AI Networking Cookbook: Practical recipes for AI-assisted network automation and development
We have 2 of 72 papers
We haven’t gathered this author’s papers yet. Follow them and we’ll fetch their work.
Not the right person? Other researchers publish under this name.
Evidence-Gated Regularization (EGR) is proposed, a modality-agnostic training objective that introduces zero inference-time overhead and derives a per-frame and per-sensor task-relevance signal to gate two state-conditional consistency objectives: invariance on low-evidence sensors, and single-sensor sufficiency on hig...
Experiments show that DenseReward outperforms general-purpose VLMs and existing robotic reward models in dense reward prediction across both simulated and real-world manipulation, and provides effective reward guidance for downstream model predictive control and reinforcement learning.
We use cookies to run the site and, with your consent, for analytics and to show ads. See our Cookie Policy.