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HFEPX Hub

CS.AI + Rubric Rating Papers

Updated from current HFEPX corpus (Mar 8, 2026). 15 papers are grouped in this hub page.

Read Full Context

Updated from current HFEPX corpus (Mar 8, 2026). 15 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: agreement. 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 2, 2026.

Papers: 15 Last published: Mar 2, 2026 Global RSS Tag RSS
Cs.AIRubric Rating

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%

15 / 15 sampled papers are not low-signal flagged.

Replication-Ready Set

2

Benchmark + metric + eval mode explicitly present.

Judge/Human Comparability

2

Papers containing both `human_eval` and `llm_as_judge`.

  • 2 papers are replication-ready (benchmark + metric + explicit evaluation mode).
  • 2 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 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.
  • Healthbench is a recurring benchmark anchor for cross-paper comparisons in this page.

Protocol Takeaways

  • 2 sampled papers report both human evaluation and LLM-as-judge, supporting direct agreement checks.
  • Most common quality-control signal is inter-annotator agreement reporting (13.3% 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 13.3% of hub papers (2/15); use this cohort for benchmark-matched comparisons.
  • CAPArena appears in 6.7% of hub papers (1/15); use this cohort for benchmark-matched comparisons.

Metric Interpretation

  • agreement is reported in 26.7% of hub papers (4/15); compare with a secondary metric before ranking methods.
  • accuracy is reported in 20% of hub papers (3/15); 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 (13.3% vs 30% target).

  • Moderate: Papers naming benchmarks/datasets

    Coverage is usable but incomplete (26.7% vs 35% target).

  • Strong: Papers naming evaluation metrics

    Coverage is strong (53.3% vs 35% target).

  • Strong: Papers with known rater population

    Coverage is strong (66.7% 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.
  • Agentic evaluation appears in 26.7% of papers.

Known Gaps

  • Only 13.3% 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 CAPArena) before comparing methods.
  • Track metric sensitivity by reporting both agreement and accuracy.
Recommended Queries (Expanded)

Recommended Queries

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
PoSh: Using Scene Graphs To Guide LLMs-as-a-Judge For Detailed Image Descriptions

Oct 21, 2025

Yes Human Eval , Llm As Judge CAPArena Spearman Not Reported
PanCanBench: A Comprehensive Benchmark for Evaluating Large Language Models in Pancreatic Oncology

Mar 2, 2026

Yes Llm As Judge , Automatic Metrics Pancanbench , Healthbench Accuracy Not 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
MENLO: From Preferences to Proficiency -- Evaluating and Modeling Native-like Quality Across 47 Languages

Sep 30, 2025

Yes Automatic Metrics Not Reported Agreement 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
A Scalable Framework for Evaluating Health Language Models

Mar 30, 2025

Yes Automatic Metrics Not Reported Accuracy , Agreement Inter Annotator Agreement Reported
Confusion-Aware Rubric Optimization for LLM-based Automated Grading

Feb 28, 2026

Yes Automatic Metrics Not Reported Accuracy , Precision Not Reported
APEX-Agents

Jan 20, 2026

Yes Automatic Metrics Not Reported Pass@1 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
Optimizing In-Context Demonstrations for LLM-based Automated Grading

Feb 28, 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 PoSh: Using Scene Graphs To Guide LLMs-as-a-Judge F… PanCanBench: A Comprehensive Benchmark for Evaluati… HEART: A Unified Benchmark for Assessing Humans and…
Human Feedback Rubric RatingRubric Rating, Expert VerificationPairwise Preference, Rubric Rating
Evaluation Modes Human Eval, Llm As JudgeLlm As Judge, Automatic MetricsHuman Eval, Llm As Judge
Benchmarks CAPArenaPancanbench, HealthbenchNot reported
Metrics SpearmanAccuracyAgreement
Quality Controls Not reportedNot reportedNot reported
Rater Population Domain ExpertsDomain ExpertsUnknown
Annotation Unit Multi Dim RubricMulti Dim RubricPairwise
Suggested Reading Order (Extended)

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

Suggested Reading Order

  1. A Scalable Framework for Evaluating Health Language Models

    Start here for detailed protocol reporting and quality-control evidence. Signals: automatic metrics + rubric ratings. Focus: accuracy. Abstract: In this work, we introduce Adaptive Precise Boolean rubrics: an.

  2. PanCanBench: A Comprehensive Benchmark for Evaluating Large Language Models in Pancreatic Oncology

    High citation traction makes this a strong baseline for protocol comparison. Signals: LLM-as-judge + rubric ratings. Focus: Pancanbench / accuracy. Abstract: Moreover, high rubric-based scores do not ensure.

  3. Optimizing In-Context Demonstrations for LLM-based Automated Grading

    High citation traction makes this a strong baseline for protocol comparison. Signals: rubric ratings. Abstract: Standard retrieval methods typically select examples based on semantic similarity, which often fails.

  4. Confusion-Aware Rubric Optimization for LLM-based Automated Grading

    High citation traction makes this a strong baseline for protocol comparison. Signals: automatic metrics + rubric ratings. Focus: accuracy. Abstract: To address these limitations, we introduce Confusion-Aware Rubric.

  5. PoSh: Using Scene Graphs To Guide LLMs-as-a-Judge For Detailed Image Descriptions

    Include a human-eval paper to calibrate against judge-based evaluation settings. Signals: human evaluation + rubric ratings. Focus: CAPArena / spearman. Abstract: In this work, we introduce PoSh, a.

  6. 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.

  7. 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.

  8. MENLO: From Preferences to Proficiency -- Evaluating and Modeling Native-like Quality Across 47 Languages

    Adds automatic metrics with pairwise preferences for broader protocol coverage within this hub. Signals: automatic metrics + pairwise preferences. Focus: agreement. Abstract: Using MENLO, we create a dataset.

Known Limitations

Known Limitations

  • Only 13.3% 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 (15)
  • Pairwise Preference (5)
  • Expert Verification (4)
  • Demonstrations (1)

Evaluation Modes

  • Automatic Metrics (6)
  • Llm As Judge (3)
  • Human Eval (2)

Top Benchmarks

  • Healthbench (2)
  • CAPArena (1)
  • MLE Bench (1)
  • Pancanbench (1)

Top Metrics

  • Agreement (4)
  • Accuracy (3)
  • Cost (1)
  • Pass@1 (1)

Rater Population Mix

  • Domain Experts (9)
  • Mixed (1)

Quality Controls

  • Inter Annotator Agreement Reported (2)
Coverage diagnostics (sample-based): human-feedback 100.0% · benchmarks 26.7% · metrics 46.7% · quality controls 13.3%.

Top Papers

  • PanCanBench: A Comprehensive Benchmark for Evaluating Large Language Models in Pancreatic Oncology

    Yimin Zhao, Sheela R. Damle, Simone E. Dekker, Scott Geng, Karly Williams Silva · Mar 2, 2026 · Citations: 0

    Rubric RatingExpert Verification Llm As JudgeAutomatic Metrics

    Large language models (LLMs) have achieved expert-level performance on standardized examinations, yet multiple-choice accuracy poorly reflects real-world clinical utility and safety.

  • PoSh: Using Scene Graphs To Guide LLMs-as-a-Judge For Detailed Image Descriptions

    Amith Ananthram, Elias Stengel-Eskin, Lorena A. Bradford, Julia Demarest, Adam Purvis · Oct 21, 2025 · Citations: 0

    Rubric Rating Human EvalLlm As Judge

    In this work, we introduce PoSh, a metric for detailed image description that uses scene graphs as structured rubrics to guide LLMs-as-a-Judge, producing aggregate scores grounded in fine-grained errors (e.g.

  • A Scalable Framework for Evaluating Health Language Models

    Neil Mallinar, A. Ali Heydari, Xin Liu, Anthony Z. Faranesh, Brent Winslow · Mar 30, 2025 · Citations: 0

    Rubric RatingExpert Verification Automatic Metrics

    As LLM-driven health applications are increasingly adopted, rigorous and efficient one-sided evaluation methodologies are crucial to ensure response quality across multiple dimensions, including accuracy, personalization and safety.

  • APEX-Agents

    Bertie Vidgen, Austin Mann, Abby Fennelly, John Wright Stanly, Lucas Rothman · Jan 20, 2026 · Citations: 0

    Rubric RatingExpert Verification Automatic Metrics Long Horizon

    We introduce the AI Productivity Index for Agents (APEX-Agents), a benchmark for assessing whether AI agents can execute long-horizon, cross-application tasks created by investment banking analysts, management consultants, and corporate…

  • MENLO: From Preferences to Proficiency -- Evaluating and Modeling Native-like Quality Across 47 Languages

    Chenxi Whitehouse, Sebastian Ruder, Tony Lin, Oksana Kurylo, Haruka Takagi · Sep 30, 2025 · Citations: 0

    Pairwise PreferenceRubric Rating Automatic Metrics

    To address this, we introduce MENLO, a framework that operationalizes the evaluation of native-like response quality based on audience design-inspired mechanisms.

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

    Laya Iyer, Kriti Aggarwal, Sanmi Koyejo, Gail Heyman, Desmond C. Ong · Jan 9, 2026 · Citations: 0

    Pairwise PreferenceRubric Rating Human EvalLlm As Judge

    Despite rapid progress in language models, we still lack a clear way to understand how their abilities in these interpersonal domains compare to those of humans.

  • Decomposing Physician Disagreement in HealthBench

    Satya Borgohain, Roy Mariathas · Feb 26, 2026 · Citations: 0

    Rubric Rating

    We decompose physician disagreement in the HealthBench medical AI evaluation dataset to understand where variance resides and what observable features can explain it.

  • KLong: Training LLM Agent for Extremely Long-horizon Tasks

    Yue Liu, Zhiyuan Hu, Flood Sung, Jiaheng Zhang, Bryan Hooi · Feb 19, 2026 · Citations: 0

    Rubric Rating Long Horizon

    Then, we introduce Research-Factory, an automated pipeline that generates high-quality training data by collecting research papers and constructing evaluation rubrics.

  • Confusion-Aware Rubric Optimization for LLM-based Automated Grading

    Yucheng Chu, Hang Li, Kaiqi Yang, Yasemin Copur-Gencturk, Joseph Krajcik · Feb 28, 2026 · Citations: 0

    Rubric Rating Automatic Metrics

    Empirical evaluations on teacher education and STEM datasets demonstrate that CARO significantly outperforms existing SOTA methods.

  • Multi-Agent Comedy Club: Investigating Community Discussion Effects on LLM Humor Generation

    Shiwei Hong, Lingyao Li, Ethan Z. Rong, Chenxinran Shen, Zhicong Lu · Feb 16, 2026 · Citations: 0

    Pairwise PreferenceRubric Rating Multi Agent

    Prior work has explored multi-turn interaction and feedback for LLM writing, but evaluations still largely center on prompts and localized feedback, leaving persistent public reception in online communities underexamined.

  • MedXIAOHE: A Comprehensive Recipe for Building Medical MLLMs

    Baorong Shi, Bo Cui, Boyuan Jiang, Deli Yu, Fang Qian · Feb 13, 2026 · Citations: 0

    Pairwise PreferenceRubric Rating Long Horizon

    MedXIAOHE achieves state-of-the-art performance across diverse medical benchmarks and surpasses leading closed-source multimodal systems on multiple capabilities.

  • Optimizing In-Context Demonstrations for LLM-based Automated Grading

    Yucheng Chu, Hang Li, Kaiqi Yang, Yasemin Copur-Gencturk, Kevin Haudek · Feb 28, 2026 · Citations: 0

    Rubric RatingDemonstrations

    GUIDE paves the way for trusted, scalable assessment systems that align closely with human pedagogical standards.

  • SibylSense: Adaptive Rubric Learning via Memory Tuning and Adversarial Probing

    Yifei Xu, Guilherme Potje, Shivam Shandilya, Tiancheng Yuan, Leonardo de Oliveira Nunes · Feb 24, 2026 · Citations: 0

    Rubric RatingRed Team

    We present SibylSense, an inference-time learning approach that adapts a frozen rubric generator through a tunable memory bank of validated rubric items.

  • Chasing the Tail: Effective Rubric-based Reward Modeling for Large Language Model Post-Training

    Junkai Zhang, Zihao Wang, Lin Gui, Swarnashree Mysore Sathyendra, Jaehwan Jeong · Sep 25, 2025 · Citations: 0

    Rubric Rating Automatic Metrics

    Reinforcement fine-tuning (RFT) often suffers from reward over-optimization, where a policy model hacks the reward signals to achieve high scores while producing low-quality outputs.

  • LFQA-HP-1M: A Large-Scale Human Preference Dataset for Long-Form Question Answering

    Rafid Ishrak Jahan, Fahmid Shahriar Iqbal, Sagnik Ray Choudhury · Feb 27, 2026 · Citations: 0

    Pairwise PreferenceRubric Rating

    We present LFQA-HP-1M, a large-scale dataset comprising 1.3M human pairwise preference annotations for LFQA.

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