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cs.SE2025
CodeMem: Architecting Reproducible Agents via Dynamic MCP and Procedural Memory
Nishant Gaurav, Adit Akarsh, Tejas Ravishankar +1
Current tool-using AI agents suffer from limited action space, context inefficiency, and probabilistic instability that makes them unsuitable for handling repetitive tasks which ar…
cs.SE2025
Dynamic ReAct: Scalable Tool Selection for Large-Scale MCP Environments
Nishant Gaurav, Adit Akarsh, Ankit Ranjan +1
We present Dynamic ReAct, a novel approach for enabling ReAct agents to efficiently operate with extensive Model Control Protocol (MCP) tool sets that exceed the contextual memory…