Title: RODS: Robust Optimization Inspired Diffusion Sampling for Detecting and Reducing Hallucination in Generative Models
Venue: NeurIPS 2025
Accepted: yes
Keywords: diffusion model

robust optimization

hallucination
Abstract: Diffusion models have achieved state-of-the-art performance in generative modeling, yet their sampling procedures remain vulnerable to hallucinations—often stemming from inaccuracies in score approximation. In this work, we reinterpret diffusion sampling through the lens of optimization and introduce RODS (Robust Optimization–inspired Diffusion Sampler), a novel method that detects and corrects high-risk sampling steps using geometric cues from the loss landscape.  RODS enforces smoother sampling trajectories and \textit{adaptively} adjusts perturbations, reducing hallucinations without retraining and at minimal additional inference cost. Experiments on AFHQv2, FFHQ, and 11k-hands demonstrate that RODS maintains comparable image quality and preserves generation diversity. More importantly, it improves both sampling fidelity and robustness, detecting over 70\% of hallucinated samples and correcting more than 25\%, all while avoiding the introduction of new artifacts. We release our code at https://github.com/Yiqi-Verna-Tian/RODS.


Peer reviews:
comments: SUMMARY:
The authors introduce an inference-time method (RODS) to reduce the number of hallucinations in diffusion models. In particular, if the curvature index $\mathcal H(x)$ of a particular sample at a particular diffusion time exceeds some hyperparameterized value $\epsilon$, RODS uses a guidance-like term $\delta = \arg\max_{||\delta|| = \rho} ||v(x+\delta)||$ to guide the sample towards stability. The method is validated over AFHQv2, FFHQ, and 11k-hands and shows good performance.

STRENGTHS AND WEAKNESSES:
Strengths:
1. Paper is clear and well written.
2. Good performance on reducing hallucinations in AFHQv2, FFHQ, and 11k-hands.
3. Method is relatively efficient in inference time.

Weaknesses:
1. Similar idea using norms to dynamically adjust diffusion sample present in other paper [1].
2. Not enough evaluations of other hallucination reducing methods, e.g. [2], [3]

[1] Constrained Diffusion with Trust Sampling.
[2] Mitigating Hallucinations in Diffusion Models through Adaptive Attention Modulation.
[3] Understanding Hallucinations in Diffusion Models through Mode Interpolation.

QUESTIONS:
See weaknesses. Would love to see further comparison with other methods.

Additionally, qualitatively examining Figure 4, RODS-CAS clearly achieves fewer hallucinations, but it also results in the background becoming blurrier. Is there an explanation for this phenomenon?

LIMITATIONS:
No real discussion of limits of current method on current task, only discussion of how it was not yet applied to another task (conditional generation).
TITLE: Official Review
CLARITY: 3
IMPACT: 2
ORIGINALITY: 2
DATE: 2025-07-01

{"IS_META_REVIEW": false, "comments": "SUMMARY:\nThe paper views the reverse diffusion process as a continuation scheme, where each timestep solves an increasingly ill-conditioned optimization subproblem. Building on this perspective, the authors integrate a curvature-based detector into standard samplers to identify regions where the score may be unreliable, and apply a worst-case-aware correction to keep the trajectory stable, without requiring retraining. This approach reduces hallucinations, as validated by experimental results.\n\nSTRENGTHS AND WEAKNESSES:\n### Strengths\n1. The paper introduces a novel mathematical perspective on diffusion sampling by framing it as a continuation method.\n2. The work is fairly complete: combining theoretical insight with a practical algorithm that is both lightweight and easy to integrate. The proposed method demonstrates meaningful empirical improvements across several datasets.\n\n### Weaknesses\nTo me, the main value of the paper is in the theoretical viewpoint, while the experiments mostly serve to support and illustrate that idea. I do wish the paper leaned a bit more into the theory: right now, the mathematical part is quite straightforward and mainly used to justify the optimization interpretation. But it doesn’t really explain why or when RODS should reduce hallucinations. It would be more convincing if there were some analysis showing, for example, that under certain conditions on the score function or the data distribution, RODS would provably outperform standard samplers. That kind of result would help bridge the gap between the conceptual contribution and the algorithmic claims.\n\nQUESTIONS:\n1. Is there any theoretical justification for why sharp curvature changes indicate high-risk regions during sampling?\n2. Is there any analysis supporting the idea that minimizing the worst-case outcome (as in RODS) can give overall more robust sampling? Under what scenarios is this true?\n3. Just to confirm my understanding: is the core idea behind Algorithm 2 to minimize the objective shown after line 181, when $\\mathcal{F}_t$ is in the form of the expression after line 219?\n4. The experiment results focus on hallucination. Is there any evaluation of overall generation quality, e.g., FID or similar metrics, to ensure RODS doesn’t degrade sample quality?\n\nLIMITATIONS:\nyes", "TITLE": "Official Review", "RECOMMENDATION": 4, "REVIEWER_CONFIDENCE": 2, "CLARITY": 2, "IMPACT": 2, "ORIGINALITY": 3, "DATE": "2025-07-01"}

{"IS_META_REVIEW": false, "comments": "SUMMARY:\nThis paper tackles the hallucination problem in diffusion models by viewing diffusion sampling as a optimization procedure, which frames each sampling steps as a proximal sub-problem on a gradually sharpening potential landscape. \nBased on this, the authors introduce Robust Optimization-inspired Diffusion Sampler, RODS, a novel curvature-based sampler detects sudden changes in score vector field, which can auto-adjust sampling directions and thus improve robustness and remove hallucination. \nImportantly, RODS does not introduce any modification to the pretrained diffusion. \nExperiments on AFHQ-v2 (cats/dogs/wild), FFHQ (faces) and 11k-hands show that RODS-CAS detects ≥ 70 % of annotated hallucinations and corrects > 25 % of faulty samples.\n\nSTRENGTHS AND WEAKNESSES:\nStrengths:\n- Paper is well structured and easy to follow, also algorithms and theorems / proofs are self-contained: \nNice derivations bridge Euler updates to the proximal optimization, and novel link between robust optimization and diffusion sampling. Unlike the previous prior temporal-variance filters, the Curvature-driven risk detector focus more on the shape of the score landscape, \n- Comprehensive experiments across three datasets with manual hallucination annotation. \n- Nearly no extra cost: since the method is plug-and-play, there is no change to the model. \n\nWeaknesses: \n- Although promising experimental results, the authors only provide hallucination related metrics. I think it will be more reasonable to also show some image quality evaluations, e.g. NLL, FID -- just want to make sure that while solving the hallucination issues, the image quality and density estimate are also guaranteed to be good. \n- It's nice to use curvature index to reflect fluctuation in score magnitude, however, making connection between curvature and hallucinate is not rigorously illustrated. To me this fluctuation in score magnitude just tells me how rapid the score changes in this area, and maybe I should adjust my step size to be smaller to avoid introducing more error. These may be just correlation not causality.\n\nQUESTIONS:\n- Why using curvature metric to detect hallucination?\nI don't see why fluctuation in score magnitude can infer hallucination. Is it possible that in some region, score magnitude can be a false alarm? Maybe some region of the image has this type of score behavior. \n- Can you show some image generation quality evaluations? For example, FID, NLL. Just want to see that this sampler doesn't tread hallucination to generation quality. \n- How you make sure that the human labeled data is trustable? \n- For hyperparameter epsilon and rho, can you provide a principled way to choose?\n\nLIMITATIONS:\nyes", "TITLE": "Official Review", "RECOMMENDATION": 5, "REVIEWER_CONFIDENCE": 3, "CLARITY": 3, "IMPACT": 2, "ORIGINALITY": 2, "DATE": "2025-07-03"}

{"IS_META_REVIEW": false, "comments": "SUMMARY:\nThe paper proposes RODS, a novel plug-and-play method for mitigating hallucinations in diffusion models, which is an under-explored field of research. The authors interpret diffusion sampling as a continuation method in numerical optimization and integrate robust optimization principles to dynamically detect and correct \"high-risk/unstable\" steps in the sampling process, thereby alleviating hallucinations. RODS considers curvature changes as a proxy for detecting hallucinations, and proposes two updating strategies (SAS and CAS). The authors evaluate RODS on AFHQv2, FFHQ, and Hands datasets, demonstrating promising results for hallucination mitigation.\n\nSTRENGTHS AND WEAKNESSES:\n**Strengths**\n- The interpretation of the diffusion process as a continuation method, followed by the adaptation of robust optimization as a mitigation strategy is an elegant and theoretical sound approach. \n- The paper addresses an underexplored research avenue and demonstrates strong hallucination detection rates and mitigation \n- The proposed work does not require retraining (plug-and-play) with a relatively lower overhead in inference time\n- Includes a detailed set of ablations\n- a clear and well-written paper where the proposed algorithm is clearly motivated\n\n**Weaknesses and Suggestions**\n- The proposed method may suppress the generative ability of a diffusion model similar to mode collapse. While this would be acceptable to a certain degree given the hallucinations are mitigated, I believe an analysis of potential trade-offs between hallucination and generation diversity is required. \n- While the use of the sharp curvature as a proxy is justifiable, I would suggest the authors discuss the underlying assumptions behind it as there could be naturally sharp but \"valid\" features\n- The authors use the qualitative analysis results as one of their main metrics but do not present details of the survey. This would be needed in making sure the survey best represents the general community and reproducibility. Further, the use of hand-wavy ratings of terms like  \"better\", \"worse\", \"same\", and \"unclear\" needs to be explicitly defined (i.e., what qualifies for each category)\n- The correction-induced changes by RODS may be unintended, where in certain cases a semantic drift and new subtle hallucinations are introduced by the correction process. For example in Fig. 9: \n    - Row 1 left: Pose and complexion change.\n    - Row 1 right: While the extra finger is removed, the pinky becomes implausibly long.\n    - Row 3 left: Floating hand is not resolved. \n    - Row 3 right: Newly introduced finger has inconsistent tone.\n    - Row 4 right: Missing sleeve and changed hand orientation.\n- The authors do not discuss on limitations, any architectural dependence, applicability to high-diveristy datasets where the notion of hallucination is itself ill-defined. \n- The following additional related work appears to be in the same domain of mitigating hallucinations in unconditional diffusion models: *Oorloff et al.  \"Mitigating Hallucinations in Diffusion Models through Adaptive Attention Modulation\"*\n\nQUESTIONS:\n- Is it possible to make \\epsilon not specific to a particular dataset? or to derive epsilon in a data-independent manner? This would help the generalization of the proposed algorithm, especially in cases of high diversity datasets\n- Please include the details of the survey and the qualitative metrics used\n- Discuss the limitations in detail along with the side-effects of corrections such as semantic drift/ over-sanitization\n- As pointed out in the weaknesses and suggestions section, I believe the paper is lacking an evaluation/analysis on the potential trade-off between generative capability and hallucination mitigation.\n\nLIMITATIONS:\nNo", "TITLE": "Official Review", "RECOMMENDATION": 5, "REVIEWER_CONFIDENCE": 4, "CLARITY": 3, "IMPACT": 3, "ORIGINALITY": 3, "DATE": "2025-07-03"}