AI Networking Cookbook: Practical recipes for AI-assisted network automation and development
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Coding agents are typically provided end-to-end behavioral specifications that the code they produce should satisfy, such as tests and input--output examples. How many such specifications must a developer provide, and which ones, for the agent to produce the developer's intended program? We argue that this cannot be de...
The proposed EDRM (Entropy Dynamics-based Reasoning Manifold), a lightweight routing framework whose default heuristic requires no router training, supports selective reasoning based on early decoding signals and demonstrates the potential of entropy-based control for efficient LLM inference.
Test-time context optimization adapts frozen language models through textual feedback, but ordinary conditional generation provides no separate control over how strongly to reinforce the distributional changes induced by a context update. We introduce EMO, a training-free framework inspired by expectation--maximization...
We study whether small LLM agents can operate effectively under explicit wall-clock time budgets by both respecting the allocated runtime and using available time productively. We evaluate Qwen3.6-27B on five competitions from MLE-Bench Lite and Qwen3-4B on Zork I (Jericho), two agentic benchmarks where additional comp...
This work systematically compares six causal structures, eight latent representations, and four training objectives, covering popular design choices in existing WAMs, to provide a systematic understanding of how core design choices affect world-action modeling and what principles can guide the development of future WAM...
Mixture-of-Experts (MoE) language models suffer from large parameter counts, which create a significant memory bottleneck. Expert pruning is the most direct approach for reducing this parameter count, yet existing methods make pruning decisions for each expert independently, and assume experts'contributions are purely...
FaithEyes, a multi-agent self-judging framework that uses a VLM to judge whether each process image helps answer the question and designs a multi-agent framework where the model itself serves as a subagent to judge the tool calls from the main agent, eliminating any dependence on external models at inference.
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