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HFEPX Hub

Coding + Rubric Rating (Last 90 Days)

Updated from current HFEPX corpus (Apr 27, 2026). 13 papers are grouped in this hub page.

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Updated from current HFEPX corpus (Apr 27, 2026). 13 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: 13 Last published: Mar 31, 2026 Global RSS Tag RSS
CodingRubric RatingLast 90d

Researcher Quick Triage

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

All Sampled Papers (13) Replication-Ready Only (3)

High-Signal Coverage

100.0%

13 / 13 sampled papers are not low-signal flagged.

Replication-Ready Set

3

Benchmark + metric + eval mode explicitly present.

Judge/Human Comparability

0

Papers containing both `human_eval` and `llm_as_judge`.

  • 3 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: Start with the top 2 papers in “Start Here”, then validate assumptions in the protocol matrix.

Currently showing only replication-ready papers in ranking and matrix sections (3 papers).

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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 46.2% 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 (7.7% of papers).
  • Rater context is mostly domain experts, and annotation is commonly multi-dimensional rubrics; use this to scope replication staffing.
  • Compare papers that report both human_eval and llm_as_judge to quantify judge-human agreement drift.

Benchmark Interpretation

  • Kernelbench appears in 7.7% of hub papers (1/13); use this cohort for benchmark-matched comparisons.
  • LongBench appears in 7.7% of hub papers (1/13); use this cohort for benchmark-matched comparisons.

Metric Interpretation

  • cost is reported in 23.1% of hub papers (3/13); compare with a secondary metric before ranking methods.
  • success rate is reported in 15.4% of hub papers (2/13); 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).

  • Moderate: Papers reporting quality controls

    Coverage is usable but incomplete (23.1% vs 30% target).

  • Strong: Papers naming benchmarks/datasets

    Coverage is strong (38.5% vs 35% target).

  • Strong: Papers naming evaluation metrics

    Coverage is strong (46.2% vs 35% target).

  • Strong: Papers with known rater population

    Coverage is strong (38.5% vs 35% target).

  • Strong: Papers with known annotation unit

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

Strengths

  • Strong human-feedback signal (100% of papers).
  • Most papers provide measurable evaluation context (38.5% benchmarks, 46.2% metrics).
  • Contains both human-eval and LLM-as-judge protocols for head-to-head methodology comparison.

Known Gaps

  • LLM-as-judge appears without enough inter-annotator agreement reporting.

Suggested Next Analyses

  • Compare papers that report both human_eval and llm_as_judge to quantify judge-human agreement drift.
  • Stratify by benchmark (Kernelbench vs LongBench) 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

Judge vs Human Agreement Benchmark Slice: Kernelbench Metric Slice: cost 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
StitchCUDA: An Automated Multi-Agents End-to-End GPU Programing Framework with Rubric-based Agentic Reinforcement Learning

Mar 3, 2026

 Yes
Rubric Rating
Automatic Metrics Kernelbench Success rate Not Reported
Document Reconstruction Unlocks Scalable Long-Context RLVR

Feb 9, 2026

 Yes
Rubric Rating
Automatic Metrics LongBench Coherence Not Reported
Paper Reconstruction Evaluation: Evaluating Presentation and Hallucination in AI-written Papers

Apr 1, 2026

 Yes
Rubric Rating
Automatic Metrics Paperwrite Bench Not Reported Not Reported

Protocol Diff (Top Papers)

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

Signal StitchCUDA: An Automated Multi-Agents End-to-End GP… Document Reconstruction Unlocks Scalable Long-Conte… Paper Reconstruction Evaluation: Evaluating Present…
Human Feedback Rubric RatingRubric RatingRubric Rating
Evaluation Modes Automatic MetricsAutomatic MetricsAutomatic Metrics
Benchmarks KernelbenchLongBenchPaperwrite Bench
Metrics Success rateCoherenceNot reported
Quality Controls Not reportedNot reportedNot 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. Small Reward Models via Backward Inference

    Include an LLM-as-judge paper to test judge design and agreement assumptions. Signals: LLM-as-judge + rubric ratings. Abstract: However, the dominant LLM-as-a-Judge paradigm relies on the strong reasoning capabilities.

  7. 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.

  8. 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.

Known Limitations

Known Limitations

  • LLM-as-judge appears without enough inter-annotator agreement reporting.
  • 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 (13)
  • Expert Verification (2)
  • Pairwise Preference (1)

Evaluation Modes

  • Automatic Metrics (6)
  • Human Eval (2)
  • Llm As Judge (1)
  • Simulation Env (1)

Top Benchmarks

  • Kernelbench (1)
  • LongBench (1)
  • MLE Bench (1)
  • Paperbench (1)

Top Metrics

  • Cost (3)
  • Success rate (2)
  • Accuracy (1)
  • Coherence (1)

Rater Population Mix

  • Domain Experts (5)

Quality Controls

  • Adjudication (1)
  • Calibration (1)
  • Gold Questions (1)
Coverage diagnostics (sample-based): human-feedback 100.0% · benchmarks 38.5% · metrics 46.2% · quality controls 23.1%.

Top Papers

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