CASHEW: Stabilizing Multimodal Reasoning via Iterative Trajectory Aggregation

Chaoyu Li; Fei Tao; Pooyan Fazli

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arXiv · 2601.08010

CASHEW: Stabilizing Multimodal Reasoning via Iterative Trajectory Aggregation

Chaoyu Li·Fei Tao·Pooyan Fazli

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Abstract

Vision-language models achieve strong performance across a wide range of multimodal understanding and reasoning tasks, yet their multi-step reasoning remains unstable. Repeated sampling over the same input often produces divergent reasoning trajectories and inconsistent final predictions. To address this, we introduce two complementary approaches inspired by test-time scaling: (1) CASHEW, an inference-time framework that stabilizes reasoning by iteratively aggregating multiple candidate trajectories into higher-quality reasoning traces, with explicit visual verification filtering hallucinated steps and grounding reasoning in visual evidence, and (2) CASHEW-RL, a learned variant that internalizes this aggregation behavior within a single model. CASHEW-RL is trained using Group Sequence Policy Optimization (GSPO) with a composite reward that encourages correct answers grounded in minimal yet sufficient visual evidence, while adaptively allocating reasoning effort based on task difficulty. This training objective enables robust self-aggregation at inference. Extensive experiments on 13 image understanding, video understanding, and video reasoning benchmarks show significant performance improvements, including gains of up to +26.2 percentage points on ScienceQA and +9.1 percentage points on EgoSchema.

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BibTeX RIS

Chaoyu Li, Fei Tao, Pooyan Fazli. 2026-07-13. CASHEW: Stabilizing Multimodal Reasoning via Iterative Trajectory Aggregation. https://arxiv.org/abs/2601.08010

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