arXiv AI
When Reasoning Helps Action: Monitoring and Steering Chain-of-Thought in Vision-Language-Action Policies
Read the original on arXiv AI →The Flow has not summarised this story yet — read it at arXiv AI.
Related stories
arXiv AI
Jul 7
Do Vision-Language-Action Models Mean What They Say? On the Role of Faithfulness in Embodied Reasoning
arXiv:2607. 04681v1 Announce Type: cross Abstract: Embodied Chain-of-Thought has emerged as a promising mechanism to enhance robot decision-making and interpretability in black-box Vision-Language Action (VLA) models.
arXiv AI
4d ago
Altered Thoughts, Altered Actions: Reasoning Chain as Control Surface for a Vision-Language-Action Policy
arXiv:2603.12717v2 Announce Type: replace-cross Abstract: Vision-language-action policies map camera images and natural-language instructions to a robot's motor actions. Some of these policies are de...
arXiv AI
Aug 5
Risky Business: Measuring The Faithfulness-Safety Tension
arXiv:2608. 03745v1 Announce Type: new Abstract: Chain-of-Thought (CoT) reasoning offers a promising window into model monitoring.
arXiv AI
Jul 21
Reasoning as a Double-Edged Sword: Architecture and Cross-Stage Robustness in Vision-Language-Action Models
arXiv:2607. 17786v1 Announce Type: cross Abstract: Does adding a reasoning step make a Vision-Language-Action (VLA) model more robust to perturbation?
arXiv AI
Aug 6
Reasoning Dynamics and the Limits of Monitoring Modality Reliance in Vision-Language Models
arXiv:2604. 14888v3 Announce Type: replace-cross Abstract: Recent advances in vision language models (VLMs) offer reasoning capabilities, yet how these unfold and integrate visual and textual information remains unclear.
arXiv AI
Aug 5
The Tell-Tale Trace: Detecting Reasoning Failures in LLMs Using Chain-of-Thought Dynamics
arXiv:2608. 03291v1 Announce Type: cross Abstract: Chain-of-thought (CoT) reasoning improves large language model (LLM) performance while also providing an observable interface to the model's reasoning process.