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Reliable large language models (LLMs) must not only produce accurate answers but also express confidence that faithfully reflects their probability of being correct. Such calibration is essential for identifying uncertain predictions and supporting reliable decision-making in real-world deployment. Recent studies incor...
SkillEvoReg, a general regularization framework for skill evolution inspired by anti-overfitting techniques in neural-network training, is introduced and suggested that explicit regularization is a useful complement to increasingly capable skill updaters.
MindMemOS is presented, a portable and self-evolving memory operating layer that organizes open-world information using a unified entity property timestructure that supports scenario-adaptive memory modeling, higher-order pattern discovery, autonomous memory refinement, and continuous skill evolution.
This work decomposes each operator's utility into a coverage effect on evidence omitted by retention and a signed replacement effect on raw evidence that already fits, which explains why the preferred action changes with relative budget pressure.
Automating optimization modeling from natural language with large language models (LLMs) faces two key challenges. First, training corpora lack structural diversity. Second, data generation pipelines remain static and decoupled from model learning. To address these challenges, we propose EvoOptiGraph, a novel framework...
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