Self-Consistent Latent Reasoning: Long Latent Sequence Reasoning for Vision-Language Model

• URL: http://arxiv.org/abs/2605.12163v2
• Date: Wed, 13 May 2026 02:23:44 GMT
• Title: Self-Consistent Latent Reasoning: Long Latent Sequence Reasoning for Vision-Language Model
• Abstract summary: SCOLAR (Self-COnsistent LAtent Reasoning) introduces a lightweight detransformer that generates auxiliary visual tokens in a single shot.<n>SCOLAR extends acceptable latent CoT length by over $30times$, achieves state-of-the-art among open-source models on real-world reasoning benchmarks.
• Score: 56.21523258053447
• License: http://creativecommons.org/licenses/by/4.0/
• Abstract: In language reasoning, longer chains of thought consistently yield better performance, which naturally suggests that visual latent reasoning may likewise benefit from longer latent sequences. However, we discover a counterintuitive phenomenon: the performance of existing latent visual reasoning methods systematically degrades as the latent sequence grows longer. We reveal the root cause: Information Gain Collapse -- autoregressive generation makes each step highly dependent on prior outputs, so subsequent tokens can barely introduce new information. We further identify that heavily pooled ($\geq 128\times$) image embeddings used as supervision targets provide no more signal than meaningless placeholders. Motivated by these insights, we propose SCOLAR (Self-COnsistent LAtent Reasoning), which introduces a lightweight detransformer that leverages the LLM's full-sequence hidden states to generate auxiliary visual tokens in a single shot, with each token independently anchored to the original visual space. Combined with three-stage SFT and ALPO reinforcement learning, SCOLAR extends acceptable latent CoT length by over $30\times$, achieves state-of-the-art among open-source models on real-world reasoning benchmarks (+14.12% over backbone), and demonstrates strong out-of-distribution generalization.

Related papers

• Why Struggle with Continuous Latents? Interpretable Discrete Latent Reasoning via Rendered Compression [42.15219720606396]
  Large language models achieve high reasoning performance via explicit chain-of-thought and reinforcement learning.<n>Latent reasoning reduces this cost by shifting computation into a latent space.<n>We propose textbfDiscrete Latent Reasoning(DLR), the first method that converts continuous latent states into explicit discrete tokens.
  arXiv  Detail & Related papers  (2026-06-29T02:34:52Z)
• Visual Latents Know More Than They Say: Unsilencing Latent Reasoning in MLLMs [54.16324124242172]
  Continuous latent-space reasoning offers a compact alternative to textual chain-of-thought for multimodal models.<n>We identify a previously overlooked optimization pathology in existing latent visual reasoning methods.<n>We show that inference-time latent optimization, without any parameter updates, effectively unleashes the suppressed reasoning capacity of visual latents.
  arXiv  Detail & Related papers  (2026-05-04T15:36:12Z)
• Reflect to Inform: Boosting Multimodal Reasoning via Information-Gain-Driven Verification [55.357038267439684]
  Visual Re-Examination (VRE) is a self-evolving training framework that enables MLLMs to autonomously perform visual introspection during reasoning without additional visual inputs.<n>VRE consistently improves reasoning accuracy and perceptual reliability, while substantially reducing hallucinations, especially in long-chain settings.
  arXiv  Detail & Related papers  (2026-03-27T12:22:13Z)
• Imagination Helps Visual Reasoning, But Not Yet in Latent Space [65.80396132375571]
  We investigate the validity of latent reasoning using Causal Mediation Analysis.<n>We show that latent tokens encode limited visual information and exhibit high similarity.<n>We propose a straightforward alternative named CapImagine, which teaches the model to explicitly imagine using text.
  arXiv  Detail & Related papers  (2026-02-26T08:56:23Z)
• CrystaL: Spontaneous Emergence of Visual Latents in MLLMs [55.34169914483764]
  We propose CrystaL (Crystallized Latent Reasoning), a single-stage framework with two paths to process intact and corrupted images.<n>By explicitly aligning the attention patterns and prediction distributions across the two paths, CrystaL crystallizes latent representations into task-relevant visual semantics.<n>Experiments on perception-intensive benchmarks demonstrate that CrystaL consistently outperforms state-of-the-art baselines.
  arXiv  Detail & Related papers  (2026-02-24T15:01:30Z)
• Forest Before Trees: Latent Superposition for Efficient Visual Reasoning [61.29300723302152]
  Laser is a novel paradigm that reformulates visual deduction via Dynamic Windowed Alignment Learning (DWAL)<n>Laser achieves state-of-the-art performance among latent reasoning methods, surpassing the strong baseline Monet by 5.03% on average.
  arXiv  Detail & Related papers  (2026-01-11T08:30:49Z)
• Beyond Exponential Decay: Rethinking Error Accumulation in Large Language Models [0.0]
  We show that errors are not uniformly distributed but are concentrated at sparse "key tokens" representing critical decision junctions.<n>We propose a framework for next-generation systems centered on selective preservation of semantically vital tokens.
  arXiv  Detail & Related papers  (2025-05-30T03:57:31Z)

This list is automatically generated from the titles and abstracts of the papers in this site.

This site does not guarantee the quality of this site (including all information) and is not responsible for any consequences.