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Latent Ordinal Evidence, Misaligned Outputs: Inference-Time Ordinal Lens Alignment for Multimodal LLMs

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TL;DR - Multimodal LLMs encode strong ordinal information for tasks such as age estimation and disease grading, but their digit-token outputs fail to reflect it. Ordinal Lens Alignment (OLA) recovers this evidence at inference time without modifying the model backbone.

  • Hidden states yielded ordinal labels with Spearman correlation up to 0.938 across four benchmarks and four MLLM backbones.
  • Native outputs lagged linear probes by 16–77 absolute accuracy points because the unembedding layer largely filtered out the ordinal direction.
  • OLA trains lightweight lenses on intermediate-to-deep decoder layers, fuses their predictions, and adjusts only digit-token logits during generation.
  • It outperformed the LoRA-tuned OrderChain baseline in most settings and improved over an offline lens in every evaluated setting.

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Latent Ordinal Evidence, Misaligned Outputs: Inference-Time Ordinal Lens Alignment for Multimodal LLMs

arXiv cs.CV Haiming Li, Yingsheng Liu, Jingmin Zhu, Siyuan Yan, Xieji Li, Jiajun Sun, Zhen Yu, Zongyuan Ge 2026-08-21 arXiv:2608.20999
Public signals Semantic Scholar citations 0 · Semantic Scholar influential citations 0
Providers: Hugging Face · N/A OpenAlex · N/A Publisher · N/A Semantic Scholar · Citations 0 · Influential citations 0 X · N/A Fetched 2026-09-17 14:29:51.253908 UTC

TL;DR - Multimodal LLMs encode strong ordinal information for tasks such as age estimation and disease grading, but their digit-token outputs fail to reflect it. Ordinal Lens Alignment (OLA) recovers this evidence at inference time without modifying the model backbone.

  • Hidden states yielded ordinal labels with Spearman correlation up to 0.938 across four benchmarks and four MLLM backbones.
  • Native outputs lagged linear probes by 16–77 absolute accuracy points because the unembedding layer largely filtered out the ordinal direction.
  • OLA trains lightweight lenses on intermediate-to-deep decoder layers, fuses their predictions, and adjusts only digit-token logits during generation.
  • It outperformed the LoRA-tuned OrderChain baseline in most settings and improved over an offline lens in every evaluated setting.
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