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HFEPX Hub

Rubric Rating Papers (Last 120 Days)

Updated from current HFEPX corpus (Mar 1, 2026). 17 papers are grouped in this hub page.

Read Full Context

Updated from current HFEPX corpus (Mar 1, 2026). 17 papers are grouped in this hub page. Common evaluation modes: Automatic Metrics, Llm As Judge. Most common rater population: Domain Experts. Common annotation unit: Multi Dim Rubric. Frequent quality control: Calibration. 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 Jan 20, 2026.

Papers: 17 Last published: Jan 20, 2026 Global RSS Tag RSS
Rubric RatingLast 120d

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%

17 / 17 sampled papers are not low-signal flagged.

Replication-Ready Set

1

Benchmark + metric + eval mode explicitly present.

Judge/Human Comparability

1

Papers containing both `human_eval` and `llm_as_judge`.

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

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

Why This Matters (Expanded)

Why This Matters For Eval Research

  • 100% of papers report explicit human-feedback signals, led by rubric ratings.
  • automatic metrics appears in 23.5% of papers in this hub.
  • Healthbench is a recurring benchmark anchor for cross-paper comparisons in this page.
Protocol Notes (Expanded)

Protocol Takeaways

  • 1 sampled papers report both human evaluation and LLM-as-judge, supporting direct agreement checks.
  • Most common quality-control signal is rater calibration (5.9% of papers).
  • Rater context is mostly domain experts, and annotation is commonly multi-dimensional rubrics; use this to scope replication staffing.

Benchmark Interpretation

  • Healthbench appears in 5.9% of hub papers (1/17); use this cohort for benchmark-matched comparisons.
  • LongBench appears in 5.9% of hub papers (1/17); use this cohort for benchmark-matched comparisons.

Metric Interpretation

  • accuracy is reported in 11.8% of hub papers (2/17); compare with a secondary metric before ranking methods.
  • agreement is reported in 11.8% of hub papers (2/17); 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 (11.8% vs 30% target).

  • Gap: Papers naming benchmarks/datasets

    Coverage is a replication risk (17.6% vs 35% target).

  • Strong: Papers naming evaluation metrics

    Coverage is strong (35.3% vs 35% target).

  • Strong: Papers with known rater population

    Coverage is strong (41.2% vs 35% target).

  • Strong: Papers with known annotation unit

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

Strengths

  • Strong human-feedback signal (100% of papers).
  • Contains both human-eval and LLM-as-judge protocols for head-to-head methodology comparison.
  • Rater population and annotation-unit details are frequently specified.

Known Gaps

  • Only 11.8% of papers report quality controls; prioritize calibration/adjudication evidence.
  • Benchmark coverage is thin (17.6% of papers mention benchmarks/datasets).

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 LongBench) before comparing methods.
  • Track metric sensitivity by reporting both accuracy and agreement.
Recommended Queries (Expanded)

Recommended Queries

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
Document Reconstruction Unlocks Scalable Long-Context RLVR

Feb 9, 2026

Yes Automatic Metrics LongBench Coherence Not Reported
Personalized Prediction of Perceived Message Effectiveness Using Large Language Model Based Digital Twins

Feb 23, 2026

Yes Automatic Metrics Not Reported Accuracy , F1 Inter Annotator Agreement Reported
HEART: A Unified Benchmark for Assessing Humans and LLMs in Emotional Support Dialogue

Jan 9, 2026

Yes Human Eval , Llm As Judge Not Reported Agreement Not Reported
Decomposing Physician Disagreement in HealthBench

Feb 26, 2026

Yes Not Reported Healthbench Not Reported Not Reported
KLong: Training LLM Agent for Extremely Long-horizon Tasks

Feb 19, 2026

Yes Not Reported SWE Bench , SWE Bench Verified Not Reported Not Reported
RuCL: Stratified Rubric-Based Curriculum Learning for Multimodal Large Language Model Reasoning

Feb 25, 2026

Yes Automatic Metrics Not Reported Accuracy Not Reported
APEX-Agents

Jan 20, 2026

Yes Automatic Metrics Not Reported Pass@1 Not Reported
Cross-Cultural Expert-Level Art Critique Evaluation with Vision-Language Models

Jan 12, 2026

Yes Not Reported Not Reported Not Reported Calibration
Multi-Agent Comedy Club: Investigating Community Discussion Effects on LLM Humor Generation

Feb 16, 2026

Yes Not Reported Not Reported Not Reported Not Reported
MedXIAOHE: A Comprehensive Recipe for Building Medical MLLMs

Feb 13, 2026

Yes Not Reported Not Reported Not Reported Not Reported
Discovering Implicit Large Language Model Alignment Objectives

Feb 17, 2026

Yes Human Eval Not Reported Not Reported Not Reported
Open Rubric System: Scaling Reinforcement Learning with Pairwise Adaptive Rubric

Feb 15, 2026

Yes Llm As Judge Not Reported Not Reported Not Reported

Protocol Diff (Top Papers)

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

Signal Document Reconstruction Unlocks Scalable Long-Conte… Personalized Prediction of Perceived Message Effect… HEART: A Unified Benchmark for Assessing Humans and…
Human Feedback Rubric RatingRubric RatingPairwise Preference, Rubric Rating
Evaluation Modes Automatic MetricsAutomatic MetricsHuman Eval, Llm As Judge
Benchmarks LongBenchNot reportedNot reported
Metrics CoherenceAccuracy, F1Agreement
Quality Controls Not reportedInter Annotator Agreement ReportedNot reported
Rater Population Domain ExpertsUnknownUnknown
Annotation Unit Multi Dim RubricScalarPairwise
Suggested Reading Order (Extended)

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

Suggested Reading Order

  1. Cross-Cultural Expert-Level Art Critique Evaluation with Vision-Language Models

    Start here for detailed protocol reporting and quality-control evidence. Signals: rubric ratings. Abstract: Existing benchmarks assess perception without interpretation, and common evaluation proxies, such as automated metrics and.

  2. Decomposing Physician Disagreement in HealthBench

    High citation traction makes this a strong baseline for protocol comparison. Signals: rubric ratings. Focus: Healthbench. Abstract: Rubric identity accounts for 15.8% of met/not-met label variance but only.

  3. RuCL: Stratified Rubric-Based Curriculum Learning for Multimodal Large Language Model Reasoning

    High citation traction makes this a strong baseline for protocol comparison. Signals: automatic metrics + rubric ratings. Focus: accuracy. Abstract: While recent rubric-based approaches offer fine-grained supervision signals,.

  4. SibylSense: Adaptive Rubric Learning via Memory Tuning and Adversarial Probing

    High citation traction makes this a strong baseline for protocol comparison. Signals: rubric ratings. Abstract: Rubrics provide structured, interpretable supervision, but scaling rubric construction is difficult: expert rubrics.

  5. HEART: A Unified Benchmark for Assessing Humans and LLMs in Emotional Support Dialogue

    Include a human-eval paper to calibrate against judge-based evaluation settings. Signals: human evaluation + pairwise preferences. Focus: agreement. Abstract: For each dialogue history, we pair human and model.

  6. Discovering Implicit Large Language Model Alignment Objectives

    Include a human-eval paper to calibrate against judge-based evaluation settings. Signals: human evaluation + rubric ratings. Abstract: Existing interpretation methods typically rely on pre-defined rubrics, risking the omission.

  7. Open Rubric System: Scaling Reinforcement Learning with Pairwise Adaptive Rubric

    Include an LLM-as-judge paper to test judge design and agreement assumptions. Signals: LLM-as-judge + pairwise preferences. Abstract: Scalar reward models compress multi-dimensional human preferences into a single opaque.

  8. APEX-Agents

    Adds automatic metrics with rubric ratings for broader protocol coverage within this hub. Signals: automatic metrics + rubric ratings. Focus: pass@1. Abstract: We open source the APEX-Agents benchmark.

Known Limitations

Known Limitations

  • Only 11.8% of papers report quality controls; prioritize calibration/adjudication evidence.
  • Benchmark coverage is thin (17.6% of papers mention benchmarks/datasets).
  • 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

  • Rubric Rating (17)
  • Pairwise Preference (5)
  • Expert Verification (2)
  • Critique Edit (1)

Evaluation Modes

  • Automatic Metrics (4)
  • Llm As Judge (3)
  • Human Eval (2)

Top Benchmarks

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

Top Metrics

  • Accuracy (2)
  • Agreement (2)
  • Coherence (1)
  • F1 (1)

Rater Population Mix

  • Domain Experts (7)

Quality Controls

  • Calibration (1)
  • Inter Annotator Agreement Reported (1)
Coverage diagnostics (sample-based): human-feedback 100.0% · benchmarks 17.6% · metrics 29.4% · quality controls 11.8%.

Top Papers

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