OmniHarness: Harnessing Generalizable Visual Generation via Symbolic Policy Learning
Unified multimodal large language models (MLLMs) and multi-agent systems have advanced visual generation. However, three limitations remain. (1) Existing methods often distill task-specific experience with limited generalizability. (2) Reflection is often deferred until task completion. (3) Knowledge is often acquired only in response to downstream task demands. To address these limitations, we introduce OmniHarness, a framework for generalizable visual generation via symbolic policy learning. OmniHarness abstracts verified executions into symbolic policies for visual generation task families, capturing shared procedures and applicability conditions while removing instance-specific inputs...
| Authors | Xu Xu, Jinxiu Liu, Zhangbo Qiao et al. |
|---|---|
| Primary category | cs.LG |
| Published | 13 September 2026 |
| Last updated at source | 13 September 2026 |
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