AI Networking Cookbook: Practical recipes for AI-assisted network automation and development
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Large language models are increasingly deployed in applications that act on users'long-term behavior histories, from personal assistants to recommendation and advertising systems. These applications demand the user dimension of LLM intelligence: inferring a person's preferences, intent, and likely future actions from w...
Rule-governed contextual decision tasks require models to apply specified rules to case-specific context and evidence. Written rules can leave gaps in decision guidance and process evaluation, while reference judgments vary in their support from the rules and evidence. To address these challenges, we introduce AdaptEvo...
This work introduces ROSS (Relearning from Self-Generated Rollouts through Selective Supervision), which preserves the full historical trajectory as context while applying loss only to selected model-generated continuations.
This work attempts to make use of high-quality public documents with small perturbations and empirically finds that LLMs can successfully generate context-dependent reasoning traces and answers, which are then used to train a student model that substantially improves the performance on CL-bench.
Executable environments are critical for post-training agents on tasks that require tool use and multi-step interaction, but constructing executable tasks together with their environments remains difficult to scale. Skills provide reusable domain knowledge, operational procedures, and tool-use instructions, but a subst...
This work introduces Compositional Environment Scaling (\textbf{CompoWorld}), which expands the task space by composing a finite library of reusable services, and guides reinforcement learning toward full task completion by emphasizing criteria with lower pass rates within each rollout group.
Policy Aligned Critic Training (PACT), which adopts an Actor-then-Critic update order to apply importance sampling correction to critic training and better align the critic with the updated policy, is developed.
D-MOPD (Dynamic Domain ScheDuling for MOPD), a zero-overhead scheduler that repurposes the per-domain reverse-KL signal already produced during training to adapt the domain mixture online to adapt the domain mixture online.
This work introduces AgenticInterleave, a single-agent ReAct framework for retrieval-supported answer generation, together with IVR-12, a 12-dimensional rubric for assessing the content, presentation, and image quality of interleaved references and model outputs, and evaluates both short-answer correctness and interlea...
This work presents Iris-mini and Iris-pro, two search agents trained at the 35B-A3B and 397B-A17B scales, together with the data pipeline and training recipe behind them, and plans to release the model weights together with the complete recipe for data construction, training, and evaluation.
Teacher-Gated On-Policy Distillation is introduced, built on the principle that teacher reliability should be verified at the prompt level before dense supervision is admitted, and outperforms Vanilla OPD in all six single-domain settings and achieves higher seven-benchmark averages at both scales under multi-domain tr...
PILOT is presented, a supervisor-worker harness for live self-improvement through two coupled mechanisms: (1) live steering lets a separate supervisor redirect or abort the active worker during execution; and (2) live self-evolution distils procedures and failure modes revealed during execution into reusable skills and...
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