Title: Confidence-Orchestrated Self-Evolution against Uncertain LLM Feedback

Source: http://arxiv.org/abs/2605.28010v1

Summary:

COSE provides a foundational framework for LLM self-evolution by using intrinsic model confidence as an uncertainty signal to filter and weigh self-generated training signals. This approach addresses the critical bottleneck of error propagation in autonomous learning loops, enabling models to improve their reasoning and mathematical capabilities without human-curated supervision or external verifiers.

Title: MUSE-Autoskill: Self-Evolving Agents via Skill Creation, Memory, Management, and Evaluation

Source: http://arxiv.org/abs/2605.27366v1

Summary:

This paper proposes a novel architectural framework for self-evolving agents that can autonomously create, store, and refine a library of reusable skills through a unified lifecycle management system. It introduces the concept of skill-level memory and unit-testable assets, representing a major advancement in building agents capable of continuous improvement and cross-task experience accumulation.

Title: Innovation: An Almost Characterization of Hallucination

Source: http://arxiv.org/abs/2605.26808v1

Summary:

This work establishes a foundational probabilistic framework that formalizes hallucination as "innovation," providing a mathematical characterization of why LLMs produce outputs outside their training data. By deriving new lower bounds on hallucination rates based on "missing mass," it offers a critical theoretical breakthrough for understanding and mitigating the core reliability limits of generative models.

Title: Multi-Agent Coordination Adaptation via Structure-Guided Orchestration

Source: http://arxiv.org/abs/2605.25746v1

Summary:

The paper introduces a novel probabilistic framework for multi-agent coordination by casting orchestration as posterior inference over task-specific structures. This foundational approach balances structural stability with dynamic adaptability, significantly improving execution efficiency and performance while reducing token overhead in complex agentic workflows.

Title: How Should LLMs Consume High-Quality Data? Optimal Data Scheduling via Quality-Aware Functional Scaling Laws

Source: http://arxiv.org/abs/2605.25698v1

Summary:

This paper establishes foundational quality-aware functional scaling laws that provide the first theoretical closed-form solution for scheduling high-quality data during LLM training. The introduced 'Drop-Stable-Rampup' schedule optimizes training dynamics across noise-limited and signal-limited regimes, yielding significant breakthroughs in mathematical reasoning performance.

Title: Efficient Agentic Reasoning Through Self-Regulated Simulative Planning

Source: http://arxiv.org/abs/2605.22138v1

Summary:

This paper introduces a foundational three-system reasoning framework—comprising reactive, simulative, and self-regulated components—that enables agents to autonomously manage their planning depth and horizon. By treating the LLM as a world model for future-state prediction, it demonstrates that structured deliberation can allow smaller models to match the performance of systems orders of magnitude larger.

Title: Compiling Agentic Workflows into LLM Weights: Near-Frontier Quality at Two Orders of Magnitude Less Cost

Source: http://arxiv.org/abs/2605.22502v1

Summary:

This work proposes the 'subterranean agent' paradigm, which replaces external orchestration frameworks by compiling agentic workflows directly into the model's weights via fine-tuning. This foundational shift addresses the cost and latency bottlenecks of frontier-model prompting while providing a more efficient and private alternative for procedural task execution.

Title: Mem-π: Adaptive Memory through Learning When and What to Generate

Source: http://arxiv.org/abs/2605.21463v1

Summary:

Mem-π presents a foundational shift in agent memory architectures by replacing static similarity-based retrieval with a dedicated generative model that produces context-specific guidance. This framework enables agents to dynamically adapt their memory usage, leading to substantial improvements in complex reasoning and long-horizon task execution.

Title: Agent JIT Compilation for Latency-Optimizing Web Agent Planning and Scheduling

Source: http://arxiv.org/abs/2605.21470v1

Summary:

This paper introduces Agent Just-In-Time (JIT) compilation, a novel architectural primitive that transforms natural language task descriptions into optimized, executable code plans. It represents a significant breakthrough in agentic efficiency by replacing traditional sequential loops with a compiled, parallelized execution framework that drastically reduces latency.

Title: PEEK: Context Map as an Orientation Cache for Long-Context LLM Agents

Source: http://arxiv.org/abs/2605.19932v1

Summary:

This work introduces 'context maps' as a novel architectural primitive for long-context agents, enabling them to cache and maintain structured orientation knowledge about recurring external datasets. By implementing a programmable cache policy for distilling and translating inference-time signals, it significantly improves efficiency and accuracy across multi-turn reasoning workloads.