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New RSIV-Harness framework improves conversational text-to-visualization agents

By PulseAugur Editorial · [1 sources] ·

Researchers have developed RSIV-Harness, a framework designed to improve open-domain conversational text-to-visualization agents. This system uses Recursive Self-Improvement to revise the agent's harness based on its own failure traces, keeping the language model weights static. A four-layer framework evaluates and refines harness revisions, ensuring stability. The new ODAC-Vis benchmark, featuring 640 tasks, assesses data discovery, ambiguity resolution, and multi-turn visualization refinement. Experiments demonstrated significant improvements across multiple metrics, including executability and chart structure, highlighting the framework's ability to learn from experience. AI

IMPACT Enhances the ability of AI agents to generate visualizations from complex, conversational user requests.

RANK_REASON The cluster describes a new framework and benchmark published in an academic paper on arXiv. [lever_c_demoted from research: ic=1 ai=1.0]

Read on arXiv cs.AI →

AI-generated summary · Google Gemini · from 1 sources. How we write summaries →

New RSIV-Harness framework improves conversational text-to-visualization agents

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The cluster describes a new framework and benchmark published in an academic paper on arXiv. [lever_c_demoted from research: ic=1 ai=1.0]
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COVERAGE [1]

  1. arXiv cs.AI TIER_1 English(EN) · Jiawei Lu, Chen Zhang, Jinwei Lu, Yuanwei Song, Yuanfeng Song, Raymond Chi-Wing Wong ·

    RSIV-Harness: A Recursive Self-Improvement Harness Framework for Open-Domain Conversational Text-to-Visualization

    arXiv:2610.35316v1 Announce Type: new Abstract: Users often request charts without knowing which database or table holds the relevant data. Such requests may be ambiguous, and users may ask follow-up questions to deepen their analysis or iteratively refine existing charts. A usef…

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