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Sponsored Design Multi-Agent AI Systems Using MCP and A2A: Engineer your own Pyt Design Multi-Agent AI Systems Using MCP and A2A: Engineer your own Pyt

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Shubham Barmase

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#reinforcement learning Open access Oct 2026

Deterministic 4D Spacetime Geodesic Planning on Hypercomplex Toroidal Manifolds

Dynamic obstacle avoidance and 4D spacetime pathfinding present severe computational bottlenecks in autonomous robotics. Classical time-expanded graph architectures (e.g., Space-Time A*) suffer from catastrophic state explosion O(|V| x T_horizon), exhausting memory over extended planning horizons. Local continuous opti...

Shubham Barmase · 0 citations
#reinforcement learning Open access Sep 2026

Deterministic Spatial Planning on Hypercomplex Manifolds: 1-Pass Maze Navigation with Zero Backpropagation and 53 KB Memory Footprint

Standard Reinforcement Learning (RL) and Frontier Foundation Models face severe structural bottlenecks in spatial planning. Deep RL algorithms (e.g., PPO, DreamerV3) suffer from extreme sample complexity (10^6–10^8 interactions) due to gradient dilution across delayed reward horizons, while Large Language Models (LLMs)...

Shubham Barmase · 0 citations

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