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HFEPX Hub

Coding + Rubric Rating Papers

Updated from current HFEPX corpus (Apr 27, 2026). 20 papers are grouped in this hub page.

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Updated from current HFEPX corpus (Apr 27, 2026). 20 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: Calibration. 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 31, 2026.

Papers: 20 Last published: Mar 31, 2026 Global RSS Tag RSS
CodingRubric Rating

Researcher Quick Triage

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

All Sampled Papers (20) Replication-Ready Only (4)

High-Signal Coverage

100.0%

20 / 20 sampled papers are not low-signal flagged.

Replication-Ready Set

4

Benchmark + metric + eval mode explicitly present.

Judge/Human Comparability

0

Papers containing both `human_eval` and `llm_as_judge`.

  • 4 papers are replication-ready (benchmark + metric + explicit evaluation mode).
  • 0 papers support judge-vs-human agreement analysis.
  • 4 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 (4 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 40% 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 rater calibration (10% 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

  • AIME appears in 5% of hub papers (1/20); use this cohort for benchmark-matched comparisons.
  • Kernelbench appears in 5% of hub papers (1/20); use this cohort for benchmark-matched comparisons.

Metric Interpretation

  • accuracy is reported in 15% of hub papers (3/20); compare with a secondary metric before ranking methods.
  • cost is reported in 15% of hub papers (3/20); 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 (20% vs 30% target).

  • Strong: Papers naming benchmarks/datasets

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

  • Strong: Papers naming evaluation metrics

    Coverage is strong (40% 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).
  • Most papers provide measurable evaluation context (35% benchmarks, 40% 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 (AIME vs Kernelbench) before comparing methods.
  • Track metric sensitivity by reporting both accuracy and cost.
  • Add inter-annotator agreement checks when reproducing these protocols.
Recommended Queries (Expanded)

Recommended Queries

Judge vs Human Agreement Benchmark Slice: AIME Metric Slice: accuracy 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
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
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
Don't Pass@k: A Bayesian Framework for Large Language Model Evaluation

Oct 5, 2025

 Yes
Rubric Rating
Automatic Metrics , Simulation Env AIME Accuracy , Pass@k 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… StitchCUDA: An Automated Multi-Agents End-to-End GP… Document Reconstruction Unlocks Scalable Long-Conte…
Human Feedback Rubric RatingRubric RatingRubric Rating
Evaluation Modes Automatic MetricsAutomatic MetricsAutomatic Metrics
Benchmarks Paperwrite BenchKernelbenchLongBench
Metrics Not reportedSuccess rateCoherence
Quality Controls Not reportedNot reportedNot reported
Rater Population UnknownUnknownDomain Experts
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. Don't Pass@k: A Bayesian Framework for Large Language Model Evaluation

    Adds automatic metrics with rubric ratings for broader protocol coverage within this hub. Signals: automatic metrics + rubric ratings. Focus: AIME / accuracy. Abstract: Evaluation outcomes are modeled.

  8. 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.

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 (20)
  • Expert Verification (2)
  • Pairwise Preference (2)
  • Critique Edit (1)

Evaluation Modes

  • Automatic Metrics (8)
  • Human Eval (3)
  • Simulation Env (2)
  • Llm As Judge (1)

Top Benchmarks

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

Top Metrics

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

Rater Population Mix

  • Domain Experts (8)

Quality Controls

  • Calibration (2)
  • Adjudication (1)
  • Gold Questions (1)
Coverage diagnostics (sample-based): human-feedback 100.0% · benchmarks 35.0% · metrics 40.0% · quality controls 20.0%.

Top Papers

  • Don't Pass@k: A Bayesian Framework for Large Language Model Evaluation

    Mohsen Hariri, Amirhossein Samandar, Michael Hinczewski, Vipin Chaudhary · Oct 5, 2025 · Citations: 0

    Rubric Rating Automatic MetricsSimulation Env

    We present a principled Bayesian evaluation framework that replaces Pass@k and average accuracy over N trials (avg@N) with posterior estimates of a model's underlying success probability and credible intervals, yielding stable rankings and…

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

    Shiyang Li, Zijian Zhang, Winson Chen, Yuebo Luo, Mingyi Hong · Mar 3, 2026 · Citations: 0

    Rubric Rating Automatic Metrics Multi Agent

    To address the challenge, in this work, we propose StitchCUDA, a multi-agent framework for end-to-end GPU program generation, with three specialized agents: a Planner to orchestrate whole system design, a Coder dedicated to implementing it…

  • Document Reconstruction Unlocks Scalable Long-Context RLVR

    Yao Xiao, Lei Wang, Yue Deng, Guanzheng Chen, Ziqi Jin · Feb 9, 2026 · Citations: 0

    Rubric Rating Automatic Metrics

    However, it often relies on gold-standard answers or explicit evaluation rubrics provided by powerful teacher models or human experts, which are costly and time-consuming.

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

    Atsuyuki Miyai, Mashiro Toyooka, Zaiying Zhao, Kenta Watanabe, Toshihiko Yamasaki · Apr 1, 2026 · Citations: 0

    Rubric Rating Automatic Metrics

    We introduce Paper Reconstruction Evaluation (PaperRecon), an evaluation framework in which an overview (overview.md) is created from an existing paper, after which an agent generates a full paper based on the overview and minimal…

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