RSIV-Harness: A Recursive Self-Improvement Harness Framework for Open-Domain Conversational Text-to-Visualization
- Published
- Source
- arXiv
- Paper number
- 1376
- Field
- AI Agents
- arXiv ID
- 2610.35316
Key points
- The paper defines the problem: when users request charts without knowing which database holds the data, the agent must discover sources, ask clarifying questions, and refine charts under earlier constraints, yet existing harnesses are authored once and never revised, so the same failures recur.
- The ODAC-Vis benchmark contains 640 dialogue tasks and jointly evaluates open-domain source discovery, ambiguity resolution, and multi-turn chart refinement.
- RSIV-Harness keeps the model weights fixed: an inner loop solves tasks, an outer loop reads failure traces and proposes harness revisions, and only revisions passing the anchor and memory checks are accepted.
- A single round of self-improvement raised all six metrics, with executability improving by 28.02 points and chart structure by 34.01 points.
- Removing the stability rules degraded all six metrics, with executability dropping by 9.73 points.
- The harness evolved with DeepSeek transferred to GPT-6 Luna and improved five of six metrics over the static harness, showing that harness improvements transfer across backbones.
Paper links
External research summaries. These are not HDATF publications or measured product results.