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HFEPX Benchmark Hub

BrowseComp Benchmark Papers

Updated from current HFEPX corpus (Mar 1, 2026). 6 papers are grouped in this benchmark page.

Read Full Context

Updated from current HFEPX corpus (Mar 1, 2026). 6 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: 6 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%

6 / 6 sampled papers are not low-signal flagged.

Replication-Ready Set

3

Papers with explicit benchmark + metric + eval mode fields.

Quality Controls

0.0%

0 papers report calibration/adjudication/IAA controls.

  • 4 papers explicitly name benchmark datasets in the sampled set.
  • 5 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

  • 16.7% of papers report explicit human-feedback signals, led by pairwise preferences.
  • automatic metrics appears in 66.7% 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 (6/6); use this cohort for benchmark-matched comparisons.
  • GAIA appears in 33.3% of hub papers (2/6); use this cohort for benchmark-matched comparisons.

Metric Interpretation

  • accuracy is reported in 50% of hub papers (3/6); compare with a secondary metric before ranking methods.
  • latency is reported in 50% of hub papers (3/6); 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
Search More, Think Less: Rethinking Long-Horizon Agentic Search for Efficiency and Generalization

Feb 26, 2026

 Automatic Metrics Not reported Accuracy, Latency Not reported
Towards Efficient Agents: A Co-Design of Inference Architecture and System

Dec 20, 2025

 Automatic Metrics Not reported Accuracy, Latency 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 (16.7% 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 (83.3% vs 35% target).

  • Moderate: Papers with known rater population

    Coverage is usable but incomplete (33.3% 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, 83.3% metrics).
  • Agentic evaluation appears in 83.3% 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 latency.

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 (4)
  • Simulation Env (1)

Human Feedback Mix

  • Pairwise Preference (1)

Top Benchmarks

  • BrowseComp (6)
  • GAIA (2)
  • HLE (1)
  • Imo Answerbench (1)

Top Metrics

  • Accuracy (3)
  • Latency (3)
  • Cost (2)
  • F1 (1)

Top Papers On This Benchmark

Related Benchmark Hubs

WebArena Ecosystem Benchmark Papers BrowseComp + Automatic Metrics Benchmark Papers BrowseComp + General Benchmark Papers BrowseComp In CS.AI Papers BrowseComp + Long Horizon Benchmark Papers BrowseComp In CS.CL Papers WebArena Ecosystem Benchmark Papers (13)

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