Context-Dependent Affordance Computation in Vision-Language Models
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arXiv
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| Natura: | Preprint |
| Pubblicazione: |
2026
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| _version_ | 1866908866712698880 |
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| author | Farzulla, Murad |
| author_facet | Farzulla, Murad |
| contents | We characterize the phenomenon of context-dependent affordance computation in vision-language models (VLMs). Through a large-scale computational study (n=3,213 scene-context pairs from COCO-2017) using Qwen-VL 30B and LLaVA-1.5-13B subject to systematic context priming across 7 agentic personas, we demonstrate massive affordance drift: mean Jaccard similarity between context conditions is 0.095 (95% CI: [0.093, 0.096], p < 0.0001), indicating that >90% of lexical scene description is context-dependent. Sentence-level cosine similarity confirms substantial drift at the semantic level (mean = 0.415, 58.5% context-dependent). Stochastic baseline experiments (2,384 inference runs across 4 temperatures and 5 seeds) confirm this drift reflects genuine context effects rather than generation noise: within-prime variance is substantially lower than cross-prime variance across all conditions. Tucker decomposition with bootstrap stability analysis (n=1,000 resamples) reveals stable orthogonal latent factors: a "Culinary Manifold" isolated to chef contexts and an "Access Axis" spanning child-mobility contrasts. These findings establish that VLMs compute affordances in a substantially context-dependent manner -- with the difference between lexical (90%) and semantic (58.5%) measures reflecting that surface vocabulary changes more than underlying meaning under context shifts -- and suggest a direction for robotics research: dynamic, query-dependent ontological projection (JIT Ontology) rather than static world modeling. We do not claim to establish processing order or architectural primacy; such claims require internal representational analysis beyond output behavior. |
| format | Preprint |
| id |
arxiv_https___arxiv_org_abs_2603_04419 |
| institution | arXiv |
| publishDate | 2026 |
| record_format | arxiv |
| spellingShingle | Context-Dependent Affordance Computation in Vision-Language Models Farzulla, Murad Computation and Language Artificial Intelligence Machine Learning 68T45, 68T50 I.2.10; I.2.7; I.2.9 We characterize the phenomenon of context-dependent affordance computation in vision-language models (VLMs). Through a large-scale computational study (n=3,213 scene-context pairs from COCO-2017) using Qwen-VL 30B and LLaVA-1.5-13B subject to systematic context priming across 7 agentic personas, we demonstrate massive affordance drift: mean Jaccard similarity between context conditions is 0.095 (95% CI: [0.093, 0.096], p < 0.0001), indicating that >90% of lexical scene description is context-dependent. Sentence-level cosine similarity confirms substantial drift at the semantic level (mean = 0.415, 58.5% context-dependent). Stochastic baseline experiments (2,384 inference runs across 4 temperatures and 5 seeds) confirm this drift reflects genuine context effects rather than generation noise: within-prime variance is substantially lower than cross-prime variance across all conditions. Tucker decomposition with bootstrap stability analysis (n=1,000 resamples) reveals stable orthogonal latent factors: a "Culinary Manifold" isolated to chef contexts and an "Access Axis" spanning child-mobility contrasts. These findings establish that VLMs compute affordances in a substantially context-dependent manner -- with the difference between lexical (90%) and semantic (58.5%) measures reflecting that surface vocabulary changes more than underlying meaning under context shifts -- and suggest a direction for robotics research: dynamic, query-dependent ontological projection (JIT Ontology) rather than static world modeling. We do not claim to establish processing order or architectural primacy; such claims require internal representational analysis beyond output behavior. |
| title | Context-Dependent Affordance Computation in Vision-Language Models |
| topic | Computation and Language Artificial Intelligence Machine Learning 68T45, 68T50 I.2.10; I.2.7; I.2.9 |
| url | https://arxiv.org/abs/2603.04419 |