Reasoning Instructions Can Break Answer Decoding in Vision--Language Models

arXiv:2609.29278 · cs.CL, cs.AI · Submitted 2026-09-24 · Read on arXiv

cs.CL, cs.AI

Submitted: 2026-09-24

Updated: 2026-09-24

Terminology

• Accuracy — 22× in this paper · explained in 1 episode
• Selection Bias — 3× in this paper · explained in 4 episodes
• Benchmark — 2× in this paper · explained in 2 episodes
• Chain-of-Thought (CoT) — 2× in this paper · explained in 4 episodes
• Instruction Tuning — 2× in this paper · explained in 2 episodes
• Linear probe — 2× in this paper · explained in 2 episodes
• Multimodal Reasoning — 2× in this paper · explained in 5 episodes
• Adaptation — 1× in this paper · explained in 1 episode
• Alignment — 1× in this paper · explained in 1 episode
• Benchmarking — 1× in this paper · explained in 3 episodes
• Chain-of-Thought (CoT) Prompting — 1× in this paper · explained in 3 episodes
• Chain-of-Thought Prompting — 1× in this paper · explained in 2 episodes
• LLM-as-a-Judge — 1× in this paper · explained in 3 episodes
• LoRA Adapters — 1× in this paper · explained in 3 episodes
• Tokenization — 1× in this paper · explained in 2 episodes
• Trace — 1× in this paper · explained in 2 episodes
• Transformer — 1× in this paper · explained in 2 episodes

Sources

• More Thought, Less Accuracy? On the Dual Nature of Reasoning in Vision-Language Models
• Qwen2.5-VL Technical Report
• Unexplored flaws in multiple-choice VQA make benchmarking unreliable
• Understanding intermediate layers using linear classifier probes

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