REVIEW 3 major objections 6 minor 1 cited by
Build the web for agents, not agents for the web
T0 review · 3 major / 6 minor · reviewed 2026-08-07 · deepseek-v4-flash
Pith's one-line read The paper argues that web agents should not be forced to navigate human-designed browser UIs and web APIs; instead, the research community should build standardized Agentic Web Interfaces (AWIs) designed for agent use, and that such…
desk verdict A clear, well-scoped position paper arguing for agent-native web interfaces; the 'essential' claim is an argument, not a result, but the paper is honest about that and worth engaging. read the letter →
The pith
A machine-rendered reading of the paper's core claim, the machinery that carries it, and where it could break.
The reading
What carries the argument
The central object is the Agentic Web Interface (AWI), defined as an interface specifically designed for agents to navigate a website, analogous to how a user interface is designed for humans. The argument is carried by two complementary design layers. The first is a set of six guiding principles: standardized, human-centric, safe, optimal representations, efficient to host, and developer-friendly. The second is five concrete suggestions: unified higher-level actions that compose primitive operations, compatibility with human user interfaces through bidirectional translation tools, access control lists plus privacy-preserving password managers for agents, progressive information transfer that sends resized or embedded content, and agentic task queues that cap concurrent agent traffic to protect human users. Together these mechanisms are what would let AWIs fix the representational, resource, and safety issues the paper attributes to current human-oriented interfaces.
What would settle it
Build an AWI for an existing website and compare agents using it against state-of-the-art DOM/screenshot/API-based agents on the same task benchmark with fixed success criteria, token cost, and safety violation counts. If the AWI-guided agent does not improve on at least one of these metrics while matching the others, the paper's claim that AWIs are essential for performance and directly fix current problems would be contradicted. A separate test would be whether any real website operator adopts an AWI voluntarily after being offered the design; if none do despite demonstrated benefits, the proposal's central adoption premise fails.
Extended reading notes
Core claim
On its own terms, the paper's central claim is that web agents should not be forced to adapt to interfaces designed for humans, neither the visual browser UI nor developer-facing web APIs, and that the research community should instead design Agentic Web Interfaces: standardized interfaces whose structure, action space, and state representations are optimized for agent use. The paper argues this is essential for web agent performance and will directly resolve representational inefficiency, resource strain on websites, narrow action spaces, and several safety and privacy risks. The argument is supported by a diagnosis of current approaches: screenshots hide occluded elements, DOM trees can exceed a million tokens, web APIs cannot perform stateful actions like sorting a list after viewing it, and internal APIs can bypass guardrails such as password prompts. The paper concludes that building AWIs should be a collaborative, iterative, multi-stakeholder effort, with machine learning researchers involved from the start.
Load-bearing premise
The proposal depends on website owners, developers, and standards bodies voluntarily adopting, implementing, and maintaining AWI interfaces, yet the paper provides neither a working prototype nor evidence of economic or political incentives for them to do so.
Editorial extensions
If this is right
- A standardized AWI action space would let the same high-level actions work across websites, so policy learning and generalization no longer depend on each site's DOM structure.
- Optimal state representations would cut token costs; the paper estimates current DOM-based interaction with a large model can cost roughly $40 for a single 20-step task, a cost AWIs would reduce.
- Built-in access control and privacy-preserving password management would let agents use sensitive information without exposing full credentials, addressing unauthorized-purchase and harmful-message risks.
- Agentic task queues would cap concurrency and spread load, keeping websites responsive for humans while absorbing growing agent traffic.
- Stateful AWI interactions would enable actions that depend on previous states, such as sorting an already-retrieved product list, without resending the entire query as stateless protocols require.
Reading between the lines
- Beyond the paper: If AWIs become widespread, web-agent benchmarks would likely move from measuring how well agents cope with human UIs to measuring task competence on agent-native interfaces, which would change what 'generalization across websites' means and make many current leaderboards non-comparable.
- Beyond the paper: The paper implicitly predicts an adoption economy, namely that websites will implement AWIs because task queues and efficient representations reduce hosting and bandwidth costs, but this is a testable empirical claim, not yet a demonstrated one; a pilot deployment on a high-traffic site could falsify or support it.
- Beyond the paper: Progressive information transfer and embedding-based representations shift content processing to website infrastructure, so one unaddressed design question is who decides what an agent may see and retain, and under what consent terms.
- Beyond the paper: Because the paper distinguishes AWIs from protocols like MCP while allowing them to be combined, an open design question is whether a future AWI standard will simply become a protocol with a stateful layer, blurring the interface/protocol boundary.
Editorial analysis
A structured set of objections, weighed in public.
Referee Report
Summary. This position paper argues that the current entry points for web agents—browser UIs (DOM trees, screenshots, accessibility trees) and web APIs—are designed for humans and are fundamentally mismatched with LLM capabilities. The authors introduce the concept of an Agentic Web Interface (AWI), a unified interface designed specifically for agents, and claim that AWIs are "essential for web agent performance" (Section 3). They list six guiding principles (standardized, human-centric, safe, optimal representations, efficient to host, developer-friendly) and offer concrete suggestions such as unified higher-level actions, access control lists, progressive information transfer, and agentic task queues (Sections 3.2 and 3.3). The paper also argues that the broader ML community must be involved in AWI design (Section 4) and distinguishes AWIs from the Model Context Protocol (MCP) (Section 5). The manuscript explicitly states that no prototype or implementation is provided.
Significance. If the core claim were established, AWIs could be an important reorientation for web-agent research, analogous to the way agent-computer interfaces helped software engineering agents (as cited via Yang et al., 2024). The paper's catalog of limitations of DOM-based, screenshot-based, and API-based agents is clear and well-documented, and its stakeholder-oriented principles (e.g., safety, developer-friendliness, efficiency) are a useful structuring device. The distinction between AWIs as stateful interfaces and MCP as a stateless protocol is a valuable conceptual contribution. However, the central claim that AWIs are 'essential' and will 'directly fix' current problems is not backed by any implementation, controlled experiment, or formal derivation; the paper itself concedes this in Section 1. The value here is as a proposal and a call to action, not as a demonstrated result.
major comments (3)
- [Section 3, first paragraph] The central claim that AWIs are "essential for web agent performance" is the load-bearing assertion of the paper, but it is supported only by a qualitative list of mechanisms in Section 3.1. Every mechanism (tailored action spaces, efficient state representations, task queues) presupposes that the interface is the binding constraint on agent performance; the opposite hypothesis—that grounding, planning, or training on existing interfaces would yield equal or larger gains—is not tested or even formulated. A controlled comparison between the same agent using an AWI-like representation (e.g., a compact structured state plus a restricted action set) and the same agent using a DOM, screenshot, or API interface on a fixed set of tasks would make the claim falsifiable. As written, the word "essential" is an unsupported modal claim. I recommend either softening it to a research hypothesis or committing to a concrete experimental protocol that could in principle falsify it.
- [Sections 3.1 and 3.3] The sentence "Developing AWIs will directly fix many of the issues highlighted above" asserts a causal relationship that none of the cited evidence demonstrates. For instance, the claim that tailored browser state representations will "significantly reduce the computational cost" (Section 3.1) is plausible but unmeasured; a DOM tree can exceed 1M tokens, but a compact representation could also lose decision-relevant information, and the net effect on task success is unknown. Similarly, "agentic task queues" and "progressive information transfer" (Section 3.3) are design sketches with no latency, throughput, or cost numbers. Because the paper explicitly provides no prototype (Section 1), the causal claims are not backed by the manuscript's own evidence. I would replace "will directly fix" with "could address" and add a short evaluation agenda with specific metrics (task success rate, token cost, wall-clock time, server load) and baseline conditions.
- [Sections 3.2 and 4] The proposal's feasibility depends on website owners and developers implementing, deploying, and maintaining AWIs, but the only incentive offered is that AWIs "would avoid development effort towards managing resources due to higher traffic from agents" (Section 3.1). This incentive is not quantified, and it is in tension with the "Developer-friendly" principle (Section 3.2), which requires that AWIs "work seamlessly with the architecture and infrastructure of the website" without disrupting existing services. The paper does not address the cost of developing a new interface, the risk that a standard will fragment, or the fact that many websites operate on thin margins and may not adopt an interface whose benefits to them are unmeasured. For a position paper, a more explicit treatment of the adoption path—for example, a minimal viable AWI that can be layered onto existing pages, or a non-profit consortium mechanism—would make the argument more credible. Without this, the proposal remains a wish-list rather than a plan.
minor comments (6)
- [Section 2.2] There is a grammatical typo: "on a online shopping website" should be "on an online shopping website"; the same phrase appears in Section 3.1.
- [Section 3.2, Table 1] The principle is named "Optimal representations" in Section 3.2 but Table 1 uses "Optimally-represented"; the inconsistency could confuse readers.
- [Section 4, AI Safety paragraph] "they will used in progressively more critical and sensitive scenarios" should read "they will be used in progressively more critical and sensitive scenarios."
- [Section 5, MCP discussion] "PostgresSQL" should be "PostgreSQL."
- [References and formatting] Several author names contain spurious spacing artifacts (e.g., "Sta ´nczak"), likely due to LaTeX accent handling; these should be cleaned up in the final version.
- [Section 2.1, Representational limitations] The phrase "token-efficient representations" is informal; specifying "token-efficient for current LLMs" would be more precise, since token efficiency is model-dependent.
Circularity Check
No circularity: the paper is a position proposal with no fitted parameters, no derived predictions, and no load-bearing self-citation chain.
full rationale
This paper is an argumentative position piece, not a derivation. It introduces the concept of an Agentic Web Interface by definition and then argues, on qualitative grounds, that interfaces designed for agents could address representational, resource, safety, and action-space limitations of browser UIs and web APIs. There are no fitted parameters, benchmark numbers produced, or 'predictions' that reduce by construction to inputs. The claim that AWIs 'will directly fix many of the issues' is an unsupported causal assertion, but unsupported assertion is an evidence and correctness concern, not circularity: the paper explicitly disclaims a prototype ('we explicitly do not provide a prototype or specific implementation details', Section 1) and offers no implementation. The one formal element, the sequential decision-making framework, is adopted from external work (Zhou et al.) and does no work in deriving the AWI proposal. Self-citations (WebLinX, AgentRewardBench, SafeArena, BrowserGym) appear as examples or as supporting evidence that existing interface workarounds generalize poorly, but the same claims are also supported by external citations (e.g., Deng et al., WebArena, VisualWebArena, Li et al., Pan et al.) and by the paper's own descriptive analysis of DOM/screenshot/API limitations. No uniqueness theorem is imported, no ansatz is smuggled in via citation, and no known result is renamed as a derivation. The central proposal is normative and would require future empirical validation, but it does not reduce to its own inputs.
Assumptions & free parameters
assumptions (3)
- domain assumption Current human-facing web interfaces fundamentally limit web agents and cannot be adequately improved by better agent design.
- domain assumption Website owners will have sufficient incentive to adopt and maintain AWIs and will participate in a standard-setting process.
- domain assumption A standardized AWI can be designed that serves all stakeholders without degrading the human web experience.
invented entities (1)
-
Agentic Web Interface (AWI)
Cite this review
Pith. "Pith review of Build the web for agents, not agents for the web." pith.science (2026). https://pith.science/paper/34MQPQNV
@misc{pith2026250610953,
author = {Pith},
title = {Pith review of: Build the web for agents, not agents for the web},
year = {2026},
howpublished = {\url{https://pith.science/paper/34MQPQNV}},
note = {Machine review of arXiv:2506.10953}
}
read the original abstract
Recent advancements in Large Language Models (LLMs) and multimodal counterparts have spurred significant interest in developing web agents -- AI systems capable of autonomously navigating and completing tasks within web environments. While holding tremendous promise for automating complex web interactions, current approaches face substantial challenges due to the fundamental mismatch between human-designed interfaces and LLM capabilities. Current methods struggle with the inherent complexity of web inputs, whether processing massive DOM trees, relying on screenshots augmented with additional information, or bypassing the user interface entirely through API interactions. This position paper advocates for a paradigm shift in web agent research: rather than forcing web agents to adapt to interfaces designed for humans, we should develop a new interaction paradigm specifically optimized for agentic capabilities. To this end, we introduce the concept of an Agentic Web Interface (AWI), an interface specifically designed for agents to navigate a website. We establish six guiding principles for AWI design, emphasizing safety, efficiency, and standardization, to account for the interests of all primary stakeholders. This reframing aims to overcome fundamental limitations of existing interfaces, paving the way for more efficient, reliable, and transparent web agent design, which will be a collaborative effort involving the broader ML community.
Forward citations
Cited by 1 Pith paper
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Agentic Web: Weaving the Next Web with AI Agents
A position paper defines the Agentic Web as the next web era and proposes a three-dimensional conceptual framework for understanding and building it.
Reference graph
Works this paper leans on
-
[1]
M. Andriushchenko, A. Souly, M. Dziemian, D. Duenas, M. Lin, J. Wang, D. Hendrycks, A. Zou, Z. Kolter, M. Fredrikson, E. Winsor, J. Wynne, Y. Gal, and X. Davies. Agentharm: A benchmark for measuring harmfulness of llm agents, 2025. URL https://arxiv.org/abs/2410.09024
arXiv 2025
-
[2]
Anthropic. Introducing claude, Mar. 2023. URL https://www.anthropic.com/news/introducing-claude
work page 2023
-
[3]
Introducing the model context protocol, Nov
Anthropic. Introducing the model context protocol, Nov. 2024. URL https://www.anthropic.com/news/model-context-protocol
work page 2024
-
[4]
P. BehnamGhader, V. Adlakha, M. Mosbach, D. Bahdanau, N. Chapados, and S. Reddy. Llm2vec: Large language models are secretly powerful text encoders. arXiv preprint arXiv:2404.05961, 2024
arXiv 2024
-
[5]
L. Boisvert, M. Bansal, C. K. R. Evuru, G. Huang, A. Puri, A. Bose, M. Fazel, Q. Cappart, J. Stanley, A. Lacoste, A. Drouin, and K. Dvijotham. Doomarena: A framework for testing ai agents against evolving security threats, 2025 a . URL https://arxiv.org/abs/2504.14064
arXiv 2025
-
[6]
L. Boisvert, M. Thakkar, M. Gasse, M. Caccia, T. L. S. D. Chezelles, Q. Cappart, N. Chapados, A. Lacoste, and A. Drouin. Workarena++: Towards compositional planning and reasoning-based common knowledge work tasks, 2025 b . URL https://arxiv.org/abs/2407.05291
arXiv 2025
-
[7]
M. Caccia, M. Thakkar, L. Boisvert, T. L. S. de Chezelles, A. Pich \'e , N. Chapados, A. Drouin, M. Gasse, and A. Lacoste. Fine-tuning web agents: It works, but it's trickier than you think. In NeurIPS 2024 Workshop on Open-World Agents, 2024. URL https://openreview.net/forum?id=SkwtxEkst2
work page 2024
-
[8]
H. Cai, Y. Li, W. Wang, F. Zhu, X. Shen, W. Li, and T.-S. Chua. Large language models empowered personalized web agents. In Proceedings of the ACM on Web Conference 2025, pages 198--215, 2025
2025
Show all 71 references
-
[9]
T. L. S. D. Chezelles, M. Gasse, A. Drouin, M. Caccia, L. Boisvert, M. Thakkar, T. Marty, R. Assouel, S. O. Shayegan, L. K. Jang, X. H. Lù, O. Yoran, D. Kong, F. F. Xu, S. Reddy, Q. Cappart, G. Neubig, R. Salakhutdinov, N. Chapados, and A. Lacoste. The browsergym ecosystem for...
2025 arXiv
-
[10]
X. Deng, Y. Gu, B. Zheng, S. Chen, S. Stevens, B. Wang, H. Sun, and Y. Su. Mind2web: Towards a generalist agent for the web. Advances in Neural Information Processing Systems, 36: 0 28091--28114, 2023
2023
-
[11]
Drouin, M
A. Drouin, M. Gasse, M. Caccia, I. H. Laradji, M. Del Verme, T. Marty, L. Boisvert, M. Thakkar, Q. Cappart, D. Vazquez, et al. Workarena: How capable are web agents at solving common knowledge work tasks? arXiv preprint arXiv:2403.07718, 2024
2024 arXiv
-
[12]
Furuta, K.-H
H. Furuta, K.-H. Lee, O. Nachum, Y. Matsuo, A. Faust, S. S. Gu, and I. Gur. Multimodal web navigation with instruction-finetuned foundation models. arXiv preprint arXiv:2305.11854, 2023
2023 arXiv
-
[13]
X. Ge, C. Xu, D. Misaki, H. R. Markus, and J. L. Tsai. How culture shapes what people want from ai. In Proceedings of the CHI Conference on Human Factors in Computing Systems, CHI ’24, page 1–15. ACM, May 2024. doi:10.1145/3613904.3642660. URL http://dx.doi.org/10.1145/3613904.3642660
2024
-
[14]
Function calling with the gemini api, May 2025
Google. Function calling with the gemini api, May 2025. URL https://ai.google.dev/gemini-api/docs/function-calling
2025
-
[15]
B. Gou, R. Wang, B. Zheng, Y. Xie, C. Chang, Y. Shu, H. Sun, and Y. Su. Navigating the digital world as humans do: Universal visual grounding for gui agents, 2025. URL https://arxiv.org/abs/2410.05243
2025 arXiv
-
[16]
Y. Gu, K. Zhang, Y. Ning, B. Zheng, B. Gou, T. Xue, C. Chang, S. Srivastava, Y. Xie, P. Qi, H. Sun, and Y. Su. Is your llm secretly a world model of the internet? model-based planning for web agents, 2025. URL https://arxiv.org/abs/2411.06559
2025 arXiv
-
[17]
I. Gur, H. Furuta, A. Huang, M. Safdari, Y. Matsuo, D. Eck, and A. Faust. A real-world webagent with planning, long context understanding, and program synthesis. arXiv preprint arXiv:2307.12856, 2023
2023 arXiv
-
[18]
K. Guu, K. Lee, Z. Tung, P. Pasupat, and M.-W. Chang. Realm: Retrieval-augmented language model pre-training, 2020. URL https://arxiv.org/abs/2002.08909
2020 arXiv
-
[19]
H. He, W. Yao, K. Ma, W. Yu, Y. Dai, H. Zhang, Z. Lan, and D. Yu. Webvoyager: Building an end-to-end web agent with large multimodal models. arXiv preprint arXiv:2401.13919, 2024
2024 arXiv
-
[20]
L. Jang, Y. Li, D. Zhao, C. Ding, J. Lin, P. P. Liang, R. Bonatti, and K. Koishida. Videowebarena: Evaluating long context multimodal agents with video understanding web tasks. arXiv preprint arXiv:2410.19100, 2024
2024 arXiv
-
[21]
W. G. JSON-RPC. Json-rpc 2.0 specification, Jan. 2013. URL https://www.jsonrpc.org/specification
2013
-
[22]
Karpukhin, B
V. Karpukhin, B. Oğuz, S. Min, P. Lewis, L. Wu, S. Edunov, D. Chen, and W. tau Yih. Dense passage retrieval for open-domain question answering, 2020. URL https://arxiv.org/abs/2004.04906
2020 arXiv
-
[23]
Khattab and M
O. Khattab and M. Zaharia. Colbert: Efficient and effective passage search via contextualized late interaction over bert, 2020. URL https://arxiv.org/abs/2004.12832
2020 arXiv
-
[24]
J. Y. Koh, R. Lo, L. Jang, V. Duvvur, M. C. Lim, P.-Y. Huang, G. Neubig, S. Zhou, R. Salakhutdinov, and D. Fried. VisualWebArena : Evaluating Multimodal Agents on Realistic Visual Web Tasks , June 2024 a . URL http://arxiv.org/abs/2401.13649. arXiv:2401.13649 [cs]
2024 arXiv
-
[25]
J. Y. Koh, S. McAleer, D. Fried, and R. Salakhutdinov. Tree search for language model agents, 2024 b . URL https://arxiv.org/abs/2407.01476
2024
-
[26]
H. Lai, X. Liu, I. L. Iong, S. Yao, Y. Chen, P. Shen, H. Yu, H. Zhang, X. Zhang, Y. Dong, and J. Tang. Autowebglm: A large language model-based web navigating agent, 2024. URL https://arxiv.org/abs/2404.03648
2024 arXiv
-
[27]
Lee, M.-W
K. Lee, M.-W. Chang, and K. Toutanova. Latent retrieval for weakly supervised open domain question answering, 2019. URL https://arxiv.org/abs/1906.00300
2019 arXiv
-
[28]
I. Levy, B. Wiesel, S. Marreed, A. Oved, A. Yaeli, and S. Shlomov. St-webagentbench: A benchmark for evaluating safety and trustworthiness in web agents, 2024. URL https://arxiv.org/abs/2410.06703
2024 arXiv
-
[29]
u ttler, M. Lewis, W.-t. Yih, T. Rockt \
P. Lewis, E. Perez, A. Piktus, F. Petroni, V. Karpukhin, N. Goyal, H. K \"u ttler, M. Lewis, W.-t. Yih, T. Rockt \"a schel, et al. Retrieval-augmented generation for knowledge-intensive nlp tasks. Advances in neural information processing systems, 33: 0 9459--9474, 2020
2020
-
[30]
W. Li, W. Bishop, A. Li, C. Rawles, F. Campbell-Ajala, D. Tyamagundlu, and O. Riva. On the effects of data scale on ui control agents. In Neural Information Processing Systems, 2024. URL https://api.semanticscholar.org/CorpusID:270285816
2024
-
[31]
Z. Liao, J. Jones, L. Jiang, E. Fosler-Lussier, Y. Su, Z. Lin, and H. Sun. Redteamcua: Realistic adversarial testing of computer-use agents in hybrid web-os environments, 2025. URL https://arxiv.org/abs/2505.21936
2025
-
[32]
E. Z. Liu, K. Guu, P. Pasupat, T. Shi, and P. Liang. Reinforcement learning on web interfaces using workflow-guided exploration. In International Conference on Learning Representations ( ICLR ) , 2018. URL https://arxiv.org/abs/1802.08802
2018 arXiv
-
[33]
J. Liu, Y. Song, B. Y. Lin, W. Lam, G. Neubig, Y. Li, and X. Yue. Visualwebbench: How far have multimodal llms evolved in web page understanding and grounding? arXiv preprint arXiv:2404.05955, 2024 a
2024 arXiv
-
[34]
X. Liu, H. Yu, H. Zhang, Y. Xu, X. Lei, H. Lai, Y. Gu, H. Ding, K. Men, K. Yang, S. Zhang, X. Deng, A. Zeng, Z. Du, C. Zhang, S. Shen, T. Zhang, Y. Su, H. Sun, M. Huang, Y. Dong, and J. Tang. Agentbench: Evaluating llms as agents, 2023. URL https://arxiv.org/abs/2308.03688
2023 arXiv
-
[35]
X. Liu, T. Zhang, Y. Gu, I. L. Iong, Y. Xu, X. Song, S. Zhang, H. Lai, X. Liu, H. Zhao, et al. Visualagentbench: Towards large multimodal models as visual foundation agents. arXiv preprint arXiv:2408.06327, 2024 b
2024 arXiv
-
[36]
X. H. L \`u , Z. Kasner, and S. Reddy. Weblinx: Real-world website navigation with multi-turn dialogue. arXiv preprint arXiv:2402.05930, 2024
2024
-
[37]
X. H. Lù, A. Kazemnejad, N. Meade, A. Patel, D. Shin, A. Zambrano, K. Stańczak, P. Shaw, C. J. Pal, and S. Reddy. Agentrewardbench: Evaluating automatic evaluations of web agent trajectories, 2025
2025
-
[38]
Murty, C
S. Murty, C. Manning, P. Shaw, M. Joshi, and K. Lee. Bagel: Bootstrapping agents by guiding exploration with language, 2024. URL https://arxiv.org/abs/2403.08140
2024 arXiv
-
[39]
Murty, H
S. Murty, H. Zhu, D. Bahdanau, and C. D. Manning. Nnetnav: Unsupervised learning of browser agents through environment interaction in the wild, 2025. URL https://arxiv.org/abs/2410.02907
2025 arXiv
-
[40]
Nakano, J
R. Nakano, J. Hilton, S. Balaji, J. Wu, L. Ouyang, C. Kim, C. Hesse, S. Jain, V. Kosaraju, W. Saunders, X. Jiang, K. Cobbe, T. Eloundou, G. Krueger, K. Button, M. Knight, B. Chess, and J. Schulman. Webgpt: Browser-assisted question-answering with human feedback, 2022. URL http...
2022 arXiv
-
[41]
Introducing chatgpt search, Oct
OpenAI. Introducing chatgpt search, Oct. 2024. URL https://openai.com/index/introducing-chatgpt-search/
2024
-
[42]
Introducing deep research, Feb
OpenAI. Introducing deep research, Feb. 2025. URL https://openai.com/index/introducing-deep-research/
2025
-
[43]
Y. Pan, D. Kong, S. Zhou, C. Cui, Y. Leng, B. Jiang, H. Liu, Y. Shang, S. Zhou, T. Wu, and Z. Wu. Webcanvas: Benchmarking web agents in online environments, 2024. URL https://arxiv.org/abs/2406.12373
2024 arXiv
-
[44]
Patel, M
A. Patel, M. Hofmarcher, C. Leoveanu-Condrei, M.-C. Dinu, C. Callison-Burch, and S. Hochreiter. Large language models can self-improve at web agent tasks, 2024. URL https://arxiv.org/abs/2405.20309
2024 arXiv
-
[45]
Prasad, A
A. Prasad, A. Koller, M. Hartmann, P. Clark, A. Sabharwal, M. Bansal, and T. Khot. Adapt: As-needed decomposition and planning with language models, 2024. URL https://arxiv.org/abs/2311.05772
2024 arXiv
-
[46]
Z. Qi, X. Liu, I. L. Iong, H. Lai, X. Sun, W. Zhao, Y. Yang, X. Yang, J. Sun, S. Yao, T. Zhang, W. Xu, J. Tang, and Y. Dong. Webrl: Training llm web agents via self-evolving online curriculum reinforcement learning, 2025. URL https://arxiv.org/abs/2411.02337
2025 arXiv
-
[47]
Y. Qin, S. Hu, Y. Lin, W. Chen, N. Ding, G. Cui, Z. Zeng, Y. Huang, C. Xiao, C. Han, Y. R. Fung, Y. Su, H. Wang, C. Qian, R. Tian, K. Zhu, S. Liang, X. Shen, B. Xu, Z. Zhang, Y. Ye, B. Li, Z. Tang, J. Yi, Y. Zhu, Z. Dai, L. Yan, X. Cong, Y. Lu, W. Zhao, Y. Huang, J. Yan, X. Ha...
2023
-
[48]
Y. Qin, Y. Ye, J. Fang, H. Wang, S. Liang, S. Tian, J. Zhang, J. Li, Y. Li, S. Huang, et al. Ui-tars: Pioneering automated gui interaction with native agents. arXiv preprint arXiv:2501.12326, 2025
2025 arXiv
-
[49]
Rawles, A
C. Rawles, A. Li, D. Rodriguez, O. Riva, and T. Lillicrap. Androidinthewild: A large-scale dataset for android device control. Advances in Neural Information Processing Systems, 36: 0 59708--59728, 2023
2023
-
[50]
Rawles, S
C. Rawles, S. Clinckemaillie, Y. Chang, J. Waltz, G. Lau, M. Fair, A. Li, W. Bishop, W. Li, F. Campbell-Ajala, et al. Androidworld: A dynamic benchmarking environment for autonomous agents. arXiv preprint arXiv:2405.14573, 2024
2024 arXiv
-
[51]
Schick, J
T. Schick, J. Dwivedi-Yu, R. Dessì, R. Raileanu, M. Lomeli, L. Zettlemoyer, N. Cancedda, and T. Scialom. Toolformer: Language models can teach themselves to use tools, 2023. URL https://arxiv.org/abs/2302.04761
2023 arXiv
-
[52]
P. Shaw, M. Joshi, J. Cohan, J. Berant, P. Pasupat, H. Hu, U. Khandelwal, K. Lee, and K. N. Toutanova. From pixels to ui actions: Learning to follow instructions via graphical user interfaces. Advances in Neural Information Processing Systems, 36: 0 34354--34370, 2023
2023
-
[53]
T. Shi, A. Karpathy, L. J. Fan, J. Z. Hern \'a ndez, and P. Liang. World of bits: An open-domain platform for web-based agents. In International Conference on Machine Learning, 2017. URL https://api.semanticscholar.org/CorpusID:34953552
2017
-
[54]
Shinn, F
N. Shinn, F. Cassano, E. Berman, A. Gopinath, K. Narasimhan, and S. Yao. Reflexion: Language agents with verbal reinforcement learning, 2023. URL https://arxiv.org/abs/2303.11366
2023 arXiv
-
[55]
R. W. Shirey. Internet Security Glossary, Version 2 . RFC 4949, Aug. 2007. URL https://www.rfc-editor.org/info/rfc4949
2007
-
[56]
Sodhi, S
P. Sodhi, S. Branavan, Y. Artzi, and R. McDonald. Step: Stacked llm policies for web actions. arXiv preprint arXiv:2310.03720, 2024
2024 arXiv
-
[57]
Y. Song, F. Xu, S. Zhou, and G. Neubig. Beyond browsing: Api-based web agents, 2025. URL https://arxiv.org/abs/2410.16464
2025 arXiv
-
[58]
Tiwary, V
N. Tiwary, V. Dongre, S. A. Chawla, A. Lamani, and D. Hakkani-Tür. From context to action: Analysis of the impact of state representation and context on the generalization of multi-turn web navigation agents, 2024. URL https://arxiv.org/abs/2410.23555
2024 arXiv
-
[59]
Trabucco, G
B. Trabucco, G. A. Sigurdsson, R. Piramuthu, and R. Salakhutdinov. Towards internet-scale training for agents. In Will Synthetic Data Finally Solve the Data Access Problem?, 2025. URL https://openreview.net/forum?id=6YFuaxXYxP
2025
-
[60]
A. D. Tur, N. Meade, X. H. Lù, A. Zambrano, A. Patel, E. Durmus, S. Gella, K. Stańczak, and S. Reddy. Safearena: Evaluating the safety of autonomous web agents, 2025. URL https://arxiv.org/abs/2503.04957
2025 arXiv
-
[61]
X. Wan, J. Yang, and J. Xiao. Towards an iterative reinforcement approach for simultaneous document summarization and keyword extraction. In Proceedings of the 45th annual meeting of the association of computational linguistics, pages 552--559, 2007
2007
-
[62]
C. H. Wu, J. Y. Koh, R. Salakhutdinov, D. Fried, and A. Raghunathan. Adversarial attacks on multimodal agents. arXiv e-prints, pages arXiv--2406, 2024
2024
-
[63]
T. Xie, D. Zhang, J. Chen, X. Li, S. Zhao, R. Cao, T. J. Hua, Z. Cheng, D. Shin, F. Lei, et al. Osworld: Benchmarking multimodal agents for open-ended tasks in real computer environments. Advances in Neural Information Processing Systems, 37: 0 52040--52094, 2024
2024
-
[64]
T. Xue, W. Qi, T. Shi, C. H. Song, B. Gou, D. Song, H. Sun, and Y. Su. An illusion of progress? assessing the current state of web agents, 2025. URL https://arxiv.org/abs/2504.01382
2025
-
[65]
R. Yan, X. Wan, J. Otterbacher, L. Kong, X. Li, and Y. Zhang. Evolutionary timeline summarization: a balanced optimization framework via iterative substitution. In Proceedings of the 34th international ACM SIGIR conference on Research and development in Information Retrieval, ...
2011
-
[66]
J. Yang, H. Zhang, F. Li, X. Zou, C. Li, and J. Gao. Set-of-mark prompting unleashes extraordinary visual grounding in gpt-4v. arXiv preprint arXiv:2310.11441, 2023
2023 arXiv
-
[67]
J. Yang, C. E. Jimenez, A. Wettig, K. Lieret, S. Yao, K. Narasimhan, and O. Press. Swe-agent: Agent-computer interfaces enable automated software engineering. Advances in Neural Information Processing Systems, 37: 0 50528--50652, 2024
2024
-
[68]
Zhang, X
H. Zhang, X. Liu, and J. Zhang. Summit: Iterative text summarization via chatgpt. arXiv preprint arXiv:2305.14835, 2023
2023 arXiv
-
[69]
Zheng, B
B. Zheng, B. Gou, J. Kil, H. Sun, and Y. Su. Gpt-4v (ision) is a generalist web agent, if grounded. arXiv preprint arXiv:2401.01614, 2024
2024 arXiv
-
[70]
Zheng, M
B. Zheng, M. Y. Fatemi, X. Jin, Z. Z. Wang, A. Gandhi, Y. Song, Y. Gu, J. Srinivasa, G. Liu, G. Neubig, and Y. Su. Skillweaver: Web agents can self-improve by discovering and honing skills. 2025. URL https://api.semanticscholar.org/CorpusID:277634081
2025
-
[71]
S. Zhou, F. F. Xu, H. Zhu, X. Zhou, R. Lo, A. Sridhar, X. Cheng, T. Ou, Y. Bisk, D. Fried, U. Alon, and G. Neubig. WebArena : A Realistic Web Environment for Building Autonomous Agents , Apr. 2024. URL http://arxiv.org/abs/2307.13854. arXiv:2307.13854 [cs]
2024 arXiv
Reviewed August 7, 2026 · model on record in the stance chip above.
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