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HFEPX Hub

Automatic Metrics + Web Browsing Papers

Updated from current HFEPX corpus (Feb 27, 2026). 12 papers are grouped in this hub page. Common evaluation modes: Automatic Metrics, Human Eval. Most common rater population: Domain Experts. Frequently cited benchmark: BrowseComp. Common metric signal: accuracy. Use this page to compare protocol setup, judge behavior, and labeling design decisions before running new eval experiments. Newest paper in this set is from Feb 25, 2026.

Papers: 12 Last published: Feb 25, 2026 Global RSS Tag RSS
Automatic MetricsWeb Browsing

Research Narrative

Grounded narrative Model: deterministic-grounded

Updated from current HFEPX corpus (Feb 27, 2026). This page covers 12 papers centered on Automatic Metrics + Web Browsing Papers. Common evaluation modes include Automatic Metrics, Human Eval, with benchmark emphasis on BrowseComp, Retrieval. Metric concentration includes accuracy, task success, and the agentic footprint highlights Web Browsing, Long Horizon. Use the anchored takeaways below to compare protocol choices, quality-control patterns, and evidence depth before allocating new eval budget.

Why This Matters For Eval Research

Protocol Takeaways

Benchmark Interpretation

  • BrowseComp appears as a recurring benchmark anchor in this page.
  • 1 papers (8.3%) mention BrowseComp.
  • Most common evaluation modes: Automatic Metrics, Simulation Env.

Metric Interpretation

  • accuracy is a common reported metric and should be paired with protocol context before ranking methods.
  • 3 papers (25%) mention accuracy.
  • Most common evaluation modes: Automatic Metrics, Simulation Env.

Researcher Checklist

  • Papers with explicit human feedback: Coverage is a replication risk (16.7% vs 45% target).
  • Papers reporting quality controls: Coverage is a replication risk (0% vs 30% target).
  • Papers naming benchmarks/datasets: Coverage is a replication risk (16.7% vs 35% target).
  • Papers naming evaluation metrics: Coverage is strong (50% vs 35% target).
  • Papers with known rater population: Coverage is a replication risk (16.7% vs 35% target).
  • Papers with known annotation unit: Coverage is a replication risk (0% vs 35% target).

Papers with explicit human feedback

Coverage is a replication risk (16.7% vs 45% target).

Papers reporting quality controls

Coverage is a replication risk (0% vs 30% target).

Papers naming benchmarks/datasets

Coverage is a replication risk (16.7% vs 35% target).

Papers naming evaluation metrics

Coverage is strong (50% vs 35% target).

Papers with known rater population

Coverage is a replication risk (16.7% vs 35% target).

Papers with known annotation unit

Coverage is a replication risk (0% vs 35% target).

Suggested Reading Order

  1. 1. GUI-Libra: Training Native GUI Agents to Reason and Act with Action-aware Supervision and Partially Verifiable RL

    Start with this anchor paper for scope and protocol framing. Covers Automatic Metrics.

  2. 2. A Benchmark for Deep Information Synthesis

    Covers Human Eval, Automatic Metrics.

  3. 3. Onboard-Targeted Segmentation of Straylight in Space Camera Sensors

    Covers Automatic Metrics.

  4. 4. Mind the Style: Impact of Communication Style on Human-Chatbot Interaction

    Covers Automatic Metrics.

  5. 5. Modeling Distinct Human Interaction in Web Agents

    Covers Automatic Metrics. Includes human-feedback signal: Pairwise Preference.

  6. 6. BrowseComp-$V^3$: A Visual, Vertical, and Verifiable Benchmark for Multimodal Browsing Agents

    Covers Automatic Metrics, Simulation Env.

  7. 7. INSURE-Dial: A Phase-Aware Conversational Dataset & Benchmark for Compliance Verification and Phase Detection

    Covers Automatic Metrics.

  8. 8. Between Search and Platform: ChatGPT Under the DSA

    Covers Automatic Metrics.

Known Limitations

  • Narrative synthesis is grounded in metadata and abstracts only; full-paper method details may be missing.
  • Extraction fields are conservative and can under-report implicit protocol details.
  • Cross-page comparisons should control for benchmark and metric mismatch.

Research Utility Links

human_eval vs automatic_metrics

both=1, left_only=0, right_only=11

1 papers use both Human Eval and Automatic Metrics.

automatic_metrics vs simulation_env

both=1, left_only=11, right_only=0

1 papers use both Automatic Metrics and Simulation Env.

human_eval vs simulation_env

both=0, left_only=1, right_only=1

0 papers use both Human Eval and Simulation Env.

Top Papers

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