Monitorable Chart Reasoning Agents via Verifiable Process Rewards
Chart reasoning agents are increasingly used to extract actionable insights in critical domains, achieving state-of-the-art performance on multiple benchmarks. Yet, high benchmark accuracy alone is insufficient for deployment, where stakeholders must be able to audit and verify how a model reaches its answer. Existing LVLM-based chart agents produce either answer-only predictions or free-form rationales that are hard to verify, obscuring whether an error arose from misreading the chart, extracting a wrong value, or miscomputing. We propose Chart-RVR, a reinforcement learning framework for training monitorable chart agents with verifiable process rewards. Chart-RVR decomposes chart reasoni...
| Authors | Sanchit Sinha, Oana Frunza, Kashif Rasul et al. |
|---|---|
| Primary category | cs.CV |
| Published | 21 September 2026 |
| Last updated at source | 21 September 2026 |
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