Title: Monitoring Risks in Test-Time Adaptation
Venue: NeurIPS 2025
Accepted: yes
Keywords: test-time adaptation

risk monitoring

sequential testing
Abstract: Encountering shifted data at test time is a ubiquitous challenge when deploying predictive machine learning models. Test-time adaptation (TTA) methods aim to address this issue by continuously adapting a deployed model using only unlabeled test data. While TTA can help extend the model's deployment lifespan, there are scenarios where, despite adaptation, the drop in the model's performance remains significant enough to warrant taking the model offline and retraining. To detect such failure cases, we propose pairing TTA with risk monitoring frameworks that track predictive performance and raise alerts when predefined performance criteria are violated. Specifically, we extend existing monitoring tools based on sequential testing with confidence sequences to accommodate scenarios where the model is updated at test time and no test labels are available to estimate the performance metrics of interest. Our extensions unlock the application of rigorous statistical risk monitoring in TTA and we demonstrate applicability of our proposed TTA monitoring framework across a representative set of TTA methods, datasets and distribution shift types.


Peer reviews:
{"IS_META_REVIEW": false, "comments": "SUMMARY:\nThis paper presents a novel unsupervised risk monitoring framework for test-time adaptation (TTA), addressing the critical challenge of detecting performance degradation in models undergoing continuous online adaptation. The authors systematically extend sequential testing frameworks to dynamic TTA scenarios, leveraging confidence sequences and loss proxies (e.g., model uncertainty) to enable rigorous risk tracking without labeled test data. The research is theoretically sound, experimentally comprehensive, and practically relevant, particularly for safety-critical application.\n\nSTRENGTHS AND WEAKNESSES:\nStrengths:\n\n1. I appreciate the theoretical rigor of this paper, which provides solid theoretical foundations for risk management in TTA. \n\n2. I also admire the writing style, which is remarkably clear and reader-friendly.\n\n3. I appreciate the sufficiently rigorous practice-based discussions in both the main text and the appendix.\n\nWeaknesses:\n\n1. I mainly worry that the practical value of this paper may be insufficient, as using proxy losses to judge model performance in unsupervised scenarios is already common knowledge, and I believe the contribution of proposing this approach might be very limited. In reality, the bigger challenge is that practitioners often do not know how to select appropriate proxies for specific scenarios, yet the paper provides minimal discussion on this critical aspect.\n\n2. My impression is that while the paper approaches the topic from a different perspective than previous work, there is little innovation in specific techniques. It still leverages model uncertainty (with a limited comparison to another metric) and sets thresholds for judgment—approaches that have become well-established in the field.\n\nQUESTIONS:\nI still hope that you can place more emphasis on the problem of how to select proxies based on real-world scenarios. Comparing the impacts of different proxy choices across a wider range of scenarios would likely contribute more than the current paper does.\n\nLIMITATIONS:\nYes.", "TITLE": "Official Review", "RECOMMENDATION": 4, "REVIEWER_CONFIDENCE": 2, "CLARITY": 4, "IMPACT": 2, "ORIGINALITY": 3, "DATE": "2025-06-17"}

{"IS_META_REVIEW": false, "comments": "SUMMARY:\nThis paper proposes a method for monitoring risks in test-time adaptation (TTA). Monitoring risk has been done before. But previous work usually only consider risk monitoring for fixed models after they are deployment. This paper is the first one (that I know of) that deals with the risk monitoring in the test-time adaptation (TTA), where the model can adapt itself after deployment. This paper proposes an algorithm based some theoretical results motivated by previous, especially [1] and [30]\n\nSTRENGTHS AND WEAKNESSES:\nStrength\n---------\n\nTest-time adaptation has become popular recently. Most TTA work focuses on developing new TTA algorithms that achieve higher accuracy. There is little work on studying the problem of detecting when the risk monitoring (i.e. the performance of the model degrades too much after adaptation for a long time, so the model needs to be retrained). I think this paper addresses a problem of practical importance in TTA.\n\nThe proposed method seems to be sound (although I am not an expert in theoretical machine learning, so I cannot comment on how novel or significant those theoretical results are). Apart from theoretical results, this paper also provides a concrete method that can be operationalized. The method is quite simple to implement in practice. \n\nWeakness\n------------\n\n1) From what I can understand, the key idea of the proposed method is to define a loss proxy u that does not require ground-truth labels. This paper chose a particular proxy in Sec 3.3 using some kind of uncertainty. But it is not clear to me whether this particular choice is somehow connected to the theoretical results in previous sections. There are many other ways of defining uncertainty (e.g. the entropy used in TENT). Would the method still work if you use other uncertainty metric? Is the particular uncertainty metric used in this paper better than other alternatives?\n\n2) For most TTA methods (e.g. TENT), they simply optimize some unlabeled loss function during adaptation. To me, the uncertainty measure in Sec 3.3 can also be used as the loss function in TTA. In other words, the TTA algorithm can directly try to optimize this risk during testing. Intuitively, the TTA method will \"overfit\" to this risk measure. I am interested in whether the proposed method still works in this case. \n\n3) The experiment compares different methods by showing some curves. I think it is important to define some numerical metric based on those curves (e.g. in other fields, people use AUC based on precision/recall curve). Otherwise, it is impossible for any follow-up work to directly compare with this paper.\n\nQUESTIONS:\nPlease refer to \"weakness\" above\n\nLIMITATIONS:\nyes", "TITLE": "Official Review", "RECOMMENDATION": 4, "REVIEWER_CONFIDENCE": 3, "CLARITY": 3, "IMPACT": 3, "ORIGINALITY": 3, "DATE": "2025-06-22"}

{"IS_META_REVIEW": false, "comments": "SUMMARY:\nThis paper proposes a risk monitoring method for TTA, aiming to address the challenge of detecting potential performance degradation during TTA when encountering distribution shifts. It extends sequential testing to the unsupervised TTA setting with no test labels used. Key components include a loss proxy using model uncertainty, online threshold calibration with labeled source subset, and theoretical analyses for false alarm control. It is validated across datasets and TTA methods, showing its effectiveness in detecting risk violations to prevent TTA collapse.\n\nSTRENGTHS AND WEAKNESSES:\n## Strengths\n1. **Novel Unsupervised Monitoring Approach**: TTA methods are proved to be vulnerable to some environments or shifts, knowing when TTA methods collapse or worse than source model is a critical task. The paper fills a significant gap by risk monitoring for TTA without test labels, leveraging model uncertainty as a proxy and extending sequential testing frameworks.\n2. **Theoretical Rigor**: The approach is grounded in confidence sequences, providing time-uniform guarantees, the authors derive false alarm control and validate assumptions both theoretically and empirically.\n3. **Practical Adaptability**: Online threshold calibration and alternative proxies enhance robustness to different TTA methods and different shifts without specific designs, which shows good potential to address real-world deployment challenges.\n\n## Weaknesses\n1. **Dynamic Environment Assumptions**: the framework assumes that the test domain is under stationary distributions. While more pratically it may not hold in scenarios with continual distribution shifts. In this more challenging setting, can risk monitoring also work well? It should handle the rapid distribution shift when facing domain change. Some theoretical anlayses and extensive experiments with continual TTA on the whole ImageNet-C with the CoTTA proposed setting may better prove its effectiveness.\n2. **Limited Collapse Prevention Comparisons**: While the framework detects collapse, It lacks of comparison with other collapse prevent methods. For example, some baseline methods like SAR’s resetting strategy, using $D_{cal}$ as a validation set for comparison between current model and source model.\n3. **Unquantified Computational Overhead**: The paper omits metrics (e.g., FLOPS, inference time) to characterize the monitoring framework’s computational cost, which is critical for resource-constrained deployments.\n\nQUESTIONS:\n1. How does the framework perform when Assumption 1 is persistently violated? Is there any failure case?\n2. Can the framework be tuned to prioritize early detection over false alarm rate control for safety-critical applications? How to deal with the trade-off.\n\nLIMITATIONS:\n1. Detection Delay: The approach introduces inevitable delay, which may may impact time-sensitive applications.\n2. Label Requirement: Verifying the key assumption (Assumption 1) needs labeled data.  Additionally, the requirements for D_cal from the source domain may violate the source-free principle to some extent.", "TITLE": "Official Review", "RECOMMENDATION": 4, "REVIEWER_CONFIDENCE": 4, "CLARITY": 3, "IMPACT": 3, "ORIGINALITY": 3, "DATE": "2025-07-01"}

comments: SUMMARY:
The paper proposes to detect the degradation in test time adaptation models over time. The core idea is to adapt the sequential testing technique for risk monitoring to the case of time-varying models and unlabeled data.

STRENGTHS AND WEAKNESSES:
**Strengths:**
- Previous risk monitoring techniques such as sequential testing was designed for static models on labeled data. The authors extend it to handle the test-time adaptation case. The key is to monitor the losses of a sequence of models updated during the adaptation, and to design proxy loss functions to eliminate the need for labels.
- The proposed method is straight-forward and easy to implement.

**Weaknesses:**
- In the paper only the classification task is considered. It is unclear for other tasks whether we can find suitable loss proxies and whether this method is still effective.
- This framework does not handle label distribution shift, where only the label distribution is changing over time, but not the input distribution.

QUESTIONS:
None

LIMITATIONS:
Yes
TITLE: Official Review
CLARITY: 3
IMPACT: 2
ORIGINALITY: 2
DATE: 2025-07-03

{"IS_META_REVIEW": false, "comments": "SUMMARY:\nThis paper proposes a method for monitoring performance during test-time adaptation (TTA) without access to test labels. By extending sequential testing with confidence sequences, it develops unsupervised alarms based on loss proxies (specifically, model uncertainty), enabling reliable detection of performance degradation. The method is supported by theoretical analysis and is empirically validated across datasets, shift types, and TTA strategies.\n\nSTRENGTHS AND WEAKNESSES:\nStrenghts\n\n- Important Problem: The paper tackles a critical challenge in test-time adaptation, due to risks such as silent model collapse or performance degradation, which current TTA methods often fail to detect.\n\n- Practical Proxy: It leverages predictive uncertainty as a simple and efficient proxy for model error, requiring no auxiliary components. The use of online threshold calibration ensures effectiveness even under continuous adaptation.\n\n- Broad Applicability: The proposed method seems to generalize well across TTA methods, datasets, and types of distribution shift.\n\nWeakness:\n\n- Unverifiable Assumption: The method relies on Assumption 1 (proxy informativeness) for its guarantees, but this assumption cannot be verified without labels, limiting trust in fully unsupervised deployments.\n\n- Proxy Sensitivity: Performance depends on the choice of proxy and threshold calibration. For example, standard uncertainty fails for last-layer TTA methods under severe distribution shifts.\n\n- Hyperparameter Robustness: The method introduces several hyperparameters (ϵ_tol, α, λ, τ), but their sensitivity to different shifts is not analyzed. Given that TTA methods are known to be HP-sensitive [1,2], it's important to study how these choices affect risk detection.\n\n[1] Parameter-Free Online Test-Time Adaptation, CVPR 2022 \\\n[2] On Pitfalls of Test-Time Adaptation, ICML 2023\n\nQUESTIONS:\nQuestions \n- Is there a practical way to diagnose whether Assumption 1 holds at deployment time?\n- How sensitive is the method to hyperparameters (e.g., ϵ_tol, α, λ, τ)?\n\nLIMITATIONS:\nYes", "TITLE": "Official Review", "RECOMMENDATION": 4, "REVIEWER_CONFIDENCE": 4, "CLARITY": 3, "IMPACT": 2, "ORIGINALITY": 2, "DATE": "2025-07-07"}