AI推理不再靠猜：双向校准堵住答案泄露
大模型做多模态推理时，常把图像信息压缩成离散文字，丢失细节。一种新思路是让模型在连续空间里“暗中推理”，但训练时模型能看到正确答案，导致它学会走捷径——测试时答案没了，推理就崩了。这篇提出双向校准法：一边让推理路径模仿有答案时的模式，一边反过来限制有答案时的路径别太依赖答案，把泄露堵住。在复杂视觉推理测试上，平均分提升10.83，单项最高涨32分。它不是你明天能用上的，但指明了让AI推理更可靠的方向：别让模型在训练时偷看答案。
📄 原文摘要(英文)
Multimodal Large Language Models (MLLMs) are often constrained by a language-space bottleneck, forcing complex visual reasoning into discrete tokens which can lose perceptual nuance. A promising alternative is continuous latent reasoning, where the goal is to discover implicit reasoning pathways that bridge the multimodal query and the final answer. However, this introduces a severe train-inference mismatch: a training-time posterior, conditioned on the ground-truth answer, can exploit answer-dependent shortcuts. Standard variational training then forces the inference-time prior to mimic a posterior that has access to information unavailable at test time, leading to poor performance. To address this, we propose Asymmetric Mutual Variational Learning (AMVL), a framework that resolves this mismatch via a bidirectional calibration objective. A forward KL divergence trains the target-agnostic prior to match the posterior, while a novel reverse KL divergence simultaneously regularizes the posterior, preventing it from collapsing into inference-incompatible regions and mitigating this ``answer leakage''. We provide theoretical analysis formalizing this leakage as prior contamination and prove that our dual-KL objective reduces it. We instantiate AMVL in a latent-integrated MLLM and show that it consistently outperforms strong discrete and latent-reasoning baselines, improving the average score on the complex BLINK benchmark by +10.83 and achieving gains of up to +32.00 on individual reasoning tasks, with analyses confirming improved latent-space stability.