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HFEPX Hub

Multi Agent + Medicine (Last 60 Days)

Updated from current HFEPX corpus (Apr 27, 2026). 10 papers are grouped in this hub page.

Read Full Context

Updated from current HFEPX corpus (Apr 27, 2026). 10 papers are grouped in this hub page. Common evaluation modes: Automatic Metrics, Simulation Env. Most common rater population: Domain Experts. Frequently cited benchmark: Medpriv-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 Mar 29, 2026.

Papers: 10 Last published: Mar 29, 2026 Global RSS Tag RSS
Multi AgentMedicineLast 60d

Researcher Quick Triage

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

All Sampled Papers (10) Replication-Ready Only (1)

High-Signal Coverage

100.0%

10 / 10 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.
  • 0 papers report explicit quality controls (calibration/adjudication/IAA).

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

Currently showing only replication-ready papers in ranking and matrix sections (1 papers).

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Why This Matters For Eval Research

  • 40% of papers report explicit human-feedback signals, led by expert verification.
  • automatic metrics appears in 80% of papers in this hub.
  • Medpriv-Bench is a recurring benchmark anchor for cross-paper comparisons in this page.

Protocol Takeaways

  • Quality-control reporting is sparse in this slice; prioritize papers with explicit calibration or adjudication steps.
  • Rater context is mostly domain experts, and annotation is commonly mixed annotation units; use this to scope replication staffing.
  • Pair this hub with llm_as_judge pages to benchmark automated-vs-human evaluation tradeoffs.

Benchmark Interpretation

  • Medpriv-Bench appears in 10% of hub papers (1/10); use this cohort for benchmark-matched comparisons.

Metric Interpretation

  • accuracy is reported in 70% of hub papers (7/10); compare with a secondary metric before ranking methods.
  • agreement is reported in 10% of hub papers (1/10); compare with a secondary metric before ranking methods.
Researcher Checklist (Expanded)

Researcher Checklist

  • Moderate: Papers with explicit human feedback

    Coverage is usable but incomplete (40% vs 45% target).

  • Gap: Papers reporting quality controls

    Coverage is a replication risk (0% vs 30% target).

  • Gap: Papers naming benchmarks/datasets

    Coverage is a replication risk (10% vs 35% target).

  • Strong: Papers naming evaluation metrics

    Coverage is strong (80% vs 35% target).

  • Strong: Papers with known rater population

    Coverage is strong (80% vs 35% target).

  • Gap: Papers with known annotation unit

    Coverage is a replication risk (0% vs 35% target).

Strengths

  • Agentic evaluation appears in 100% of papers.

Known Gaps

  • Only 0% of papers report quality controls; prioritize calibration/adjudication evidence.
  • Annotation unit is under-specified (0% coverage).
  • Benchmark coverage is thin (10% of papers mention benchmarks/datasets).

Suggested Next Analyses

  • Pair this hub with llm_as_judge pages to benchmark automated-vs-human evaluation tradeoffs.
  • Track metric sensitivity by reporting both accuracy and agreement.
Recommended Queries (Expanded)

Recommended Queries

Human Eval Protocols Benchmark Slice: Medpriv-Bench Metric Slice: accuracy 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
MedPriv-Bench: Benchmarking the Privacy-Utility Trade-off of Large Language Models in Medical Open-End Question Answering

Mar 15, 2026

 No
Not Reported
Automatic Metrics Medpriv Bench Accuracy Not Reported
Suggested Reading Order (Extended)

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

Suggested Reading Order

  1. CCD-CBT: Multi-Agent Therapeutic Interaction for CBT Guided by Cognitive Conceptualization Diagram

    Start here for detailed protocol reporting and quality-control evidence. Signals: simulation environments. Abstract: Large language models show potential for scalable mental-health support by simulating Cognitive Behavioral Therapy (CBT).

  2. Improving Clinical Diagnosis with Counterfactual Multi-Agent Reasoning

    Start here for detailed protocol reporting and quality-control evidence. Signals: human evaluation + expert verification. Focus: accuracy. Abstract: Human evaluation further indicates that our framework produces more clinically.

  3. ClinicalAgents: Multi-Agent Orchestration for Clinical Decision Making with Dual-Memory

    Start here for detailed protocol reporting and quality-control evidence. Signals: automatic metrics + expert verification. Focus: accuracy. Abstract: While Large Language Models (LLMs) have demonstrated potential in healthcare,.

  4. TherapyProbe: Generating Design Knowledge for Relational Safety in Mental Health Chatbots Through Adversarial Simulation

    Include a human-eval paper to calibrate against judge-based evaluation settings. Signals: simulation environments + expert verification. Abstract: As mental health chatbots proliferate to address the global treatment gap,.

  5. A Multidisciplinary AI Board for Multimodal Dementia Characterization and Risk Assessment

    Adds automatic metrics with expert verification for broader protocol coverage within this hub. Signals: automatic metrics + expert verification. Focus: accuracy. Abstract: Modern clinical practice increasingly depends on.

  6. MedPriv-Bench: Benchmarking the Privacy-Utility Trade-off of Large Language Models in Medical Open-End Question Answering

    Adds automatic metrics for broader protocol coverage within this hub. Signals: automatic metrics. Focus: Medpriv-Bench / accuracy. Abstract: We establish a standardized evaluation protocol leveraging a pre-trained RoBERTa-Natural.

  7. Agentic Automation of BT-RADS Scoring: End-to-End Multi-Agent System for Standardized Brain Tumor Follow-up Assessment

    Adds automatic metrics for broader protocol coverage within this hub. Signals: automatic metrics. Focus: accuracy. Abstract: The Brain Tumor Reporting and Data System (BT-RADS) standardizes post-treatment MRI response.

  8. RexDrug: Reliable Multi-Drug Combination Extraction through Reasoning-Enhanced LLMs

    Adds automatic metrics for broader protocol coverage within this hub. Signals: automatic metrics. Focus: accuracy. Abstract: Automated Drug Combination Extraction (DCE) from large-scale biomedical literature is crucial for.

Known Limitations

Known Limitations

  • Only 0% of papers report quality controls; prioritize calibration/adjudication evidence.
  • Annotation unit is under-specified (0% coverage).
  • 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

  • Expert Verification (4)

Evaluation Modes

  • Automatic Metrics (8)
  • Simulation Env (2)
  • Human Eval (1)

Top Benchmarks

  • Medpriv Bench (1)

Top Metrics

  • Accuracy (7)
  • Agreement (1)
  • Auroc (1)
  • Cost (1)

Rater Population Mix

  • Domain Experts (8)

Quality Controls

Coverage diagnostics (sample-based): human-feedback 40.0% · benchmarks 10.0% · metrics 80.0% · quality controls 0.0%.

Top Papers

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