storms-c8b9742e·3 events·first seen Aliases: STORMS, STORM
Researchers introduce GEIS, a framework that decomposes long-form Wikipedia-style article generation into named, declarative agent skills organized in a generation-evaluation-improvement loop. Implemented in the Tasi Harness, GEIS outperforms STORM on structural and content quality dimensions and improves over the default writer by 8.0 points on a 100-point rubric across 20 Wikipedia Featured Article topics. A key contribution is the improvement skill, which maps recurrent evaluation findings into permanent patches to the writing skill, enabling iterative self-refinement without rewriting fixed pipelines.
STORM is an open-source Python system from Stanford OVAL that uses LLMs to autonomously research a topic and generate full-length reports with citations. The repository has accumulated 28,804 GitHub stars with 199 added today, indicating sustained and active community interest. The system represents a practical agentic research pipeline combining retrieval, synthesis, and structured writing.
STORMS is a two-stage training framework that teaches large vision-language models to perform spatial-temporal video reasoning through bounded continuous latent trajectories rather than explicit textual chain-of-thought, keyframe selection, or external tool use. In Stage I, latent tokens are aligned with thought-video representations derived from generated videos; in Stage II, answer-only supervision internalizes the reasoning process. At inference time, no video regeneration or frame reinsertion is required, reducing latency and engineering complexity. Evaluations on VideoMME, MVBench, TempCompass, and MMVU show improved accuracy with substantially lower inference overhead versus tool-based pipelines.