Title: Unbiased Visual Reasoning with Controlled Visual Inputs
Primary area: applications to computer vision, audio, language, and other modalities
Keywords: Visual Question Answering, Reasoning

Abstract: End-to-end Vision-language models (VLMs) often rely on spurious visual cues, conflating perception with decision-making. We introduce VISTA (Visual Information Separation for Text-based Analysis), which enforces an explicit information bottleneck between a text-only reasoner and a stateless VLM sensor. The LLM reasoner decomposes each question and iteratively queries a VLM for visual facts; the VLM is instructed to reject queries that require high-level inference, creating an explicit information bottleneck. Trained on only 641 questions, VISTA yields large robustness gains on SpuriVerse across two vision backbones (+16.29\% with Qwen-2.5-VL-7B and +6.77\% with Llama-3.2-Vision-11B), while direct SFT or RL on the VLM fails to remedy spuriosity and can even exacerbate it. Despite never exposing the reasoner to raw pixels, VISTA slightly improves or remains on par with VLMs on everyday-scene benchmarks, including MMVP and SeedBench. Our learned reasoners transfer across sensors, indicating algorithmic rather than model-specific generalization. Together, VISTA enables spurious-resistant VQA by upgrading the brain, not the eyes.