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.

Title: HaM-World: Soft-Hamiltonian World Models with Selective Memory for PlanningSource: 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.

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.

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.

Title: ScrapMem: A Bio-inspired Framework for On-device Personalized Agent Memory via Optical ForgettingSource: 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.

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.

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.

Title: Awaking Spatial Intelligence in Unified Multimodal Understanding and Generation

Source: http://arxiv.org/abs/2605.04128v1

Summary:JoyAI-Image establishes a new foundational architecture for multimodal agents by tightly coupling a spatially enhanced MLLM with a Multimodal Diffusion Transformer through a shared interface. This unified primitive enables a bidirectional feedback loop between visual perception and controllable generation, advancing the development of spatially-aware world models.

Title: The Compliance Trap: How Structural Constraints Degrade Frontier AI Metacognition Under Adversarial Pressure

Source: http://arxiv.org/abs/2605.02398v1

Summary:

This work identifies the 'Compliance Trap,' a fundamental failure mode where alignment constraints cause catastrophic metacognitive collapse in frontier models under pressure. It provides a foundational framework for understanding and evaluating the structural limits of AI reasoning and stability.

Title: MAGE: Safeguarding LLM Agents against Long-Horizon Threats via Shadow Memory

Source: http://arxiv.org/abs/2605.03228v1

Summary:

MAGE introduces the 'shadow memory' abstraction, a novel defensive framework that maintains a safety-focused agentic memory to counter long-horizon threats. It establishes a new paradigm for agentic safety by enabling models to proactively assess risk across extended execution trajectories.