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HFEPX Metric Hub

Accuracy & Pass Rate Metric Papers + Coding

Updated from current HFEPX corpus (Mar 1, 2026). 17 papers are grouped in this metric page.

Read Full Context

Updated from current HFEPX corpus (Mar 1, 2026). 17 papers are grouped in this metric page. Common evaluation modes: Automatic Metrics, Simulation Env. Most common rater population: Domain Experts. Common annotation unit: Trajectory. Frequent quality control: Gold Questions. 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 Feb 15, 2026.

Papers: 17 Last published: Feb 15, 2026 Global RSS

Researcher Quick Triage

Use this page to compare metric behavior across protocols and benchmarks before selecting your reporting stack. Quality band: Medium .

Metric Coverage

94.1%

16 sampled papers include metric names.

Benchmark Anchoring

29.4%

Papers with explicit dataset/benchmark anchors for fair comparison.

Quality Controls

5.9%

1 papers report calibration/adjudication/IAA controls.

  • 17 papers are not low-signal flagged in this sample.
  • Use the protocol matrix below to avoid comparing metrics across incompatible eval setups.

Primary action: Use the top metric-reliable papers first, then compare benchmark context in the matrix before drawing conclusions.

Why This Matters (Expanded)

Why This Matters For Eval Research

  • 35.3% of papers report explicit human-feedback signals, led by expert verification.
  • automatic metrics appears in 94.1% of papers in this hub.
  • DROP is a recurring benchmark anchor for cross-paper comparisons in this page.
Metric Notes (Expanded)

Metric-Driven Protocol Takeaways

  • Most common quality-control signal is gold-question checks (5.9% of papers).
  • Rater context is mostly domain experts, and annotation is commonly trajectory-level annotation; use this to scope replication staffing.
  • Stratify by benchmark (DROP vs SWE-bench) before comparing methods.

Metric Interpretation

  • accuracy is reported in 82.4% of hub papers (14/17); compare with a secondary metric before ranking methods.
  • cost is reported in 17.6% of hub papers (3/17); compare with a secondary metric before ranking methods.

Benchmark Context

  • DROP appears in 11.8% of hub papers (2/17); use this cohort for benchmark-matched comparisons.
  • SWE-bench appears in 11.8% of hub papers (2/17); use this cohort for benchmark-matched comparisons.

Start Here (Metric-Reliable First 6)

Ranked for metric reporting completeness and comparability.

Metric Protocol Matrix (Top 10)

Compare metric, benchmark, and evaluation context side by side.

Paper Metrics Benchmarks Eval Modes Quality Controls
CricBench: A Multilingual Benchmark for Evaluating LLMs in Cricket Analytics

Dec 26, 2025

 Accuracy DROP, BIRD Automatic Metrics Gold Questions
AD-Bench: A Real-World, Trajectory-Aware Advertising Analytics Benchmark for LLM Agents

Feb 15, 2026

 Pass@1, Pass@3 Ad Bench Simulation Env Not reported
SWE-Protégé: Learning to Selectively Collaborate With an Expert Unlocks Small Language Models as Software Engineering Agents

Feb 25, 2026

 Pass@1, Latency SWE Bench, SWE Bench Verified Automatic Metrics Not reported
Dyslexify: A Mechanistic Defense Against Typographic Attacks in CLIP

Aug 28, 2025

 Accuracy DROP Automatic Metrics Not reported
MEDSYN: Benchmarking Multi-EviDence SYNthesis in Complex Clinical Cases for Multimodal Large Language Models

Feb 25, 2026

 Accuracy Not reported Automatic Metrics Not reported
SurGo-R1: Benchmarking and Modeling Contextual Reasoning for Operative Zone in Surgical Video

Feb 25, 2026

 Accuracy Not reported Automatic Metrics Not reported
Precise Attribute Intensity Control in Large Language Models via Targeted Representation Editing

Oct 14, 2025

 Accuracy Not reported Automatic Metrics Not reported
Test-Time Scaling with Diffusion Language Models via Reward-Guided Stitching

Feb 26, 2026

 Accuracy, Latency Not reported Automatic Metrics Not reported
Continuous Telemonitoring of Heart Failure using Personalised Speech Dynamics

Feb 23, 2026

 Accuracy, Cost Not reported Automatic Metrics Not reported
AgentDropoutV2: Optimizing Information Flow in Multi-Agent Systems via Test-Time Rectify-or-Reject Pruning

Feb 26, 2026

 Accuracy Not reported Automatic Metrics Not reported
Researcher Workflow (Detailed)

Checklist

  • Moderate: Papers with explicit human feedback

    Coverage is usable but incomplete (35.3% vs 45% target).

  • Gap: Papers reporting quality controls

    Coverage is a replication risk (5.9% vs 30% target).

  • Moderate: Papers naming benchmarks/datasets

    Coverage is usable but incomplete (29.4% vs 35% target).

  • Strong: Papers naming evaluation metrics

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

  • Strong: Papers with known rater population

    Coverage is strong (35.3% vs 35% target).

  • Moderate: Papers with known annotation unit

    Coverage is usable but incomplete (29.4% vs 35% target).

Strengths

  • Agentic evaluation appears in 58.8% of papers.

Known Gaps

  • Only 5.9% of papers report quality controls; prioritize calibration/adjudication evidence.

Suggested Next Analyses

  • Stratify by benchmark (DROP vs SWE-bench) before comparing methods.
  • Track metric sensitivity by reporting both accuracy and cost.
  • Add inter-annotator agreement checks when reproducing these protocols.

Recommended Queries

Benchmark Slice: DROPMetric Slice: accuracyRecent High-Signal Papers
Known Limitations
  • Only 5.9% of papers report quality controls; prioritize calibration/adjudication evidence.
  • Narrative synthesis is grounded in metadata and abstracts only; full-paper implementation details are not parsed.
  • Cross-page comparisons should be benchmark- and metric-matched to avoid protocol confounding.
Research Utility Snapshot (Detailed)

Top Metrics

  • Accuracy (14)
  • Cost (3)
  • Pass@1 (3)
  • Latency (2)

Evaluation Modes

  • Automatic Metrics (16)
  • Simulation Env (3)

Top Benchmarks

  • DROP (2)
  • SWE Bench (2)
  • SWE Bench Verified (2)
  • Ad Bench (1)

Agentic Mix

  • Long Horizon (9)
  • Multi Agent (1)
  • Web Browsing (1)

Top Papers Reporting This Metric

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