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HFEPX Hub

Automatic Metrics + Critique Edit + General Papers

Updated from current HFEPX corpus (Apr 12, 2026). 11 papers are grouped in this hub page.

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Updated from current HFEPX corpus (Apr 12, 2026). 11 papers are grouped in this hub page. Common evaluation modes: Automatic Metrics. Common annotation unit: Multi Dim Rubric. Frequent quality control: Inter Annotator Agreement Reported. Frequently cited benchmark: AIME. 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 22, 2026.

Papers: 11 Last published: Mar 22, 2026 Global RSS Tag RSS
Automatic MetricsCritique EditGeneral

Researcher Quick Triage

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

All Sampled Papers (11) Replication-Ready Only (2)

High-Signal Coverage

100.0%

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

Replication-Ready Set

2

Benchmark + metric + eval mode explicitly present.

Judge/Human Comparability

0

Papers containing both `human_eval` and `llm_as_judge`.

  • 2 papers are replication-ready (benchmark + metric + explicit evaluation mode).
  • 0 papers support judge-vs-human agreement analysis.
  • 1 papers report explicit quality controls (calibration/adjudication/IAA).

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

Currently showing only replication-ready papers in ranking and matrix sections (2 papers).

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Why This Matters For Eval Research

  • 100% of papers report explicit human-feedback signals, led by critique/edit feedback.
  • automatic metrics appears in 100% of papers in this hub.
  • AIME is a recurring benchmark anchor for cross-paper comparisons in this page.

Protocol Takeaways

  • Most common quality-control signal is inter-annotator agreement reporting (9.1% of papers).
  • Rater context is mostly unspecified rater pools, and annotation is commonly multi-dimensional rubrics; use this to scope replication staffing.
  • Stratify by benchmark (AIME vs Paperbananabench) before comparing methods.

Benchmark Interpretation

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

Metric Interpretation

  • accuracy is reported in 36.4% of hub papers (4/11); compare with a secondary metric before ranking methods.
  • cost is reported in 27.3% of hub papers (3/11); compare with a secondary metric before ranking methods.
Researcher Checklist (Expanded)

Researcher Checklist

  • Strong: Papers with explicit human feedback

    Coverage is strong (100% vs 45% target).

  • Gap: Papers reporting quality controls

    Coverage is a replication risk (9.1% vs 30% target).

  • Gap: Papers naming benchmarks/datasets

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

  • Strong: Papers naming evaluation metrics

    Coverage is strong (72.7% vs 35% target).

  • Gap: Papers with known rater population

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

  • Moderate: Papers with known annotation unit

    Coverage is usable but incomplete (27.3% vs 35% target).

Strengths

  • Strong human-feedback signal (100% of papers).

Known Gaps

  • Only 9.1% of papers report quality controls; prioritize calibration/adjudication evidence.
  • Rater population is under-specified (0% coverage).
  • Benchmark coverage is thin (18.2% of papers mention benchmarks/datasets).

Suggested Next Analyses

  • Stratify by benchmark (AIME vs Paperbananabench) before comparing methods.
  • Track metric sensitivity by reporting both accuracy and cost.
Recommended Queries (Expanded)

Recommended Queries

Benchmark Slice: AIME Metric Slice: accuracy IAA-Reported Evaluations Recent High-Signal Papers

Start with These 3

Use these when you need one protocol anchor, one benchmark anchor, and one recent comparison point before reading the wider hub.

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
PaperBanana: Automating Academic Illustration for AI Scientists

Jan 30, 2026

 Yes
Critique Edit
Automatic Metrics Paperbananabench Faithfulness , Conciseness Not Reported
Critique-GRPO: Advancing LLM Reasoning with Natural Language and Numerical Feedback

Jun 3, 2025

 Yes
Critique Edit
Automatic Metrics AIME Pass@1 Not Reported

Protocol Diff (Top Papers)

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

Signal PaperBanana: Automating Academic Illustration for A… Critique-GRPO: Advancing LLM Reasoning with Natural…
Human Feedback Critique EditCritique Edit
Evaluation Modes Automatic MetricsAutomatic Metrics
Benchmarks PaperbananabenchAIME
Metrics Faithfulness, ConcisenessPass@1
Quality Controls Not reportedNot reported
Rater Population UnknownUnknown
Annotation Unit UnknownUnknown
Suggested Reading Order (Extended)

This section is intentionally expanded only when needed; use “Start Here” above for a faster pass.

Suggested Reading Order

  1. How Much LLM Does a Self-Revising Agent Actually Need?

    Start here for detailed protocol reporting and quality-control evidence. Signals: automatic metrics + critique/edit feedback. Focus: f1. Abstract: Recent LLM-based agents often place world modeling, planning, and reflection.

  2. BeliefShift: Benchmarking Temporal Belief Consistency and Opinion Drift in LLM Agents

    Start here for detailed protocol reporting and quality-control evidence. Signals: automatic metrics + pairwise preferences. Focus: accuracy. Abstract: The dataset includes 2,400 human-annotated multi-session interaction trajectories spanning health,.

  3. ReasonScaffold: A Scaffolded Reasoning-based Annotation Protocol for Human-AI Co-Annotation

    Start here for detailed protocol reporting and quality-control evidence. Signals: automatic metrics + critique/edit feedback. Focus: accuracy. Abstract: Human annotation is central to NLP evaluation, yet subjective tasks.

  4. PaperBanana: Automating Academic Illustration for AI Scientists

    Include a human-eval paper to calibrate against judge-based evaluation settings. Signals: automatic metrics + critique/edit feedback. Focus: Paperbananabench / faithfulness. Abstract: Despite rapid advances in autonomous AI scientists.

  5. Critique-GRPO: Advancing LLM Reasoning with Natural Language and Numerical Feedback

    Adds automatic metrics with critique/edit feedback for broader protocol coverage within this hub. Signals: automatic metrics + critique/edit feedback. Focus: AIME / pass@1. Abstract: Recent advances in reinforcement.

  6. CAMEL: Confidence-Gated Reflection for Reward Modeling

    Adds automatic metrics with pairwise preferences for broader protocol coverage within this hub. Signals: automatic metrics + pairwise preferences. Focus: accuracy. Abstract: Reward models play a fundamental role.

  7. Distilling Feedback into Memory-as-a-Tool

    Adds automatic metrics with rubric ratings for broader protocol coverage within this hub. Signals: automatic metrics + rubric ratings. Focus: cost. Abstract: We evaluate this method on the.

  8. Error-Aware Knowledge Distillation via Targeted Revision for Customer-Service Summarization

    Adds automatic metrics with critique/edit feedback for broader protocol coverage within this hub. Signals: automatic metrics + critique/edit feedback. Focus: accuracy. Abstract: We introduce an Analyze-Revise-Finetune (ARF) pipeline.

Known Limitations

Known Limitations

  • Only 9.1% of papers report quality controls; prioritize calibration/adjudication evidence.
  • Rater population 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)

Research Utility Snapshot

Human Feedback Mix

  • Critique Edit (11)
  • Pairwise Preference (2)
  • Rubric Rating (1)

Evaluation Modes

  • Automatic Metrics (11)

Top Benchmarks

  • AIME (1)
  • Paperbananabench (1)

Top Metrics

  • Accuracy (4)
  • Cost (3)
  • Inference cost (2)
  • Agreement (1)

Rater Population Mix

Quality Controls

  • Inter Annotator Agreement Reported (1)
Coverage diagnostics (sample-based): human-feedback 100.0% · benchmarks 18.2% · metrics 72.7% · quality controls 9.1%.

Top Papers

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