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HFEPX Hub

Coding + Rubric Rating (Last 60 Days)

Updated from current HFEPX corpus (Apr 27, 2026). 10 papers are grouped in this hub page.

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Updated from current HFEPX corpus (Apr 27, 2026). 10 papers are grouped in this hub page. Common evaluation modes: Automatic Metrics, Human Eval. Most common rater population: Domain Experts. Common annotation unit: Multi Dim Rubric. Frequent quality control: Adjudication. Frequently cited benchmark: Kernelbench. Common metric signal: cost. 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 31, 2026.

Papers: 10 Last published: Mar 31, 2026 Global RSS Tag RSS
CodingRubric RatingLast 60d

Researcher Quick Triage

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

High-Signal Coverage

100.0%

10 / 10 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.
  • 3 papers report explicit quality controls (calibration/adjudication/IAA).

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

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Why This Matters For Eval Research

  • 100% of papers report explicit human-feedback signals, led by rubric ratings.
  • automatic metrics appears in 50% of papers in this hub.
  • Kernelbench is a recurring benchmark anchor for cross-paper comparisons in this page.

Protocol Takeaways

  • Most common quality-control signal is adjudication (10% of papers).
  • Rater context is mostly domain experts, and annotation is commonly multi-dimensional rubrics; use this to scope replication staffing.
  • Pair this hub with llm_as_judge pages to benchmark automated-vs-human evaluation tradeoffs.

Benchmark Interpretation

  • Kernelbench appears in 10% of hub papers (1/10); use this cohort for benchmark-matched comparisons.
  • Paperwrite-Bench appears in 10% of hub papers (1/10); use this cohort for benchmark-matched comparisons.

Metric Interpretation

  • cost is reported in 30% of hub papers (3/10); compare with a secondary metric before ranking methods.
  • success rate is reported in 20% of hub papers (2/10); 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).

  • Strong: Papers reporting quality controls

    Coverage is strong (30% vs 30% target).

  • Moderate: Papers naming benchmarks/datasets

    Coverage is usable but incomplete (30% vs 35% target).

  • Strong: Papers naming evaluation metrics

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

  • Strong: Papers with known rater population

    Coverage is strong (40% vs 35% target).

  • Strong: Papers with known annotation unit

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

Strengths

  • Strong human-feedback signal (100% of papers).
  • Quality-control evidence appears in 30% of papers.
  • Rater population and annotation-unit details are frequently specified.

Known Gaps

  • No dominant metadata gap detected in current extraction coverage.

Suggested Next Analyses

  • Pair this hub with llm_as_judge pages to benchmark automated-vs-human evaluation tradeoffs.
  • Stratify by benchmark (Kernelbench vs Paperwrite-Bench) before comparing methods.
  • Track metric sensitivity by reporting both cost and success rate.
  • Add inter-annotator agreement checks when reproducing these protocols.
Recommended Queries (Expanded)

Recommended Queries

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
Paper Reconstruction Evaluation: Evaluating Presentation and Hallucination in AI-written Papers

Apr 1, 2026

Yes Automatic Metrics Paperwrite Bench Not Reported Not Reported
Is this Idea Novel? An Automated Benchmark for Judgment of Research Ideas

Mar 11, 2026

Yes Human Eval Rinobench Not Reported Gold Questions
StitchCUDA: An Automated Multi-Agents End-to-End GPU Programing Framework with Rubric-based Agentic Reinforcement Learning

Mar 3, 2026

Yes Automatic Metrics Kernelbench Success rate Not Reported
Beyond the Resumé: A Rubric-Aware Automatic Interview System for Information Elicitation

Mar 2, 2026

Yes Automatic Metrics Not Reported Not Reported Calibration
CounselReflect: A Toolkit for Auditing Mental-Health Dialogues

Mar 31, 2026

Yes Human Eval Not Reported Not Reported Adjudication
PRBench: End-to-end Paper Reproduction in Physics Research

Mar 29, 2026

Yes Automatic Metrics , Simulation Env Not Reported Accuracy , Success rate Not Reported
QED-Nano: Teaching a Tiny Model to Prove Hard Theorems

Apr 6, 2026

Yes Automatic Metrics Not Reported Inference cost Not Reported
Comparing Developer and LLM Biases in Code Evaluation

Mar 25, 2026

Yes Not Reported Not Reported Not Reported Not Reported
When Names Change Verdicts: Intervention Consistency Reveals Systematic Bias in LLM Decision-Making

Mar 19, 2026

Yes Not Reported Not Reported Not Reported Not Reported
Extending Minimal Pairs with Ordinal Surprisal Curves and Entropy Across Applied Domains

Mar 15, 2026

Yes Not Reported Not Reported Not Reported Not Reported

Protocol Diff (Top Papers)

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

Signal Paper Reconstruction Evaluation: Evaluating Present… Is this Idea Novel? An Automated Benchmark for Judg… StitchCUDA: An Automated Multi-Agents End-to-End GP…
Human Feedback Rubric RatingRubric RatingRubric Rating
Evaluation Modes Automatic MetricsHuman EvalAutomatic Metrics
Benchmarks Paperwrite BenchRinobenchKernelbench
Metrics Not reportedNot reportedSuccess rate
Quality Controls Not reportedGold QuestionsNot reported
Rater Population UnknownDomain ExpertsUnknown
Annotation Unit Multi Dim RubricMulti Dim RubricMulti Dim Rubric
Suggested Reading Order (Extended)

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

Suggested Reading Order

  1. CounselReflect: A Toolkit for Auditing Mental-Health Dialogues

    Start here for detailed protocol reporting and quality-control evidence. Signals: human evaluation + rubric ratings. Abstract: The system integrates two families of evaluation signals: (i) 12 model-based metrics.

  2. Is this Idea Novel? An Automated Benchmark for Judgment of Research Ideas

    Start here for detailed protocol reporting and quality-control evidence. Signals: human evaluation + rubric ratings. Focus: Rinobench. Abstract: Yet, evaluation of these approaches remains largely inconsistent and is.

  3. Beyond the Resumé: A Rubric-Aware Automatic Interview System for Information Elicitation

    Start here for detailed protocol reporting and quality-control evidence. Signals: automatic metrics + rubric ratings. Focus: cost. Abstract: We present a system that leverages an LLM interviewer to.

  4. QED-Nano: Teaching a Tiny Model to Prove Hard Theorems

    High citation traction makes this a strong baseline for protocol comparison. Signals: automatic metrics + rubric ratings. Focus: cost. Abstract: Our training recipe has three stages: (1) supervised.

  5. Paper Reconstruction Evaluation: Evaluating Presentation and Hallucination in AI-written Papers

    High citation traction makes this a strong baseline for protocol comparison. Signals: automatic metrics + rubric ratings. Focus: Paperwrite-Bench / cost. Abstract: PaperRecon disentangles the evaluation of the.

  6. StitchCUDA: An Automated Multi-Agents End-to-End GPU Programing Framework with Rubric-based Agentic Reinforcement Learning

    Adds automatic metrics with rubric ratings for broader protocol coverage within this hub. Signals: automatic metrics + rubric ratings. Focus: Kernelbench / success rate. Abstract: To fundamentally improve.

  7. PRBench: End-to-end Paper Reproduction in Physics Research

    Adds automatic metrics with rubric ratings for broader protocol coverage within this hub. Signals: automatic metrics + rubric ratings. Focus: accuracy. Abstract: All tasks are contributed by domain.

  8. Comparing Developer and LLM Biases in Code Evaluation

    Adds evaluation protocol evidence with pairwise preferences for broader protocol coverage within this hub. Signals: pairwise preferences. Abstract: As LLMs are increasingly used as judges in code applications,.

Known Limitations

Known Limitations

  • No dominant metadata gap detected in current extraction coverage.
  • 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)

Research Utility Snapshot

Human Feedback Mix

  • Rubric Rating (10)
  • Expert Verification (2)
  • Pairwise Preference (1)

Evaluation Modes

  • Automatic Metrics (5)
  • Human Eval (2)
  • Simulation Env (1)

Top Benchmarks

  • Kernelbench (1)
  • Paperwrite Bench (1)
  • Rinobench (1)

Top Metrics

  • Cost (3)
  • Success rate (2)
  • Accuracy (1)
  • Inference cost (1)

Rater Population Mix

  • Domain Experts (4)

Quality Controls

  • Adjudication (1)
  • Calibration (1)
  • Gold Questions (1)
Coverage diagnostics (sample-based): human-feedback 100.0% · benchmarks 30.0% · metrics 50.0% · quality controls 30.0%.

Top Papers

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