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HFEPX Hub

Automatic Metrics + General + Web Browsing Papers

Updated from current HFEPX corpus (Apr 19, 2026). 21 papers are grouped in this hub page.

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Updated from current HFEPX corpus (Apr 19, 2026). 21 papers are grouped in this hub page. Common evaluation modes: Automatic Metrics, Simulation Env. Most common rater population: Domain Experts. Common annotation unit: Multi Dim Rubric. 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 Mar 19, 2026.

Papers: 21 Last published: Mar 19, 2026 Global RSS Tag RSS
Automatic MetricsGeneralWeb Browsing

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%

21 / 21 sampled papers are not low-signal flagged.

Replication-Ready Set

5

Benchmark + metric + eval mode explicitly present.

Judge/Human Comparability

0

Papers containing both `human_eval` and `llm_as_judge`.

  • 5 papers are replication-ready (benchmark + metric + explicit evaluation mode).
  • 0 papers support judge-vs-human agreement analysis.
  • 0 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

  • 33.3% 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

  • Quality-control reporting is sparse in this slice; prioritize papers with explicit calibration or adjudication steps.
  • Rater context is mostly domain experts, and annotation is commonly multi-dimensional rubrics; use this to scope replication staffing.
  • Stratify by benchmark (BIRD vs BrowseComp) before comparing methods.

Benchmark Interpretation

  • BIRD appears in 4.8% of hub papers (1/21); use this cohort for benchmark-matched comparisons.
  • BrowseComp appears in 4.8% of hub papers (1/21); use this cohort for benchmark-matched comparisons.

Metric Interpretation

  • accuracy is reported in 57.1% of hub papers (12/21); compare with a secondary metric before ranking methods.
  • cost is reported in 9.5% of hub papers (2/21); compare with a secondary metric before ranking methods.
Researcher Checklist (Expanded)

Researcher Checklist

  • Moderate: Papers with explicit human feedback

    Coverage is usable but incomplete (33.3% vs 45% target).

  • Gap: Papers reporting quality controls

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

  • Moderate: Papers naming benchmarks/datasets

    Coverage is usable but incomplete (28.6% vs 35% target).

  • Strong: Papers naming evaluation metrics

    Coverage is strong (95.2% vs 35% target).

  • Gap: Papers with known rater population

    Coverage is a replication risk (9.5% vs 35% target).

  • Gap: Papers with known annotation unit

    Coverage is a replication risk (4.8% vs 35% target).

Strengths

  • Agentic evaluation appears in 100% of papers.

Known Gaps

  • Only 0% of papers report quality controls; prioritize calibration/adjudication evidence.
  • Rater population is under-specified (9.5% coverage).
  • Annotation unit is under-specified (4.8% coverage).

Suggested Next Analyses

  • Stratify by benchmark (BIRD vs BrowseComp) before comparing methods.
  • Track metric sensitivity by reporting both accuracy and cost.
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
MemoryArena: Benchmarking Agent Memory in Interdependent Multi-Session Agentic Tasks

Feb 18, 2026

Yes Automatic Metrics Memoryarena Recall 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
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
SpatiaLab: Can Vision-Language Models Perform Spatial Reasoning in the Wild?

Feb 3, 2026

No
Not Reported
Automatic Metrics DROP Accuracy Not Reported
Role-Augmented Intent-Driven Generative Search Engine Optimization

Aug 15, 2025

Yes Automatic Metrics Not Reported Perplexity Not Reported
CowPilot: A Framework for Autonomous and Human-Agent Collaborative Web Navigation

Jan 28, 2025

Yes Automatic Metrics Not Reported Success rate , Task success Not Reported
BrowseComp-$V^3$: A Visual, Vertical, and Verifiable Benchmark for Multimodal Browsing Agents

Feb 13, 2026

No
Not Reported
Automatic Metrics , Simulation Env Not Reported Accuracy Not Reported
Mind over Space: Can Multimodal Large Language Models Mentally Navigate?

Mar 23, 2026

No
Not Reported
Automatic Metrics , Simulation Env Not Reported Accuracy Not Reported
Rethinking SAR ATR: A Target-Aware Frequency-Spatial Enhancement Framework with Noise-Resilient Knowledge Guidance

Mar 23, 2026

No
Not Reported
Automatic Metrics Not Reported Accuracy , Precision 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 MemoryArena: Benchmarking Agent Memory in Interdepe… RedTeamCUA: Realistic Adversarial Testing of Comput…
Human Feedback Expert VerificationPairwise PreferenceRed Team
Evaluation Modes Automatic MetricsAutomatic MetricsAutomatic Metrics
Benchmarks Sodium BenchMemoryarenaRtc Bench
Metrics AccuracyRecallJailbreak success rate
Quality Controls Not reportedNot reportedNot reported
Rater Population Domain ExpertsUnknownUnknown
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. Mind over Space: Can Multimodal Large Language Models Mentally Navigate?

    Start here for detailed protocol reporting and quality-control evidence. Signals: automatic metrics. Focus: accuracy. Abstract: Despite the widespread adoption of MLLMs in embodied agents, their capabilities remain largely.

  2. Rethinking SAR ATR: A Target-Aware Frequency-Spatial Enhancement Framework with Noise-Resilient Knowledge Guidance

    Start here for detailed protocol reporting and quality-control evidence. Signals: automatic metrics. Focus: accuracy. Abstract: Synthetic aperture radar automatic target recognition (SAR ATR) is of considerable importance in.

  3. SODIUM: From Open Web Data to Queryable Databases

    Start here for detailed protocol reporting and quality-control evidence. Signals: automatic metrics + expert verification. Focus: Sodium-Bench / accuracy. Abstract: During research, domain experts often ask analytical questions.

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

    Include a human-eval paper to calibrate against judge-based evaluation settings. Signals: automatic metrics + pairwise preferences. Focus: Memoryarena / recall. Abstract: MemoryArena supports evaluation across web navigation, preference-constrained.

  5. 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.

  6. Role-Augmented Intent-Driven Generative Search Engine Optimization

    Adds automatic metrics with rubric ratings for broader protocol coverage within this hub. Signals: automatic metrics + rubric ratings. Focus: perplexity. Abstract: To better evaluate the method under.

  7. BEACON: Language-Conditioned Navigation Affordance Prediction under Occlusion

    Adds automatic metrics for broader protocol coverage within this hub. Signals: automatic metrics. Focus: BIRD / accuracy. Abstract: Language-conditioned local navigation requires a robot to infer a nearby.

  8. MUSE: A Run-Centric Platform for Multimodal Unified Safety Evaluation of Large Language Models

    Adds automatic metrics with red-team protocols for broader protocol coverage within this hub. Signals: automatic metrics + red-team protocols. Focus: success rate. Abstract: We present MUSE (Multimodal Unified.

Known Limitations

Known Limitations

  • Only 0% of papers report quality controls; prioritize calibration/adjudication evidence.
  • Rater population is under-specified (9.5% 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 (3)
  • Red Team (2)
  • Demonstrations (1)
  • Expert Verification (1)

Evaluation Modes

  • Automatic Metrics (21)
  • Simulation Env (3)

Top Benchmarks

  • BIRD (1)
  • BrowseComp (1)
  • DROP (1)
  • Memoryarena (1)

Top Metrics

  • Accuracy (12)
  • Cost (2)
  • Jailbreak success rate (2)
  • Precision (2)

Rater Population Mix

  • Domain Experts (2)

Quality Controls

Coverage diagnostics (sample-based): human-feedback 33.3% · benchmarks 23.8% · metrics 95.2% · quality controls 0.0%.

Top Papers

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