EP300: Tensors prevent multi-agent LLM collisions

EP300: Tensors prevent multi-agent LLM collisions

Title: Tensor-Coord: Algebraic Decomposition of Joint Plan Tensors for Conflict-Free Multi-Agent LLM Planning Source: http://arxiv.org/abs/2606.16478v1 Summary: This work establishes a foundational algebraic primitive for multi-agent coordination by representing joint plans as third-order tensors and using Canonical Polyadic decomposition to identify latent conflicts. It introduces a computable coordination complexity measure that allows agents to localize and resolve planning failures through interpretable mathematical factors.

EP299: STRIDE grades the AI scratchpad

Title: STRIDE: Strategic Trajectory Reasoning via Discriminative Estimation for Verifiable Reinforcement Learning Source: http://arxiv.org/abs/2606.15866v1 Summary: STRIDE introduces a novel Reinforcement Learning with Verifiable Rewards (RLVR) framework that identifies and rewards specific decision-relevant strategic patterns using discriminative estimation. By solving the sparse supervision bottleneck and enabling precise credit assignment, it provides a foundational methodology for advancing the reasoning capabilities of large language models.

EP298: LLM-as-Code Fixes Unreliable AI Agents

Title: LLM-as-Code Agentic Programming for Agent Harness Source: http://arxiv.org/abs/2606.15874v1 Summary: This paper proposes a fundamental architectural shift by replacing probabilistic LLM-based orchestration with deterministic 'Agentic Programming' where code governs the control flow. This framework eliminates control-flow hallucinations and token explosion, establishing a robust new primitive for building reliable, long-horizon agentic systems.

EP297: How T-Mem fixes the associative blind spot

Title: T-Mem: Memory That Anticipates, Not Archives Source: http://arxiv.org/abs/2606.15405v1 Summary: T-Mem presents a foundational shift in agentic memory by transitioning from passive similarity-based retrieval to an anticipatory 'episodic future thinking' architecture. By using write-time rehearsal triggers, it enables agents to recall information across latent semantic arcs, effectively bridging the gap between descriptive and associative long-term memory.

EP296: Stop parallel AI agents from crashing production

Title: CoAgent: Concurrency Control for Multi-Agent Systems Source: http://arxiv.org/abs/2606.15376v1 Summary: This paper introduces CoAgent, a novel concurrency control framework that leverages LLM reasoning to resolve conflicts in multi-agent systems without the bottlenecks of traditional locking or retries. It establishes the MTPO protocol as a foundational primitive for high-concurrency agentic workflows, significantly improving pass rates and efficiency in shared environments.