EP202: Why ADEMA AI Never Loses The Plot

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.

EP222: Gyan Beats GPT-4o Without Using GPUs

Title: Gyan: An Explainable Neuro-Symbolic Language Model Source: http://arxiv.org/abs/2605.04759v1 Summary: Gyan proposes a breakthrough non-transformer architecture that decouples language modeling from knowledge representation to eliminate hallucinations and drastically reduce compute requirements. It introduces a neuro-symbolic framework that mimics human compositional context, offering a more trustable and efficient foundational primitive for AI development.

EP221: ScrapMem Mimics Human Memory Through Forgetting

Title: ScrapMem: A Bio-inspired Framework for On-device Personalized Agent Memory via Optical Forgetting Source: http://arxiv.org/abs/2605.03804v1 Summary: ScrapMem introduces a novel on-device memory architecture for agents that employs bio-inspired 'Optical Forgetting' to maintain long-term multimodal context with extreme storage efficiency. It establishes a foundational framework for personalized agentic reasoning on resource-constrained edge devices through structured episodic memory management.

EP220: How PARSE Makes AI Four Times Faster

Title: Parallel Prefix Verification for Speculative Generation Source: http://arxiv.org/abs/2605.04263v1 Summary: This paper introduces PARSE, a novel speculative generation primitive that enables semantic-level verification across multiple prefixes in a single forward pass. By eliminating sequential bottlenecks in speculative decoding, it achieves up to 4.3x throughput gains, representing a major efficiency breakthrough for frontier LLM inference.

EP219: OpenSeeker V2 Shatters The AI Compute Myth

Title: OpenSeeker-v2: Pushing the Limits of Search Agents with Informative and High-Difficulty Trajectories Source: http://arxiv.org/abs/2605.04036v1 Summary: This paper establishes a high-efficiency paradigm for training frontier search agents using only supervised fine-tuning on high-quality synthesized trajectories, challenging resource-intensive industry standards. It provides a foundational methodology for achieving state-of-the-art agentic reasoning and search capabilities with significantly reduced computational requirements.