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REVIEW 4 major objections 2 minor 66 references

Understanding Action Effects through Instrumental Empowerment in Multi-Agent Reinforcement Learning

T0 review · 4 major / 2 minor · reviewed 2026-08-05 · deepseek-v4-flash

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Pith's one-line read The paper claims that in cooperative and competitive MARL, an agent's causal influence on teammates can be read solely from policy-distribution changes, quantified by Shapley values of instrumental empowerment.

desk verdict Only the abstract is in view, so this is a promise, not a paper; the idea is worth checking, but the causal language is doing more work than the abstract can support. read the letter →

arxiv 2508.15652 v2 pith:BWNDAHRF submitted 2025-08-21 cs.AI cs.ITcs.LGcs.MAmath.IT

Ardian Selmonaj , Miroslav Strupl , Oleg Szehr , Alessandro Antonucci This is my paper · ORCID
classification cs.AIcs.ITcs.LGcs.MAmath.IT
keywords multi-agentreinforcementlearningcausalattributionShapleyvaluesinstrumentalempowermentpolicydistributionsexplainabilityreward-freeanalysiscooperative-competitiveMARL
verification ladder T0 review T1 audit T2 compute T3 formal

The pith

A machine-rendered reading of the paper's core claim, the machinery that carries it, and where it could break.

The reading

This paper sets out to show that in multi-agent reinforcement learning, an agent's contribution to team success can be identified without any reward or value feedback—simply by watching how its actions shift teammates' policy distributions. It introduces Intended Cooperation Values (ICVs), information-theoretic Shapley values that decompose an agent's action effect into two components: whether it makes teammates more or less decisive, and whether it pulls their preferences toward or away from its own. A sympathetic reader would care because current MARL explainability usually starts from explicit rewards, which are often absent, sparse, or uninformative. If the claim holds, behavior attribution and cooperation diagnostics become available from policies alone.

What carries the argument

Intended Cooperation Values (ICVs): an information-theoretic Shapley-value attribution that measures one agent's causal influence on co-players' instrumental empowerment by scoring changes in decision uncertainty and preference alignment within teammates' policy distributions. This machinery carries the argument by turning 'who helped the team' into a quantity computed solely from policies, with no reward or value function.

What would settle it

In a two-agent gridworld, make agent A's action unobservable to agent B (remove A from B's observation vector) while B still changes its policy because of shared environment dynamics. If ICVs still attribute nonzero causal influence to A on B's decisions, the method measures correlation rather than causation.

Watch

Extended reading notes

Core claim

The central claim is that meaningful causal attribution in MARL does not require rewards: the policy distribution itself carries the information needed. ICVs quantify each agent's causal influence on its co-players' instrumental empowerment—the practical room for action a teammate has after observing the agent—via an information-theoretic Shapley value. Concretely, an agent's action effect on a teammate is scored by how much it changes that teammate's decision (un)certainty and how much it aligns or misaligns the teammate's preferences with its own. Across cooperative and competitive tasks, the method identifies behaviors that help the team, distinguishing between actions that foster determi

Load-bearing premise

The method assumes that when a teammate's policy distribution becomes more or less certain and its preferences shift, that change was caused by the focal agent's action; in reality, shared environment dynamics, other agents' actions, and the teammate's own exploration also move the policy, so the attribution may capture correlation with team success rather than causal influence.

Editorial extensions

If this is right

  • Teams can be audited for individual contribution without any reward or value signal, which matters when rewards are sparse, delayed, or unavailable.
  • Agent behavior becomes classifiable by its effect: committing teammates to deterministic choices versus preserving their flexibility for future actions.
  • The degree to which agents adopt similar or diverse strategies becomes measurable from policy data alone.
  • Cooperative success can be attributed to specific agents in both cooperative and competitive settings, improving explainability of trained MARL systems.

Reading between the lines

Editorial extensions of the paper, not claims the author makes directly.

  • The supplied extract contains only the abstract; the derivation, implementation, and experiments for ICVs are not present here, so the claims rest on the abstract alone.
  • If policy-only attribution holds, ICVs could serve as a reward-free credit-assignment signal for shaping or diagnosing MARL training where rewards are absent—an extension the paper does not state.
  • A natural test is to check whether ICV attributions stay stable under shared-environment confounders, for instance when a teammate's policy shift is induced by the environment rather than by the focal agent's action.
  • The preference-alignment component might generalize to human-agent teams as a signal of whether a robot's actions expand or narrow a human partner's perceived action options.
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Editorial analysis

A structured set of objections, weighed in public.

Desk editor's note, referee report, and a circularity audit.

Referee Report

4 major / 2 minor

Summary. The submission, arXiv:2508.15652, proposes 'Intended Cooperation Values' (ICVs), an information-theoretic Shapley-value method intended to quantify each agent's causal influence on co-players' instrumental empowerment in multi-agent reinforcement learning. The abstract claims that by analyzing only policy distributions—specifically decision (un)certainty and preference alignment—the method can identify which agent behaviors benefit team success in cooperative and competitive tasks, without any reward feedback. However, the full text supplied with the submission is not this paper at all: it is arXiv:2508.15647, 'CausalMesh: A Formally Verified Causally Consistent Distributed Cache with Support for Client Migration,' a distributed-systems paper. The actual MARL paper contains no visible equations, definitions, algorithms, experiments, or evaluation. Thus the submission as provided consists of a title and abstract asserting a specific technical contribution, with no technical content to verify.

Significance. If the claimed results were true, the work could be relevant to MARL explainability and to the broader question of attributing agent influence without reward signals. The stated ambition—extracting meaningful causal insight solely from policy distributions—would be a useful contribution if supported by a rigorous identification argument and empirical validation. However, the significance cannot be assessed from the current submission. The abstract alone provides no derivations, no formal definitions of the quantities being measured, no causal model or identification strategy, and no experimental evidence. The submission therefore does not meet the standard of a verifiable technical claim.

major comments (4)
  1. [Full text (overall submission)] The full text supplied for this submission is an unrelated paper (arXiv:2508.15647, 'CausalMesh'), not the MARL/ICV paper advertised by the title and abstract. No equations, definitions, algorithms, or experiments for ICVs appear anywhere in the submission. The central claim of the paper is entirely unsupported because the actual manuscript is absent. This is a load-bearing defect: the review process cannot evaluate a paper that does not contain the claimed technical content.
  2. [Abstract, 'causal influence'] The abstract states that ICVs 'quantify each agent's causal influence on their co-players' instrumental empowerment' but does not specify whether the underlying Shapley value is computed over interventional conditionals (e.g., do(a_i)) or over observational policy distributions. In the latter case, changes in a teammate's decision certainty or preference alignment could arise from shared environment dynamics, other agents' actions, or the teammate's own exploration, making the focal agent's action merely correlated with—not causally responsible for—the change. Without a stated causal model, a defined intervention, or an identifiability argument, the word 'causal' is not supported. The manuscript needs to provide this information or weaken the claim to 'covariation.'
  3. [Abstract, 'solely by analyzing the policy distribution'] The submission does not define 'decision (un)certainty' or 'preference alignment' mathematically, nor does it state how an information-theoretic Shapley value is constructed from policy distributions. It is impossible to assess whether the proposed ICVs are well-defined, normalized, permutation-invariant, or identifiable from finite samples. The central claim that 'meaningful insights into agent behaviors can be extracted solely by analyzing the policy distribution' is therefore an assertion without formal content.
  4. [Abstract, 'cooperative and competitive MARL tasks'] No experiments are reported in the submission. The abstract claims that the method 'identifies which agent behaviors are beneficial to team success,' including fostering deterministic decisions, preserving flexibility, and revealing similar or diverse strategies, but no environments, baselines, metrics, or results are provided. The empirical component of the claim is completely unsubstantiated.
minor comments (2)
  1. [Abstract] The term 'instrumental empowerment' is used without a formal definition or citation to the empowerment literature. Please define it explicitly (e.g., mutual information between an action and future state entropy) and explain how it relates to the proposed ICV construction.
  2. [Abstract] The acronym 'ICV' is introduced but never expanded in the abstract; the paper should include a notation table and formal definitions of the Shapley-value estimator, including the coalition game and value function.

Circularity Check

0 steps flagged · score 0.0 of 10

No circularity can be established: the supplied full text is a different paper, so no derivation chain or equations from arXiv:2508.15652 are available to inspect.

full rationale

The abstract describes arXiv:2508.15652, a MARL paper introducing Intended Cooperation Values (ICVs) from information-theoretic Shapley values over policy distributions. The supplied full text, however, is arXiv:2508.15647 (CausalMesh), a distributed cache paper with no definitions, equations, or experiments related to ICVs, Shapley values, or MARL. Without the actual derivation chain, no circular step can be quoted or exhibited, and the rules require specific reduction (e.g., Eq. X = Eq. Y by construction, or a fitted parameter renamed as prediction) before flagging circularity. The abstract's claim that ICVs quantify 'causal influence' from policy distributions may be under-identified or unsupported, but that is a correctness/evidence concern, not a demonstration of circularity. The honest finding is therefore no significant circularity, with score 0.

Assumptions & free parameters 0 free parameters · 3 assumptions · 0 invented entities

Review based on abstract only; no free parameters or invented entities are identifiable. Three domain assumptions are listed because they are load-bearing in the abstract's argument.

assumptions (3)
  • domain assumption Intelligent agents tend to pursue convergent instrumental values, so policy certainty and alignment are indicative of instrumental goal pursuit.
    Stated in the abstract as the inspiration for ICVs; it is a behavioral postulate about MARL agents, not a proven theorem.
  • domain assumption An agent's causal influence on co-players' instrumental empowerment can be estimated from policy distributions alone, without reward or value feedback.
    The core premise of ICVs; the abstract asserts it but provides no identifiability argument in the available text.
  • domain assumption Information-theoretic Shapley values over per-agent policy quantities provide a valid attribution of causal influence in multi-agent settings.
    Shapley attribution has known sensitivity to the underlying game/value definition; the abstract does not specify the coalition value function or prove its causal semantics.

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Cite this review

Pith. "Pith review of Understanding Action Effects through Instrumental Empowerment in Multi-Agent Reinforcement Learning." pith.science (2026). https://pith.science/paper/BWNDAHRF

@misc{pith2026250815652,
  author       = {Pith},
  title        = {Pith review of: Understanding Action Effects through Instrumental Empowerment in Multi-Agent Reinforcement Learning},
  year         = {2026},
  howpublished = {\url{https://pith.science/paper/BWNDAHRF}},
  note         = {Machine review of arXiv:2508.15652}
}
read the original abstract

To reliably deploy Multi-Agent Reinforcement Learning (MARL) systems, it is crucial to understand individual agent behaviors. While prior work typically evaluates overall team performance based on explicit reward signals, it is unclear how to infer agent contributions in the absence of any value feedback. In this work, we investigate whether meaningful insights into agent behaviors can be extracted solely by analyzing the policy distribution. Inspired by the phenomenon that intelligent agents tend to pursue convergent instrumental values, we introduce Intended Cooperation Values (ICVs), a method based on information-theoretic Shapley values for quantifying each agent's causal influence on their co-players' instrumental empowerment. Specifically, ICVs measure an agent's action effect on its teammates' policies by assessing their decision (un)certainty and preference alignment. By analyzing action effects on policies and value functions across cooperative and competitive MARL tasks, our method identifies which agent behaviors are beneficial to team success, either by fostering deterministic decisions or by preserving flexibility for future action choices, while also revealing the extent to which agents adopt similar or diverse strategies. Our proposed method offers novel insights into cooperation dynamics and enhances explainability in MARL systems.

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Reference graph

Works this paper leans on

66 extracted references · 65 canonical work pages

  1. [1]

    In: Proceedings of the ACM European Conference on Computer Systems (EuroSys) (2023)

    Abdi, M., Ginzburg, S., Lin, X.C., Faleiro, J., Chaudhry, G.I., Goiri, I., Bianchini, R., Berger, D.S., Fonseca, R.: Palette load balancing: Locality hints for serverless func- tions. In: Proceedings of the ACM European Conference on Computer Systems (EuroSys) (2023)

    work page 2023

  2. [2]

    In: Proceedings of the USENIX Symposium on Networked Systems Design and Implementation (NSDI) (2020)

    Agache, A., Brooker, M., Iordache, A., Liguori, A., Neugebauer, R., Piwonka, P., Popa, D.M.: Firecracker: Lightweight virtualization for serverless applications. In: Proceedings of the USENIX Symposium on Networked Systems Design and Implementation (NSDI) (2020)

    work page 2020

  3. [3]

    In: International Conference on Distributed Computing Sys- tems (ICDCS) (2016)

    Akkoorath, D.D., Tomsic, A.Z., Bravo, M., Li, Z., Crain, T., Bieniusa, A., Pregui¸ ca, N., Shapiro, M.: Cure: Strong semantics meets high availability and low latency. In: International Conference on Distributed Computing Sys- tems (ICDCS) (2016)

    work page 2016

  4. [4]

    In: Proceedings of the ACM European Conference on Computer Systems (EuroSys) (2013)

    Almeida, S., Leit˜ ao, J., Rodrigues, L.: Chainreaction: a causal+ consistent datastore based on chain replication. In: Proceedings of the ACM European Conference on Computer Systems (EuroSys) (2013)

    work page 2013

  5. [5]

    In: Proceedings of the ACM SIG- MOD Conference (SIGMOD) (2013)

    Bailis, P., Ghodsi, A., Hellerstein, J.M., Stoica, I.: Bolt- on causal consistency. In: Proceedings of the ACM SIG- MOD Conference (SIGMOD) (2013)

    work page 2013

  6. [6]

    In: Proceedings of the USENIX Sympo- sium on Networked Systems Design and Implementation (NSDI) (2006)

    Belaramani, N.M., Dahlin, M., Gao, L., Nayate, A., Venkataramani, A., Yalagandula, P., Zheng, J.: Practi replication. In: Proceedings of the USENIX Sympo- sium on Networked Systems Design and Implementation (NSDI) (2006)

    work page 2006

  7. [7]

    In: Workshop on Middleware for Service Oriented Computing (MW4SOC) (2011)

    Bermbach, D., Tai, S.: Eventual consistency: How soon is eventual? an evaluation of amazon s3’s consistency be- havior. In: Workshop on Middleware for Service Oriented Computing (MW4SOC) (2011)

    work page 2011

  8. [8]

    SIGMOD Record54(1) (2025)

    Bernstein, P.A.: A technical perspective of CausalMesh. SIGMOD Record54(1) (2025)

    work page 2025

Show all 66 references
  1. [9]

    In: Proceedings of the ACM Symposium on Cloud Computing (SOCC) (2022)

    Bhasi, V.M., Gunasekaran, J.R., Sharma, A., Kandemir, M.T., Das, C.: Cypress: input size-sensitive container provisioning and request scheduling for serverless plat- forms. In: Proceedings of the ACM Symposium on Cloud Computing (SOCC) (2022)

    2022

  2. [10]

    Cloudlab.https://cloudlab.us

  3. [11]

    Proceedings of the International Conference on Very Large Data Bases (VLDB) (2008)

    Cooper, B.F., Ramakrishnan, R., Srivastava, U., Silber- stein, A., Bohannon, P., Jacobsen, H.A., Puz, N., Weaver, D., Yerneni, R.: Pnuts: Yahoo!’s hosted data serving plat- form. Proceedings of the International Conference on Very Large Data Bases (VLDB) (2008)

    2008

  4. [12]

    cosmosdb.https://azure.microsoft.com/en-us/prod ucts/cosmos-db/

  5. [13]

    couchbase.https://www.couchbase.com/

  6. [14]

    Dax and dynamodb consistency models.https://docs .aws.amazon.com/amazondynamodb/latest/developerg uide/DAX.consistency.html

  7. [15]

    DeathStarBench.https://github.com/delimitrou/De athStarBench/

  8. [16]

    Proceedings of the ACM Sympo- sium on Operating Systems Principles (SOSP) (2007)

    DeCandia, G., Hastorun, D., Jampani, M., Kakulap- ati, G., Lakshman, A., Pilchin, A., Sivasubramanian, S., Vosshall, P., Vogels, W.: Dynamo: Amazon’s highly avail- able key-value store. Proceedings of the ACM Sympo- sium on Operating Systems Principles (SOSP) (2007)

    2007

  9. [17]

    In: Proceedings of the ACM Symposium on Cloud Computing (SOCC) (2013)

    Du, J., Elnikety, S., Roy, A., Zwaenepoel, W.: Orbe: Scal- able causal consistency using dependency matrices and physical clocks. In: Proceedings of the ACM Symposium on Cloud Computing (SOCC) (2013)

    2013

  10. [18]

    In: Proceedings of the ACM Symposium on Cloud Computing (SOCC) (2014)

    Du, J., Iorgulescu, C., Roy, A., Zwaenepoel, W.: Gen- tlerain: Cheap and scalable causal consistency with phys- ical clocks. In: Proceedings of the ACM Symposium on Cloud Computing (SOCC) (2014)

    2014

  11. [19]

    evmap: A lock-free, eventually consistent, concurrent multi-value map.https://github.com/jonhoo/evmap

  12. [20]

    In: Proceedings of the International Conference on Architectural Support for Programming Languages and Operating Systems (ASPLOS) (2019)

    Gan, Y., Zhang, Y., Cheng, D., Shetty, A., Rathi, P., Katarki, N., Bruno, A., Hu, J., Ritchken, B., Jackson, B., Hu, K., Pancholi, M., He, Y., Clancy, B., Colen, C., Wen, F., Leung, C., Wang, S., Zaruvinsky, L., Es- pinosa, M., Lin, R., Liu, Z., Padilla, J., Delimitrou, C.: Ca...

    2019

  13. [21]

    grpc.https://grpc.io/

  14. [22]

    In: Proceedings of the ACM/IFIP/USENIX International Middleware Con- ference (Middleware) (2020)

    Gunasekaran, J.R., Thinakaran, P., Nachiappan, N.C., Kandemir, M.T., Das, C.R.: Fifer: Tackling resource un- derutilization in the serverless era. In: Proceedings of the ACM/IFIP/USENIX International Middleware Con- ference (Middleware) (2020)

    2020

  15. [23]

    In: SOSP (2015)

    Hawblitzel, C., Howell, J., Kapritsos, M., Lorch, J.R., Parno, B., Roberts, M.L., Setty, S.T.V., Zill, B.: Ironfleet: Proving practical distributed systems correct. In: SOSP (2015)

    2015

  16. [24]

    In: Con- ference on Innovative Data Systems Research (CIDR) (2019)

    Hellerstein, J.M., Faleiro, J., Gonzalez, J.E., Schleier- Smith, J., Sreekanti, V., Tumanov, A., Wu, C.: Serverless computing: One step forward, two steps back. In: Con- ference on Innovative Data Systems Research (CIDR) (2019)

    2019

  17. [25]

    In: Proceedings of the USENIX Annual Technical Conference (ATC) (2010)

    Hunt, P., Konar, M., Junqueira, F.P., Reed, B.: Zookeeper: Wait-free coordination for internet-scale sys- tems. In: Proceedings of the USENIX Annual Technical Conference (ATC) (2010)

    2010

  18. [26]

    In: Proceedings of the USENIX Annual Technical Conference (ATC) (2023)

    Huye, D., Shkuro, Y., Sambasivan, R.R.: Lifting the veil on meta’s microservice architecture: Analyses of topology and request workflows. In: Proceedings of the USENIX Annual Technical Conference (ATC) (2023)

    2023

  19. [27]

    In: Proceedings of the International Con- ference on Architectural Support for Programming Lan- guages and Operating Systems (ASPLOS) (2021)

    Jia, Z., Witchel, E.: Nightcore: efficient and scalable serverless computing for latency-sensitive, interactive mi- croservices. In: Proceedings of the International Con- ference on Architectural Support for Programming Lan- guages and Operating Systems (ASPLOS) (2021)

    2021

  20. [28]

    In: Pro- ceedings of the ACM Symposium on Cloud Computing (SOCC) (2019)

    Kaffes, K., Yadwadkar, N.J., Kozyrakis, C.: Centralized core-granular scheduling for serverless functions. In: Pro- ceedings of the ACM Symposium on Cloud Computing (SOCC) (2019)

    2019

  21. [29]

    In: Proceedings of the ACM Symposium on Cloud Computing (SOCC) (2022)

    Kaffes, K., Yadwadkar, N.J., Kozyrakis, C.: Hermod: principled and practical scheduling for serverless func- tions. In: Proceedings of the ACM Symposium on Cloud Computing (SOCC) (2022)

    2022

  22. [30]

    In: Proceedings of the USENIX Sym- posium on Operating Systems Design and Implementa- tion (OSDI) (2018)

    Klimovic, A., Wang, Y., Stuedi, P., Trivedi, A., Pfefferle, J., Kozyrakis, C.: Pocket: Elastic ephemeral storage for serverless analytics. In: Proceedings of the USENIX Sym- posium on Operating Systems Design and Implementa- tion (OSDI) (2018)

    2018

  23. [31]

    How long does A WS Lambda keep your idle functions around before a cold start?https://acloudguru.com/b log/engineering/how-long-does-aws-lambda-keep-y our-idle-functions-around-before-a-cold-start

  24. [32]

    ACM Trans

    Lamport, L.: The temporal logic of actions. ACM Trans. Program. Lang. Syst.16(3), 872–923 (1994)

    1994

  25. [33]

    Lamport, L.: Specifying Systems, The TLA+ Language and Tools for Hardware and Software Engineers (2002)

    2002

  26. [34]

    In: Communications of the ACM (2019)

    Lamport, L.: Time, clocks, and the ordering of events in a distributed system. In: Communications of the ACM (2019)

    2019

  27. [35]

    In: International conference on logic for programming artificial intelligence and reason- ing, pp

    Leino, K.R.M.: Dafny: An automatic program verifier for functional correctness. In: International conference on logic for programming artificial intelligence and reason- ing, pp. 348–370 (2010)

    2010

  28. [36]

    URLhttps://lampor t.azurewebsites.net/tla/current-tools.pdf

    Leslie: Tlc model checker (2021). URLhttps://lampor t.azurewebsites.net/tla/current-tools.pdf

    2021

  29. [37]

    In: Proceedings of the USENIX Symposium on Networked Systems Design and Imple- mentation (NSDI) (2023)

    Liu, D.H., Noghabi, S., Burckhardt, S., Levy, A.: Do- ing more with less: Orchestrating serverless applications without an orchestrator. In: Proceedings of the USENIX Symposium on Networked Systems Design and Imple- mentation (NSDI) (2023)

    2023

  30. [38]

    In: Proceedings of the ACM Symposium on Operating Systems Principles (SOSP) (2011)

    Lloyd, W., Freedman, M.J., Kaminsky, M., Andersen, D.G.: Don’t settle for eventual: Scalable causal consis- tency for wide-area storage with cops. In: Proceedings of the ACM Symposium on Operating Systems Principles (SOSP) (2011)

    2011

  31. [39]

    In: Proceedings of the USENIX Symposium on Networked Systems Design and Implementation (NSDI) (2013)

    Lloyd, W., Freedman, M.J., Kaminsky, M., Andersen, D.G.: Stronger semantics for low-latency geo-replicated storage. In: Proceedings of the USENIX Symposium on Networked Systems Design and Implementation (NSDI) (2013)

    2013

  32. [40]

    In: Pro- ceedings of the ACM Symposium on Cloud Computing (SOCC) (2021)

    Luo, S., Xu, H., Lu, C., Ye, K., Xu, G., Zhang, L., Ding, Y., He, J., Xu, C.: Characterizing microservice depen- dency and performance: Alibaba trace analysis. In: Pro- ceedings of the ACM Symposium on Cloud Computing (SOCC) (2021)

    2021

  33. [41]

    In: Proceedings of the ACM/IFIP/USENIX Inter- national Middleware Conference (Middleware) (2021)

    Lykhenko, T., Soares, R., Rodrigues, L.: Faastcc: efficient transactional causal consistency for serverless comput- ing. In: Proceedings of the ACM/IFIP/USENIX Inter- national Middleware Conference (Middleware) (2021)

    2021

  34. [42]

    Mahajan, P., Alvisi, L., Dahlin, M.: Consistency, avail- ability. Tech. rep., and convergence. Technical Report TR-11-22, Univ. Texas at Austin (2011)

    2011

  35. [43]

    In: Proceedings of the USENIX Symposium on Networked Systems Design and Implementation (NSDI) (2017)

    Mehdi, S.A., Littley, C., Crooks, N., Alvisi, L., Bron- son, N., Lloyd, W.: I can’t believe it’s not causal! scal- able causal consistency with no slowdown cascades. In: Proceedings of the USENIX Symposium on Networked Systems Design and Implementation (NSDI) (2017)

    2017

  36. [44]

    mongodb.https://www.mongodb.com/

  37. [45]

    In: Proceedings of the USENIX Annual Technical Conference (ATC) (2019)

    Pang, R., Caceres, R., Burrows, M., Chen, Z., Dave, P., Germer, N., Golynski, A., Graney, K., Kang, N., Kissner, L., et al.: Zanzibar: Google’s consistent, global autho- rization system. In: Proceedings of the USENIX Annual Technical Conference (ATC) (2019)

    2019

  38. [46]

    In: Proceedings of the ACM Symposium on Operating Systems Principles (SOSP) (1997)

    Petersen, K., Spreitzer, M.J., Terry, D.B., Theimer, M.M., Demers, A.J.: Flexible update propagation for weakly consistent replication. In: Proceedings of the ACM Symposium on Operating Systems Principles (SOSP) (1997)

    1997

  39. [47]

    In: Proceedings of the USENIX Symposium on Networked Systems Design and Implementation (NSDI) (2019)

    Pu, Q., Venkataraman, S., Stoica, I.: Shuffling, fast and slow: Scalable analytics on serverless infrastructure. In: Proceedings of the USENIX Symposium on Networked Systems Design and Implementation (NSDI) (2019)

    2019

  40. [48]

    redis.https://redis.io/

  41. [49]

    In: Proceedings of the ACM Sym- posium on Cloud Computing (SOCC) (2021)

    Romero, F., Chaudhry, G.I., Goiri, ´I., Gopa, P., Batum, P., Yadwadkar, N.J., Fonseca, R., Kozyrakis, C., Bian- chini, R.: Faa$t: A transparent auto-scaling cache for serverless applications. In: Proceedings of the ACM Sym- posium on Cloud Computing (SOCC) (2021)

    2021

  42. [50]

    Information Processing Letters (1992)

    Singhal, M., Kshemkalyani, A.: An efficient implemen- tation of vector clocks. Information Processing Letters (1992)

    1992

  43. [51]

    Proceedings of the Inter- national Conference on Very Large Data Bases (VLDB) (2020)

    Sreekanti, V., Wu, C., Lin, X.C., Schleier-Smith, J., Gon- zalez, J.E., Hellerstein, J.M., Tumanov, A.: Cloudburst: stateful functions-as-a-service. Proceedings of the Inter- national Conference on Very Large Data Bases (VLDB) (2020)

    2020

  44. [52]

    In: Proceedings of the USENIX Symposium on Operating Systems Design and Implementation (OSDI) (2020)

    Tang, C., Yu, K., Veeraraghavan, K., Kaldor, J., Michel- son, S., Kooburat, T., Anbudurai, A., Clark, M., Gogia, K., Cheng, L., et al.: Twine: A unified cluster manage- ment system for shared infrastructure. In: Proceedings of the USENIX Symposium on Operating Systems Design a...

    2020

  45. [53]

    clock and causal consistency in mongodb

    Tyulenev, M., Schwerin, A., Kamsky, A., Tan, R., Cabral, A., Mulrow, J.: Implementation of cluster-wide logical 26 Haoran Zhang et al. clock and causal consistency in mongodb. In: Proceedings of the ACM SIGMOD Conference (SIGMOD) (2019)

    2019

  46. [54]

    In: Proceed- ings of the USENIX Symposium on Operating Systems Design and Implementation (OSDI) (2004)

    Van Renesse, R., Schneider, F.B.: Chain replication for supporting high throughput and availability. In: Proceed- ings of the USENIX Symposium on Operating Systems Design and Implementation (OSDI) (2004)

    2004

  47. [55]

    In: Proceedings of the USENIX Conference on File and Storage Technologies (F AST) (2020)

    Wang, A., Zhang, J., Ma, X., Anwar, A., Rupprecht, L., Skourtis, D., Tarasov, V., Yan, F., Cheng, Y.: Infini- cache: Exploiting ephemeral serverless functions to build a cost-effective memory cache. In: Proceedings of the USENIX Conference on File and Storage Technologies (F A...

    2020

  48. [56]

    wrk2: A constant throughput, correct latency recording variant of wrk.https://github.com/giltene/wrk2

  49. [57]

    IEEE Transactions on Knowledge and Data Engineering (TKDE) (2019)

    Wu, C., Faleiro, J.M., Lin, Y., Hellerstein, J.M.: Anna: A kvs for any scale. IEEE Transactions on Knowledge and Data Engineering (TKDE) (2019)

    2019

  50. [58]

    Proceedings of the Inter- national Conference on Very Large Data Bases (VLDB) (2019)

    Wu, C., Sreekanti, V., Hellerstein, J.M.: Autoscaling tiered cloud storage in anna. Proceedings of the Inter- national Conference on Very Large Data Bases (VLDB) (2019)

    2019

  51. [59]

    In: Proceed- ings of the ACM SIGMOD Conference (SIGMOD) (2020)

    Wu, C., Sreekanti, V., Hellerstein, J.M.: Transactional causal consistency for serverless computing. In: Proceed- ings of the ACM SIGMOD Conference (SIGMOD) (2020)

    2020

  52. [60]

    In: Proceedings of the USENIX Symposium on Operating Systems Design and Implementation (OSDI) (2000)

    Yu, H.: Design and evaluation of a continuous consis- tency model for replicated services. In: Proceedings of the USENIX Symposium on Operating Systems Design and Implementation (OSDI) (2000)

    2000

  53. [61]

    arXiv:2109.13492 (2021).https://arxiv.org/ abs/2109.13492

    Yu, M., Cao, T., Wang, W., Chen, R.: Restructuring serverless computing with data-centric function orches- tration. arXiv:2109.13492 (2021).https://arxiv.org/ abs/2109.13492

    2021 arXiv

  54. [62]

    In: Proceedings of the ACM/IFIP/USENIX International Middleware Con- ference (Middleware) (2015)

    Zawirski, M., Pregui¸ ca, N., Duarte, S., Bieniusa, A., Bale- gas, V., Shapiro, M.: Write fast, read in the past: Causal consistency for client-side applications. In: Proceedings of the ACM/IFIP/USENIX International Middleware Con- ference (Middleware) (2015)

    2015

  55. [63]

    In: Proceedings of the USENIX Symposium on Operating Systems Design and Implementation (OSDI) (2020)

    Zhang, H., Cardoza, A., Chen, P.B., Angel, S., Liu, V.: Fault-tolerant and transactional stateful serverless work- flows. In: Proceedings of the USENIX Symposium on Operating Systems Design and Implementation (OSDI) (2020)

    2020

  56. [64]

    The Pro- ceedings of the VLDB Endowment (PVLDB)17(13) (2024)

    Zhang, H., Mu, S., Angel, S., Liu, V.: Causalmesh: A causal cache for stateful serverless computing. The Pro- ceedings of the VLDB Endowment (PVLDB)17(13) (2024)

    2024

  57. [65]

    In: Proceedings of the USENIX Symposium on Networked Systems Design and Implementation (NSDI) (2021)

    Zhang, H., Tang, Y., Khandelwal, A., Chen, J., Stoica, I.: Caerus: Nimble task scheduling for serverless ana- lytics. In: Proceedings of the USENIX Symposium on Networked Systems Design and Implementation (NSDI) (2021)

    2021

  58. [66]

    In: SOSP (2025)

    Zhang, Z., Zhou, T., Jenkins, C., Chowdhury, O., Mu, S.: AutoMan: Facilitating verified distributed systems devel- opment through automatic code generation and manual optimizations. In: SOSP (2025)

    2025

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