Title: Robustifying Learning-Augmented Caching Efficiently without Compromising 1-Consistency
Venue: NeurIPS 2025
Accepted: yes
Keywords: Learning-augmented Algorithms

Paging

Caching

Robustness

Consistency
Abstract: The online caching problem aims to minimize cache misses when serving a sequence of requests under a limited cache size. While naive learning-augmented caching algorithms achieve ideal $1$-consistency, they lack robustness guarantees. Existing robustification methods either sacrifice $1$-consistency or introduce excessive computational overhead. In this paper, we introduce Guard, a lightweight robustification framework that enhances the robustness of a broad class of learning-augmented caching algorithms to $2H_{k-1} + 2$, while preserving their $1$-consistency. Guard achieves the current best-known trade-off between consistency and robustness, with only $\mathcal{O}(1)$ additional per-request overhead, thereby maintaining the original time complexity of the base algorithm. Extensive experiments across multiple real-world datasets and prediction models validate the effectiveness of Guard in practice.


Peer reviews:
comments: SUMMARY:
This paper addresses the online caching problem, focusing on minimizing cache misses under limited cache size. While existing learning-augmented caching algorithms achieve ideal 1-consistency, they often lack robustness guarantees. To tackle this, the authors propose GUARD, a lightweight robustification framework that significantly improves robustness to  2Hk+2 without sacrificing 1-consistency. GUARD maintains the original algorithm’s time complexity by adding only a constant per-request overhead. Extensive experiments on various real-world datasets and prediction models demonstrate the practical effectiveness of GUARD.

STRENGTHS AND WEAKNESSES:
Strengths:

1. The paper provides thorough and rigorous theoretical analysis
2. The writing is clear and well-structured.
3. This work is well-motivated.

Weaknesses:

1. The experiments could include more ablation studies to isolate the effects of different components of GUARD.
2. It is unclear how GUARD performs under extreme or adversarial conditions that may arise in some practical caching scenarios.
3. The paper could provide more detailed discussion on the potential limitations or failure cases of the method.

QUESTIONS:
see weakness

LIMITATIONS:
No
TITLE: Official Review
CLARITY: 3
IMPACT: 2
ORIGINALITY: 3
DATE: 2025-07-02

comments: SUMMARY:
The paper studies the online paging problem in the learning-augmented setting. It provides a framework that, given a consistent learning-augmented algorithm following a relaxed version of Belady's rule, maintains consistency while improving robustness to $2H_k+2$. That is, the final algorithm performs optimally for perfect predictions, and in the worst case, its cost is at most $2H_k+2$  times more than the optimal cost. The authors also perform extensive experiments to empirically compare the proposed algorithms with the baselines.

STRENGTHS AND WEAKNESSES:
1. Unlike the common approach in the framework of algorithms with predictions, which focuses on predicting the input, this work starts with a learning-augmented algorithm with specific properties and robustifies it without losing consistency. This is an interesting approach and may have applications in other problems studied in the learning-augmented setting.

2. They achieve a state-of-the-art consistency-robustness tradeoff.

3. The proposed robustification technique is simple, lightweight, and introduces minimal computational overhead, making it practical for real-world deployment.

4. The authors conduct comprehensive experiments that demonstrate performance improvements over existing baselines.

5. The paper is well-written and well-organized, making the technical content accessible and easy to follow. In particular, the detailed comparison with recent learning-augmented caching algorithms is helpful.

QUESTIONS:
N/A

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

{"IS_META_REVIEW": false, "comments": "SUMMARY:\nThe paper investigates the caching problem, where the objective is to store $k$ pages to minimize cache misses---a scenario where a requested page is not present in the cache. The authors specifically examines learning-augmented caching policies, which leverage predictions to guide decisions about page retention and eviction. A key aspect of these policies is their performance under varying prediction accuracy: a consistent policy performs as well as the best caching algorithm when predictions are perfect, while a robust policy guarantees a bounded competitive ratio against the optimal solution even when predictions are flawed. The paper's primary contribution is the proposed algorithm GUARD, which serves as a wrapper to enhance the robustness of any learning-augmented algorithm without compromising its consistency.\n\nSTRENGTHS AND WEAKNESSES:\n**Strengths.**\n\n* The paper does a good job of situating its contributions within the relevant literature, and the research gap it seeks to fill is clearly articulated. The individual contributions are presented in a logical and easily understandable manner, and the work itself appears to be comprehensive and technically solid.\n\n**Weaknesses.**\n\n* It's unclear why the system model necessitates a specific cache always storing the requested file. While this aligns with the operation of many caching policies (e.g., LRU, LFU), its inclusion as a general system model constraint lacks clear justification. Allowing for greater flexibility in this regard could potentially lead to even stronger comparative optimums.\n\n* The authors claim their approach is lightweight; however, the complexity of the \"guard\" mechanism appears to depend on the size of the request sequence (n). In real-world systems, where request sequences can be very large and frequent, it's not clear how this algorithm would remain practical.\n\nQUESTIONS:\n* Could you clarify the necessity of the system model's constraint that a specific cache must always store the requested file? What benefits or challenges might arise if this constraint were more flexible?\n\n* How does the \"guard\" mechanism's complexity, which depends on the request sequence size (n), impact the practical scalability of your \"lightweight\" approach, especially with very large and frequent request sequences in real-world systems?\n\nLIMITATIONS:\nyes", "TITLE": "Official Review", "RECOMMENDATION": 5, "REVIEWER_CONFIDENCE": 4, "CLARITY": 3, "IMPACT": 2, "ORIGINALITY": 3, "DATE": "2025-07-03"}

{"IS_META_REVIEW": false, "comments": "SUMMARY:\nThe authors address the problem of enhancing the robustness of learning-augmented caching algorithms. They propose GUARD, a general framework designed to robustify caching strategies that adhere to a relaxed version of Belady’s rule. GUARD achieves a robustness guarantee of $​2H_k + 2$ while incurring minimal additional computational overhead. The authors conduct experiments on multiple datasets to verify the effectiveness of the GUARD framework.\n\nSTRENGTHS AND WEAKNESSES:\n**Strengths.** The authors consider an important problem of robustifying online caching algorithms and develop a general framework that can be utilized to robustify learning-augmented caching algorithms that follow the relaxed Belady’s rule. The algorithm is intuitive, while the results presented are impactful. The paper is well written and easy to follow even for a non-expert. The presented experiments demonstrate the effectiveness of the proposed framework. \n\n**Weaknesses.** The paper has only minor weaknesses; however, I provide a few concerns that may be worth addressing.\n\n- There is an issue with the notation, in  Section 3, OPT is defined to be an algorithm. Then, in Section 4, the authors provide upper and lower bounds on OPT. Please correct. \n\n- The discussion in observation 2 can be strengthened with some references or examples. Currently, the argument appears somewhat speculative and lacks rigor. \n\n- The authors first present a weaker robustness result of $2 H_k + 6$ and provide its proof. Then, the authors present the improved result with a robustness guarantee of $2 H_k + 2$. What is the need to present two types of guarantees on robustness? \n\n- It would be useful to provide some discussion on the proof of Theorem 4.6. Specifically, how the proof differs from Theorem 4.5.\n\n- I believe the introduction could be strengthened by more clearly articulating the relevance and potential impact of the proposed algorithm and analysis for the machine learning community.\n\nQUESTIONS:\nPlease see weaknesses above.\n\nLIMITATIONS:\nyes", "TITLE": "Official Review", "RECOMMENDATION": 4, "REVIEWER_CONFIDENCE": 3, "CLARITY": 4, "IMPACT": 3, "ORIGINALITY": 3, "DATE": "2025-07-05"}