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HFEPX Hub

Rubric Rating Papers (Last 30 Days)

Updated from current HFEPX corpus (Apr 27, 2026). 16 papers are grouped in this hub page.

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Updated from current HFEPX corpus (Apr 27, 2026). 16 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: Healthbench. 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: 16 Last published: Mar 31, 2026 Global RSS Tag RSS
Rubric RatingLast 30d

Researcher Quick Triage

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

All Sampled Papers (16) Replication-Ready Only (4)

High-Signal Coverage

100.0%

16 / 16 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.
  • 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.

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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 43.8% of papers in this hub.
  • Healthbench is a recurring benchmark anchor for cross-paper comparisons in this page.

Protocol Takeaways

  • Most common quality-control signal is adjudication (18.8% 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

  • Healthbench appears in 6.3% of hub papers (1/16); use this cohort for benchmark-matched comparisons.
  • IFEval appears in 6.3% of hub papers (1/16); use this cohort for benchmark-matched comparisons.

Metric Interpretation

  • accuracy is reported in 18.8% of hub papers (3/16); compare with a secondary metric before ranking methods.
  • agreement is reported in 18.8% of hub papers (3/16); 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 (18.8% vs 30% target).

  • Strong: Papers naming benchmarks/datasets

    Coverage is strong (37.5% vs 35% target).

  • Strong: Papers naming evaluation metrics

    Coverage is strong (56.3% vs 35% target).

  • Strong: Papers with known rater population

    Coverage is strong (43.8% 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 (37.5% benchmarks, 56.3% metrics).
  • Contains both human-eval and LLM-as-judge protocols for head-to-head methodology comparison.

Known Gaps

  • Only 18.8% of papers report quality controls; prioritize calibration/adjudication evidence.

Suggested Next Analyses

  • Compare papers that report both human_eval and llm_as_judge to quantify judge-human agreement drift.
  • Stratify by benchmark (Healthbench vs IFEval) before comparing methods.
  • Track metric sensitivity by reporting both accuracy and agreement.
Recommended Queries (Expanded)

Recommended Queries

Judge vs Human Agreement Benchmark Slice: Healthbench 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
Personalized RewardBench: Evaluating Reward Models with Human Aligned Personalization

Apr 8, 2026

 Yes
Pairwise Preference , Rubric Rating
Human Eval , Automatic Metrics Rewardbench Accuracy , Helpfulness Not Reported
Beyond Paper-to-Paper: Structured Profiling and Rubric Scoring for Paper-Reviewer Matching

Apr 7, 2026

 Yes
Rubric Rating
Automatic Metrics Scirepeval Recall 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
Rethinking Atomic Decomposition for LLM Judges: A Prompt-Controlled Study of Reference-Grounded QA Evaluation

Mar 30, 2026

 Yes
Rubric Rating
Automatic Metrics TruthfulQA Accuracy Not Reported
LLM Essay Scoring Under Holistic and Analytic Rubrics: Prompt Effects and Bias

Mar 31, 2026

 Yes
Rubric Rating
Human Eval Not Reported Kappa , Agreement Inter Annotator Agreement Reported , Adjudication
More Human, More Efficient: Aligning Annotations with Quantized SLMs

Apr 1, 2026

 Yes
Rubric Rating
Automatic Metrics Not Reported Agreement Inter Annotator Agreement Reported , Adjudication
Self-Preference Bias in Rubric-Based Evaluation of Large Language Models

Apr 8, 2026

 Yes
Pairwise Preference , Rubric Rating
Llm As Judge IFEval , Healthbench Not Reported Not Reported
MiroEval: Benchmarking Multimodal Deep Research Agents in Process and Outcome

Mar 30, 2026

 Yes
Rubric Rating
Not Reported Miroeval Not Reported Not Reported
CounselReflect: A Toolkit for Auditing Mental-Health Dialogues

Mar 31, 2026

 Yes
Rubric Rating , Expert Verification
Human Eval Not Reported Not Reported Adjudication
PRBench: End-to-end Paper Reproduction in Physics Research

Mar 29, 2026

 Yes
Rubric Rating , Expert Verification
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
Rubric Rating
Automatic Metrics Not Reported Inference cost Not Reported
FrontierFinance: A Long-Horizon Computer-Use Benchmark of Real-World Financial Tasks

Apr 7, 2026

 Yes
Rubric Rating
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 Personalized RewardBench: Evaluating Reward Models… Beyond Paper-to-Paper: Structured Profiling and Rub… Paper Reconstruction Evaluation: Evaluating Present…
Human Feedback Pairwise Preference, Rubric RatingRubric RatingRubric Rating
Evaluation Modes Human Eval, Automatic MetricsAutomatic MetricsAutomatic Metrics
Benchmarks RewardbenchScirepevalPaperwrite Bench
Metrics Accuracy, HelpfulnessRecallNot reported
Quality Controls Not reportedNot reportedNot reported
Rater Population UnknownDomain ExpertsUnknown
Annotation Unit PairwiseMulti 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. More Human, More Efficient: Aligning Annotations with Quantized SLMs

    Start here for detailed protocol reporting and quality-control evidence. Signals: automatic metrics + rubric ratings. Focus: agreement. Abstract: As Large Language Model (LLM) capabilities advance, the demand for.

  2. 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.

  3. Aggregate vs. Personalized Judges in Business Idea Evaluation: Evidence from Expert Disagreement

    High citation traction makes this a strong baseline for protocol comparison. Signals: rubric ratings. Abstract: This paper studies a methodological question raised by such disagreement: should an automatic.

  4. Emergent Strategic Reasoning Risks in AI: A Taxonomy-Driven Evaluation Framework

    High citation traction makes this a strong baseline for protocol comparison. Signals: rubric ratings. Abstract: ESRRSim generates evaluation scenarios designed to elicit faithful reasoning, paired with dual rubrics.

  5. Personalized RewardBench: Evaluating Reward Models with Human Aligned Personalization

    High citation traction makes this a strong baseline for protocol comparison. Signals: human evaluation + pairwise preferences. Focus: Rewardbench / accuracy. Abstract: While benchmarks for general response quality.

  6. LLM Essay Scoring Under Holistic and Analytic Rubrics: Prompt Effects and Bias

    Include a human-eval paper to calibrate against judge-based evaluation settings. Signals: human evaluation + rubric ratings. Focus: kappa. Abstract: In particular, we observe large and stable negative directional.

  7. Self-Preference Bias in Rubric-Based Evaluation of Large Language Models

    Include an LLM-as-judge paper to test judge design and agreement assumptions. Signals: LLM-as-judge + pairwise preferences. Focus: IFEval. Abstract: LLM-as-a-judge has become the de facto approach for evaluating.

  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

  • Only 18.8% 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)

Research Utility Snapshot

Human Feedback Mix

  • Rubric Rating (16)
  • Expert Verification (3)
  • Pairwise Preference (2)
  • Critique Edit (1)

Evaluation Modes

  • Automatic Metrics (7)
  • Human Eval (3)
  • Llm As Judge (1)
  • Simulation Env (1)

Top Benchmarks

  • Healthbench (1)
  • IFEval (1)
  • Miroeval (1)
  • Paperwrite Bench (1)

Top Metrics

  • Accuracy (3)
  • Agreement (3)
  • Cost (2)
  • Helpfulness (1)

Rater Population Mix

  • Domain Experts (7)

Quality Controls

  • Adjudication (3)
  • Inter Annotator Agreement Reported (2)
Coverage diagnostics (sample-based): human-feedback 100.0% · benchmarks 37.5% · metrics 50.0% · quality controls 18.8%.

Top Papers

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