Position: The Systemic Lack of Agency in Visual Reasoning
- URL: http://arxiv.org/abs/2606.14795v1
- Date: Thu, 11 Jun 2026 14:13:42 GMT
- Title: Position: The Systemic Lack of Agency in Visual Reasoning
- Abstract summary: Implicit reasoning refers to the ability to autonomously discover and utilize hidden visual evidence.<n>This capacity underlies human visual understanding and everyday reasoning.<n>We introduce the Visual Implicit Reasoning Diagnosing Benchmark (V-IRD) to measure this capacity.
- Score: 16.497809678344566
- License: http://arxiv.org/licenses/nonexclusive-distrib/1.0/
- Abstract: This paper argues that a systemic lack of Agency constrains the implicit reasoning capabilities of current Vision-Language Models (VLMs). Implicit reasoning refers to the ability to autonomously discover and utilize hidden visual evidence to bridge information gaps, rather than merely relying on explicitly specified targets. This capacity underlies human visual understanding and everyday reasoning. We argue that this limitation arises from a tendency to approach visual reasoning primarily as passive semantic retrieval, rather than as active, situated reasoning that depends on autonomous visual exploration. As a result, most existing benchmarks primarily assess Passive Capacity, leaving this aspect of reasoning largely unmeasured. To address this gap, we introduce the Visual Implicit Reasoning Diagnosing Benchmark (V-IRD), which targets this missing quadrant by requiring models to derive answers strictly through autonomous visual analysis. Our results show that, despite strong retrieval abilities, prominent VLMs struggle to utilize reference objects and to attend to visual evidence that requires self-directed inquiry. Simply put, strong semantic recognition does not equate to active visual exploration, revealing a critical gap in current VLMs. More information can be found at https://haoychen.github.io/Implicit-Reasoning/
Related papers
- Cognitive Pivot Points and Visual Anchoring: Unveiling and Rectifying Hallucinations in Multimodal Reasoning Models [65.57985131861399]
hallucinations are strongly correlated with cognitive bifurcation points that often exhibit high entropy states.<n>We propose V-STAR, a lightweight, holistic training paradigm designed to internalize visually aware reasoning capabilities.
arXiv Detail & Related papers (2026-04-11T13:59:05Z) - Through the Lens of Contrast: Self-Improving Visual Reasoning in VLMs [60.93949629734977]
We propose Visual Contrastive Self-Taught Reasoner (VC-STaR) to mitigate hallucinations in model-generated rationales.<n>We collect a diverse suite of VQA datasets, curate contrastive pairs according to multi-modal similarity, and generate rationales using VC-STaR.<n>Extensive experiments show that VC-STaR not only outperforms existing self-improving approaches but also surpasses models finetuned on the SoTA visual reasoning datasets.
arXiv Detail & Related papers (2026-03-03T03:18:31Z) - Diagnosing Causal Reasoning in Vision-Language Models via Structured Relevance Graphs [18.83755844366017]
We introduce Vision-Language Causal Graphs (VLCGs), a structured, query-conditioned representation that explicitly encodes causally relevant objects, attributes, relations, and scene-grounded assumptions.<n>We present ViLCaR, a diagnostic benchmark comprising tasks for Causal Attribution, Causal Inference, and Question Answering, along with graph-aligned evaluation metrics.<n> Experiments in state-of-the-art LVLMs show that injecting structured relevance information significantly improves attribution and inference compared to zero-shot and standard in-context learning.
arXiv Detail & Related papers (2026-02-24T13:20:07Z) - VRIQ: Benchmarking and Analyzing Visual-Reasoning IQ of VLMs [3.8552182839941884]
We introduce VRIQ, a novel benchmark designed to assess and analyze the visual reasoning ability of Vision Language Models (VLMs)<n>We find that on abstract puzzles, performance remains near random with an average accuracy of around 28%, while natural tasks yield better but still weak results with 45% accuracy.<n>Our analysis demonstrates that around 56% of failures arise from perception alone, 43% from both perception and reasoning, and only a mere 1% from reasoning alone.
arXiv Detail & Related papers (2026-02-05T07:07:27Z) - BLINK-Twice: You see, but do you observe? A Reasoning Benchmark on Visual Perception [67.89135437537179]
We introduce BLINK-Twice, a vision-centric reasoning benchmark grounded in challenging perceptual tasks.<n>Instead of relying on external knowledge, our tasks require models to reason from visual content alone.<n>Compared to prior perception benchmarks, it moves beyond shallow perception and requires fine-grained observation and analytical reasoning.
arXiv Detail & Related papers (2025-10-10T13:14:13Z) - When Thinking Drifts: Evidential Grounding for Robust Video Reasoning [68.75730050161219]
Chain-of-Thought (CoT) mechanism has enhanced reasoning in text-based tasks.<n>CoT often degrades performance in video reasoning, generating verbose but misleading internal monologues.<n>Visual Evidence Reward (VER) is a reinforcement learning framework that explicitly rewards the generation of reasoning traces that are verifiably grounded in visual evidence.
arXiv Detail & Related papers (2025-10-07T16:03:33Z) - What's Missing in Vision-Language Models? Probing Their Struggles with Causal Order Reasoning [26.671128120554457]
causal reasoning is fundamental to solving complex high-level reasoning tasks.<n>Existing benchmarks often include a mixture of reasoning questions.<n>We introduce VQA-Causal and VCR-Causal to isolate and rigorously evaluate causal reasoning abilities.
arXiv Detail & Related papers (2025-06-01T07:17:46Z) - Why Is Spatial Reasoning Hard for VLMs? An Attention Mechanism Perspective on Focus Areas [69.56484419619919]
We study the spatial reasoning challenge from the lens of mechanistic interpretability.<n>We observe that successful spatial reasoning correlates strongly with the model's ability to align its attention with actual object locations.<n>Motivated by these findings, we propose ADAPTVIS to sharpen the attention on highly relevant regions when confident.
arXiv Detail & Related papers (2025-03-03T17:57:03Z) - Retrieval-Based Interleaved Visual Chain-of-Thought in Real-World Driving Scenarios [69.00444996464662]
We propose RIV-CoT, a Retrieval-Based Interleaved Visual Chain-of-Thought method that enables vision-language models to reason using visual crops corresponding to relevant entities.<n>Our experiments demonstrate that RIV-CoT improves answer accuracy by 3.1% and reasoning accuracy by 4.6% over vanilla CoT prompting.
arXiv Detail & Related papers (2025-01-08T18:31:16Z) - Interpretable Visual Question Answering via Reasoning Supervision [4.76359068115052]
Transformer-based architectures have recently demonstrated remarkable performance in the Visual Question Answering (VQA) task.
We propose a novel architecture for visual question answering that leverages common sense reasoning as a supervisory signal.
We demonstrate both quantitatively and qualitatively that the proposed approach can boost the model's visual perception capability and lead to performance increase.
arXiv Detail & Related papers (2023-09-07T14:12:31Z) - Guiding Visual Question Answering with Attention Priors [76.21671164766073]
We propose to guide the attention mechanism using explicit linguistic-visual grounding.
This grounding is derived by connecting structured linguistic concepts in the query to their referents among the visual objects.
The resultant algorithm is capable of probing attention-based reasoning models, injecting relevant associative knowledge, and regulating the core reasoning process.
arXiv Detail & Related papers (2022-05-25T09:53:47Z)
This list is automatically generated from the titles and abstracts of the papers in this site.
This site does not guarantee the quality of this site (including all information) and is not responsible for any consequences.