AI Networking Cookbook: Practical recipes for AI-assisted network automation and development
We have 6 of 14 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.
These findings provide functional support for the view that prototype-based categorization contributes to behavioral adaptability and show that transferring this representational principle to LLMs yields generalizable safety under adversarial conditions.
The CVPR 2026@AdvML Workshop Challenge on adversarial multimodal attacks against autonomous-driving VLAs is presented, providing a practical reference for future robustness evaluation and defense design in multimodal autonomous-driving systems.
The routed-state interface unifies contextual memory and persistent capability adaptation within the model's native computation, reframing memory from a passive record of prior context into an active substrate for maintaining and evolving model behavior.
This Perspective proposes machine experientialism, positing that LLMs build their own form of understanding from training corpora, and shifts from cataloging LLMs’ human-like traits to uncovering their distinct logic that emerges from this text-based world.
Memory-Anchor Routing across Context History (MARCH), a network architecture that effectively scales state-space models beyond a fixed-size dimension, while maintaining computational efficiency over long-sequences, is introduced.
This work proposes the Evolutionary Markov Hypergraph Attack (EMHA), a black-box policy that performs feedback-driven environment evolution by coordinating authorized state transitions without requiring parameter updates, and establishes OpenART as a scalable foundation for studying agent safety in complex, evolving en...
We use cookies to run the site and, with your consent, for analytics and to show ads. See our Cookie Policy.