AI Networking Cookbook: Practical recipes for AI-assisted network automation and development
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On-policy distillation (OPD) trains a target policy on its own rollouts using token-level supervision from a stronger teacher, but typically relies on the teacher being substantially more capable than the target. Recent work relaxes this requirement by transferring the relative policy difference between an expert and a...
Proxy OPD is introduced, an asynchronous post-training framework that transfers reward-induced policy improvements rather than absolute policy distributions and establishes relative policy updates as highly reusable, adjustable assets for scalable, reward-based post-training.
This paper presents IterCAD, a unified multimodal agent framework for closed-loop, interactive CAD generation and editing, and introduces the IterCAD-Bench evaluation suite, establishing a survivor-bias-free standard that unifies code validity and geometric precision.
IndustryForge-27B will serve as the common substrate for downstream industrial-agent projects, providing a unified starting point for a full-stack industrial agent that spans from CAD design to industrial-software operation, from parts to assemblies, and from single-shot generation to closed-loop self-improvement.
This work conceptualize Agent-Centric Interactive World Proxies, shifting the fundamental paradigm from physical state transitions to agent-usable information transitions, such as execution outcomes, retrieved experiences or skills, and verification signals, broadening the scope of world modeling to provide versatile f...
REOPD combines a token-level compatibility weight with a batch-level adaptive budget, yielding a token-wise coefficient $\lambda_{b,t}=1+\gamma_b q_t$ that preserves teacher alignment while selectively extrapolating along reliable teacher-reference directions, demonstrating effective fine-grained reliability adaptation...
TransMem is proposed, a lightweight inference-time parametric memory module that transforms sparse historical hidden states from a frozen LLM backbone into reusable memory representations and introduces evidence-conditioned self-distillation to learn transferable memory utilization rather than task-specific knowledge.
AssemCAD is presented, an axiom-grounded framework for production-ready CAD assembly generation from natural language that extends Text-to-CAD from isolated part generation toward production-ready mechanical assembly design.
MUSE is a framework that enables iterative self-improvement through a hierarchical Memory Module that organizes cross-domain insights to facilitate the orchestration of long-horizon workflows and demonstrates that MUSE’s performance scales with the accumulation of insights and exhibits strong cross-task transferability...
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