EP224: Soft-Hamiltonian world models for robust planning

EP227: FlowAgent fixes the AI tool bottleneck

Title: Tools as Continuous Flow for Evolving Agentic Reasoning Source: http://arxiv.org/abs/2605.07339v1 Summary: FlowAgent reconceptualizes agentic reasoning by replacing discrete, step-wise tool orchestration with continuous trajectory generation using conditional flow matching. This foundational framework provides theoretical guarantees for error attenuation and global planning, representing a significant shift in how agents execute long-horizon reasoning tasks.

EP226: MELT Decouples AI Reasoning from Memory

Title: Memory-Efficient Looped Transformer: Decoupling Compute from Memory in Looped Language Models Source: http://arxiv.org/abs/2605.07721v1 Summary: This paper introduces a novel architectural primitive that decouples reasoning depth from memory consumption in looped language models, enabling constant-memory iterative reasoning. By sharing a single KV cache across loops via a learnable gating mechanism, it provides a foundational efficiency breakthrough for models performing multi-step computation in embedding space.

EP225: Turning AI into its own lie detector

Title: Logic-Regularized Verifier Elicits Reasoning from LLMs Source: http://arxiv.org/abs/2605.05893v1 Summary: This work presents a novel reasoning framework that uses logical consistency rules to regularize unsupervised verifiers, eliminating the need for expensive supervised datasets. By treating verification as a binary latent variable problem, it achieves performance comparable to supervised models in eliciting complex reasoning from off-the-shelf LLMs.

EP224: Soft-Hamiltonian world models for robust planning

Title: HaM-World: Soft-Hamiltonian World Models with Selective Memory for Planning Source: http://arxiv.org/abs/2605.05951v1 Summary: This paper introduces a foundational architectural primitive for world models by combining Hamiltonian geometric structures with Mamba-based selective memory to stabilize long-horizon planning. It provides agents with a structured latent state for dynamics, rewards, and action search, significantly improving robustness in out-of-distribution planning tasks.

EP223: UNO-ORCHESTRA Slashes AI Costs via Selective Delegation

Title: Uno-Orchestra: Parsimonious Agent Routing via Selective Delegation Source: http://arxiv.org/abs/2605.05007v1 Summary: This paper introduces a novel orchestration policy that jointly optimizes task decomposition and agent routing, establishing a new frontier for efficiency and accuracy in multi-agent systems. It moves beyond rigid workflows by learning selective delegation from RL trajectories to achieve high performance at an order of magnitude lower cost.