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HFEPX Hub

Web Browsing + Automatic Metrics Papers

Updated from current HFEPX corpus (Apr 12, 2026). 36 papers are grouped in this hub page.

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Updated from current HFEPX corpus (Apr 12, 2026). 36 papers are grouped in this hub page. Common evaluation modes: Automatic Metrics, Simulation Env. Most common rater population: Domain Experts. Common annotation unit: Trajectory. Frequent quality control: Adjudication. Frequently cited benchmark: BIRD. 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 Jul 15, 2025.

Papers: 36 Last published: Jul 15, 2025 Global RSS Tag RSS
Web BrowsingAutomatic Metrics

Researcher Quick Triage

This hub is best used for protocol triage and replication planning from abstract-level evidence. Quality band: Medium .

High-Signal Coverage

100.0%

36 / 36 sampled papers are not low-signal flagged.

Replication-Ready Set

9

Benchmark + metric + eval mode explicitly present.

Judge/Human Comparability

0

Papers containing both `human_eval` and `llm_as_judge`.

  • 9 papers are replication-ready (benchmark + metric + explicit evaluation mode).
  • 0 papers support judge-vs-human agreement analysis.
  • 1 papers report explicit quality controls (calibration/adjudication/IAA).

Primary action: Start with the top 2 papers in “Start Here”, then validate assumptions in the protocol matrix.

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Why This Matters For Eval Research

  • 30.6% of papers report explicit human-feedback signals, led by pairwise preferences.
  • automatic metrics appears in 100% of papers in this hub.
  • BIRD is a recurring benchmark anchor for cross-paper comparisons in this page.

Protocol Takeaways

  • Most common quality-control signal is adjudication (2.8% of papers).
  • Rater context is mostly domain experts, and annotation is commonly trajectory-level annotation; use this to scope replication staffing.
  • Stratify by benchmark (BIRD vs BrowseComp) before comparing methods.

Benchmark Interpretation

  • BIRD appears in 5.6% of hub papers (2/36); use this cohort for benchmark-matched comparisons.
  • BrowseComp appears in 2.8% of hub papers (1/36); use this cohort for benchmark-matched comparisons.

Metric Interpretation

  • accuracy is reported in 58.3% of hub papers (21/36); compare with a secondary metric before ranking methods.
  • cost is reported in 13.9% of hub papers (5/36); compare with a secondary metric before ranking methods.
Researcher Checklist (Expanded)

Researcher Checklist

  • Moderate: Papers with explicit human feedback

    Coverage is usable but incomplete (30.6% vs 45% target).

  • Gap: Papers reporting quality controls

    Coverage is a replication risk (2.8% vs 30% target).

  • Moderate: Papers naming benchmarks/datasets

    Coverage is usable but incomplete (30.6% vs 35% target).

  • Strong: Papers naming evaluation metrics

    Coverage is strong (94.4% vs 35% target).

  • Gap: Papers with known rater population

    Coverage is a replication risk (8.3% vs 35% target).

  • Gap: Papers with known annotation unit

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

Strengths

  • Agentic evaluation appears in 100% of papers.

Known Gaps

  • Only 2.8% of papers report quality controls; prioritize calibration/adjudication evidence.
  • Rater population is under-specified (8.3% coverage).
  • Annotation unit is under-specified (16.7% coverage).

Suggested Next Analyses

  • Stratify by benchmark (BIRD vs BrowseComp) before comparing methods.
  • Track metric sensitivity by reporting both accuracy and cost.
  • Add inter-annotator agreement checks when reproducing these protocols.
Recommended Queries (Expanded)

Recommended Queries

Start with These 3

Use these when you need one protocol anchor, one benchmark anchor, and one recent comparison point before reading the wider hub.

Start Here (Best First 6)

Ranked for protocol completeness (human signal, benchmark + metric anchors, quality controls, and judge/human overlap).

Protocol Matrix (Top 12)

Use this to quickly compare protocol ingredients instead of scanning long prose.

Paper HF Signal Eval Modes Benchmarks Metrics QC
SODIUM: From Open Web Data to Queryable Databases

Mar 19, 2026

Yes Automatic Metrics Sodium Bench Accuracy Not Reported
Vision-DeepResearch Benchmark: Rethinking Visual and Textual Search for Multimodal Large Language Models

Feb 2, 2026

Yes Automatic Metrics Vdr Bench Not Reported Adjudication
MemoryArena: Benchmarking Agent Memory in Interdependent Multi-Session Agentic Tasks

Feb 18, 2026

Yes Automatic Metrics Memoryarena Recall Not Reported
Let's Think in Two Steps: Mitigating Agreement Bias in MLLMs with Self-Grounded Verification

Jul 15, 2025

Yes Automatic Metrics , Simulation Env VisualWebArena , OSWorld Accuracy Not Reported
RedTeamCUA: Realistic Adversarial Testing of Computer-Use Agents in Hybrid Web-OS Environments

May 28, 2025

Yes Automatic Metrics Rtc Bench Jailbreak success rate Not Reported
Don't Overthink It: Inter-Rollout Action Agreement as a Free Adaptive-Compute Signal for LLM Agents

Apr 9, 2026

No
Not Reported
Automatic Metrics GSM8K Accuracy Not Reported
LRC-WeatherNet: LiDAR, RADAR, and Camera Fusion Network for Real-time Weather-type Classification in Autonomous Driving

Mar 23, 2026

No
Not Reported
Automatic Metrics BIRD Precision Not Reported
Vibe Code Bench: Evaluating AI Models on End-to-End Web Application Development

Mar 4, 2026

Yes Automatic Metrics Not Reported Accuracy , Agreement Not Reported
Sabiá-4 Technical Report

Mar 10, 2026

Yes Automatic Metrics Not Reported Accuracy , Cost Not Reported
BEACON: Language-Conditioned Navigation Affordance Prediction under Occlusion

Mar 10, 2026

No
Not Reported
Automatic Metrics , Simulation Env BIRD Accuracy Not Reported
MUSE: A Run-Centric Platform for Multimodal Unified Safety Evaluation of Large Language Models

Mar 3, 2026

Yes Automatic Metrics Not Reported Success rate , Jailbreak success rate Not Reported
Modeling Distinct Human Interaction in Web Agents

Feb 19, 2026

Yes Automatic Metrics Not Reported Accuracy Not Reported

Protocol Diff (Top Papers)

Fast side-by-side comparison for the highest-ranked papers in this hub.

Signal SODIUM: From Open Web Data to Queryable Databases Vision-DeepResearch Benchmark: Rethinking Visual an… MemoryArena: Benchmarking Agent Memory in Interdepe…
Human Feedback Expert VerificationExpert VerificationPairwise Preference
Evaluation Modes Automatic MetricsAutomatic MetricsAutomatic Metrics
Benchmarks Sodium BenchVdr BenchMemoryarena
Metrics AccuracyNot reportedRecall
Quality Controls Not reportedAdjudicationNot reported
Rater Population Domain ExpertsDomain ExpertsUnknown
Annotation Unit UnknownUnknownUnknown
Suggested Reading Order (Extended)

This section is intentionally expanded only when needed; use “Start Here” above for a faster pass.

Suggested Reading Order

  1. Don't Overthink It: Inter-Rollout Action Agreement as a Free Adaptive-Compute Signal for LLM Agents

    Start here for detailed protocol reporting and quality-control evidence. Signals: automatic metrics. Focus: GSM8K / accuracy. Abstract: Inference-time compute scaling has emerged as a powerful technique for improving.

  2. AgentGL: Towards Agentic Graph Learning with LLMs via Reinforcement Learning

    Start here for detailed protocol reporting and quality-control evidence. Signals: automatic metrics. Focus: accuracy. Abstract: Large Language Models (LLMs) increasingly rely on agentic capabilities-iterative retrieval, tool use, and.

  3. From Guessing to Placeholding: A Cost-Theoretic Framework for Uncertainty-Aware Code Completion

    Start here for detailed protocol reporting and quality-control evidence. Signals: automatic metrics. Focus: cost. Abstract: While Large Language Models (LLMs) have demonstrated exceptional proficiency in code completion, they.

  4. Let's Think in Two Steps: Mitigating Agreement Bias in MLLMs with Self-Grounded Verification

    Include a human-eval paper to calibrate against judge-based evaluation settings. Signals: automatic metrics + pairwise preferences. Focus: VisualWebArena / accuracy. Abstract: Multimodal LLMs (MLLMs) offer a promising solution,.

  5. Vision-DeepResearch Benchmark: Rethinking Visual and Textual Search for Multimodal Large Language Models

    Include a human-eval paper to calibrate against judge-based evaluation settings. Signals: automatic metrics + expert verification. Focus: Vdr-Bench. Abstract: Multimodal Large Language Models (MLLMs) have advanced VQA and.

  6. SODIUM: From Open Web Data to Queryable Databases

    Adds automatic metrics with expert verification for broader protocol coverage within this hub. Signals: automatic metrics + expert verification. Focus: Sodium-Bench / accuracy. Abstract: During research, domain experts.

  7. MemoryArena: Benchmarking Agent Memory in Interdependent Multi-Session Agentic Tasks

    Adds automatic metrics with pairwise preferences for broader protocol coverage within this hub. Signals: automatic metrics + pairwise preferences. Focus: Memoryarena / recall. Abstract: MemoryArena supports evaluation across.

  8. RedTeamCUA: Realistic Adversarial Testing of Computer-Use Agents in Hybrid Web-OS Environments

    Adds automatic metrics with red-team protocols for broader protocol coverage within this hub. Signals: automatic metrics + red-team protocols. Focus: Rtc-Bench / jailbreak success rate. Abstract: Computer-use agents.

Known Limitations

Known Limitations

  • Only 2.8% of papers report quality controls; prioritize calibration/adjudication evidence.
  • Rater population is under-specified (8.3% coverage).
  • Narrative synthesis is grounded in metadata and abstracts only; full-paper implementation details are not parsed.
Research Utility Snapshot (Detailed)

Research Utility Snapshot

Human Feedback Mix

  • Pairwise Preference (6)
  • Expert Verification (2)
  • Red Team (2)
  • Demonstrations (1)

Evaluation Modes

  • Automatic Metrics (36)
  • Simulation Env (4)

Top Benchmarks

  • BIRD (2)
  • BrowseComp (1)
  • DROP (1)
  • GSM8K (1)

Top Metrics

  • Accuracy (21)
  • Cost (5)
  • Precision (4)
  • Agreement (3)

Rater Population Mix

  • Domain Experts (3)

Quality Controls

  • Adjudication (1)
Coverage diagnostics (sample-based): human-feedback 30.6% · benchmarks 27.8% · metrics 94.4% · quality controls 2.8%.

Top Papers

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