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TimeWarp: Evaluating Web Agents by Revisiting the Past

Md Farhan Ishmam, Kenneth Marino · Mar 5, 2026 · Citations: 0

Demonstrations General Web Browsing
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How to use this page

Moderate trust

Use this for comparison and orientation, not as your only source.

Best use

Background context only

What to verify

Read the full paper before copying any benchmark, metric, or protocol choices.

Evidence quality

Moderate

Derived from extracted protocol signals and abstract evidence.

Abstract

The improvement of web agents on current benchmarks raises the question: Do today's agents perform just as well when the web changes? We introduce TimeWarp, a benchmark that emulates the evolving web using containerized environments that vary in UI, design, and layout. TimeWarp consists of three web environments, each with six UI versions spanning different eras of the internet, paired with a set of complex, realistic tasks requiring different forms of web navigation. Our experiments reveal web agents' vulnerability to changes and the limitations of behavior cloning (BC) on single-version trajectories. To address this, we propose TimeTraj, a simple yet effective algorithm that uses plan distillation to collect trajectories across multiple versions. By training agents on teacher rollouts using our BC-variant, we achieve substantial performance gains: $20.4\%\rightarrow37.7\%$ for Qwen-3 4B and $0\%\rightarrow27.0\%$ for Llama-3.1 8B models. We hope our work helps researchers study generalization across web designs and unlock a new paradigm for collecting plans rather than trajectories, thereby improving the robustness of web agents.

Low-signal caution for protocol decisions

Use this page for context, then validate protocol choices against stronger HFEPX references before implementation decisions.

  • The abstract does not clearly name benchmarks or metrics.
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Should You Rely On This Paper?

This paper is adjacent to HFEPX scope and is best used for background context, not as a primary protocol reference.

Best use

Background context only

Use if you need

A secondary eval reference to pair with stronger protocol papers.

Main weakness

The abstract does not clearly name benchmarks or metrics.

Trust level

Moderate

Usefulness score

40/100 • Low

Treat as adjacent context, not a core eval-method reference.

Human Feedback Signal

Detected

Evaluation Signal

Detected

Usefulness for eval research

Adjacent candidate

Extraction confidence 50%

If you are doing eval pipeline work, start here:

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What We Could Verify

These are the protocol signals we could actually recover from the available paper metadata. Use them to decide whether this paper is worth deeper reading.

Human Feedback Types

strong

Demonstrations

Directly usable for protocol triage.

"The improvement of web agents on current benchmarks raises the question: Do today's agents perform just as well when the web changes?"

Evaluation Modes

missing

None explicit

Validate eval design from full paper text.

"The improvement of web agents on current benchmarks raises the question: Do today's agents perform just as well when the web changes?"

Quality Controls

missing

Not reported

No explicit QC controls found.

"The improvement of web agents on current benchmarks raises the question: Do today's agents perform just as well when the web changes?"

Benchmarks / Datasets

missing

Not extracted

No benchmark anchors detected.

"The improvement of web agents on current benchmarks raises the question: Do today's agents perform just as well when the web changes?"

Reported Metrics

missing

Not extracted

No metric anchors detected.

"The improvement of web agents on current benchmarks raises the question: Do today's agents perform just as well when the web changes?"

Human Feedback Details

  • Uses human feedback: Yes
  • Feedback types: Demonstrations
  • Rater population: Not reported
  • Unit of annotation: Trajectory
  • Expertise required: General

Evaluation Details

  • Evaluation modes:
  • Agentic eval: Web Browsing
  • Quality controls: Not reported
  • Evidence quality: Moderate
  • Use this page as: Background context only

Protocol And Measurement Signals

Benchmarks / Datasets

No benchmark or dataset names were extracted from the available abstract.

Reported Metrics

No metric terms were extracted from the available abstract.

Research Brief

Metadata summary

The improvement of web agents on current benchmarks raises the question: Do today's agents perform just as well when the web changes?

Based on abstract + metadata only. Check the source paper before making high-confidence protocol decisions.

Key Takeaways

  • The improvement of web agents on current benchmarks raises the question: Do today's agents perform just as well when the web changes?
  • We introduce TimeWarp, a benchmark that emulates the evolving web using containerized environments that vary in UI, design, and layout.
  • TimeWarp consists of three web environments, each with six UI versions spanning different eras of the internet, paired with a set of complex, realistic tasks requiring different forms of web navigation.

Researcher Actions

  • Compare this paper against nearby papers in the same arXiv category before using it for protocol decisions.
  • Check the full text for explicit evaluation design choices (raters, protocol, and metrics).
  • Use related-paper links to find stronger protocol-specific references.

Caveats

  • Generated from abstract + metadata only; no PDF parsing.
  • Signals below are heuristic and may miss details reported outside the abstract.

Recommended Queries

Research Summary

Contribution Summary

  • The improvement of web agents on current benchmarks raises the question: Do today's agents perform just as well when the web changes?
  • We introduce TimeWarp, a benchmark that emulates the evolving web using containerized environments that vary in UI, design, and layout.
  • To address this, we propose TimeTraj, a simple yet effective algorithm that uses plan distillation to collect trajectories across multiple versions.

Why It Matters For Eval

  • The improvement of web agents on current benchmarks raises the question: Do today's agents perform just as well when the web changes?
  • We introduce TimeWarp, a benchmark that emulates the evolving web using containerized environments that vary in UI, design, and layout.

Researcher Checklist

  • Pass: Human feedback protocol is explicit

    Detected: Demonstrations

  • Gap: Evaluation mode is explicit

    No clear evaluation mode extracted.

  • Gap: Quality control reporting appears

    No calibration/adjudication/IAA control explicitly detected.

  • Gap: Benchmark or dataset anchors are present

    No benchmark/dataset anchor extracted from abstract.

  • Gap: Metric reporting is present

    No metric terms extracted.

Related Papers

Papers are ranked by protocol overlap, extraction signal alignment, and semantic proximity.

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