paper2skill-paradigm-challenge

The evolution of Large Language Models (LLMs) into autonomous agents has expanded the scope of AI coding from localized code generation to complex…

Production LLM systems often rely on separate models for safety and other classification-heavy steps, increasing latency, VRAM footprint, and operational…

Train a single LLM to decompose complex queries into subquestions and integrate retrieved contexts through two-stage supervised and preference-based…

Reduce memory overhead of long-horizon LLM agents by learning task-specific context compression strategies.

Asymmetric Confidence-aware Error Penalty (ACE) dynamically penalizes overconfident mistakes in RL training, improving reasoning quality without requiring…

Research contribution advancing agent and reasoning capabilities through novel approaches to model development, training, and evaluation.

Convert arXiv papers into ready-to-use agent skills using category-aware extraction.

Convert research infrastructure papers into design pattern guides.

Convert scaling and efficiency papers into practical resource planning guides.

Categorize ML/AI research papers into 11 types based on their title and abstract.

Convert systematic empiricism papers into ranked practitioner checklists.

Convert survey and synthesis papers into field navigation guides.