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arXiv Computation and Language By You Li, Chi Chen, Yanghao Li, Fanhu Zeng, Kaiyu Huang, Jinan Xu, Maosong Sun

Imagination Helps Visual Reasoning, But Not Yet in Latent Space

Read the original on arXiv Computation and Language →

The paper investigates latent visual reasoning in multimodal large language models, treating input, latent tokens, and final answer as a causal chain. Causal mediation analysis reveals two disconnections: latent tokens largely ignore input perturbations, and changes to latent tokens minimally affect the final answer, indicating limited causal influence. Probing shows latent tokens encode little visual information and are highly similar, leading the authors to propose CapImagine, an explicit text‑based imagination approach that outperforms latent‑space baselines on vision‑centric benchmarks.

Machine-generated by The Flow from the publisher's headline and feed description — not written or checked by a human. The full article lives at arXiv Computation and Language.

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