From the archive
Tool Learning with Frozen Language Models is rapidly emerging as a scalable strategy to empower LLMs with real-world functionality. This article introduces Chain-of-Tools (CoTools), a novel approach that enables frozen…
The ReaRAG factuality reasoning model introduces a breakthrough in retrieval-augmented generation by combining structured reasoning with external knowledge retrieval. Built around a Thought → Action → Observation (TAO) loop, ReaRAG…
SEARCH-R1 is a groundbreaking reinforcement learning framework for search-augmented LLMs, enabling AI to think, search, and reason autonomously. Unlike traditional models constrained by static training data, SEARCH-R1 dynamically retrieves, verifies,…
Reasoning LLMs with Tool Integration represent a significant leap forward in AI capabilities, addressing critical challenges like hallucinations and computational errors common to traditional reasoning models. START, a groundbreaking Self-Taught…
Evaluate open AI models through their exact licenses, deployment requirements, security boundaries, and performance on the work your team needs to do.
A review of Chain of Draft prompting, its reported benchmark results, and the cost assumptions and evaluation limits that matter before deployment.
Discover how AI Research Agents, powered by MLGym and MLGym-Bench, are transforming scientific discovery. This article explores the architecture and capabilities of these advanced systems, automating complex tasks like hypothesis…
Natively Sparse Attention (NSA) is transforming the way Large Language Models (LLMs) handle long-context modeling. As tasks like detailed reasoning, code generation, and multi-turn dialogues require processing extensive sequences, traditional…
AI-driven financial models are now influencing billion-dollar decisions, from investment strategies to regulatory compliance. However, financial Large Language Models (LLMs) face critical challenges, including hallucinations, sensitivity to query variations, and…
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