AI Networking Cookbook: Practical recipes for AI-assisted network automation and development
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This work introduces Polarity-Prompt Contrastive Decoding (PopCD), a test-time behavior control method that generalizes contrastive decoding to broader enhancement settings and is applicable to both LLMs and Vision-Language Models without additional training.
AgentCompass is introduced, an open-source, lightweight, and extensible infrastructure for evaluating LLM-based agents that organizes the evaluation process around three independent components, thereby enabling flexible configurations without requiring the reimplementation of complex execution logic.
OSReward is introduced, a realistic, high-quality benchmark that evaluates VLM judges on CUA trajectories, and an open corpus of reasoning-annotated trajectory judgments for the CUA community, to close the gap in reliable CUA reward at scale.
Evaluations across scientific, multimodal, agentic, and general-purpose benchmarks show that Intern-S2-Preview-397B achieves competitive or leading results in multiple settings.
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