The Problem We Started With Creating meeting minutes is probably the most boring yet critical task in corporate environments. Picture this scenario: a one‑hour...
Self-evolution methods enhance code generation through iterative 'generate-verify-refine' cycles, yet existing approaches suffer from low exploration efficiency...
Self-evolution methods enhance code generation through iterative 'generate-verify-refine' cycles, yet existing approaches suffer from low exploration efficiency...
Self-evolution methods enhance code generation through iterative 'generate-verify-refine' cycles, yet existing approaches suffer from low exploration efficiency...
Swahili – An AI & ML Representation Gap The cover image is also under‑represented! Swahili is significantly under‑represented in AI research and applications,...
Large language models (LLMs) exhibit complementary strengths arising from differences in pretraining data, model architectures, and decoding behaviors. Inferenc...
Reinforcement learning (RL) has emerged as a critical technique for enhancing LLM-based deep search agents. However, existing approaches primarily rely on binar...
Recent advancements in Large Language Model (LLM) agents have enabled complex multi-turn agentic tasks requiring extensive tool calling, where conversations can...
Large language models (LLMs) often fail to learn effective long chain-of-thought (Long CoT) reasoning from human or non-Long-CoT LLMs imitation. To understand t...
We propose a framework that amortizes the cost of inference-time reasoning by converting transient critiques into retrievable guidelines, through a file-based m...
Autonomous machine learning agents have revolutionized scientific discovery, yet they remain constrained by a Generate-Execute-Feedback paradigm. Previous appro...
We release Pantagruel models, a new family of self-supervised encoder models for French text and speech. Instead of predicting modality-tailored targets such as...
