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HFEPX Benchmark Hub

DROP Benchmark Papers (Last 45 Days)

Updated from current HFEPX corpus (Mar 17, 2026). 11 papers are grouped in this benchmark page.

Read Full Context

Updated from current HFEPX corpus (Mar 17, 2026). 11 papers are grouped in this benchmark page. Common evaluation modes: Automatic Metrics. Most common rater population: Domain Experts. Frequently cited benchmark: DROP. 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 16, 2026.

Papers: 11 Last published: Mar 16, 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%

11 / 11 sampled papers are not low-signal flagged.

Replication-Ready Set

2

Papers with explicit benchmark + metric + eval mode fields.

Quality Controls

0.0%

0 papers report calibration/adjudication/IAA controls.

  • 3 papers explicitly name benchmark datasets in the sampled set.
  • 2 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

  • 18.2% of papers report explicit human-feedback signals, led by expert verification.
  • automatic metrics appears in 18.2% of papers in this hub.
  • DROP 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 (DROP vs BrowseComp) before comparing methods.

Benchmark Interpretation

  • DROP appears in 100% of hub papers (11/11); use this cohort for benchmark-matched comparisons.
  • BrowseComp appears in 9.1% of hub papers (1/11); use this cohort for benchmark-matched comparisons.

Metric Interpretation

  • accuracy is reported in 45.5% of hub papers (5/11); compare with a secondary metric before ranking methods.
  • cost is reported in 18.2% of hub papers (2/11); 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
FairMed-XGB: A Bayesian-Optimised Multi-Metric Framework with Explainability for Demographic Equity in Critical Healthcare Data

Mar 16, 2026

 Automatic Metrics Expert Verification Accuracy, Auroc Not reported
SpatiaLab: Can Vision-Language Models Perform Spatial Reasoning in the Wild?

Feb 3, 2026

 Automatic Metrics Not reported Accuracy Not reported
ReCoN-Ipsundrum: An Inspectable Recurrent Persistence Loop Agent with Affect-Coupled Control and Mechanism-Linked Consciousness Indicator Assays

Feb 26, 2026

 Not reported Pairwise Preference Not reported Not reported
MXNorm: Reusing MXFP block scales for efficient tensor normalisation

Mar 13, 2026

 Not reported Not reported Not reported Not reported
LABSHIELD: A Multimodal Benchmark for Safety-Critical Reasoning and Planning in Scientific Laboratories

Mar 12, 2026

 Not reported Not reported Not reported Not reported
UIS-Digger: Towards Comprehensive Research Agent Systems for Real-world Unindexed Information Seeking

Mar 9, 2026

 Not reported Not reported Not reported Not reported
Dynamic Vehicle Routing Problem with Prompt Confirmation of Advance Requests

Mar 8, 2026

 Not reported Not reported Not reported Not reported
Confidence-Calibrated Small-Large Language Model Collaboration for Cost-Efficient Reasoning

Mar 4, 2026

 Not reported Not reported Not reported Not reported
Polynomial Mixing for Efficient Self-supervised Speech Encoders

Feb 28, 2026

 Not reported Not reported Not reported Not reported
Draft-Thinking: Learning Efficient Reasoning in Long Chain-of-Thought LLMs

Feb 28, 2026

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

Checklist

  • Gap: Papers with explicit human feedback

    Coverage is a replication risk (18.2% 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 (63.6% vs 35% target).

  • Gap: Papers with known rater population

    Coverage is a replication risk (9.1% 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, 63.6% metrics).

Known Gaps

  • Only 0% of papers report quality controls; prioritize calibration/adjudication evidence.
  • Rater population is under-specified (9.1% coverage).
  • Annotation unit is under-specified (0% coverage).

Suggested Next Analyses

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

Recommended Queries

Benchmark Slice: DROPMetric Slice: accuracyRecent High-Signal Papers
Known Limitations
  • Only 0% of papers report quality controls; prioritize calibration/adjudication evidence.
  • Rater population is under-specified (9.1% 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)

Human Feedback Mix

  • Expert Verification (1)
  • Pairwise Preference (1)

Top Benchmarks

  • DROP (11)
  • BrowseComp (1)
  • GAIA (1)
  • MATH 500 (1)

Top Metrics

  • Accuracy (5)
  • Cost (2)
  • Precision (2)
  • Auroc (1)

Top Papers On This Benchmark

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