXiv Computer Vision
Sep 22
What do VLM-Based Vision-Language Navigation Models Rely on: Interpreting and Steering Policy Behavior
arXiv:2609.24576v1 Announce Type: cross Abstract: Modern Vision-Language Navigation (VLN) models rely mostly on pre-trained large Vision-Language Models (VLMs) to predict navigation actions. While th...
arXiv Computer Vision
Sep 10
Learning to Adapt and Calibrate: Score Distribution Alignment for Few-Shot Uncertainty Prediction in Medical VLMs
arXiv:2609.10333v1 Announce Type: new Abstract: Uncertainty estimation for medical vision--language models (VLMs) using conformal prediction has gained increasing attention due to its distribution-fr...
arXiv AI
Jun 2
Domain-Shift-Aware Conformal Prediction for Large Language Models
arXiv:2510. 05566v2 Announce Type: replace-cross Abstract: Large language models have achieved impressive performance across diverse tasks.
arXiv Computer Vision
Sep 18
Latent-Centroid Steering: Single-Pass Classifier-Free Guidance for Command-Aligned Autonomous Driving
The paper introduces Latent-Centroid Steering (LCS), a single-pass classifier-free guidance method for vision‑language autonomous driving models. LCS replaces instance‑level residuals with class‑level latent shifts, projecting conditional representations toward precomputed command‑specific centroids to enhance command adherence. Experiments on Bench2Drive and nuScenes show that LCS cuts inference latency by about 50% while improving driving performance.