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Image Reasoning Moves Into the Latent Space
LARC replaces token-level image reasoning with continuous hidden-state reasoning and reports a seven-point GenEval gain over BAGEL.
Summary
LARC replaces token-level image reasoning with continuous hidden-state reasoning and reports a seven-point GenEval gain over BAGEL.
Unified multimodal models often reason in discrete language tokens before generating in a continuous image space. LARC treats that modality mismatch as the bottleneck. Its curriculum first converts token reasoning into continuous latent reasoning, then an information-gain reinforcement-learning stage continues without ground-truth reasoning traces. Across the reported text-to-image evaluations, the method improved generation quality, prompt following and compositional fidelity; on GenEval it beat BAGEL by seven percentage points and showed gains from additional test-time computation. Those are benchmark results from the authors, not evidence that the hidden reasoning is human-readable or causally faithful.
Why it matters
LARC replaces token-level image reasoning with continuous hidden-state reasoning and reports a seven-point GenEval gain over BAGEL.
Limits and context
- Those are benchmark results from the authors, not evidence that the hidden reasoning is human-readable or causally faithful.
Key claims
LARC replaces token-level image reasoning with continuous hidden-state reasoning and reports a seven-point GenEval gain over BAGEL.
Qualification: Those are benchmark results from the authors, not evidence that the hidden reasoning is human-readable or causally faithful.
Evidence: source-2026-11-02-003
Sources
- arXiv preprint 2610.42149, original v1arXiv · primary research
Corrections
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