Autonomous Web Navigation

Description from the archive archive 2025-07-28.

Benchmarks archive 2025-07-28

No benchmark for this task in the archive.

Libraries

Not in the archive: the export carries no per-task library table, so there is nothing to show at snapshot 2025-07-28.

Datasets archive 2025-07-28

No dataset record in the archive lists this task.

Subtasks archive 2025-07-28

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Parent tasks archive 2025-07-28

Most implemented papers archive 2025-07-28

4 shown of 4 papers with code (9 tagged with this task in all), ordered by repositories listed in the archive, not by stars (the archive holds no stars, so PwC's “Social” and “Latest” sorts cannot be reproduced). Papers without a page here are shown as plain text.

• REAL: Benchmarking Autonomous Agents on Deterministic Simulations of Real Websites
  15 Apr 2025 2 repositories listed Syntology ran 1 of 5 samples · 4 unverified
  We introduce REAL, a benchmark and framework for multi-turn agent evaluations on deterministic simulations of real-world websites.
• Magma: A Foundation Model for Multimodal AI Agents
  18 Feb 2025 2 repositories listed Syntology ran 2 of 16 samples · 14 unverified
  We present Magma, a foundation model that serves multimodal AI agentic tasks in both the digital and physical worlds.
• Agent-E: From Autonomous Web Navigation to Foundational Design Principles in Agentic Systems
  17 Jul 2024 2 repositories listed
  AI Agents are changing the way work gets done, both in consumer and enterprise domains.
• ST-WebAgentBench: A Benchmark for Evaluating Safety and Trustworthiness in Web Agents
  9 Oct 2024 1 repository listed Syntology ran 1 of 1 samples · 0 unverified · 1 pointer-only (licence)
  Autonomous web agents solve complex browsing tasks, yet existing benchmarks measure only whether an agent finishes a task, ignoring whether it does so safely or in a way enterprises can trust.

Syntology lines on 3 of the papers shown; no Syntology record for the others (a paper without an arXiv id cannot be joined to the graph, and absence from the graph layer is not a recorded non-run). “Ran” means the sample executed on a synthesized fixture, not that the paper's result was reproduced. Read from the graph 2026-09-24.