AI Networking Cookbook: Practical recipes for AI-assisted network automation and development
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Long horizon language model agents continually accumulate reasoning history, increasing context length and inference cost even after earlier decisions have been executed and observed. Unlike static Chain of Thought compression, removing historical reasoning can change future actions and the resulting interaction trajec...
Long-horizon agents continuously accumulate interaction history during task execution, yet the importance of past interactions changes as the agent state evolves. Existing context management methods largely compress history based on fixed windows, periodic schedules, or current relevance, overlooking a more fundamental...
Long horizon language model agents continuously accumulate interaction history, increasing computational cost while making relevant information harder to preserve and reuse. Existing context management methods mainly focus on how to compress or retrieve history, but largely leave open whether the model itself already r...
Direct Relational Set-Risk Pruning is introduced, which formulates agent-history compression as risk-constrained selection over deletion sets and shows that decision-conditioned relations, retained-context information, pair interactions, and abstention each contribute to reliable pruning.
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