Title: Deontic Policies for Runtime Governance of Agentic AI Systems

Source: http://arxiv.org/abs/2606.19464v1

Summary:

This paper proposes AgenticRei, a deontic policy framework that enables runtime governance, constraints, and deontic reasoning for autonomous multi-agent systems. By decoupling policy enforcement from the LLM via an external logic engine, it establishes a foundational security and compliance architecture for agentic AI.

Title: MagicSim: A Unified Infrastructure for Executable Embodied Interaction

Source: http://arxiv.org/abs/2606.17511v1

Summary:

This paper is foundational because it provides a unified execution substrate and shared Markov Decision Process that bridges high-level agent planning with low-level physical control. By enabling deterministic multi-modal episode rollouts, evaluation, and annotation under a single interface, it removes a major barrier in scaling embodied AI.

Title: MODE-RAG: Manifold Outlier Diagnosis and Energy-based Retrieval-Augmented Generation Evaluation

Source: http://arxiv.org/abs/2606.17449v1

Summary:

MODE-RAG proposes a multi-agent system driven by Variational Free Energy and Monte Carlo Tree Search to dynamically gate and guide interventions in multimodal RAG. It establishes a principled energy-based reasoning loop to quantify and mitigate complex failure modes like cross-modal hallucinations and sycophancy in generative models.

Title: Beyond Domains: Reusing Web Skills via Transferable Interaction Patterns

Source: http://arxiv.org/abs/2606.17645v1

Summary:

This paper introduces SkillMigrator and Transferable Interaction Patterns, a novel framework that allows web agents to generalize interaction skills across diverse sites by matching layout structures rather than specific element references. This approach addresses a core scalability bottleneck in agentic AI by significantly reducing action counts and token consumption while maintaining high success rates.

Title: VeriGraph: Towards Verifiable Data-Analytic Agents

Source: http://arxiv.org/abs/2606.16603v1

Summary:

This paper introduces a novel neuro-symbolic reasoning framework that replaces linear text trajectories with explicit evidence-based directed acyclic graphs to ensure structural traceability. It provides a significant breakthrough in agentic verifiability by grounding natural-language claims in deterministic computations and raw data sources.

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.

Title: STRIDE: Strategic Trajectory Reasoning via Discriminative Estimation for Verifiable Reinforcement LearningSource: 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.

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.

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.

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.