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HFEPX Hub

Multi Agent + Automatic Metrics (Last 45 Days)

Updated from current HFEPX corpus (Apr 27, 2026). 24 papers are grouped in this hub page.

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Updated from current HFEPX corpus (Apr 27, 2026). 24 papers are grouped in this hub page. Common evaluation modes: Automatic Metrics, Llm As Judge. Most common rater population: Domain Experts. Common annotation unit: Ranking. Frequent quality control: Calibration. Frequently cited benchmark: HLE. 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: 24 Last published: Mar 29, 2026 Global RSS Tag RSS
Multi AgentAutomatic MetricsLast 45d

Researcher Quick Triage

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

All Sampled Papers (24) Replication-Ready Only (7)

High-Signal Coverage

100.0%

24 / 24 sampled papers are not low-signal flagged.

Replication-Ready Set

7

Benchmark + metric + eval mode explicitly present.

Judge/Human Comparability

0

Papers containing both `human_eval` and `llm_as_judge`.

  • 7 papers are replication-ready (benchmark + metric + explicit evaluation mode).
  • 0 papers support judge-vs-human agreement analysis.
  • 2 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.

Currently showing only replication-ready papers in ranking and matrix sections (7 papers).

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Why This Matters For Eval Research

  • 16.7% of papers report explicit human-feedback signals, led by expert verification.
  • automatic metrics appears in 100% of papers in this hub.
  • HLE is a recurring benchmark anchor for cross-paper comparisons in this page.

Protocol Takeaways

  • Most common quality-control signal is rater calibration (8.3% of papers).
  • Rater context is mostly domain experts, and annotation is commonly ranking annotation; 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

  • HLE appears in 4.2% of hub papers (1/24); use this cohort for benchmark-matched comparisons.
  • Medpriv-Bench appears in 4.2% of hub papers (1/24); use this cohort for benchmark-matched comparisons.

Metric Interpretation

  • accuracy is reported in 75% of hub papers (18/24); compare with a secondary metric before ranking methods.
  • cost is reported in 12.5% of hub papers (3/24); compare with a secondary metric before ranking methods.
Researcher Checklist (Expanded)

Researcher Checklist

  • Gap: Papers with explicit human feedback

    Coverage is a replication risk (16.7% vs 45% target).

  • Gap: Papers reporting quality controls

    Coverage is a replication risk (8.3% vs 30% target).

  • Moderate: Papers naming benchmarks/datasets

    Coverage is usable but incomplete (29.2% vs 35% target).

  • Strong: Papers naming evaluation metrics

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

  • Strong: Papers with known rater population

    Coverage is strong (37.5% vs 35% target).

  • Gap: Papers with known annotation unit

    Coverage is a replication risk (4.2% vs 35% target).

Strengths

  • Contains both human-eval and LLM-as-judge protocols for head-to-head methodology comparison.
  • Agentic evaluation appears in 100% of papers.

Known Gaps

  • Only 8.3% of papers report quality controls; prioritize calibration/adjudication evidence.
  • Annotation unit is under-specified (4.2% coverage).
  • LLM-as-judge appears without enough inter-annotator agreement reporting.

Suggested Next Analyses

  • Compare papers that report both human_eval and llm_as_judge to quantify judge-human agreement drift.
  • Stratify by benchmark (HLE vs Medpriv-Bench) before comparing methods.
  • Track metric sensitivity by reporting both accuracy and cost.
  • Add inter-annotator agreement checks when reproducing these protocols.
Recommended Queries (Expanded)

Recommended Queries

Judge vs Human Agreement Benchmark Slice: HLE 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
SODIUM: From Open Web Data to Queryable Databases

Mar 19, 2026

 Yes
Expert Verification
Automatic Metrics Sodium Bench Accuracy Not Reported
Navigating Large-Scale Document Collections: MuDABench for Multi-Document Analytical QA

Apr 24, 2026

 No
Not Reported
Automatic Metrics Mudabench Accuracy Not Reported
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
From Skills to Talent: Organising Heterogeneous Agents as a Real-World Company

Apr 24, 2026

 No
Not Reported
Automatic Metrics Prdbench Success rate Not Reported
Agent Q-Mix: Selecting the Right Action for LLM Multi-Agent Systems through Reinforcement Learning

Apr 1, 2026

 No
Not Reported
Automatic Metrics HLE Accuracy , Token cost Not Reported
Effective Strategies for Asynchronous Software Engineering Agents

Mar 23, 2026

 No
Not Reported
Automatic Metrics Paperbench Accuracy Not Reported
Cross-Context Verification: Hierarchical Detection of Benchmark Contamination through Session-Isolated Analysis

Mar 23, 2026

 No
Not Reported
Automatic Metrics SWE Bench , SWE Bench Verified Accuracy , Recall Not Reported

Protocol Diff (Top Papers)

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

Signal SODIUM: From Open Web Data to Queryable Databases Navigating Large-Scale Document Collections: MuDABe… MedPriv-Bench: Benchmarking the Privacy-Utility Tra…
Human Feedback Expert VerificationNot reportedNot reported
Evaluation Modes Automatic MetricsAutomatic MetricsAutomatic Metrics
Benchmarks Sodium BenchMudabenchMedpriv Bench
Metrics AccuracyAccuracyAccuracy
Quality Controls Not reportedNot reportedNot reported
Rater Population Domain ExpertsDomain ExpertsDomain Experts
Annotation Unit UnknownUnknownUnknown
Suggested Reading Order (Extended)

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

Suggested Reading Order

  1. From Skills to Talent: Organising Heterogeneous Agents as a Real-World Company

    Start here for detailed protocol reporting and quality-control evidence. Signals: automatic metrics. Focus: Prdbench / success rate. Abstract: Individual agent capabilities have advanced rapidly through modular skills and.

  2. Navigating Large-Scale Document Collections: MuDABench for Multi-Document Analytical QA

    Start here for detailed protocol reporting and quality-control evidence. Signals: automatic metrics. Focus: Mudabench / accuracy. Abstract: We also propose an evaluation protocol that measures final answer accuracy.

  3. EvoAgent: An Evolvable Agent Framework with Skill Learning and Multi-Agent Delegation

    Start here for detailed protocol reporting and quality-control evidence. Signals: LLM-as-judge. Focus: accuracy. Abstract: Under a five-dimensional LLM-as-Judge evaluation protocol, the overall average score increases by approximately 28%.

  4. Improving Clinical Diagnosis with Counterfactual Multi-Agent Reasoning

    Include a human-eval paper to calibrate against judge-based evaluation settings. Signals: human evaluation + expert verification. Focus: accuracy. Abstract: Human evaluation further indicates that our framework produces more.

  5. SODIUM: From Open Web Data to Queryable Databases

    Include a human-eval paper to calibrate against judge-based evaluation settings. Signals: automatic metrics + expert verification. Focus: Sodium-Bench / accuracy. Abstract: During research, domain experts often ask analytical.

  6. Multi-Agent Dialectical Refinement for Enhanced Argument Classification

    Include an LLM-as-judge paper to test judge design and agreement assumptions. Signals: LLM-as-judge. Focus: f1. Abstract: MAD-ACC utilizes a Proponent-Opponent-Judge model where agents defend conflicting interpretations of ambiguous.

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

    Adds automatic metrics with expert verification for broader protocol coverage within this hub. Signals: automatic metrics + expert verification. Focus: accuracy. Abstract: While Large Language Models (LLMs) have.

  8. 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.

Known Limitations

Known Limitations

  • Only 8.3% of papers report quality controls; prioritize calibration/adjudication evidence.
  • Annotation unit is under-specified (4.2% 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 (24)
  • Llm As Judge (2)
  • Simulation Env (2)
  • Human Eval (1)

Top Benchmarks

  • HLE (1)
  • Medpriv Bench (1)
  • Mudabench (1)
  • Paperbench (1)

Top Metrics

  • Accuracy (18)
  • Cost (3)
  • Recall (3)
  • Agreement (1)

Rater Population Mix

  • Domain Experts (9)

Quality Controls

  • Calibration (2)
Coverage diagnostics (sample-based): human-feedback 16.7% · benchmarks 29.2% · metrics 100.0% · quality controls 8.3%.

Top Papers

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