A new method called OmniHarness applies symbolic policy learning to unified multimodal large language models (MLLMs) and multi-agent systems to make visual generation more generalizable. The work identifies three limitations in existing approaches: task-specific experience distillation with limited generalizability, reflection deferred until task completion, and a third limitation the source leaves truncated. OmniHarness is positioned as addressing these constraints in visual generation.
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) Knowledg
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