EP250: Architecting Intelligence

EP291: The Spatial AI Blind Spot

Title: Ouroboros-Spatial: Closing the Data-Model Loop for Spatial Reasoning Source: http://arxiv.org/abs/2606.11719v1 Summary: This paper introduces a self-evolving training framework that enables models to co-evolve their training distribution with their own capabilities by acting as both proposer and solver. It represents a significant breakthrough in efficiency and reasoning by closing the data-model loop, allowing for substantial performance gains with an order of magnitude less training data.

EP290: LLMs+Graphs Synergistic AI

Title: LLMs+Graphs: Toward Graph-Native, Synergistic AI Systems Source: http://arxiv.org/abs/2606.11560v1 Summary: This paper defines the architectural principles for next-generation graph-native AI systems by synergizing LLMs with structured graph computation for grounded, multi-hop reasoning. It establishes a unified framework for LLM-augmented retrieval, knowledge graph integration, and graph-algorithm-strengthened agentic planning.

EP289: Governing Autonomous AI

Title: A Five-Plane Reference Architecture for Runtime Governance of Production AI Agents Source: http://arxiv.org/abs/2606.12320v1 Summary: This paper proposes a formal Five-Plane Reference Architecture for agent governance, defining critical architectural primitives like reasoning planes and composite principals for autonomous systems. It provides the foundational structural framework necessary for the runtime management and secure deployment of production-level agentic infrastructure.

EP288: Power of Test-Time Training

Title: The Power of Test-Time Training for Approximate Sampling Source: http://arxiv.org/abs/2606.11437v1 Summary: This work provides a foundational theoretical framework for Test-Time Training (TTT) by formalizing it as an approximate sampling problem and establishing query complexity lower bounds. It offers principled mathematical backing for the emerging paradigm of adapting model weights at inference time to solve complex reasoning tasks.

EP287: Role-Agent LLM Co-Evolution

Title: Role-Agent: Bootstrapping LLM Agents via Dual-Role Evolution Source: http://arxiv.org/abs/2606.10917v1 Summary: The Role-Agent framework introduces a dual-role evolution cycle where an LLM functions as both agent and environment to bootstrap its own reasoning capabilities. This novel agentic framework addresses the limitations of static training data by enabling self-correcting co-evolution through internal state prediction and failure mode analysis.