AI Networking Cookbook: Practical recipes for AI-assisted network automation and development
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The proposed **Lean4AgenticRL** is the first framework that grounds multi-level process rewards in the unified Lean4 formalization schema for open-ended agentic RL, and provides multi-level rewards to support more effective agentic RL with the workflow-stage-normalized GRPO algorithm.
This work introduces Minimal Intervention Reinforcement Learning (MInTRL), which expands the exploration frontier through sparse, local interventions in otherwise on-policy rollouts, and establishes minimal intervention as an effective paradigm for enhancing on-policy RL.
We introduce Mitra-v2, a tabular foundation model that delivers state-of-the-art performance on real-world classification and regression problems, from credit-risk scoring and clinical prediction to equipment-failure detection and house-price estimation. Mitra-v2 is trained only on synthetic data, with a pretraining di...
Cliff, a reward shaping strategy that utilizes an off-the-shelf LLM as a teacher to identify the first mistake in each rollout, is proposed and established as a simple, general and effective approach for improving RLVR with richer, fine-grained supervision.
The Human-LLM Reflection Framework is introduced, a controlled two-pass protocol comparing human and LLM revision under identical conditions across self-, peer-, and cross-agent settings, using an information-theoretic analysis based on per-iteration cross-entropy reduction.
The degradation rate across neural models, both sentence embeddings and decoder-only LLMs, is studied, and how consistent it is depends on the scale of the noise: under word-level noise, models with very different architectures decline along nearly the same curve, while under character-level noise they separate.
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