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HFEPX Hub

Rubric Rating Papers (Last 30 Days)

Updated from current HFEPX corpus (Mar 1, 2026). 14 papers are grouped in this hub page.

Read Full Context

Updated from current HFEPX corpus (Mar 1, 2026). 14 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: Inter Annotator Agreement Reported. 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 Feb 9, 2026.

Papers: 14 Last published: Feb 9, 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 .

High-Signal Coverage

100.0%

14 / 14 sampled papers are not low-signal flagged.

Replication-Ready Set

1

Benchmark + metric + eval mode explicitly present.

Judge/Human Comparability

0

Papers containing both `human_eval` and `llm_as_judge`.

  • 1 papers are replication-ready (benchmark + metric + explicit evaluation mode).
  • 0 papers support judge-vs-human agreement analysis.
  • 1 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 21.4% of papers in this hub.
  • Healthbench is a recurring benchmark anchor for cross-paper comparisons in this page.
Protocol Notes (Expanded)

Protocol Takeaways

  • Most common quality-control signal is inter-annotator agreement reporting (7.1% 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 7.1% of hub papers (1/14); use this cohort for benchmark-matched comparisons.
  • LongBench appears in 7.1% of hub papers (1/14); use this cohort for benchmark-matched comparisons.

Metric Interpretation

  • accuracy is reported in 14.3% of hub papers (2/14); compare with a secondary metric before ranking methods.
  • agreement is reported in 7.1% of hub papers (1/14); 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 (7.1% vs 30% target).

  • Moderate: Papers naming benchmarks/datasets

    Coverage is usable but incomplete (21.4% vs 35% target).

  • Moderate: Papers naming evaluation metrics

    Coverage is usable but incomplete (28.6% vs 35% target).

  • Strong: Papers with known rater population

    Coverage is strong (35.7% 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 7.1% 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 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
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
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
Small Reward Models via Backward Inference

Feb 14, 2026

Yes Llm As Judge Not Reported Not Reported Not Reported
SibylSense: Adaptive Rubric Learning via Memory Tuning and Adversarial Probing

Feb 24, 2026

Yes Not Reported Not Reported Not Reported Not Reported
Quantifying and Mitigating Socially Desirable Responding in LLMs: A Desirability-Matched Graded Forced-Choice Psychometric Study

Feb 19, 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 Document Reconstruction Unlocks Scalable Long-Conte… Personalized Prediction of Perceived Message Effect… Decomposing Physician Disagreement in HealthBench
Human Feedback Rubric RatingRubric RatingRubric Rating
Evaluation Modes Automatic MetricsAutomatic MetricsNot reported
Benchmarks LongBenchNot reportedHealthbench
Metrics CoherenceAccuracy, F1Not reported
Quality Controls Not reportedInter Annotator Agreement ReportedNot reported
Rater Population Domain ExpertsUnknownDomain Experts
Annotation Unit Multi Dim RubricScalarMulti 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. Decomposing Physician Disagreement in HealthBench

    Start here for detailed protocol reporting and quality-control evidence. Signals: rubric ratings. Focus: Healthbench. Abstract: Rubric identity accounts for 15.8% of met/not-met label variance but only 3.6-6.9% of.

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

    Start here for detailed protocol reporting and quality-control evidence. Signals: automatic metrics + rubric ratings. Focus: accuracy. Abstract: While recent rubric-based approaches offer fine-grained supervision signals, they suffer.

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

    Start here for detailed protocol reporting and quality-control evidence. Signals: rubric ratings. Abstract: Rubrics provide structured, interpretable supervision, but scaling rubric construction is difficult: expert rubrics are costly,.

  4. 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.

  5. Document Reconstruction Unlocks Scalable Long-Context RLVR

    Include a human-eval paper to calibrate against judge-based evaluation settings. Signals: automatic metrics + rubric ratings. Focus: LongBench / coherence. Abstract: However, it often relies on gold-standard answers.

  6. 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.

  7. 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.

  8. Personalized Prediction of Perceived Message Effectiveness Using Large Language Model Based Digital Twins

    Adds automatic metrics with rubric ratings for broader protocol coverage within this hub. Signals: automatic metrics + rubric ratings. Focus: accuracy. Abstract: Perceived message effectiveness (PME) by potential.

Known Limitations

Known Limitations

  • Only 7.1% 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 (14)
  • Pairwise Preference (4)
  • Expert Verification (1)
  • Red Team (1)

Evaluation Modes

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

Top Benchmarks

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

Top Metrics

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

Rater Population Mix

  • Domain Experts (5)

Quality Controls

  • Inter Annotator Agreement Reported (1)
Coverage diagnostics (sample-based): human-feedback 100.0% · benchmarks 21.4% · metrics 21.4% · quality controls 7.1%.

Top Papers

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