AI Networking Cookbook: Practical recipes for AI-assisted network automation and development
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Multimodal in-context learning (ICL) is an important capability of multimodal large language models (MLLMs). Its performance is closely tied to the demonstrations it uses, making demonstration retrieval a core problem for ICL. Common methods use fixed scoring methods to retrieve all the demonstrations at once, which ma...
Computer-use agents need to capture procedural knowledge of how people use software. User telemetry offers a scalable source of this knowledge. However, learning reusable skills from these logs requires addressing three challenges: (1) Goal Underspecification, since logs do not record the goal behind each action; (2) N...
While post-training improves the capabilities of large language models (LLMs), it generally lowers their output diversity and creativity, negatively impacting tasks that explicitly require creativity (e.g., story generation) as well as those that require it implicitly, e.g., reinforcement learning (RL). We instead prop...
FrankenReport, an interface for long-form knowledge-seeking report generation that supports adaptive early exiting per section, is presented, an interface for long-form knowledge-seeking report generation that evaluates intermediate outputs during generation and predicts whether further targeted computation will yield...
It is found that current LLMs have the potential for cumulative cultural evolution -- previously attested only in humans -- with mixed populations of agents developing capacities that go beyond their lowest common denominator.
An evaluation method is proposed that pinpoints which agent introduced each error by locally testing agent invocations for faithfulness and verifiability relative to their own inputs and proposes a four-type taxonomy to categorize the discovered errors: hallucination, uncited input reliance, uncited output, or insuffic...
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