EP301: How VeriGraph Makes AI Auditable

EP303: MODE-RAG AI Hallucinations

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.

EP302: SKILLMIGRATOR AI

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.

EP301: How VeriGraph Makes AI Auditable

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.

EP300: Tensor-Coord AI Teamwork

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: How STRIDE Teaches AI

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.