AI Networking Cookbook: Practical recipes for AI-assisted network automation and development
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This paper recast context compression as a causal decision preservation problem over discrete interaction units and introduces FOCUS, a training-free context compression framework that operates entirely at test time that requires no offline data collection or fine-tuning.
Pseudo Self-Distillation is presented, a framework that enables small language models to construct hierarchical memory representations by distilling behavior from a strong black-box oracle through a multi-stage training pipeline, with off-policy PSD achieving the strongest results across most conditions.
Evaluating mainstream coding agents on DEPBENCH, a benchmark consisting of 203 real-world dependency-upgrade tasks across five package ecosystems spanning five language communities, each involving hidden code-level changes that require source code adaptation.
TSGen, an automated pipeline for generating high-quality, structured TSGs from historical incident reports using large language models (LLMs), consists of filtering and classifying incident data into diagnostically relevant categories and distilling core incidents to ensure diversity and generalizability.
This work introduces WebXSkill, a framework that bridges a grounding gap with executable skills, each pairing a parameterized action program with step-level natural-language guidance, and finds that better skill deployment mode depends on a model's plan and execution capability.
DepRepair is proposed, a single-call LLM approach that grounds repair in structured upstream evidence through three components: an evidence filter that distills relevant upstream changes, a usage locator that identifies affected consumer sites, and a subcategory-aware guide that tailors repairs to the breaking-change t...
The proposed AutoSaddler, an automatic harness optimization framework that formulates harness improvement as an offline learning problem and iteratively updates the harness using failure signals from mini-batches, suggests that automatic harness optimization is a promising path toward more performant and reliable agent...
Coding agent infrastructure is shifting from harness engineering toward loop engineering as coding agents are deployed for sustained long-horizon software development. Existing benchmarks often center on localized tasks or end-state outcomes, offering limited insight into sustained execution. We introduce LOOPSBENCH, a...
Large language model (LLM) agents are increasingly used for multi-step, stateful tool-use tasks, yet production reliability remains limited. Unlike static software repair, agent repair must recover dynamic trajectories whose early decisions can propagate into later errors and external state changes. Existing automatic...
This work presents Change2Task, a system grounded in repository history that converts merged pull requests into verified tasks on healthy modern revisions of the same repository, and provides executable data for coding agent training and evaluation while reducing repeated environment setup, storage, and task constructi...
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