Spatial Hallucinations in Visual AI Systems

Updated 10 December 2025

Spatial hallucinations are perceptual errors where AI assigns nonexistent objects or spatial relations to specific image regions, impacting model fidelity.
They emerge in systems like VLMs through image tokenization and language priors, causing mislocalizations and erroneous spatial relationships.
Mitigation strategies including knowledge erasure and constraint-aware prompting significantly reduce hallucination rates to enhance model reliability.

Spatial hallucinations are systematic, spatially localized perceptual or representational errors in which an information processing system—biological, artificial, or generative—attributes nonexistent structures, objects, or spatial relations to visual data. In modern computational settings, spatial hallucinations arise most prominently in vision–LLMs (VLMs), large vision–LLMs (LVLMs), generative image restoration pipelines, and mathematical models of cortical dynamics, where such errors undermine the fidelity of reasoning, perception, and content generation. Addressing spatial hallucinations is central to improving the reliability, interpretability, and deployment safety of visual AI systems.

1. Formal Definitions and Taxonomies

Spatial hallucinations are distinct from generic object hallucinations by virtue of their spatial specificity. In VLMs, a spatial hallucination occurs when the system not only invents (hallucinates) an object or spatial attribute but also localizes this entity to a specific patch, region, or relation within the visual input, despite no corresponding ground-truth support (Jiang et al., 3 Oct 2024). This includes both false-positive object detections assigned to a region and incorrect spatial relationships between real objects.

A unified taxonomy observed across domains includes:

Object spatial hallucination: Hallucinated entity is localized by model (e.g., “giraffe” highlighted over a sofa).
Relationship (spatial predicate) hallucination: False assertion of spatial predicates (e.g., “cup left of book” when truly right) (Wu et al., 12 Feb 2025, Wu et al., 24 Jun 2024, Peng et al., 18 Feb 2025).
State hallucination: Incorrect assignment of a state or attribute (e.g., “refrigerator open” when none exist) (Chakraborty et al., 18 Jun 2025).
Intrinsic vs. extrinsic (image restoration): Intrinsic violates data consistency; extrinsic introduces plausible but incorrect content in measurement-consistent nullspaces (Kim et al., 3 Dec 2025).
Cortical geometric hallucination: Localized activity patterns in neural fields not driven by actual stimulus but by intrinsic dynamics or symmetry-breaking (Tamekue et al., 2022, Faugeras et al., 2021).

Quantitatively, spatial hallucinations are diagnosed via false-positive rates on spatial queries, segmentation consistency, confidence thresholds per patch (as in $C_o$ ), and, in generative domains, patch-level IoU drops or drops in specialized spatial metrics.

2. Mechanisms Underlying Spatial Hallucinations

In VLMs and LVLMs, spatial hallucinations emerge from the interaction between learned language priors, weak visual grounding, and the structure of encoder-decoder architectures. A typical pipeline involves:

Image Tokenization: Vision encoder produces $n$ spatial patch features $k_i\in\mathbb{R}^d$ preserving image layout.
Projection to Language Space: Patch features mapped to language embeddings via shared/deeper “vision-to-language” MLPs, yielding $h_l(k_i)$ at each decoder layer (Jiang et al., 3 Oct 2024).
Patch-to-token Probabilities: Unembedding maps patch features to logits over the language vocabulary $f_l(k_i)=W_U h_l(k_i)$ ; softmax yields per-patch, per-token spatial “probabilities” $p_l(y=v|k_i)$ .
Spatial Localization: For each object or relation token $o$ , spatial scores $s_i(o)$ are extracted by max-pooling over layers; up-sampled spatial heatmaps $H_o(x,y)$ localize support for spatial concepts (Jiang et al., 3 Oct 2024, Wu et al., 24 Jun 2024).
Relation Extraction and Hallucination: Pattern-matching and language priors can cause the model to output high probabilities for objects/relations in unsupported locations or between unrelated objects.

These mechanisms generalize to 3D-LLMs (via point-cloud-based object/relationship representations) (Peng et al., 18 Feb 2025), embodied agent planning pipelines ( $x_s$ for scene, $x_{td}$ for task description) (Chakraborty et al., 18 Jun 2025), and cortical field models (pattern formation in neural operator equations) (Tamekue et al., 2022, Faugeras et al., 2021).

3. Empirical Manifestations and Quantitative Assessment

Extensive benchmarks reveal consistent, quantifiable spatial hallucination rates and highlight failure modes. Key patterns include:

Image Captioning (COCO2014): Prevalence of captions containing spatially hallucinated objects (CHAIR_S), with standard VLMs showing 14–15% rates, which can be reduced by up to 25.7% with knowledge erasure interventions (Jiang et al., 3 Oct 2024).
Spatial Relation QA (ARO, GQA, MMRel): LVLMs yield error rates $>$ 30% on spatial relation questions unless explicitly constrained (Wu et al., 12 Feb 2025).
3D Scene Understanding: Relation hallucination rates for direction, containment, contact, and distance queries reach $\sim 30\%$ ; “opposite question” protocols show over 50% failure to distinguish left/right analogues (Peng et al., 18 Feb 2025).
Embodied Agents: In LLM-driven task planning (VirtualHome), object-level spatial hallucinations reach $C_O\sim49\%$ under scene–task contradictions (Chakraborty et al., 18 Jun 2025).
Restoration Pipelines: Hallucination synthesis drops segmentation IoU in target patches from 0.86 $\to$ 0.36, revealing that even visually plausible enhancements can inject severe spatial errors (Kim et al., 3 Dec 2025).
Cortical Models: Spatially periodic and localized activity patterns arise spontaneously or under symmetry breaking, in the absence of matching stimulus input (Tamekue et al., 2022, Faugeras et al., 2021).

Table 1: Examples of Quantitative Metrics for Spatial Hallucination

Domain	Metric	Typical Value (Baseline)	Reference
VLM Captioning	CHAIR_I	52–53% (pre-intervention)	(Jiang et al., 3 Oct 2024)
3D Relation QA	$HR_{random}$	$\sim30\%$ spatial relations	(Peng et al., 18 Feb 2025)
Restoration (IoU)	Segm. IoU (m)	0.86 $\to$ 0.36 (within halluc. mask)	(Kim et al., 3 Dec 2025)
Embodied Planning	$C_O$ (VirtualHome)	49% (scene–task contradiction)	(Chakraborty et al., 18 Jun 2025)

4. Root Causes: Data and Model Biases

Systematic investigation attributes spatial hallucinations to specific causes:

Data Distribution Imbalance: Long-tailed object/relation representations in instruction-tuning data induce strong language priors; spatial predicates are particularly underrepresented (Wu et al., 24 Jun 2024, Peng et al., 18 Feb 2025).
Pattern Co-occurrence: Conditional frequencies (subject–relation, relation–relation, relation–object) bias models toward hallucinating plausible but absent spatial relations. Example: the presence of “bus” inflates hallucination of “parked at bus stop” (Wu et al., 24 Jun 2024).
Overreliance on LLM Knowledge: Counterfactual tests show LVLMs ignore image features in favor of associative memory (e.g., answer “Yes, two wheels” for a one-wheeled “motorcycle”) (Wu et al., 24 Jun 2024, Peng et al., 18 Feb 2025).
Intrinsic Model Properties: Neural-field and mathematical models demonstrate that localized or periodic “hallucination” patterns can emerge from symmetry-breaking, parameter shifts (gain $\gamma$ , threshold $\epsilon$ ), or stochasticity, independent of external input (Faugeras et al., 2021, Tamekue et al., 2022).
Cross-modal Insufficiency: Absence of strong visual–text cross-attention or region-token alignment enables textual hallucination to be spatially mapped without real support (Jiang et al., 3 Oct 2024, Chakraborty et al., 18 Jun 2025).

5. Mitigation and Detection Strategies

Several algorithmic and architectural interventions have demonstrated efficacy in reducing spatial hallucinations:

Projection and Confidence-based Detection: Projecting image patch features into the language vocabulary and thresholding per-object internal confidences ( $C_o$ ) sharply separates real from hallucinated objects; low $C_o (<0.1)$ signifies hallucination (Jiang et al., 3 Oct 2024).
Knowledge Erasure via Linear Orthogonalization: The ProjectAway method subtracts a hallucinated-object’s text embedding direction $t_o$ from all patch embeddings, effectively suppressing hallucination in the generated text—yielding up to 25.7% relative reductions in CHAIR_I (Jiang et al., 3 Oct 2024).
Constraint-aware Prompting: Enforcing bidirectionality ( $r(a,b)=\textrm{inv}(r(b,a))$ ) and transitivity (if $a$ left_of $b$ and $b$ left_of $c$ then $a$ left_of $c$ ) in prompts, LVLMs maintain more spatially coherent predictions; prompt engineering can increase accuracy/F1 by 3–8 points (Wu et al., 12 Feb 2025).
Region-level Alignment and Balanced Tuning: Explicitly associating relation tokens with image regions (region-guided instruction, alignment losses) and oversampling rare spatial predicates during training reduce hallucination rates and improve generalization to spatial reasoning benchmarks (Wu et al., 24 Jun 2024).
Contrastive Training, Cross-modal Grounding: Penalizing identical responses to random/scenically opposite 3D inputs promotes true visual grounding; joint image+text input reduces embodied hallucination rates by up to 30% (Chakraborty et al., 18 Jun 2025, Peng et al., 18 Feb 2025).
Feature-based Hallucination Detection: SHAFE (Semantic Hallucination Assessment via Feature Evaluation) improves AUC for hallucination detection over LPIPS (AUC of 0.82 vs 0.51) (Kim et al., 3 Dec 2025); reference-free classifiers transfer to real failures.

6. Broader Implications: Zero-shot Segmentation and Cortical Hallucination Models

Interpreting and editing the spatial structure of model representations enables not only hallucination mitigation but unlocks new capabilities:

Zero-shot Segmentation: The same per-patch spatial scores that distinguish real and hallucinated objects can be used to generate segmentation masks; on ImageNet-Segmentation these achieve Pixel Acc 76.16%, mIoU 54.26%, mAP 79.90%, matching specialized segmenters (Jiang et al., 3 Oct 2024).
Neurocomputational Models: Spatial hallucination patterns in cortical field models emerge either via subthreshold symmetry-breaking (external localized input triggers complementary geometric patterns) (Tamekue et al., 2022) or spontaneous bifurcations to stripe/spot planforms as gain and threshold parameters are modulated (bifurcation analysis, Equivariant Branching Lemma) (Faugeras et al., 2021).
Image Restoration Synthesis: Controlled spatial hallucination synthesis enables systematic benchmarking and detector training for safety-critical restoration tasks such as MRI, bridging the annotation–detection circularity (Kim et al., 3 Dec 2025).

7. Open Problems and Future Directions

Despite progress, important challenges remain:

Unified Metrics: No single metric yet robustly captures the perceptual and semantic impact of spatial hallucinations across all domains; feature-based and region-aware metrics show promise but require cross-domain validation (Kim et al., 3 Dec 2025).
Training Data Design: Fully correcting spatial hallucination requires not only balanced, region-annotated supervision but augmentation with counterfactual and “illusion” scene constructs (Wu et al., 24 Jun 2024, Peng et al., 18 Feb 2025).
Rejection and Abstention Policies: Embodied agents and LVLMs frequently plan/actions hallucinated objects rather than refusing infeasible queries, highlighting the need for robust “I cannot” policies and environment-aware goal filtering (Chakraborty et al., 18 Jun 2025).
Theoretical Characterization: Mathematical models reveal rich bifurcation scenarios for spatial hallucinations in neural fields, but the precise stability and physiological correlates require further paper (Faugeras et al., 2021).
Real-world Generalization: Detectors and mitigation schemes must be validated for robustness on open-world, out-of-distribution, and high-stakes applications (e.g., medical imaging, autonomous robotics).

Spatial hallucinations thus remain a central concern both for the fidelity of AI vision models and for neuroscientific understanding of cortical pattern formation. Precise diagnosis, principled mitigation, and theoretical synthesis of spatial hallucination phenomena constitute an active area of interdisciplinary research in computational neuroscience, machine learning, and visual AI.