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 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.

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.

Title: No Accidental Software Agent First Canonical Code for Human Code Entropy Reduction and 30 to 500 times Lower Frontier Model Requirements

Source: 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 RL

Source: 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.