AI Networking Cookbook: Practical recipes for AI-assisted network automation and development
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This work proposes Adaptive Multi-view Budgeted Elo Reranking (AMBER), an online, budgeted multi-view reranking framework that dynamically optimizes global resource allocation and shows that each Elo update corresponds to a stochastic gradient ascent step on the Bradley-Terry log-likelihood.
MCPGen is introduced, an executable benchmark for Model Context Protocol (MCP) workflow development that evaluates three diagnostic tasks: workflow reconstruction, tool creation, and backward-compatible workflow extension and evaluates 11 representative LLMs in a single-turn foundation-model setting.
The proposed REALM framework models long-term memory as a continual lifecycle by autonomously organizing memories into a heterogeneous cognitive graph, retrieving evidence via adaptively composed graph-search atoms, and continually reconsolidating memories based on retrieval feedback.
The key observation is that although expert trajectories are scarce, high-quality final artifacts such as literature reviews, analyst reports and legal judgments, are abundant in pre-training data and can be viewed as compressed traces of the evidence-seeking processes that produced them.
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