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HFEPX Hub

Coding Or Medicine Papers

Updated from current HFEPX corpus (Apr 12, 2026). 317 papers are grouped in this hub page.

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Updated from current HFEPX corpus (Apr 12, 2026). 317 papers are grouped in this hub page. Common evaluation modes: Automatic Metrics, Simulation Env. Most common rater population: Domain Experts. Common annotation unit: Multi Dim Rubric. Frequent quality control: Calibration. Frequently cited benchmark: SWE-bench. 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 15, 2026.

Papers: 317 Last published: Feb 15, 2026 Global RSS Tag RSS
CodingMedicine

Researcher Quick Triage

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

Analysis blocks below are computed from the currently loaded sample (60 of 317 total papers in this hub).

High-Signal Coverage

100.0%

60 / 60 sampled papers are not low-signal flagged.

Replication-Ready Set

17

Benchmark + metric + eval mode explicitly present.

Judge/Human Comparability

0

Papers containing both `human_eval` and `llm_as_judge`.

  • 17 papers are replication-ready (benchmark + metric + explicit evaluation mode).
  • 0 papers support judge-vs-human agreement analysis.
  • 18 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

  • 65.3% of papers report explicit human-feedback signals, led by pairwise preferences.
  • automatic metrics appears in 57.1% of papers in this hub.
  • SWE-bench is a recurring benchmark anchor for cross-paper comparisons in this page.

Protocol Takeaways

  • Most common quality-control signal is rater calibration (3.2% 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

  • SWE-bench appears in 1.6% of hub papers (5/317); use this cohort for benchmark-matched comparisons.
  • DROP appears in 1.3% of hub papers (4/317); use this cohort for benchmark-matched comparisons.

Metric Interpretation

  • accuracy is reported in 31.9% of hub papers (101/317); compare with a secondary metric before ranking methods.
  • cost is reported in 12.3% of hub papers (39/317); compare with a secondary metric before ranking methods.
Researcher Checklist (Expanded)

Researcher Checklist

  • Strong: Papers with explicit human feedback

    Coverage is strong (65.3% vs 45% target).

  • Gap: Papers reporting quality controls

    Coverage is a replication risk (7.6% vs 30% target).

  • Moderate: Papers naming benchmarks/datasets

    Coverage is usable but incomplete (23.3% vs 35% target).

  • Strong: Papers naming evaluation metrics

    Coverage is strong (61.5% vs 35% target).

  • Strong: Papers with known rater population

    Coverage is strong (35.6% vs 35% target).

  • Moderate: Papers with known annotation unit

    Coverage is usable but incomplete (30.6% vs 35% target).

Strengths

  • Strong human-feedback signal (65.3% of papers).
  • Contains both human-eval and LLM-as-judge protocols for head-to-head methodology comparison.
  • Agentic evaluation appears in 42.9% of papers.

Known Gaps

  • Only 7.6% 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 (SWE-bench vs DROP) before comparing methods.
  • Track metric sensitivity by reporting both accuracy and cost.
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
CricBench: A Multilingual Benchmark for Evaluating LLMs in Cricket Analytics

Dec 26, 2025

Yes Automatic Metrics DROP , BIRD Accuracy Gold Questions
PubMed Reasoner: Dynamic Reasoning-based Retrieval for Evidence-Grounded Biomedical Question Answering

Mar 28, 2026

Yes Llm As Judge , Automatic Metrics MMLU Accuracy , Relevance Not Reported
Xpertbench: Expert Level Tasks with Rubrics-Based Evaluation

Mar 27, 2026

Yes Automatic Metrics Xpertbench Success rate Not Reported
Paper Reconstruction Evaluation: Evaluating Presentation and Hallucination in AI-written Papers

Apr 1, 2026

Yes Automatic Metrics Paperwrite Bench Cost Not Reported
FairMed-XGB: A Bayesian-Optimised Multi-Metric Framework with Explainability for Demographic Equity in Critical Healthcare Data

Mar 16, 2026

Yes Automatic Metrics DROP Accuracy , Auroc Not Reported
Modeling and Benchmarking Spoken Dialogue Rewards with Modality and Colloquialness

Mar 16, 2026

Yes Automatic Metrics Esdr Bench Accuracy Not Reported
AD-Bench: A Real-World, Trajectory-Aware Advertising Analytics Benchmark for LLM Agents

Feb 15, 2026

Yes Simulation Env Ad Bench Pass@1 , Pass@3 Not Reported
Is this Idea Novel? An Automated Benchmark for Judgment of Research Ideas

Mar 11, 2026

Yes Human Eval Rinobench Not Reported Gold Questions
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
Jailbreak Foundry: From Papers to Runnable Attacks for Reproducible Benchmarking

Feb 27, 2026

Yes Llm As Judge AdvBench , Jbf Eval Success rate , Jailbreak success rate Not Reported
Vision-DeepResearch Benchmark: Rethinking Visual and Textual Search for Multimodal Large Language Models

Feb 2, 2026

Yes Automatic Metrics Vdr Bench Not Reported Adjudication
StitchCUDA: An Automated Multi-Agents End-to-End GPU Programing Framework with Rubric-based Agentic Reinforcement Learning

Mar 3, 2026

Yes Automatic Metrics Kernelbench Success rate Not Reported

Protocol Diff (Top Papers)

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

Signal CricBench: A Multilingual Benchmark for Evaluating… PubMed Reasoner: Dynamic Reasoning-based Retrieval… Xpertbench: Expert Level Tasks with Rubrics-Based E…
Human Feedback Expert VerificationExpert VerificationRubric Rating, Expert Verification
Evaluation Modes Automatic MetricsLlm As Judge, Automatic MetricsAutomatic Metrics
Benchmarks DROP, BIRDMMLUXpertbench
Metrics AccuracyAccuracy, RelevanceSuccess rate
Quality Controls Gold QuestionsNot reportedNot reported
Rater Population Domain ExpertsDomain ExpertsDomain Experts
Annotation Unit UnknownUnknownMulti 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. Guideline-Grounded Evidence Accumulation for High-Stakes Agent Verification

    Start here for detailed protocol reporting and quality-control evidence. Signals: automatic metrics + expert verification. Focus: brier score. Abstract: As LLM-powered agents have been used for high-stakes decision-making,.

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

    High citation traction makes this a strong baseline for protocol comparison. Signals: LLM-as-judge + pairwise preferences. Focus: IFEval. Abstract: LLM-as-a-judge has become the de facto approach for evaluating.

  5. Jailbreak Foundry: From Papers to Runnable Attacks for Reproducible Benchmarking

    Include an LLM-as-judge paper to test judge design and agreement assumptions. Signals: LLM-as-judge + red-team protocols. Focus: AdvBench / success rate. Abstract: This system enables a standardized AdvBench.

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

    Include an LLM-as-judge paper to test judge design and agreement assumptions. Signals: LLM-as-judge + rubric ratings. Focus: Pancanbench / accuracy. Abstract: Moreover, high rubric-based scores do not ensure.

Known Limitations

Known Limitations

  • Only 7.6% 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

  • Pairwise Preference (71)
  • Expert Verification (66)
  • Rubric Rating (36)
  • Critique Edit (20)

Evaluation Modes

  • Automatic Metrics (181)
  • Simulation Env (30)
  • Llm As Judge (23)
  • Human Eval (17)

Top Benchmarks

  • SWE Bench (5)
  • DROP (4)
  • LiveCodeBench (4)
  • LMSYS Chatbot Arena (4)

Top Metrics

  • Accuracy (101)
  • Cost (39)
  • Agreement (15)
  • Latency (14)

Rater Population Mix

  • Domain Experts (110)
  • Mixed (3)

Quality Controls

  • Calibration (10)
  • Adjudication (8)
  • Gold Questions (4)
  • Inter Annotator Agreement Reported (3)
Coverage diagnostics (sample-based): human-feedback 98.3% · benchmarks 50.0% · metrics 71.7% · quality controls 30.0%.

Top Papers

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