René's URL Explorer Experiment


Title: GitHub - SAIL-Research-Lab/agentic-web: Agentic Web: Weaving the Next Web with AI Agents. · GitHub

Open Graph Title: GitHub - SAIL-Research-Lab/agentic-web: Agentic Web: Weaving the Next Web with AI Agents.

X Title: GitHub - SAIL-Research-Lab/agentic-web: Agentic Web: Weaving the Next Web with AI Agents.

Description: Agentic Web: Weaving the Next Web with AI Agents. Contribute to SAIL-Research-Lab/agentic-web development by creating an account on GitHub.

Open Graph Description: Agentic Web: Weaving the Next Web with AI Agents. Contribute to SAIL-Research-Lab/agentic-web development by creating an account on GitHub.

X Description: Agentic Web: Weaving the Next Web with AI Agents. Contribute to SAIL-Research-Lab/agentic-web development by creating an account on GitHub.

Mail addresses
shangding.gu@berkeley.edu
shangding.gu@berkeley.edu

Opengraph URL: https://github.com/SAIL-Research-Lab/agentic-web

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Domain: github.com

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Agentic Web Developmenthttps://github.com/SafeRL-Lab/agentic-web#agentic-web-development
Information Retrievalhttps://github.com/SafeRL-Lab/agentic-web#information-retrieval
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https://github.com/SAIL-Research-Lab/agentic-web/blob/main/docs/figures/web-evolution.png
https://github.com/SAIL-Research-Lab/agentic-web/blob/main/docs/figures/timeline-web-evolution.png
https://github.com/SafeRL-Lab/agentic-web#agentic-web-development
BetaWeb: Towards a Blockchain-enabled Trustworthy Agentic Webhttps://arxiv.org/pdf/2508.13787
A Survey of AI Agent Registry Solutionshttps://arxiv.org/pdf/2508.03095
Using the NANDA Index Architecture in Practice: An Enterprise Perspectivehttps://www.arxiv.org/pdf/2508.03101
Web3 x AI Agents: Landscape, Integrations, and Foundational Challengeshttps://arxiv.org/pdf/2508.02773
Plan-and-act: Improving planning of agents for long-horizon taskshttps://arxiv.org/pdf/2503.09572
A survey of webagents: Towards next-generation ai agents for web automation with large foundation modelshttps://arxiv.org/pdf/2503.23350v4
WebDancer: Towards Autonomous Information Seeking Agencyhttps://arxiv.org/pdf/2505.22648
From Web Search towards Agentic Deep Research: Incentivizing Search with Reasoning Agentshttps://arxiv.org/pdf/2506.18959
MA-RAG: Multi-Agent Retrieval-Augmented Generation via Collaborative Chain-of-Thought Reasoninghttps://arxiv.org/pdf/2505.20096
Deep Research Agents: A Systematic Examination And Roadmaphttps://arxiv.org/pdf/2506.18096
Smartagent: Chain-of-user-thought for embodied personalized agent in cyber worldhttps://arxiv.org/pdf/2412.07472
ArchRAG: Attributed Community-based Hierarchical Retrieval-Augmented Generationhttps://arxiv.org/pdf/2502.09891
Macrec: A multi-agent collaboration framework for recommendationhttps://dl.acm.org/doi/pdf/10.1145/3626772.3657669
Webarena: A realistic web environment for building autonomous agentshttps://arxiv.org/pdf/2307.13854
Toolllm: Facilitating large language models to master 16000+ real-world apishttps://arxiv.org/pdf/2307.16789
Api-bank: A comprehensive benchmark for tool-augmented llmshttps://arxiv.org/pdf/2304.08244
React: Synergizing reasoning and acting in language modelshttps://par.nsf.gov/servlets/purl/10451467
Voyager: An open-ended embodied agent with large language modelshttps://arxiv.org/pdf/2305.16291
Toolformer: Language models can teach themselves to use toolshttps://proceedings.neurips.cc/paper_files/paper/2023/file/d842425e4bf79ba039352da0f658a906-Paper-Conference.pdf
Swe-bench: Can language models resolve real-world github issues?https://arxiv.org/pdf/2310.06770
Hugginggpt: Solving ai tasks with chatgpt and its friends in hugging facehttps://proceedings.neurips.cc/paper_files/paper/2023/file/77c33e6a367922d003ff102ffb92b658-Paper-Conference.pdf
Training language models to follow instructions with human feedbackhttps://proceedings.neurips.cc/paper_files/paper/2022/file/b1efde53be364a73914f58805a001731-Paper-Conference.pdf
Webgpt: Browser-assisted question-answering with human feedbackhttps://arxiv.org/pdf/2112.09332
Deep reinforcement learning for list-wise recommendationshttps://arxiv.org/pdf/1801.00209
SlateQ: A Tractable Decomposition for Reinforcement Learning with Recommendation Sets.https://www.cs.toronto.edu/~cebly/Papers/SlateQ_IJCAI_2019.pdf
https://github.com/SafeRL-Lab/agentic-web#information-retrieval
Agentic information retrievalhttps://arxiv.org/pdf/2410.09713
Large language models for generative information extraction: A surveyhttps://link.springer.com/content/pdf/10.1007/s11704-024-40555-y.pdf
Bias and unfairness in information retrieval systems: New challenges in the llm erahttps://dl.acm.org/doi/pdf/10.1145/3637528.3671458
Retrieval-augmented code generation for universal information extractionhttps://arxiv.org/pdf/2311.02962
Large language models for information retrieval: A surveyhttps://arxiv.org/pdf/2308.07107
Inpars-v2: Large language models as efficient dataset generators for information retrievalhttps://arxiv.org/pdf/2301.01820
Unified structure generation for universal information extractionhttps://arxiv.org/pdf/2203.12277
An Adversarial Imitation Click Model for Information Retrievalhttps://dl.acm.org/doi/pdf/10.1145/3442381.3449913?casa_token=uxTFt1GHdzYAAAAA:oYcBl_NFP7Rx-rB1c6Ol_aGVykWPtWWBUmkjuulSP_eEONMgYsnJJOJRHobEUI5aTic8j88fz7qd
A survey on deep matrix factorizationshttps://www.sciencedirect.com/science/article/pii/S1574013721000630?casa_token=X2qx6lyiRwgAAAAA:DDn-5yrnsytdljuWnDKVW0B_xmJGsM67KFR1nosP_37MiozL88O-dfysupN-KenOJ1YX7SDPJQ
Deepcf: A unified framework of representation learning and matching function learning in recommender systemhttps://arxiv.org/pdf/1901.04704
On the Equilibrium of Query Reformulation and Document Retrievalhttps://dl.acm.org/doi/pdf/10.1145/3234944.3234962?casa_token=uohit0RoaXgAAAAA:kBBh30W1qHWnaYytiXtgLMGnvTszUPxfBSzLNA29Tik0gQ7-DeECH2lqzUM1Qim9kZ4OhisDxyHT
Irgan: A minimax game for unifying generative and discriminative information retrieval modelshttps://dl.acm.org/doi/pdf/10.1145/3077136.3080786?casa_token=ewZ3STWowa0AAAAA:GOB0cDaNh9gqsZd41Yu2CR_BPRiOrmaWoVa8iHhXw3FEASrhqe3W6rqs21FUYCWrCzuYuuV_nOyS
Neural collaborative filteringhttps://dl.acm.org/doi/pdf/10.1145/3038912.3052569?casa_token=voDsTiXaNKAAAAAA:RCPudBDnAYslj7UDu02FBvZAEN-vgTXy0ttzVpjDOVPi6TiXakxTw3vK18WV0POM8lcZTUh0e8fp
DeepFM: a factorization-machine based neural network for CTR predictionhttps://arxiv.org/pdf/1703.04247
Wide & deep learning for recommender systemshttps://dl.acm.org/doi/pdf/10.1145/2988450.2988454
Autorec: Autoencoders meet collaborative filteringhttps://dl.acm.org/doi/pdf/10.1145/2740908.2742726?casa_token=TPRVrykjKM0AAAAA:eOATJu7QXZu8KoFHwK6Zfbw6PJS1LrTCMDTXzIGKiwW02QHqTwmJDlDqCVoClCDKLYtrvFQBdvSm
Top-k Retrieval using Facility Location Analysishttps://citeseerx.ist.psu.edu/document?repid=rep1&type=pdf&doi=f34d6ac976f927f30b91618437604500006de3d2
Mean-variance analysis: A new document ranking theory in information retrievalhttp://web4.cs.ucl.ac.uk/staff/jun.wang/papers/2009-ecir09-mva.pdf
Portfolio theory of information retrievalhttps://dl.acm.org/doi/pdf/10.1145/1571941.1571963?casa_token=D8WiNZD_4b8AAAAA:f2oduiAJ3xYRq6oNRlRV-w7FYKAIq9-SVwcEUm9BXXu0KloUsgg_mazWw6VRM4nm_X6jpY23eC2W
The probabilistic relevance framework: BM25 and beyondhttps://www.nowpublishers.com/article/Details/INR-019
Matrix factorization techniques for recommender systemshttps://ieeexplore.ieee.org/document/5197422?denied=
Internet advertising and the generalized second-price auction: Selling billions of dollars worth of keywordshttps://www.nber.org/system/files/working_papers/w11765/w11765.pdf
A user-item relevance model for log-based collaborative filteringhttps://citeseerx.ist.psu.edu/document?repid=rep1&type=pdf&doi=53d0c7d5563edb817f02b41515e68ffbc5a649fd
Item-based collaborative filtering recommendation algorithmshttps://dl.acm.org/doi/pdf/10.1145/371920.372071?casa_token=tJ1WytFOvQYAAAAA:VH6P6CmYuDJQkFZMw-YiD_zqnuJ_sDYXXcvn1QxZXRCe9q13cK6xZJs-gd7UfAv-bzF6vo6OjTYu
The PageRank citation ranking: Bringing order to the webhttp://ilpubs.stanford.edu:8090/422/1/1999-66.pdf
Indexing by latent semantic analysishttp://ryanrossi.com/teaching/search/papers/indexing_by_LSA.pdf
A statistical interpretation of term specificity and its application in retrievalhttps://www.emerald.com/insight/content/doi/10.1108/eb026526/full/html
https://github.com/SafeRL-Lab/agentic-web#recommendation
A survey of large language model empowered agents for recommendation and search: Towards next-generation information retrievalhttps://arxiv.org/pdf/2503.05659
A survey on llm-powered agents for recommender systemshttps://arxiv.org/pdf/2502.10050
AgentRecBench: Benchmarking LLM Agent-based Personalized Recommender Systemshttps://arxiv.org/pdf/2505.19623
Deep reinforcement learning based resource allocation for network slicing with massive MIMOhttps://ieeexplore.ieee.org/stamp/stamp.jsp?arnumber=10186882
Macrec: A multi-agent collaboration framework for recommendationhttps://dl.acm.org/doi/pdf/10.1145/3626772.3657669
Raserec: Retrieval-augmented sequential recommendationhttps://arxiv.org/pdf/2412.18378
Probing early modification of gravity with Planck, ACT and SPThttps://arxiv.org/pdf/2308.12345
SlateQ: A Tractable Decomposition for Reinforcement Learning with Recommendation Setshttps://www.cs.toronto.edu/~cebly/Papers/SlateQ_IJCAI_2019.pdf
Novelty and diversity metrics for recommender systems: choice, discovery and relevancehttps://citeseerx.ist.psu.edu/document?repid=rep1&type=pdf&doi=ecb23352b3fd8abd32332790fda7aca59c498fdf
https://github.com/SafeRL-Lab/agentic-web#agent-planning
Plangenllms: A modern survey of llm planning capabilitieshttps://arxiv.org/pdf/2502.11221
Plan-and-act: Improving planning of agents for long-horizon taskshttps://arxiv.org/pdf/2503.09572
Acpbench: Reasoning about action, change, and planninghttps://arxiv.org/pdf/2410.05669
Natural plan: Benchmarking llms on natural language planninghttps://arxiv.org/pdf/2406.04520
Adaplanner: Adaptive planning from feedback with language modelshttps://proceedings.neurips.cc/paper_files/paper/2023/file/b5c8c1c117618267944b2617add0a766-Paper-Conference.pdf
Toolllm: Facilitating large language models to master 16000+ real-world apishttps://arxiv.org/pdf/2307.16789
Webarena: A realistic web environment for building autonomous agentshttps://arxiv.org/pdf/2307.13854
https://github.com/SafeRL-Lab/agentic-web#multi-agent-learning
Realm-bench: A real-world planning benchmark for llms and multi-agent systemshttps://arxiv.org/pdf/2502.18836
Autogen: Enabling next-gen LLM applications via multi-agent conversationshttps://openreview.net/pdf?id=BAakY1hNKS
Agentboard: An analytical evaluation board of multi-turn llm agentshttps://proceedings.neurips.cc/paper_files/paper/2024/file/877b40688e330a0e2a3fc24084208dfa-Paper-Datasets_and_Benchmarks_Track.pdf
Learning to use tools via cooperative and interactive agentshttps://arxiv.org/pdf/2403.03031
Camel: Communicative agents for" mind" exploration of large language model societyhttps://proceedings.neurips.cc/paper_files/paper/2023/file/a3621ee907def47c1b952ade25c67698-Paper-Conference.pdf
Agentverse: Facilitating multi-agent collaboration and exploring emergent behaviors in agentshttps://cz5waila03cyo0tux1owpyofgoryroob.itic-sci.com/9C/54/B4/9C54B47616795A320E36FCB1EA595C91.pdf
Metagpt: Meta programming for multi-agent collaborative frameworkhttps://arxiv.org/pdf/2308.00352
Taxai: A dynamic economic simulator and benchmark for multi-agent reinforcement learninghttps://arxiv.org/pdf/2309.16307
A Game-Theoretic Framework for Managing Risk in Multi-Agent Systemshttps://proceedings.mlr.press/v202/slumbers23a/slumbers23a.pdf
Chatdev: Communicative agents for software developmenthttps://arxiv.org/pdf/2307.07924
MarlRank: Multi-agent Reinforced Learning to Rankhttps://dl.acm.org/doi/pdf/10.1145/3357384.3358075?casa_token=e_YD3uJQKMgAAAAA:rFSxrP-KC0DtNPoeYgtkhZrWUHSA50bC3Dfsju1q5mftTb9EuFdsfH4Vg2Gor1gKtDwXF6Ibx8MV
Magent: A many-agent reinforcement learning platform for artificial collective intelligencehttps://arxiv.org/pdf/1712.00600
Real-Time Bidding with Multi-Agent Reinforcement Learning in Display Advertisinghttps://dl.acm.org/doi/pdf/10.1145/3269206.3272021?casa_token=MKQh5dDcz2oAAAAA:lYMjUiSArBDQRIM8pNzoAkh4R0eSA6-dl1UjW76veLPs4WoTYWSu_cjmssd7Qu5FXfsBj0ptW2Kk
https://github.com/SafeRL-Lab/agentic-web#safety-and-security
Securing agentic ai: A comprehensive threat model and mitigation framework for generative ai agentshttps://arxiv.org/pdf/2504.19956
Open challenges in multi-agent security: Towards secure systems of interacting ai agentshttps://arxiv.org/pdf/2505.02077
Model context protocol (mcp): Landscape, security threats, and future research directionshttps://arxiv.org/pdf/2503.23278
Ai agents under threat: A survey of key security challenges and future pathwayshttps://dl.acm.org/doi/pdf/10.1145/3716628
Enterprise-grade security for the model context protocol (mcp): Frameworks and mitigation strategieshttps://arxiv.org/pdf/2504.08623
Position: AI Safety Must Embrace an Antifragile Perspectivehttps://openreview.net/pdf?id=WpePuya3Ki
Red-teaming llm multi-agent systems via communication attackshttps://arxiv.org/pdf/2502.14847
Skin-in-the-game: Decision making via multi-stakeholder alignment in llmshttps://arxiv.org/pdf/2405.12933
Ai safety in generative ai large language models: A surveyhttps://arxiv.org/pdf/2407.18369
Agent-safetybench: Evaluating the safety of llm agentshttps://arxiv.org/pdf/2412.14470
Mart: Improving llm safety with multi-round automatic red-teaminghttps://arxiv.org/pdf/2311.07689
Aart: Ai-assisted red-teaming with diverse data generation for new llm-powered applicationshttps://arxiv.org/pdf/2311.08592
Red teaming language models with language modelshttps://arxiv.org/pdf/2202.03286
Improving alignment of dialogue agents via targeted human judgementshttps://arxiv.org/pdf/2209.14375
Adversarial training for high-stakes reliabilityhttps://proceedings.neurips.cc/paper_files/paper/2022/file/3c44405d619a6920384a45bce876b41e-Paper-Conference.pdf
Analyzing dynamic adversarial training data in the limithttps://arxiv.org/pdf/2110.08514
Dynabench: Rethinking benchmarking in NLPhttps://arxiv.org/pdf/2104.14337
Beyond accuracy: Behavioral testing of NLP models with CheckListhttps://arxiv.org/pdf/2005.04118
HateCheck: Functional tests for hate speech detection modelshttps://arxiv.org/pdf/2012.15606
Recipes for safety in open-domain chatbotshttps://arxiv.org/pdf/2010.07079
Counterfactual fairness in text classification through robustnesshttps://dl.acm.org/doi/pdf/10.1145/3306618.3317950
Avoiding reasoning shortcuts: Adversarial evaluation, training, and model development for multi-hop QAhttps://arxiv.org/pdf/1906.07132
Build it break it fix it for dialogue safety: Robustness from adversarial human attackhttps://arxiv.org/pdf/1908.06083
Adversarial NLI: A new benchmark for natural language understandinghttps://arxiv.org/pdf/1910.14599
The malicious use of artificial intelligence: Forecasting, prevention, and mitigationhttps://arxiv.org/pdf/1802.07228
Measuring and mitigating unintended bias in text classificationhttps://dl.acm.org/doi/pdf/10.1145/3278721.3278729
Adversarial examples for evaluating reading comprehension systemshttps://arxiv.org/pdf/1707.07328
Concrete problems in AI safetyhttps://arxiv.org/pdf/1606.06565
https://github.com/SafeRL-Lab/agentic-web#benchmark
ClawBench: Can AI Agents Complete Everyday Online Tasks?https://arxiv.org/abs/2604.08523
Mind2web 2: Evaluating agentic search with agent-as-a-judgehttps://proceedings.neurips.cc/paper_files/paper/2025/file/fdcec9f5b99aa4fc8f4fb8487802d737-Paper-Datasets_and_Benchmarks_Track.pdf
Safearena: Evaluating the safety of autonomous web agentshttps://arxiv.org/pdf/2503.04957
An Illusion of Progress? Assessing the Current State of Web Agentshttps://arxiv.org/pdf/2504.01382
Workarena: How capable are web agents at solving common knowledge work tasks?https://arxiv.org/pdf/2403.07718
The browsergym ecosystem for web agent researchhttps://arxiv.org/pdf/2412.05467
AssistantBench: Can Web Agents Solve Realistic and Time-Consuming Tasks?https://arxiv.org/pdf/2407.15711
VisualWebArena: Evaluating Multimodal Agents on Realistic Visual Web Taskshttps://arxiv.org/pdf/2401.13649
St-webagentbench: A benchmark for evaluating safety and trustworthiness in web agentshttps://arxiv.org/pdf/2410.06703
R-Judge: Benchmarking Safety Risk Awareness for LLM Agentshttps://arxiv.org/pdf/2401.10019
Webcanvas: Benchmarking web agents in online environmentshttps://arxiv.org/pdf/2406.12373
WebVoyager: Building an End-to-End Web Agent with Large Multimodal Modelshttps://arxiv.org/pdf/2401.13919
Mind2web: Towards a generalist agent for the webhttps://arxiv.org/pdf/2306.06070
Webshop: Towards scalable real-world web interaction with grounded language agentshttps://proceedings.neurips.cc/paper_files/paper/2022/file/82ad13ec01f9fe44c01cb91814fd7b8c-Paper-Conference.pdf
https://github.com/SafeRL-Lab/agentic-web#citation
arxiv.org/pdf/2507.21206https://arxiv.org/pdf/2507.21206
Readmehttps://github.com/SafeRL-Lab/agentic-web#readme-ov-file
Apache-2.0 licensehttps://github.com/SafeRL-Lab/agentic-web#Apache-2.0-1-ov-file
Activityhttps://github.com/SAIL-Research-Lab/agentic-web/activity
Custom propertieshttps://github.com/SAIL-Research-Lab/agentic-web/custom-properties
43 forkshttps://github.com/SAIL-Research-Lab/agentic-web/forks
Report repositoryhttps://github.com/contact/report-content?content_url=https%3A%2F%2Fgithub.com%2FSAIL-Research-Lab%2Fagentic-web&report=SAIL-Research-Lab+%28user%29
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Statushttps://www.githubstatus.com/
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