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REVIEW 2 major objections 6 minor 19 cited by

Infrastructure for AI Agents

T0 review · 2 major / 6 minor · reviewed 2026-08-10 · deepseek-v4-flash

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Pith's one-line read External protocols and identity systems, not just model training, will be what makes AI agents safe and useful, this paper argues.

desk verdict Useful conceptual synthesis and research agenda for external agent infrastructure, with an abstract that overstates the inevitability of adoption; the framework survives the overreach. read the letter →

arxiv 2501.10114 v3 pith:J6DEHZ66 submitted 2025-01-17 cs.AI

Alan Chan , Kevin Wei , Sihao Huang , Nitarshan Rajkumar , Elija Perrier , Seth Lazar , Gillian K. Hadfield , Markus Anderljung This is my paper · ORCID
classification cs.AI
keywords AIagentsagentinfrastructureidentitybindingcertificationinter-agentcommunicationrollbackmechanismsgovernanceprotocols
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 tries to establish that safe, useful AI agents will require external infrastructure, not just better models. It defines agent infrastructure as technical systems and shared protocols outside agents that shape how agents interact with the world, and argues these systems will be as central to agent ecosystems as HTTPS and TCP are to the Internet. The paper identifies three functions for such infrastructure—attribution, interaction, and response—and catalogs concrete research directions for each, from identity binding and agent IDs to communication protocols and rollback tools. A sympathetic reader would care because the claim redirects attention from model-level training alone toward the protocols, standards, and institutions that surround agents.

What carries the argument

The central object is the concept of agent infrastructure itself, defined as technical systems and shared protocols external to agents that are designed to mediate and influence agents' interactions with and impacts on their environments. The paper's analytic machinery is a three-function taxonomy—attribution, interaction, response—paired with an analogy: just as HTTPS, TCP, and BGP enable the Internet, external protocols and systems will enable agent ecosystems. A companion unit is the agent instance, an instantiation of a model with a user, interaction history, and tools, which gives IDs and certification something to attach to. The taxonomy does the work of turning a broad intuition into eight concrete research directions, each with a use case, an adoption path, and stated limitations.

What would settle it

A concrete falsifier: if a large ecosystem of heterogeneous, independently deployed agents provably coordinates and stays safe without any shared identity, communication, or rollback protocol—relying only on model-level training—then the paper's central claim fails. A more modest check: after several years, if no inter-agent communication protocol has reached meaningful adoption across independent developers while agent use grows, the 'indispensable' claim is undercut.

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Extended reading notes

Core claim

The paper's central claim is that making AI agents useful and safe will require more than directly training or prompting the models: it will require agent infrastructure—technical systems and shared protocols external to agents that mediate and influence how agents interact with their environments. The paper argues this infrastructure will be as indispensable to ecosystems of agents as HTTPS and TCP are to the Internet, and organizes it under three functions: attribution, which binds actions and properties to agents or legal actors; interaction, which shapes how agents encounter services and one another; and response, which detects and remedies harm. It then catalogs eight research directions, from identity binding, certification, and agent IDs to agent channels, oversight layers, inter-agent communication, commitment devices, incident reporting, and rollbacks. Alongside each direction the paper analyzes use cases, adoption dynamics, limitations, and open questions, and it takes no stance on which pieces should be prioritized.

Load-bearing premise

The argument rests on enough independent parties choosing to adopt the same external identity, communication, and response systems; the paper itself notes that without such coordination, individual tools remain useful but the 'indispensable' claim does not follow.

Editorial extensions

If this is right

  • If agent infrastructure is indispensable, safety work on agents must include protocols and systems that surround agents, not just training, fine-tuning, and prompting.
  • Attribution tools such as identity binding and agent IDs would give counterparties a way to seek recourse, which could make agents more widely trusted in commerce and services.
  • Agent channels and oversight layers would give operators a way to contain incidents, for example by suspending agent traffic during a worm outbreak.
  • Because communication protocols and IDs depend on network effects, early choices by large platforms could lock in standards that are hard to revise, as happened with BGP.
  • Governments and standards bodies would need to participate early if agent infrastructure is to be interoperable and updatable.

Reading between the lines

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

  • Beyond the paper, the taxonomy suggests a market-failure prediction: because most agent infrastructure is a coordination good, purely private provision will likely underproduce it, and agent ecosystems may fragment into incompatible standards unless a public body or a dominant platform coordinates.
  • A testable extension would compare agent marketplaces that require identity binding or IDs against those that do not, measuring rates of fraud, spam, and contested transactions.
  • The paper's rollback and oversight ideas imply a natural experiment: platforms offering reversible agent transactions versus irreversible ones, and whether reversibility changes user willingness to delegate consequential actions.
  • If private actors build the infrastructure, design choices around identity could concentrate power in identity providers; the paper notes the privacy risks but does not develop this political-economy implication.
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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

2 major / 6 minor

Summary. This paper proposes a new concept, 'agent infrastructure': technical systems and shared protocols external to AI agents that mediate and influence agents' interactions with their environments. It argues that such infrastructure will be as indispensable to future agent ecosystems as protocols like HTTPS and TCP are to the Internet, and it identifies three functions: attribution, interaction, and response. The paper catalogs nine research directions under these functions (identity binding, certification, agent IDs, agent channels, oversight layers, inter-agent communication, commitment devices, incident reporting, and rollbacks), each with use cases, adoption considerations, limitations, and open questions. It also discusses cross-cutting challenges including adoption dynamics, lack of interoperability, and lock-in. The paper is a position paper; it contains no new empirical data or formal derivation.

Significance. The paper's main contribution is a useful synthesis: it gathers diverse proposals for agent governance and safety into a single taxonomy and connects them to institutional and adoption concerns. If agent ecosystems become open and multi-party, this framework could genuinely structure both research and policy, and Table 1 provides a clear starting point for that agenda. The paper is careful to hedge within individual sections and to acknowledge that the catalog is incomplete, which is appropriate for a position paper. However, the central 'indispensable' claim is a forward-looking conjecture that rests on assumptions about open, decentralized agent ecosystems that are not defended; the paper's own adoption analysis highlights how fragile such assumptions are. The most defensible version of the claim is conditional: if agents operate across many organizations and actors, then external infrastructure will be needed for accountability, coordination, and incident response. As a piece of agenda-setting conceptual work, the paper is strong; as a proof of inevitability, it is not.

major comments (2)
  1. [Abstract; Section 1; Section 6] The central claim that agent infrastructure 'will be similarly indispensable' is stronger than the evidence presented in the paper. In Section 1 the authors say only that infrastructure 'will likely be crucial,' and Sections 6.1-6.3 document network effects, lack of interoperability, and lock-in without proposing a concrete governance or incentive mechanism that would overcome the collective-action problem. The claim also presupposes an open, multi-party agent ecosystem; if agents remain mostly on vertically integrated platforms (as current deployments from major vendors suggest), a single provider could implement the Table 1 functions internally and shared external protocols would be optional. The paper should either weaken the abstract to a conditional claim or supply a substantive argument and evidence that open, multi-party adoption will actually occur; its own BGP/RPKI example in Section 6.3 shows that even security-critical protocols can remain partially adopted for decades.
  2. [Section 2.1; Section 4.2; Section 5.2] The definition of agent infrastructure as 'external to agents' and explicitly 'not system-level interventions' is not applied consistently. Oversight layers (Section 4.2) are described as a monitoring system plus an interface for intervention, which could be embedded in the agent's own control loop; rollbacks (Section 5.2) are illustrated with Patil et al.'s LM runtime, which operates on the agent's internal state and could reverse the agent's actions. If these count as infrastructure, then 'external' cannot mean outside the agent's software; if they do not count, then Table 1 includes non-infrastructure items. The authors should clarify whether 'external' is defined relative to the model weights, relative to the agent's scaffolding/runtime, or relative to the agent's decision-making process, and adjust the examples and the definition accordingly.
minor comments (6)
  1. [Section 4.1] The sentence 'agents could soon become capable of interacting with with human interfaces' contains a duplicated 'with' and should be corrected.
  2. [Section 4.3] 'Google is collaborating with the number of large enterprises' should read 'with a number of large enterprises'.
  3. [Section 6.3] The statement 'Adoption of the Border Gateway Protocol (BGP) ... ran into similar problems' is imprecise: BGP itself is almost universally deployed, whereas the slow-adoption example is RPKI, the verifiable variant. Please rephrase to avoid conflating the protocol with its security extension.
  4. [Section 3.2] The name 'TrustARC (formerly TRUSTe)' appears as 'TrustARC' throughout the discussion; this should be consistent, and 'Analagously' should be 'Analogously'.
  5. [Section 5.1] The phrase 'existing incident report systems in other countries' appears to be a leftover or misphrasing; the surrounding discussion is about different domains and reporting mechanisms, not countries.
  6. [Abstract; Section 1] The abstract's 'similarly indispensable' is stronger than the body's 'will likely be crucial'; unless the claim is defended, these should be harmonized.

Circularity Check

0 steps flagged · score 0.0 of 10

No circularity found: the paper is a conceptual taxonomy; its central claim is an analogical conjecture and the same-author citations are background, not load-bearing derivations.

full rationale

This paper is a conceptual/taxonomic proposal, not a derivation. It defines 'agent infrastructure' and organizes research directions under three functions; there are no equations, fitted parameters, or empirical predictions that could reduce to inputs. The strongest claim—that agent infrastructure will be 'similarly indispensable' to agent ecosystems as internet protocols—is an analogical conjecture supported by examples and adoption analysis, not a result derived from the definition. The same-author citations (e.g., Chan et al. 2024b for agent IDs, Hammond et al. 2025 for threat-model priorities, Perrier and Lazar 2025 for agent ontologies) point to prior conceptual work and do not function as premises whose conclusion is already assumed; the taxonomy stands or falls on its usefulness rather than on those citations. The paper's own acknowledgments of adoption, interoperability, and lock-in challenges (Sections 6.1-6.3) are substantive caveats to the 'indispensable' conjecture, but they are not circularity. No uniqueness theorem or formal result is imported from the authors' prior work. I therefore find no circular step and assign score 0.

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

This is a conceptual paper; the 'free parameters' and 'invented entities' categories do not apply. The listed axioms are the load-bearing premises the paper states or assumes.

assumptions (3)
  • domain assumption System-level interventions are insufficient to ensure beneficial adoption of agents and to mitigate their risks.
    Motivates the whole paper in Section 1 ('system-level interventions ... may not be sufficient').
  • domain assumption External infrastructure can meaningfully shape agent behavior and interactions.
    The central premise; argued by analogy to traffic infrastructure and Internet protocols in Sections 1 and 2.
  • domain assumption Relevant actors will adopt agent infrastructure at scale.
    Needed for network-effect infrastructure to work; Section 6.1 explicitly flags adoption as a common challenge.

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Cite this review

Pith. "Pith review of Infrastructure for AI Agents." pith.science (2026). https://pith.science/paper/J6DEHZ66

@misc{pith2026250110114,
  author       = {Pith},
  title        = {Pith review of: Infrastructure for AI Agents},
  year         = {2026},
  howpublished = {\url{https://pith.science/paper/J6DEHZ66}},
  note         = {Machine review of arXiv:2501.10114}
}
read the original abstract

AI agents plan and execute interactions in open-ended environments. For example, OpenAI's Operator can use a web browser to do product comparisons and buy online goods. Much research on making agents useful and safe focuses on directly modifying their behaviour, such as by training them to follow user instructions. Direct behavioural modifications are useful, but do not fully address how heterogeneous agents will interact with each other and other actors. Rather, we will need external protocols and systems to shape such interactions. For instance, agents will need more efficient protocols to communicate with each other and form agreements. Attributing an agent's actions to a particular human or other legal entity can help to establish trust, and also disincentivize misuse. Given this motivation, we propose the concept of \textbf{agent infrastructure}: technical systems and shared protocols external to agents that are designed to mediate and influence their interactions with and impacts on their environments. Just as the Internet relies on protocols like HTTPS, our work argues that agent infrastructure will be similarly indispensable to ecosystems of agents. We identify three functions for agent infrastructure: 1) attributing actions, properties, and other information to specific agents, their users, or other actors; 2) shaping agents' interactions; and 3) detecting and remedying harmful actions from agents. We provide an incomplete catalog of research directions for such functions. For each direction, we include analysis of use cases, infrastructure adoption, relationships to existing (internet) infrastructure, limitations, and open questions. Making progress on agent infrastructure can prepare society for the adoption of more advanced agents.

Figures

Figures reproduced from arXiv: 2501.10114 by the authors.

Figure 1
Figure 1. Agent infrastructure consists of technical systems and shared protocols external to agents that are designed to mediate and influence their interactions with and impacts on their environments, including interactions with existing institutions (e.g., legal and economic systems) and actors (e.g., digital service providers, humans, other AI agents). More general-purpose agents could automate a wide range of both benefi… view at source ↗
Figure 2
Figure 2. Identity binding links an agent or its actions to an existing legal identity, certification would provide assurance about behaviour and properties of an agent instance, and an agent IDs would be a container of information about an agent instance. We illustrate identity binding for a user, but an agent could also be bound to other actors, such as those involved in overseeing it 3.1 Identity Binding Description: AI ag… view at source ↗
Figure 3
Figure 3. Agent channels separate agent traffic from other digital traffic. Oversight layers enable actors to intervene on an agent’s behaviour. Inter-agent communication help to facilitate joint activities amongst groups of agents. Commitment devices enforce commitments between agents. See Section 4 for discussion. • How can counterparties incentivize or enforce ID usage (e.g., by making sure that agents use unique channels … view at source ↗
Figures from the paper (1 more)
Figure 4
Figure 4. Figure 4: Incident reporting systems would collect information about and respond to events that could result in harm, while Rollbacks are tools that would void or undo an agent’s actions. See Section 5 for further discussion. 5 Response Response infrastructure consists of tools …

Discussion (0). Continue with ORCID to comment.

Forward citations

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Reference graph

Works this paper leans on

169 extracted references · 77 canonical work pages · cited by 19 Pith papers

  1. [1]

    Rogers Communications Inc . v. Voltage Pictures , September 2018

    2018

  2. [2]

    15 U . S . Code § 1666 - Correction of billing errors, 2023

    2023

  3. [3]

    Regulation ( EU ) 2024/1689 of the European Parliament and of the Council of 13 June 2024 laying down harmonised rules on artificial intelligence and amending Regulations ( EC ) No 300/2008, ( EU ) No 167/2013, ( EU ) No 168/2013, ( EU ) 2018/858, ( EU ) 2018/1139 and ( EU ) 2019/2144 and Directives 2014/90/ EU , ( EU ) 2016/797 and ( EU ) 2020/1828 ( Art...

    2024

  4. [4]

    Leibowicz, Srikanth Nadhamuni, Alan Z

    Steven Adler, Zoë Hitzig, Shrey Jain, Catherine Brewer, Wayne Chang, Renée DiResta, Eddy Lazzarin, Sean McGregor, Wendy Seltzer, Divya Siddarth, Nouran Soliman, Tobin South, Connor Spelliscy, Manu Sporny, Varya Srivastava, John Bailey, Brian Christian, Andrew Critch, Ronnie Falcon, Heather Flanagan, Kim Hamilton Duffy, Eric Ho, Claire R. Leibowicz, Srikan...

    2024

  5. [5]

    Anthony Aguirre, Gaia Dempsey, Harry Surden, and Peter B. Reiner. AI loyalty: A New Paradigm for Aligning Stakeholder Interests , March 2020

    2020

  6. [6]

    AI Vulnerability Database , 2024

    AI Risk and Vulnerability Alliance . AI Vulnerability Database , 2024

    2024

  7. [7]

    AI , algorithmic, and automation incidents, 2024

    AIAAIC . AI , algorithmic, and automation incidents, 2024

    2024

  8. [8]

    Bowman, Ethan Perez, Roger Baker Grosse, and David Duvenaud

    Cem Anil, Esin Durmus, Nina Rimsky, Mrinank Sharma, Joe Benton, Sandipan Kundu, Joshua Batson, Meg Tong, Jesse Mu, Daniel J Ford, Francesco Mosconi, Rajashree Agrawal, Rylan Schaeffer, Naomi Bashkansky, Samuel Svenningsen, Mike Lambert, Ansh Radhakrishnan, Carson Denison, Evan J Hubinger, Yuntao Bai, Trenton Bricken, Timothy Maxwell, Nicholas Schiefer, Ja...

    2024

Show all 169 references
  1. [9]

    Expanding our model safety bug bounty program, August 2024 a

    Anthropic . Expanding our model safety bug bounty program, August 2024 a

    2024

  2. [10]

    Model Context Protocol , 2024 b

    Anthropic . Model Context Protocol , 2024 b

    2024

  3. [11]

    Responsible Scaling Policy

    Anthropic . Responsible Scaling Policy . Technical report, October 2024 c

    2024

  4. [12]

    The Role of Cooperation in Responsible AI Development

    Amanda Askell, Miles Brundage, and Gillian Hadfield. The Role of Cooperation in Responsible AI Development . July 2019

    2019

  5. [13]

    AI Safety Summit - Enhancing Frontier AI Safety , 2023

    AWS. AI Safety Summit - Enhancing Frontier AI Safety , 2023

    2023

  6. [14]

    Agent57: Outperforming the Atari Human Benchmark , March 2020

    Adrià Puigdomènech Badia, Bilal Piot, Steven Kapturowski, Pablo Sprechmann, Alex Vitvitskyi, Daniel Guo, and Charles Blundell. Agent57: Outperforming the Atari Human Benchmark , March 2020

    2020

  7. [15]

    Mark Bagnoli and Barton L. Lipman. Provision of Public Goods : Fully Implementing the Core through Private Contributions . The Review of Economic Studies, 56 0 (4), October 1989. ISSN 0034-6527

    1989

  8. [16]

    Bowman, Zac Hatfield-Dodds, Ben Mann, Dario Amodei, Nicholas Joseph, Sam McCandlish, Tom Brown, and Jared Kaplan

    Yuntao Bai, Saurav Kadavath, Sandipan Kundu, Amanda Askell, Jackson Kernion, Andy Jones, Anna Chen, Anna Goldie, Azalia Mirhoseini, Cameron McKinnon, Carol Chen, Catherine Olsson, Christopher Olah, Danny Hernandez, Dawn Drain, Deep Ganguli, Dustin Li, Eli Tran-Johnson, Ethan P...

    2022

  9. [17]

    Guan, Aleksander Madry, Wojciech Zaremba, Jakub Pachocki, and David Farhi

    Bowen Baker, Joost Huizinga, Leo Gao, Zehao Dou, Melody Y. Guan, Aleksander Madry, Wojciech Zaremba, Jakub Pachocki, and David Farhi. Monitoring Reasoning Models for Misbehavior and the Risks of Promoting Obfuscation , March 2025

    2025

  10. [18]

    Compromising Google Accounts : Malwares Exploiting Undocumented OAuth2 Functionality for session hijacking, 2024

    Anirudh Batra, Sparsh Kulshrestha, and Abhishek Mathew. Compromising Google Accounts : Malwares Exploiting Undocumented OAuth2 Functionality for session hijacking, 2024

    2024

  11. [19]

    Designing Fiduciary Artificial Intelligence , July 2023

    Sebastian Benthall and David Shekman. Designing Fiduciary Artificial Intelligence , July 2023

    2023

  12. [20]

    Berkowsky and Thaier Hayajneh

    Jake A. Berkowsky and Thaier Hayajneh. Security issues with certificate authorities. In 2017 IEEE 8th Annual Ubiquitous Computing , Electronics and Mobile Communication Conference ( UEMCON ) , October 2017

    2017

  13. [21]

    Societal Adaptation to Advanced AI , May 2024

    Jamie Bernardi, Gabriel Mukobi, Hilary Greaves, Lennart Heim, and Markus Anderljung. Societal Adaptation to Advanced AI , May 2024

    2024

  14. [22]

    Purple Llama CyberSecEval : A Secure Coding Benchmark for Language Models , December 2023

    Manish Bhatt, Sahana Chennabasappa, Cyrus Nikolaidis, Shengye Wan, Ivan Evtimov, Dominik Gabi, Daniel Song, Faizan Ahmad, Cornelius Aschermann, Lorenzo Fontana, Sasha Frolov, Ravi Prakash Giri, Dhaval Kapil, Yiannis Kozyrakis, David LeBlanc, James Milazzo, Aleksandar Straumann...

    2023

  15. [23]

    Project Zero : From Naptime to Big Sleep : Using Large Language Models To Catch Vulnerabilities In Real - World Code , November 2024

    Big Sleep Team . Project Zero : From Naptime to Big Sleep : Using Large Language Models To Catch Vulnerabilities In Real - World Code , November 2024

    2024

  16. [24]

    C2PA Technical Specification , 2023

    C2PA. C2PA Technical Specification , 2023

    2023

  17. [25]

    Harms from Increasingly Agentic Algorithmic Systems

    Alan Chan, Rebecca Salganik, Alva Markelius, Chris Pang, Nitarshan Rajkumar, Dmitrii Krasheninnikov, Lauro Langosco, Zhonghao He, Yawen Duan, Micah Carroll, Michelle Lin, Alex Mayhew, Katherine Collins, Maryam Molamohammadi, John Burden, Wanru Zhao, Shalaleh Rismani, Konstanti...

    2023

  18. [26]

    Visibility into AI Agents

    Alan Chan, Carson Ezell, Max Kaufmann, Kevin Wei, Lewis Hammond, Herbie Bradley, Emma Bluemke, Nitarshan Rajkumar, David Krueger, Noam Kolt, Lennart Heim, and Markus Anderljung. Visibility into AI Agents . In Proceedings of the 2024 ACM Conference on Fairness , Accountability ...

    2024

  19. [27]

    IDs for AI Systems , June 2024 b

    Alan Chan, Noam Kolt, Peter Wills, Usman Anwar, Christian Schroeder de Witt, Nitarshan Rajkumar, Lewis Hammond, David Krueger, Lennart Heim, and Markus Anderljung. IDs for AI Systems , June 2024 b

    2024

  20. [28]

    LangChain 0.0.77 Docs , 2022

    Harrison Chase. LangChain 0.0.77 Docs , 2022

    2022

  21. [29]

    Finance worker pays out \ 25 million after video call with deepfake ‘chief financial officer’

    Heather Chen and Kathleen Magramo. Finance worker pays out \ 25 million after video call with deepfake ‘chief financial officer’. CNN, February 2024

    2024

  22. [30]

    Bryan H. Choi. The Anonymous Internet . Maryland Law Review, 72, 2012

    2012

  23. [31]

    Jimenez, John Yang, Kevin Liu, and Aleksander Madry

    Neil Chowdhury, James Aung, Chan Jun Shern, Oliver Jaffe, Dane Sherburn, Giulio Starace, Evan Mays, Rachel Dias, Marwan Aljubeh, Mia Glaese, Carlos E. Jimenez, John Yang, Kevin Liu, and Aleksander Madry. Introducing SWE -bench verified, 2024

    2024

  24. [32]

    Data, privacy, and security for Azure OpenAI Service - Azure AI services, June 2023

    ChrisHMSFT, PatrickFarley, mrbullwinkle, eric urban, and aahill. Data, privacy, and security for Azure OpenAI Service - Azure AI services, June 2023

    2023

  25. [33]

    Deep Reinforcement Learning from Human Preferences

    Paul F Christiano, Jan Leike, Tom Brown, Miljan Martic, Shane Legg, and Dario Amodei. Deep Reinforcement Learning from Human Preferences . In Advances in Neural Information Processing Systems , volume 30. Curran Associates, Inc., 2017

    2017

  26. [34]

    When would AGIs engage in conflict?, October 2022

    Jesse Clifton, Samuel Martin, and Anthony DiGiovanni. When would AGIs engage in conflict?, October 2022

    2022

  27. [35]

    Generative AI , Privacy , and Google Cloud

    Google Cloud. Generative AI , Privacy , and Google Cloud . Technical report, 2023

    2023

  28. [36]

    Here Comes The AI Worm : Unleashing Zero -click Worms that Target GenAI - Powered Applications , March 2024

    Stav Cohen, Ron Bitton, and Ben Nassi. Here Comes The AI Worm : Unleashing Zero -click Worms that Target GenAI - Powered Applications , March 2024

    2024

  29. [37]

    Privacy Self - Regulation in Crisis ? – TRUSTe 's ‘ Deceptive ’ Practices , December 2014

    Chris Connolly, Graham Greenleaf, and Nigel Waters. Privacy Self - Regulation in Crisis ? – TRUSTe 's ‘ Deceptive ’ Practices , December 2014

    2014

  30. [38]

    Who Audits the Auditors ? Recommendations from a field scan of the algorithmic auditing ecosystem

    Sasha Costanza-Chock, Inioluwa Deborah Raji, and Joy Buolamwini. Who Audits the Auditors ? Recommendations from a field scan of the algorithmic auditing ecosystem. In Proceedings of the 2022 ACM Conference on Fairness , Accountability , and Transparency , FAccT '22, New York, ...

    2022

  31. [39]

    Information Supplement : Guidance for PCI DSS Scoping and Network Segmentation

    PCI Security Standards Council. Information Supplement : Guidance for PCI DSS Scoping and Network Segmentation . Technical report, December 2016

    2016

  32. [40]

    Cooperative AI : machines must learn to find common ground

    Allan Dafoe, Yoram Bachrach, Gillian Hadfield, Eric Horvitz, Kate Larson, and Thore Graepel. Cooperative AI : machines must learn to find common ground. Nature, 593 0 (7857), 2021

    2021

  33. [41]

    Towards Guaranteed Safe AI : A Framework for Ensuring Robust and Reliable AI Systems , May 2024

    David "davidad" Dalrymple, Joar Skalse, Yoshua Bengio, Stuart Russell, Max Tegmark, Sanjit Seshia, Steve Omohundro, Christian Szegedy, Ben Goldhaber, Nora Ammann, Alessandro Abate, Joe Halpern, Clark Barrett, Ding Zhao, Tan Zhi-Xuan, Jeannette Wing, and Joshua Tenenbaum. Towar...

    2024

  34. [42]

    Frontier Safety Framework

    DeepMind . Frontier Safety Framework . Technical report, May 2024

    2024

  35. [43]

    agent.ai The Professional Network for AI Agents , 2024

    Dharmesh . agent.ai The Professional Network for AI Agents , 2024

    2024

  36. [44]

    Network segmentation - An introduction for health and care organisations, November 2023

    NHS England Digital. Network segmentation - An introduction for health and care organisations, November 2023

    2023

  37. [45]

    Dasher Identity Verification FAQ , 2025

    DoorDash . Dasher Identity Verification FAQ , 2025

    2025

  38. [46]

    Failures of public key infrastructure: 53 year survey, January 2024

    Adrian-Tudor Dumitrescu and Johan Pouwelse. Failures of public key infrastructure: 53 year survey, January 2024

    2024

  39. [47]

    RFC 9293: Transmission control protocol (tcp), 2022

    W Eddy. RFC 9293: Transmission control protocol (tcp), 2022

    2022

  40. [48]

    Adverse selection in online "trust" certifications

    Benjamin Edelman. Adverse selection in online "trust" certifications. In Proceedings of the 11th International Conference on Electronic Commerce , ICEC '09, New York, NY, USA, August 2009. Association for Computing Machinery

    2009

  41. [49]

    Unmasking Jane and John Doe : Online Anonymity and the First Amendment

    Victoria Smith Ekstrand. Unmasking Jane and John Doe : Online Anonymity and the First Amendment . Communication Law and Policy, 8 0 (4), October 2003. ISSN 1081-1680

    2003

  42. [50]

    LLM Agents can Autonomously Exploit One -day Vulnerabilities , April 2024 a

    Richard Fang, Rohan Bindu, Akul Gupta, and Daniel Kang. LLM Agents can Autonomously Exploit One -day Vulnerabilities , April 2024 a

    2024

  43. [51]

    Voice- Enabled AI Agents can Perform Common Scams , October 2024 b

    Richard Fang, Dylan Bowman, and Daniel Kang. Voice- Enabled AI Agents can Perform Common Scams , October 2024 b

    2024

  44. [52]

    James D. Fearon. Rationalist Explanations for War . International Organization, 49 0 (3), 1995. ISSN 0020-8183

    1995

  45. [53]

    RFC 9110: HTTP semantics, 2022

    R Fielding, M Nottingham, and J Reschke. RFC 9110: HTTP semantics, 2022

    2022

  46. [54]

    Gonczarowski, and Ran I

    Sara Fish, Yannai A. Gonczarowski, and Ran I. Shorrer. Algorithmic Collusion by Large Language Models , March 2024

    2024

  47. [55]

    Exposing Limitations of Language Model Agents in Sequential - Task Compositions on the Web , February 2024

    Hiroki Furuta, Yutaka Matsuo, Aleksandra Faust, and Izzeddin Gur. Exposing Limitations of Language Model Agents in Sequential - Task Compositions on the Web , February 2024

    2024

  48. [56]

    Iason Gabriel, Arianna Manzini, Geoff Keeling, Lisa Anne Hendricks, Verena Rieser, Hasan Iqbal, Nenad Tomašev, Ira Ktena, Zachary Kenton, Mikel Rodriguez, Seliem El-Sayed, Sasha Brown, Canfer Akbulut, Andrew Trask, Edward Hughes, A. Stevie Bergman, Renee Shelby, Nahema Marchal...

    2024

  49. [57]

    Automation bias: a systematic review of frequency, effect mediators, and mitigators

    Kate Goddard, Abdul Roudsari, and Jeremy C Wyatt. Automation bias: a systematic review of frequency, effect mediators, and mitigators. Journal of the American Medical Informatics Association, 19 0 (1), January 2012. ISSN 1067-5027

    2012

  50. [58]

    Where to Report a Cyber Incident , May 2022

    Government of the United Kingdom . Where to Report a Cyber Incident , May 2022

    2022

  51. [59]

    Reporting vehicle and driving offences, 2024

    Government of the United Kingdom . Reporting vehicle and driving offences, 2024

    2024

  52. [60]

    The flaws of policies requiring human oversight of government algorithms

    Ben Green. The flaws of policies requiring human oversight of government algorithms. Computer Law & Security Review, 45, July 2022. ISSN 0267-3649

    2022

  53. [61]

    AI Control : Improving Safety Despite Intentional Subversion , January 2024

    Ryan Greenblatt, Buck Shlegeris, Kshitij Sachan, and Fabien Roger. AI Control : Improving Safety Despite Intentional Subversion , January 2024

    2024

  54. [62]

    Not what you've signed up for: Compromising Real - World LLM - Integrated Applications with Indirect Prompt Injection , May 2023

    Kai Greshake, Sahar Abdelnabi, Shailesh Mishra, Christoph Endres, Thorsten Holz, and Mario Fritz. Not what you've signed up for: Compromising Real - World LLM - Integrated Applications with Indirect Prompt Injection , May 2023

    2023

  55. [63]

    Agent smith: A single image can jailbreak one million multimodal llm agents exponentially fast

    Xiangming Gu, Xiaosen Zheng, Tianyu Pang, Chao Du, Qian Liu, Ye Wang, Jing Jiang, and Min Lin. Agent smith: A single image can jailbreak one million multimodal llm agents exponentially fast. arXiv preprint arXiv:2402.08567, 2024

    2024 arXiv

  56. [64]

    A Real - World WebAgent with Planning , Long Context Understanding , and Program Synthesis , February 2024

    Izzeddin Gur, Hiroki Furuta, Austin Huang, Mustafa Safdari, Yutaka Matsuo, Douglas Eck, and Aleksandra Faust. A Real - World WebAgent with Planning , Long Context Understanding , and Program Synthesis , February 2024

    2024

  57. [65]

    Cooperative Inverse Reinforcement Learning

    Dylan Hadfield-Menell, Stuart J Russell, Pieter Abbeel, and Anca Dragan. Cooperative Inverse Reinforcement Learning . In Advances in Neural Information Processing Systems , volume 29. Curran Associates, Inc., 2016

    2016

  58. [66]

    Lewis Hammond, Alan Chan, Jesse Clifton, Jason Hoelscher-Obermaier, Akbir Khan, Euan McLean, Chandler Smith, Wolfram Barfuss, Jakob Foerster, Tomáš Gavenčiak, The Anh Han, Edward Hughes, Vojtěch Kovařík, Jan Kulveit, Joel Z. Leibo, Caspar Oesterheld, Christian Schroeder de Wit...

    2025

  59. [67]

    21 CFR Part 820 - Quality System Regulation

    Health and Human Services Department and Food and Drug Administration . 21 CFR Part 820 - Quality System Regulation . 1996

    1996

  60. [68]

    Technologies for an agent-based world, 2024

    Joe Hewett. Technologies for an agent-based world, 2024

    2024

  61. [69]

    A Low - Cost Attack against the hCaptcha System

    Md Imran Hossen and Xiali Hei. A Low - Cost Attack against the hCaptcha System . In 2021 IEEE Security and Privacy Workshops ( SPW ) , May 2021

    2021

  62. [70]

    An Object Detection based Solver for \ Google ’s\ Image \ reCAPTCHA \ v2

    Md Imran Hossen, Yazhou Tu, Md Fazle Rabby, Md Nazmul Islam, Hui Cao, and Xiali Hei. An Object Detection based Solver for \ Google ’s\ Image \ reCAPTCHA \ v2. 2020

    2020

  63. [71]

    Other - Play

    Hengyuan Hu, Adam Lerer, Alex Peysakhovich, and Jakob Foerster. " Other - Play " for zero-shot coordination. In Proceedings of the 37th International Conference on Machine Learning , volume 119 of ICML '20 . JMLR.org, July 2020

    2020

  64. [72]

    TrustAgent : Towards Safe and Trustworthy LLM -based Agents through Agent Constitution , August 2024

    Wenyue Hua, Xianjun Yang, Mingyu Jin, Wei Cheng, Ruixiang Tang, and Yongfeng Zhang. TrustAgent : Towards Safe and Trustworthy LLM -based Agents through Agent Constitution , August 2024

    2024

  65. [73]

    Liao, Esin Durmus, Alex Tamkin, and Deep Ganguli

    Saffron Huang, Divya Siddarth, Liane Lovitt, Thomas I. Liao, Esin Durmus, Alex Tamkin, and Deep Ganguli. Collective Constitutional AI : Aligning a Language Model with Public Input . In Proceedings of the 2024 ACM Conference on Fairness , Accountability , and Transparency , FAc...

    2024

  66. [74]

    A roadmap of agent research and development

    Nicholas R Jennings, Katia Sycara, and Michael Wooldridge. A roadmap of agent research and development. Autonomous agents and multi-agent systems, 1, 1998

    1998

  67. [75]

    Jimenez, John Yang, Alexander Wettig, Shunyu Yao, Kexin Pei, Ofir Press, and Karthik Narasimhan

    Carlos E. Jimenez, John Yang, Alexander Wettig, Shunyu Yao, Kexin Pei, Ofir Press, and Karthik Narasimhan. SWE -bench: Can Language Models Resolve Real - World GitHub Issues ?, April 2024

    2024

  68. [76]

    Deborah G. Johnson. Software Agents , Anticipatory Ethics , and Accountability . In Gary E. Marchant, Braden R. Allenby, and Joseph R. Herkert (eds.), The Growing Gap Between Emerging Technologies and Legal - Ethical Oversight : The Pacing Problem . Springer Netherlands, Dordr...

    2011

  69. [77]

    Adversaries Can Misuse Combinations of Safe Models , June 2024

    Erik Jones, Anca Dragan, and Jacob Steinhardt. Adversaries Can Misuse Combinations of Safe Models , June 2024

    2024

  70. [78]

    The digital library project volume 1: the world of knowbots ( DRAFT )

    Robert E Kahn and Vinton G Cerf. The digital library project volume 1: the world of knowbots ( DRAFT ). Corporation for National Research Initiatives, 1988

    1988

  71. [79]

    Adding payments to your LLM agentic workflows, November 2024

    Steve Kaliski. Adding payments to your LLM agentic workflows, November 2024

    2024

  72. [80]

    Why do people seek anonymity on the internet? informing policy and design

    Ruogu Kang, Stephanie Brown, and Sara Kiesler. Why do people seek anonymity on the internet? informing policy and design. In Proceedings of the SIGCHI Conference on Human Factors in Computing Systems , CHI '13, New York, NY, USA, April 2013. Association for Computing Machinery

    2013

  73. [81]

    Siegel, Nitya Nadgir, and Arvind Narayanan

    Sayash Kapoor, Benedikt Stroebl, Zachary S. Siegel, Nitya Nadgir, and Arvind Narayanan. AI Agents That Matter , July 2024

    2024

  74. [82]

    Hannah Rose Kirk, Alexander Whitefield, Paul Röttger, Andrew Bean, Katerina Margatina, Juan Ciro, Rafael Mosquera, Max Bartolo, Adina Williams, He He, Bertie Vidgen, and Scott A. Hale. The PRISM Alignment Project : What Participatory , Representative and Individualised Human F...

    2024

  75. [83]

    Assessing anonymous communication on the internet: Policy deliberations

    Rob Kling, Ya-ching Lee, Al Teich, and Mark S Frankel. Assessing anonymous communication on the internet: Policy deliberations. The Information Society, 15 0 (2), 1999

    1999

  76. [84]

    Governing AI Agents , April 2024

    Noam Kolt. Governing AI Agents , April 2024

    2024

  77. [85]

    Preparing for the ( Non - Existent ?) Future of Work , June 2022

    Anton Korinek and Megan Juelfs. Preparing for the ( Non - Existent ?) Future of Work , June 2022

    2022

  78. [86]

    Scenarios for the Transition to AGI , March 2024

    Anton Korinek and Donghyun Suh. Scenarios for the Transition to AGI , March 2024

    2024

  79. [87]

    Frontier AI Ethics : Anticipating and Evaluating the Societal Impacts of Generative Agents , April 2024

    Seth Lazar. Frontier AI Ethics : Anticipating and Evaluating the Societal Impacts of Generative Agents , April 2024

    2024

  80. [88]

    The Moral Case for Using Language Model Agents for Recommendation , October 2024

    Seth Lazar, Luke Thorburn, Tian Jin, and Luca Belli. The Moral Case for Using Language Model Agents for Recommendation , October 2024

    2024

  81. [89]

    Vazquez, Misha Yagudin, Niclas Kupper, and Laurence Aitchison

    Gavin Leech, Juan J. Vazquez, Misha Yagudin, Niclas Kupper, and Laurence Aitchison. Questionable practices in machine learning, July 2024

    2024

  82. [90]

    Leibo, Edgar Duéñez-Guzmán, Alexander Sasha Vezhnevets, John P

    Joel Z. Leibo, Edgar Duéñez-Guzmán, Alexander Sasha Vezhnevets, John P. Agapiou, Peter Sunehag, Raphael Koster, Jayd Matyas, Charles Beattie, Igor Mordatch, and Thore Graepel. Scalable Evaluation of Multi - Agent Reinforcement Learning with Melting Pot , July 2021

    2021

  83. [91]

    Scalable agent alignment via reward modeling: a research direction, November 2018

    Jan Leike, David Krueger, Tom Everitt, Miljan Martic, Vishal Maini, and Shane Legg. Scalable agent alignment via reward modeling: a research direction, November 2018

    2018

  84. [92]

    Learning Existing Social Conventions via Observationally Augmented Self - Play

    Adam Lerer and Alexander Peysakhovich. Learning Existing Social Conventions via Observationally Augmented Self - Play . In Proceedings of the 2019 AAAI / ACM Conference on AI , Ethics , and Society , AIES '19, New York, NY, USA, January 2019. Association for Computing Machinery

    2019

  85. [93]

    Large Language Models Understand and Can be Enhanced by Emotional Stimuli , October 2023

    Cheng Li, Jindong Wang, Yixuan Zhang, Kaijie Zhu, Wenxin Hou, Jianxun Lian, Fang Luo, Qiang Yang, and Xing Xie. Large Language Models Understand and Can be Enhanced by Emotional Stimuli , October 2023

    2023

  86. [94]

    Autonomous interface agents

    Henry Lieberman. Autonomous interface agents. In Proceedings of the ACM SIGCHI Conference on Human factors in computing systems , CHI '97, New York, NY, USA, March 1997. Association for Computing Machinery

    1997

  87. [95]

    AgentBench : Evaluating LLMs as Agents , October 2023

    Xiao Liu, Hao Yu, Hanchen Zhang, Yifan Xu, Xuanyu Lei, Hanyu Lai, Yu Gu, Hangliang Ding, Kaiwen Men, Kejuan Yang, Shudan Zhang, Xiang Deng, Aohan Zeng, Zhengxiao Du, Chenhui Zhang, Sheng Shen, Tianjun Zhang, Yu Su, Huan Sun, Minlie Huang, Yuxiao Dong, and Jie Tang. AgentBench ...

    2023

  88. [96]

    ToolSandbox : A Stateful , Conversational , Interactive Evaluation Benchmark for LLM Tool Use Capabilities , August 2024

    Jiarui Lu, Thomas Holleis, Yizhe Zhang, Bernhard Aumayer, Feng Nan, Felix Bai, Shuang Ma, Shen Ma, Mengyu Li, Guoli Yin, Zirui Wang, and Ruoming Pang. ToolSandbox : A Stateful , Conversational , Interactive Evaluation Benchmark for LLM Tool Use Capabilities , August 2024

    2024

  89. [97]

    Agents that reduce work and information overload

    Pattie Maes. Agents that reduce work and information overload. Communications of the ACM, 37 0 (7), July 1994. ISSN 0001-0782

    1994

  90. [98]

    Artificial life meets entertainment: lifelike autonomous agents

    Pattie Maes. Artificial life meets entertainment: lifelike autonomous agents. Communications of the ACM, 38 0 (11), November 1995. ISSN 0001-0782

    1995

  91. [99]

    A Scalable Communication Protocol for Networks of Large Language Models , October 2024

    Samuele Marro, Emanuele La Malfa, Jesse Wright, Guohao Li, Nigel Shadbolt, Michael Wooldridge, and Philip Torr. A Scalable Communication Protocol for Networks of Large Language Models , October 2024

    2024

  92. [100]

    Where in the world is the internet? Locating political power in internet infrastructure

    Ashwin Jacob Mathew. Where in the world is the internet? Locating political power in internet infrastructure . PhD thesis, University of California, Berkeley, 2014

    2014

  93. [101]

    GAIA : a benchmark for General AI Assistants , November 2023

    Grégoire Mialon, Clémentine Fourrier, Craig Swift, Thomas Wolf, Yann LeCun, and Thomas Scialom. GAIA : a benchmark for General AI Assistants , November 2023

    2023

  94. [102]

    Threat actors misuse OAuth applications to automate financially driven attacks, December 2023

    Microsoft Threat Intelligence . Threat actors misuse OAuth applications to automate financially driven attacks, December 2023

    2023

  95. [103]

    CVE : Common Vulnerabilities and Exposures , 2025

    MITRE Corporation . CVE : Common Vulnerabilities and Exposures , 2025

    2025

  96. [104]

    Playing Atari with Deep Reinforcement Learning , December 2013

    Volodymyr Mnih, Koray Kavukcuoglu, David Silver, Alex Graves, Ioannis Antonoglou, Daan Wierstra, and Martin Riedmiller. Playing Atari with Deep Reinforcement Learning , December 2013

    2013

  97. [105]

    MultiOn AI , 2024

    MultiOn. MultiOn AI , 2024

    2024

  98. [106]

    Verification and validation in scientific computing

    William L Oberkampf and Christopher J Roy. Verification and validation in scientific computing. Cambridge university press, 2010

    2010

  99. [107]

    Law- Following AI : Designing AI Agents to Obey Human Laws , 2025

    Cullen O'Keefe, Ketan Ramakrishnan, Janna Tay, and Christoph Winter. Law- Following AI : Designing AI Agents to Obey Human Laws , 2025

    2025

  100. [108]

    OpenAI Charter , 2018

    OpenAI. OpenAI Charter , 2018

    2018

  101. [109]

    Coordinated vulnerability disclosure policy, July 2023 a

    OpenAI . Coordinated vulnerability disclosure policy, July 2023 a

    2023

  102. [110]

    OpenAI Evals , September 2023 b

    OpenAI . OpenAI Evals , September 2023 b

    2023

  103. [111]

    Preparedness Framework ( Beta )

    OpenAI . Preparedness Framework ( Beta ). Technical report, December 2023 c

    2023

  104. [112]

    GPT Actions , 2024

    OpenAI. GPT Actions , 2024

    2024

  105. [113]

    How should I report a GPT ?, 2024

    OpenAI . How should I report a GPT ?, 2024

    2024

  106. [114]

    Model Spec , May 2024

    OpenAI. Model Spec , May 2024

    2024

  107. [115]

    Humans and Automation : Use , Misuse , Disuse , Abuse

    Raja Parasuraman and Victor Riley. Humans and Automation : Use , Misuse , Disuse , Abuse . Human Factors, 39 0 (2), June 1997. ISSN 0018-7208

    1997

  108. [116]

    Patil, Tianjun Zhang, Vivian Fang, Noppapon C., Roy Huang, Aaron Hao, Martin Casado, Joseph E

    Shishir G. Patil, Tianjun Zhang, Vivian Fang, Noppapon C., Roy Huang, Aaron Hao, Martin Casado, Joseph E. Gonzalez, Raluca Ada Popa, and Ion Stoica. GoEX : Perspectives and Designs Towards a Runtime for Autonomous LLM Applications , April 2024

    2024

  109. [117]

    Classical and Language Model Multi - Agent Infrastructure : A Comparative Review

    Elija Perrier. Classical and Language Model Multi - Agent Infrastructure : A Comparative Review . Forthcoming, 2025

    2025

  110. [118]

    Language Model Agent Ontologies

    Elija Perrier and Seth Lazar. Language Model Agent Ontologies . Forthcoming, 2025

    2025

  111. [119]

    Specifying protocols for multi-agent systems interaction

    Stefan Poslad. Specifying protocols for multi-agent systems interaction. ACM Transactions on Autonomous and Adaptive Systems (TAAS), 2 0 (4), 2007

    2007

  112. [120]

    War as a Commitment Problem

    Robert Powell. War as a Commitment Problem . International Organization, 60 0 (1), 2006. ISSN 0020-8183

    2006

  113. [121]

    Language Models Can Reduce Asymmetry in Information Markets , March 2024

    Nasim Rahaman, Martin Weiss, Manuel Wüthrich, Yoshua Bengio, Li Erran Li, Chris Pal, and Bernhard Schölkopf. Language Models Can Reduce Asymmetry in Information Markets , March 2024

    2024

  114. [122]

    White, Margaret Mitchell, Timnit Gebru, Ben Hutchinson, Jamila Smith-Loud, Daniel Theron, and Parker Barnes

    Inioluwa Deborah Raji, Andrew Smart, Rebecca N. White, Margaret Mitchell, Timnit Gebru, Ben Hutchinson, Jamila Smith-Loud, Daniel Theron, and Parker Barnes. Closing the AI accountability gap: defining an end-to-end framework for internal algorithmic auditing. In Proceedings of...

    2020

  115. [123]

    Outsider Oversight : Designing a Third Party Audit Ecosystem for AI Governance

    Inioluwa Deborah Raji, Peggy Xu, Colleen Honigsberg, and Daniel Ho. Outsider Oversight : Designing a Third Party Audit Ecosystem for AI Governance . In Proceedings of the 2022 AAAI / ACM Conference on AI , Ethics , and Society , AIES '22, New York, NY, USA, July 2022. Associat...

    2022

  116. [124]

    RFC 4271: A border gateway protocol 4 ( BGP -4), 2006

    Yakov Rekhter, Tony Li, and Susan Hares. RFC 4271: A border gateway protocol 4 ( BGP -4), 2006

    2006

  117. [125]

    Artificial Intelligence Incident Database , 2024

    Responsible AI Collaborative . Artificial Intelligence Incident Database , 2024

    2024

  118. [126]

    Russell and Peter Norvig

    Stuart J. Russell and Peter Norvig. Artificial Intelligence : A Modern Approach . 4 edition, 2021

    2021

  119. [127]

    AI Rights for Human Safety , 2024

    Peter Salib and Simon Goldstein. AI Rights for Human Safety , 2024

    2024

  120. [128]

    The SEC Whistleblower Program Is Dominating Regulatory Enforcement , October 2024

    Bruce Schneier and Nathan Sanders. The SEC Whistleblower Program Is Dominating Regulatory Enforcement , October 2024

    2024

  121. [129]

    An Empirical Study & Evaluation of Modern CAPTCHAs , July 2023

    Andrew Searles, Yoshimichi Nakatsuka, Ercan Ozturk, Andrew Paverd, Gene Tsudik, and Ai Enkoji. An Empirical Study & Evaluation of Modern CAPTCHAs , July 2023

    2023

  122. [130]

    Whistleblower Program , August 2024

    Securities and Exchange Commission . Whistleblower Program , August 2024

    2024

  123. [131]

    Robinson

    Yonadav Shavit, Sandhini Agarwal, Miles Brundage, Steven Adler, Cullen O’Keefe, Rosie Campbell, Teddy Lee, Pamela Mishkin, Tyna Eloundou, Alan Hickey, Katarina Slama, Lama Ahmad, Paul McMillan, Alex Beutel, Alexandre Passos, and David G. Robinson. Practices for Governing Agent...

    2023

  124. [132]

    ' Crypto King ' Sam Bankman - Fried faces decades in jail after guilty verdict

    Natalie Sherman and Peter Hoskins. ' Crypto King ' Sam Bankman - Fried faces decades in jail after guilty verdict. November 2023

    2023

  125. [133]

    Auto- GPT - Plugins , 2024

    Significant-Gravitas. Auto- GPT - Plugins , 2024

    2024

  126. [134]

    A general reinforcement learning algorithm that masters chess, shogi, and Go through self-play

    David Silver, Thomas Hubert, Julian Schrittwieser, Ioannis Antonoglou, Matthew Lai, Arthur Guez, Marc Lanctot, Laurent Sifre, Dharshan Kumaran, Thore Graepel, Timothy Lillicrap, Karen Simonyan, and Demis Hassabis. A general reinforcement learning algorithm that masters chess, ...

    2018

  127. [135]

    Skitka, KATHLEEN L

    LINDA J. Skitka, KATHLEEN L. Mosier, and MARK Burdick. Does automation bias decision-making? International Journal of Human-Computer Studies, 51 0 (5), November 1999. ISSN 1071-5819

    1999

  128. [136]

    Beyond Browsing : API - Based Web Agents , October 2024

    Yueqi Song, Frank Xu, Shuyan Zhou, and Graham Neubig. Beyond Browsing : API - Based Web Agents , October 2024

    2024

  129. [137]

    Verifiable Credentials Data Model v2.0

    Manu Sporny, Dave Longley, David Chadwick, and Orie Steele. Verifiable Credentials Data Model v2.0. Technical report, W3C, May 2024

    2024

  130. [138]

    eCommerce - Worldwide , 2024

    Statista . eCommerce - Worldwide , 2024

    2024

  131. [139]

    Cooperative AI via Decentralized Commitment Devices , November 2023

    Xinyuan Sun, Davide Crapis, Matt Stephenson, Barnabé Monnot, Thomas Thiery, and Jonathan Passerat-Palmbach. Cooperative AI via Decentralized Commitment Devices , November 2023

    2023

  132. [140]

    Sunstein

    Cass R. Sunstein. Brave New World ? Human Welfare and Paternalistic AI , 2024

    2024

  133. [141]

    Announcing the Agent2Agent Protocol ( A2A ) - Google Developers Blog , April 2025

    Rao Surapaneni, Miku Jha, Michael Vakoc, and Todd Segal. Announcing the Agent2Agent Protocol ( A2A ) - Google Developers Blog , April 2025

    2025

  134. [142]

    Sutton and Andrew G

    Richard S. Sutton and Andrew G. Barto. Reinforcement learning: An introduction . Adaptive computation and machine learning series. The MIT Press, Cambridge, Massachusetts, second edition edition, 2018

    2018

  135. [143]

    The Private Provision of Public Goods via Dominant Assurance Contracts

    Alexander Tabarrok. The Private Provision of Public Goods via Dominant Assurance Contracts . Public Choice, 96 0 (3/4), 1998. ISSN 0048-5829

    1998

  136. [144]

    Tamper- Resistant Safeguards for Open - Weight LLMs , August 2024

    Rishub Tamirisa, Bhrugu Bharathi, Long Phan, Andy Zhou, Alice Gatti, Tarun Suresh, Maxwell Lin, Justin Wang, Rowan Wang, Ron Arel, Andy Zou, Dawn Song, Bo Li, Dan Hendrycks, and Mantas Mazeika. Tamper- Resistant Safeguards for Open - Weight LLMs , August 2024

    2024

  137. [145]

    WebWISE : Web Interface Control and Sequential Exploration with Large Language Models , October 2023

    Heyi Tao, Sethuraman T V, Michal Shlapentokh-Rothman, and Derek Hoiem. WebWISE : Web Interface Control and Sequential Exploration with Large Language Models , October 2023

    2023

  138. [146]

    Trapping misbehaving bots in an AI Labyrinth , March 2025

    Reid Tatoris, Harsh Saxena, and Luis Miglietti. Trapping misbehaving bots in an AI Labyrinth , March 2025

    2025

  139. [147]

    Identifying Current Barriers in RPKI Adoption , September 2024

    Cecilia Testart, Josephine Wolff, Deepak Gouda, and Romain Fontugne. Identifying Current Barriers in RPKI Adoption , September 2024

    2024

  140. [148]

    How to audit an AI model owned by someone else (part 1), July 2023

    Andrew Trask, Akshay Sukumar, Antti Kalliokoski, Bennett Farkas, Callis Ezenwaka, Carmen Popa, Curtis Mitchell, Dylan Hrebenach, George-Cristian Muraru, Ionesio Junior, Irina Bejan, Ishan Mishra, Ivoline Ngong, Jack Bandy, Jess Stahl, Julian Cardonnet, Kellye Trask, Khoa Nguye...

    2023

  141. [149]

    Beyond Privacy Trade -offs with Structured Transparency , March 2024

    Andrew Trask, Emma Bluemke, Teddy Collins, Ben Garfinkel Eric Drexler, Claudia Ghezzou Cuervas-Mons, Iason Gabriel, Allan Dafoe, and William Isaac. Beyond Privacy Trade -offs with Structured Transparency , March 2024

    2024

  142. [150]

    The Rise of AI Agent Infrastructure , June 2024

    Jon Turow. The Rise of AI Agent Infrastructure , June 2024

    2024

  143. [151]

    Identity Verification Checks , 2025

    Uber . Identity Verification Checks , 2025

    2025

  144. [152]

    Attorney's Office

    Southern District of New York U.S. Attorney's Office. North Carolina Musician Charged With Music Streaming Fraud Aided By Artificial Intelligence , September 2024

    2024

  145. [153]

    Digital Market Perfection

    Rory Van Loo. Digital Market Perfection . Michigan Law Review, 117 0 (5), January 2019. ISSN 0026-2234

    2019

  146. [154]

    Artificial Artificial Artificial Intelligence : Crowd Workers Widely Use Large Language Models for Text Production Tasks , June 2023

    Veniamin Veselovsky, Manoel Horta Ribeiro, and Robert West. Artificial Artificial Artificial Intelligence : Crowd Workers Widely Use Large Language Models for Text Production Tasks , June 2023

    2023

  147. [155]

    The instruction hierarchy: Training llms to prioritize privileged instructions

    Eric Wallace, Kai Xiao, Reimar Leike, Lilian Weng, Johannes Heidecke, and Alex Beutel. The instruction hierarchy: Training llms to prioritize privileged instructions. arXiv preprint arXiv:2404.13208, 2024

    2024 arXiv

  148. [156]

    Soft Self - Consistency Improves Language Models Agents

    Han Wang, Archiki Prasad, Elias Stengel-Eskin, and Mohit Bansal. Soft Self - Consistency Improves Language Models Agents . In Lun-Wei Ku, Andre Martins, and Vivek Srikumar (eds.), Proceedings of the 62nd Annual Meeting of the Association for Computational Linguistics ( Volume ...

    2024

  149. [157]

    Learning From Failure : Integrating Negative Examples when Fine -tuning Large Language Models as Agents , April 2024 b

    Renxi Wang, Haonan Li, Xudong Han, Yixuan Zhang, and Timothy Baldwin. Learning From Failure : Integrating Negative Examples when Fine -tuning Large Language Models as Agents , April 2024 b

    2024

  150. [158]

    Designing Incident Reporting Systems for Harms from AI

    Kevin Wei and Lennart Heim. Designing Incident Reporting Systems for Harms from AI . May 2024

    2024

  151. [159]

    RE - Bench : Evaluating frontier AI R & D capabilities of language model agents against human experts, November 2024

    Hjalmar Wijk, Tao Lin, Joel Becker, Sami Jawhar, Neev Parikh, Thomas Broadley, Lawrence Chan, Michael Chen, Josh Clymer, Jai Dhyani, Elena Ericheva, Katharyn Garcia, Brian Goodrich, Nikola Jurkovic, Megan Kinniment, Aron Lajko, Seraphina Nix, Lucas Sato, William Saunders, Maks...

    2024

  152. [160]

    Care for Chatbots , May 2024

    Peter Wills. Care for Chatbots , May 2024

    2024

  153. [161]

    An Introduction to Multiagent Systems

    Michael Wooldridge. An Introduction to Multiagent Systems. John Wiley & Sons, 2009

    2009

  154. [162]

    White, Doug Burger, and Chi Wang

    Qingyun Wu, Gagan Bansal, Jieyu Zhang, Yiran Wu, Beibin Li, Erkang Zhu, Li Jiang, Xiaoyun Zhang, Shaokun Zhang, Jiale Liu, Ahmed Hassan Awadallah, Ryen W. White, Doug Burger, and Chi Wang. AutoGen : Enabling Next - Gen LLM Applications via Multi - Agent Conversation Framework . 2023

    2023

  155. [163]

    Introducing Devin , the first AI software engineer, March 2024

    Scott Wu. Introducing Devin , the first AI software engineer, March 2024

    2024

  156. [164]

    OSWorld : Benchmarking Multimodal Agents for Open - Ended Tasks in Real Computer Environments , April 2024

    Tianbao Xie, Danyang Zhang, Jixuan Chen, Xiaochuan Li, Siheng Zhao, Ruisheng Cao, Toh Jing Hua, Zhoujun Cheng, Dongchan Shin, Fangyu Lei, Yitao Liu, Yiheng Xu, Shuyan Zhou, Silvio Savarese, Caiming Xiong, Victor Zhong, and Tao Yu. OSWorld : Benchmarking Multimodal Agents for O...

    2024

  157. [165]

    Zhang, Neil Perry, Riya Dulepet, Eliot Jones, Justin W

    Andy K. Zhang, Neil Perry, Riya Dulepet, Eliot Jones, Justin W. Lin, Joey Ji, Celeste Menders, Gashon Hussein, Samantha Liu, Donovan Jasper, Pura Peetathawatchai, Ari Glenn, Vikram Sivashankar, Daniel Zamoshchin, Leo Glikbarg, Derek Askaryar, Mike Yang, Teddy Zhang, Rishi Allu...

    2024

  158. [166]

    Edelman, Danilo Francati, Daniele Venturi, Giuseppe Ateniese, and Boaz Barak

    Hanlin Zhang, Benjamin L. Edelman, Danilo Francati, Daniele Venturi, Giuseppe Ateniese, and Boaz Barak. Watermarks in the Sand : Impossibility of Strong Watermarking for Generative Models , November 2023

    2023

  159. [167]

    Zittrain

    Jonathan L. Zittrain. The Generative Internet . Harvard Law Review, 119 0 (7), 2006. ISSN 0017-811X

    2006

  160. [168]

    Improving Alignment and Robustness with Circuit Breakers , July 2024

    Andy Zou, Long Phan, Justin Wang, Derek Duenas, Maxwell Lin, Maksym Andriushchenko, Rowan Wang, Zico Kolter, Matt Fredrikson, and Dan Hendrycks. Improving Alignment and Robustness with Circuit Breakers , July 2024

    2024

  161. [169]

    write newline

    " write newline "" before.all 'output.state := FUNCTION n.dashify 't := "" t empty not t #1 #1 substring "-" = t #1 #2 substring "--" = not "--" * t #2 global.max substring 't := t #1 #1 substring "-" = "-" * t #2 global.max substring 't := while if t #1 #1 substring * t #2 gl...

Pith tools

Reviewed August 10, 2026 · model on record in the stance chip above.