AI Networking Cookbook: Practical recipes for AI-assisted network automation and development
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Long-horizon research agents must decide both how to investigate and what to investigate next as evidence accumulates. This is hard to learn because such decisions are sparse in long execution traces, and their consequences may emerge several investigations later. We introduce Meta-reasoning for Iterative Research Agen...
This work adapts HVE to Terminal Bench, a leading benchmark of real-world terminal and coding tasks, and introduces Hack-Verifiable Terminal Bench (HVTB), to measure reward-hacking rates across frontier models and study whether prompts with varying amounts of information on the hack can mitigate this behavior.
This work develops a black-box method for learning a single context-independent logit-bias vector, added at every decoding step, without modifying model weights or requiring gradients, and suggests that learned logit bias is a lightweight mechanism for adapting language models under minimal access requirements.
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