AI Networking Cookbook: Practical recipes for AI-assisted network automation and development
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This work introduces RecreationWorld, a five-platform framework built around recreation: given a running reference, an agent must discover its behavior and build a faithful implementation with no prescribed workflow, and introduces RecreationBench, comprising 250 diverse tasks across domains and platforms.
Gradient-Aligned Reward (GAR), which operates in the policy's own gradient space: truncated backpropagation through the output projection layer extracts a compact gradient vector for each rollout, and cosine similarity with an expert-anchor gradient yields a dense, reasoning-aware reward with less than 9% wall-clock ov...
Terminal-Universe is introduced, a framework which turns each trajectory into a reusable environment and explores it for synthesizing new tasks and continued interactions, and produces 37.3k task-sufficient environments.
This paper examines whether synthetic training artifacts can support the complete SFT-to-RL cycle for competitive programming, and synthesizes tasks, verified solutions, and reliable test cases that serve as reward signals for reinforcement learning.
This work introduces credit-addressable reasoning, in which the semantic units exposed during inference also define where learning compares alternatives and assigns credit, and instantiates Code-CoT, which retains the diagram, represents visual relations as line-addressable executable code, and organizes reasoning into...
DPIAgent is proposed, a structured agentic framework built on three principles, Divide, Protocol, Isolate (DPI), that mitigates compound-objective ambiguity and goal drift, and shows that architectural structure and backbone capability are complementary axes rather than substitutes, demonstrating DPI's generalizability...
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