arXiv:2512.11995v2 Announce Type: replace-cross Abstract: While many vision-language models (VLMs) are developed to answer well-defined, straightforward questions with highly specified targets, as in most benchmarks, they often struggle in practice with complex open-ended tasks, which usually require multiple rounds of exploration and reasoning in the visual space. Such visual thinking paths not only provide step-by-step exploration and verification as an AI detective but also produce better interpretations of the final answers. However, these paths are challenging to evaluate due to the large
The proliferation of advanced vision-language models necessitates more sophisticated benchmarking methods to evaluate their real-world exploratory reasoning capabilities beyond simple Q&A.
Improving benchmarks for exploratory visual reasoning is critical for developing more capable and robust AI systems that can handle complex, open-ended tasks encountered in practical applications.
This research introduces a new benchmark, V-REX, that shifts evaluation from single-shot questions to a 'chain-of-questions' approach, better reflecting human-like investigative processes and exposing limitations in current VLM architectures.
- · AI researchers focusing on agentic vision systems
- · Developers of advanced vision-language models
- · Industries requiring complex visual data interpretation
- · AI models focused solely on direct, single-query answers
- · Benchmarking methodologies lacking exploratory depth
VLMs will be trained and optimized against more challenging exploratory reasoning tasks, leading to more robust models.
This will accelerate the development of AI agents capable of autonomous visual investigation and problem-solving in unstructured environments.
These advanced agentic systems could begin to automate complex visual analysis tasks across scientific research, industrial inspection, and even detective work, impacting white-collar visual analysis professions.
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Read at arXiv cs.LG