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HFEPX Hub

Web Browsing + Automatic Metrics (Last 90 Days)

Updated from current HFEPX corpus (Mar 1, 2026). 10 papers are grouped in this hub page.

Read Full Context

Updated from current HFEPX corpus (Mar 1, 2026). 10 papers are grouped in this hub page. Common evaluation modes: Automatic Metrics, Simulation Env. 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 18, 2026.

Papers: 10 Last published: Feb 18, 2026 Global RSS Tag RSS
Web BrowsingAutomatic MetricsLast 90d

Researcher Quick Triage

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

All Sampled Papers (10) Replication-Ready Only (1)

High-Signal Coverage

100.0%

10 / 10 sampled papers are not low-signal flagged.

Replication-Ready Set

1

Benchmark + metric + eval mode explicitly present.

Judge/Human Comparability

0

Papers containing both `human_eval` and `llm_as_judge`.

  • 1 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: Use this page for scouting only; collect additional papers before attempting replication-critical comparisons.

Why This Matters (Expanded)

Why This Matters For Eval Research

  • 20% of papers report explicit human-feedback signals, led by pairwise preferences.
  • automatic metrics appears in 100% of papers in this hub.
  • BrowseComp is a recurring benchmark anchor for cross-paper comparisons in this page.
Protocol Notes (Expanded)

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 mixed annotation units; use this to scope replication staffing.
  • Stratify by benchmark (BrowseComp vs Memoryarena) before comparing methods.

Benchmark Interpretation

  • BrowseComp appears in 10% of hub papers (1/10); use this cohort for benchmark-matched comparisons.
  • Memoryarena appears in 10% of hub papers (1/10); use this cohort for benchmark-matched comparisons.

Metric Interpretation

  • accuracy is reported in 40% of hub papers (4/10); compare with a secondary metric before ranking methods.
  • latency is reported in 20% of hub papers (2/10); compare with a secondary metric before ranking methods.
Researcher Checklist (Expanded)

Researcher Checklist

  • Gap: Papers with explicit human feedback

    Coverage is a replication risk (20% vs 45% target).

  • Gap: Papers reporting quality controls

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

  • Gap: Papers naming benchmarks/datasets

    Coverage is a replication risk (20% vs 35% target).

  • Strong: Papers naming evaluation metrics

    Coverage is strong (90% vs 35% target).

  • Gap: Papers with known rater population

    Coverage is a replication risk (10% vs 35% target).

  • Gap: Papers with known annotation unit

    Coverage is a replication risk (0% 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 (10% coverage).
  • Annotation unit is under-specified (0% coverage).

Suggested Next Analyses

  • Stratify by benchmark (BrowseComp vs Memoryarena) before comparing methods.
  • Track metric sensitivity by reporting both accuracy and latency.
Recommended Queries (Expanded)

Recommended Queries

Benchmark Slice: BrowseComp Metric Slice: accuracy Recent High-Signal Papers

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

Feb 18, 2026

 Yes
Pairwise Preference
Automatic Metrics Memoryarena Recall Not Reported
Modeling Distinct Human Interaction in Web Agents

Feb 19, 2026

 Yes
Pairwise Preference
Automatic Metrics Not Reported Accuracy 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
A Benchmark for Deep Information Synthesis

Feb 24, 2026

 No
Not Reported
Automatic Metrics Not Reported F1 Not Reported
Spatio-Temporal Token Pruning for Efficient High-Resolution GUI Agents

Feb 26, 2026

 No
Not Reported
Automatic Metrics Not Reported Precision , Latency Not Reported
GUI-Libra: Training Native GUI Agents to Reason and Act with Action-aware Supervision and Partially Verifiable RL

Feb 25, 2026

 No
Not Reported
Automatic Metrics Not Reported Accuracy , Task success Not Reported
Mind the Style: Impact of Communication Style on Human-Chatbot Interaction

Feb 19, 2026

 No
Not Reported
Automatic Metrics Not Reported Task success Not Reported
The Automatic Verification of Image-Text Claims (AVerImaTeC) Shared Task

Feb 11, 2026

 No
Not Reported
Automatic Metrics Not Reported Accuracy Not Reported
INSURE-Dial: A Phase-Aware Conversational Dataset & Benchmark for Compliance Verification and Phase Detection

Jan 28, 2026

 No
Not Reported
Automatic Metrics Not Reported Latency Not Reported
Onboard-Targeted Segmentation of Straylight in Space Camera Sensors

Feb 24, 2026

 No
Not Reported
Automatic Metrics Not Reported Not Reported Not Reported

Protocol Diff (Top Papers)

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

Signal MemoryArena: Benchmarking Agent Memory in Interdepe… Modeling Distinct Human Interaction in Web Agents BrowseComp-$V^3$: A Visual, Vertical, and Verifiabl…
Human Feedback Pairwise PreferencePairwise PreferenceNot reported
Evaluation Modes Automatic MetricsAutomatic MetricsAutomatic Metrics, Simulation Env
Benchmarks MemoryarenaNot reportedNot reported
Metrics RecallAccuracyAccuracy
Quality Controls Not reportedNot reportedNot reported
Rater Population UnknownUnknownDomain Experts
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. Spatio-Temporal Token Pruning for Efficient High-Resolution GUI Agents

    Start here for detailed protocol reporting and quality-control evidence. Signals: automatic metrics. Focus: precision. Abstract: Pure-vision GUI agents provide universal interaction capabilities but suffer from severe efficiency bottlenecks.

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

    Start here for detailed protocol reporting and quality-control evidence. Signals: automatic metrics. Focus: accuracy. Abstract: Open-source native GUI agents still lag behind closed-source systems on long-horizon navigation tasks.

  3. A Benchmark for Deep Information Synthesis

    Start here for detailed protocol reporting and quality-control evidence. Signals: automatic metrics. Focus: f1. Abstract: When evaluated on DEEPSYNTH, 11 state-of-the-art LLMs and deep research agents achieve a.

  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. Modeling Distinct Human Interaction in Web Agents

    Include a human-eval paper to calibrate against judge-based evaluation settings. Signals: automatic metrics + pairwise preferences. Focus: accuracy. Abstract: Despite rapid progress in autonomous web agents, human involvement.

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

    Adds automatic metrics for broader protocol coverage within this hub. Signals: automatic metrics. Focus: accuracy. Abstract: Multimodal large language models (MLLMs), equipped with increasingly advanced planning and tool-use.

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

    Adds automatic metrics for broader protocol coverage within this hub. Signals: automatic metrics. Focus: task success. Abstract: Conversational agents increasingly mediate everyday digital interactions, yet the effects of.

  8. The Automatic Verification of Image-Text Claims (AVerImaTeC) Shared Task

    Adds automatic metrics for broader protocol coverage within this hub. Signals: automatic metrics. Focus: accuracy. Abstract: The Automatic Verification of Image-Text Claims (AVerImaTeC) shared task aims to advance.

Known Limitations

Known Limitations

  • Only 0% of papers report quality controls; prioritize calibration/adjudication evidence.
  • Rater population is under-specified (10% 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 (2)

Evaluation Modes

  • Automatic Metrics (10)
  • Simulation Env (1)

Top Benchmarks

  • BrowseComp (1)
  • Memoryarena (1)

Top Metrics

  • Accuracy (4)
  • Latency (2)
  • Task success (2)
  • F1 (1)

Rater Population Mix

  • Domain Experts (1)

Quality Controls

Coverage diagnostics (sample-based): human-feedback 20.0% · benchmarks 10.0% · metrics 90.0% · quality controls 0.0%.

Top Papers

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