Large multimodal models are increasingly used as the reasoning core of embodied agents operating in 3D environments, yet they remain prone to hallucinations that can produce unsafe and ungrounded decisions. Existing inference-time hallucination mitigation methods largely target 2D vision-language settings and do not transfer to embodied 3D reasoning, where failures arise from object presence, spatial layout, and geometric grounding rather than pixel-level inconsistencies. We introduce 3D-VCD, the first inference-time visual contrastive decoding framework for hallucination mitigation in 3D embodied agents. 3D-VCD constructs a distorted 3D scene graph by applying semantic and geometric perturbations to object-centric representations, such as category substitutions and coordinate or extent corruption. By contrasting predictions under the original and distorted 3D contexts, our method suppresses tokens that are insensitive to grounded scene evidence and are therefore likely driven by language priors. We evaluate 3D-VCD on the 3D-POPE and HEAL benchmarks and show that it consistently improves grounded reasoning without any retraining, establishing inference-time contrastive decoding over structured 3D representations as an effective and practical route to more reliable embodied intelligence.

• 3D-VCD as a New Inference Framework. We introduce 3D-VCD, the first training-free, inference-time contrastive decoding framework for hallucination mitigation in 3D embodied agents.
• Semantic and Geometric Distortion Operators. We propose a simple and effective 3D counterfactual grounding mechanism that constructs distorted scene graphs through semantic and geometric perturbations, and uses dual-context logit fusion to suppress predictions unsupported by the underlying 3D evidence.
• Strong Generalization Without Retraining. We demonstrate 3D-VCD improves grounded reasoning across embodied hallucination benchmarks, reducing over-affirmation on 3D-POPE and lowering hallucination on HEAL with minimal computational overhead.

Effect of Scene Graph Distortion Types. We conduct a systematic study to analyze how different scene graph perturbations affect the performance of 3D- VCD. By independently corrupting semantic, geometric, and structural attributes, we isolate how each type of information contributes to grounded reasoning.

Semantic Corruptions. We perturb object categories through synonym substitution and modifier removal to test robustness to lexical ambiguity and reduced contextual specificity.

Geometric Corruptions. We introduce Gaussian noise to object centroids and extents to simulate sensor noise and imperfect 3D reconstruction.

Structural Corruptions. We modify scene structure through object sparsification, relation flipping, and distractor injection to test reliance on relational and contextual cues.

Mixed Corruptions. We combine semantic and geometric perturbations to mimic realistic embodied sensing noise where category uncertainty and spatial variance co-occur.

Across all settings, 3D- VCD consistently outperforms the baseline, demonstrating strong robustness to corrupted 3D inputs. These results indicate that contrastive decoding effectively leverages discrepancies between clean and perturbed scene representations, reinforcing grounded reasoning and suppressing hallucinations.

We present qualitative examples showing how 3D-VCD improves factual grounding across both 3D-POPE and HEAL. Across these examples, the baseline model exhibits several failure modes, including missing objects that are present, hallucinating objects that do not exist, and generating spurious ungrounded content. In contrast, 3D-VCD predictions remain more faithful to the scene graph and the underlying physical environment.

Taken together, these examples highlight the dual advantage of 3D-VCD: it reduces false positives by suppressing hallucinated objects and attributes, while also improving recall by attending to grounded, semantically coherent evidence. This leads to more reliable 3D reasoning in both embodied question answering and action-oriented grounding settings.