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HFEPX Benchmark Hub

BrowseComp In CS.AI Papers

Updated from current HFEPX corpus (Mar 1, 2026). 4 papers are grouped in this benchmark page.

Read Full Context

Updated from current HFEPX corpus (Mar 1, 2026). 4 papers are grouped in this benchmark 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 11, 2026.

Papers: 4 Last published: Feb 11, 2026 Global RSS

Researcher Quick Triage

Use this page for benchmark-matched method comparisons and eval protocol selection. Quality band: Developing .

High-Signal Coverage

100.0%

4 / 4 sampled papers are not low-signal flagged.

Replication-Ready Set

1

Papers with explicit benchmark + metric + eval mode fields.

Quality Controls

0.0%

0 papers report calibration/adjudication/IAA controls.

  • 2 papers explicitly name benchmark datasets in the sampled set.
  • 3 papers report at least one metric term in metadata extraction.
  • Start with the ranked shortlist below before reading all papers.

Primary action: Use this page to map benchmark mentions first; wait for stronger metric/QC coverage before strict comparisons.

Why This Matters (Expanded)

Why This Matters For Eval Research

  • 25% of papers report explicit human-feedback signals, led by pairwise preferences.
  • automatic metrics appears in 50% 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 GAIA) before comparing methods.

Benchmark Interpretation

  • BrowseComp appears in 100% of hub papers (4/4); use this cohort for benchmark-matched comparisons.
  • GAIA appears in 25% of hub papers (1/4); use this cohort for benchmark-matched comparisons.

Metric Interpretation

  • accuracy is reported in 25% of hub papers (1/4); compare with a secondary metric before ranking methods.
  • cost is reported in 25% of hub papers (1/4); compare with a secondary metric before ranking methods.

Start Here (Benchmark-Matched First 6)

Ranked by protocol completeness so you can quickly find papers suitable for comparison studies.

Protocol Matrix (Top 10)

Compare protocol ingredients quickly before deep-reading full papers.

Paper Eval Modes Human Feedback Metrics Quality Controls
Step 3.5 Flash: Open Frontier-Level Intelligence with 11B Active Parameters

Feb 11, 2026

 Not reported Pairwise Preference Latency, Cost Not reported
Hybrid Deep Searcher: Scalable Parallel and Sequential Search Reasoning

Aug 26, 2025

 Automatic Metrics Not reported F1 Not reported
BrowseComp-$V^3$: A Visual, Vertical, and Verifiable Benchmark for Multimodal Browsing Agents

Feb 13, 2026

 Automatic Metrics, Simulation Env Not reported Accuracy Not reported
MiroFlow: Towards High-Performance and Robust Open-Source Agent Framework for General Deep Research Tasks

Feb 26, 2026

 Not reported Not reported Not reported Not reported
Researcher Workflow (Detailed)

Checklist

  • Gap: Papers with explicit human feedback

    Coverage is a replication risk (25% vs 45% target).

  • Gap: Papers reporting quality controls

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

  • Strong: Papers naming benchmarks/datasets

    Coverage is strong (100% vs 35% target).

  • Strong: Papers naming evaluation metrics

    Coverage is strong (75% vs 35% target).

  • Strong: Papers with known rater population

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

  • Gap: Papers with known annotation unit

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

Strengths

  • Most papers provide measurable evaluation context (100% benchmarks, 75% metrics).
  • Agentic evaluation appears in 75% of papers.

Known Gaps

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

Suggested Next Analyses

  • Stratify by benchmark (BrowseComp vs GAIA) before comparing methods.
  • Track metric sensitivity by reporting both accuracy and cost.

Recommended Queries

Benchmark Slice: BrowseCompMetric Slice: accuracyRecent High-Signal Papers
Known Limitations
  • Only 0% of papers report quality controls; prioritize calibration/adjudication evidence.
  • Annotation unit is under-specified (0% coverage).
  • Narrative synthesis is grounded in metadata and abstracts only; full-paper implementation details are not parsed.
Research Utility Snapshot (Detailed)

Evaluation Modes

  • Automatic Metrics (2)
  • Simulation Env (1)

Human Feedback Mix

  • Pairwise Preference (1)

Top Benchmarks

  • BrowseComp (4)
  • GAIA (1)
  • HLE (1)
  • Imo Answerbench (1)

Top Metrics

  • Accuracy (1)
  • Cost (1)
  • F1 (1)
  • Latency (1)

Top Papers On This Benchmark

Related Benchmark Hubs

BrowseComp Benchmark Papers WebArena Ecosystem Benchmark Papers CS.AI Human Feedback And Eval Papers General Or Math Papers Coding Or General Papers CS.AI Papers (Last 30 Days) WebArena Ecosystem Benchmark Papers (13)

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