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.

Title: No Accidental Software Agent First Canonical Code for Human Code Entropy Reduction and 30 to 500 times Lower Frontier Model RequirementsSource: http://arxiv.org/abs/2606.14357v1

Summary:

This paper proposes a 'proof-carrying substrate' that canonicalizes software behavior to drastically reduce the complexity and model capacity required for autonomous coding. It introduces a novel architectural approach to software representation that aims for a 100-fold reduction in the cost of verified correct changes.

Title: Closing the Reflection Gap: A Free Calibration Bonus for Agentic RLSource: http://arxiv.org/abs/2606.14211v1

Summary:

It introduces RefGRPO, a novel reinforcement learning algorithm that utilizes environment feedback to calibrate an agent's self-reflection without additional reward models. This framework creates a grounded reasoning loop that allows agents to serve as their own verifiers, significantly boosting task accuracy and reliability.

Title: Reward Modeling for Multi-Agent Orchestration

Source: http://arxiv.org/abs/2606.13598v1

Summary:

This paper presents OrchRM, a self-supervised framework that enables the training of multi-agent orchestrators without human annotations, achieving a 10x improvement in token efficiency. It establishes orchestration-level reward modeling as a scalable and foundational approach for coordinating specialized agents across diverse reasoning tasks and test-time scaling scenarios.

Title: Agents-K1: Towards Agent-native Knowledge Orchestration

Source: http://arxiv.org/abs/2606.13669v1

Summary:

This work introduces a novel end-to-end pipeline for converting raw scientific documents into 'agent-native' knowledge graphs, moving beyond simple abstract-based retrieval to enable complex scientific reasoning. By establishing a unifying theoretical foundation for multimodal parsing and cross-document traversal, it provides a new architectural primitive for scientific knowledge orchestration in agentic systems.

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.

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.

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.

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.

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.