Multimodal Continuous Reasoning via Asymmetric Mutual Variational Learning

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multimodal-reasoning variational-learning continuous-reasoning latent-reasoning answer-leakage kl-divergence

Summary

Proposes Asymmetric Mutual Variational Learning (AMVL) to resolve train-inference mismatch in multimodal continuous reasoning by using bidirectional calibration to prevent answer leakage and improve latent-space stability, achieving significant gains on the BLINK benchmark.

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.
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Source: https://huggingface.co/papers/2607.00461

Abstract

Asymmetric Mutual Variational Learning addresses train-inference mismatch in multimodal reasoning by using bidirectional calibration to prevent answer leakage and improve latent-space stability.

Multimodal Large Language Models(MLLMs) are often constrained by alanguage-space bottleneck, forcing complex visual reasoning into discrete tokens which can lose perceptual nuance. A promising alternative iscontinuous latent reasoning, where the goal is to discover implicit reasoning pathways that bridge the multimodal query and the final answer. However, this introduces a severetrain-inference mismatch: a training-timeposterior, conditioned on the ground-truth answer, can exploit answer-dependent shortcuts. Standardvariational trainingthen forces the inference-timepriorto mimic aposteriorthat 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 abidirectional calibrationobjective. Aforward KL divergencetrains the target-agnosticpriorto match theposterior, while a novelreverse KL divergencesimultaneously regularizes theposterior, preventing it from collapsing into inference-incompatible regions and mitigating this ``answer leakage’’. We provide theoretical analysis formalizing this leakage aspriorcontamination and prove that our dual-KL objective reduces it. We instantiate AMVL in alatent-integrated MLLMand show that it consistently outperforms strong discrete and latent-reasoning baselines, improving the average score on the complexBLINK benchmarkby +10.83 and achieving gains of up to +32.00 on individual reasoning tasks, with analyses confirming improved latent-space stability.

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