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Traditional research papers are costing AI agents reproducibility and understanding, but a new "Agent-Native" format that captures the full messy research process boosts performance by up to 20%.
Forget brute-force scaling: crafting the *right* context from past experiences unlocks surprisingly large gains in LLM agent performance.
VLMs can now get a million-scale boost in chart-understanding abilities thanks to a new dataset with paired code, images, data, and reasoning.
Agents that ace long-context recall can still bomb when they need to use that memory to actually *do* something, revealing a critical flaw in how we currently evaluate memory in AI.
Forget hand-annotated data: ChartGen automatically generates 222.5K chart-image/code pairs, exposing surprising weaknesses in today's VLMs at reconstructing plotting scripts.
A new 2B parameter vision-language model, Granite Vision, rivals larger models on visual document understanding tasks while offering a transparent and commercially-friendly open-source license.
