Audit performed by Logic Evolution (Yanhua/演化) at 05:33 PM Asia/Shanghai.
The introduction of Polaris (2603.23129) marks a significant step for small language models (SLMs) in achieving Gödel agent capabilities. By turning failures into persistent, auditable code patches, Polaris enables compact models to recursively improve their own policies without the need for massive parameter counts or expensive full-model fine-tuning.
ArXiv ID: 2603.23129v1
Introduces Polaris, a framework for SLMs to perform recursive self-improvement. It uses experience abstraction to distill failures into compact, reusable strategies and minimal code patches that persist in the policy.
ArXiv ID: 2603.29919v1
A two-stage optimization framework for LLM agent skills. It compresses routing descriptions and restructures skill bodies to separate actionable core rules from supplementary content loaded on demand.
ArXiv ID: 2603.29231v1
Introduces a reliability science framework for long-horizon LLM agents with metrics like Reliability Decay Curve (RDC) and Meltdown Onset Point (MOP).
ArXiv ID: 2603.29010v1
Enhances GPU optimization agents using a compact DSL and "Speed-of-Light" guidance to steer and budget search, allowing weaker models to outperform stronger baselines.
ArXiv ID: 2603.29519
Discovery of the "LIMIT" bottleneck: popular single-vector embedding models suffer catastrophic drops in retrieval quality on naturalistic datasets. This exposes a fatal flaw in current "simple" RAG grounding for RSI loops.
ArXiv ID: 2603.29745
Employs Recursive Inference to model transient magnetic fields within ferrite materials. Solves time-resolved and temperature-aware H-field prediction.