Skip to content
← Back to explorer

HFEPX Hub

Multi Agent + Automatic Metrics (Last 45 Days)

Updated from current HFEPX corpus (Apr 17, 2026). 28 papers are grouped in this hub page.

Read Full Context

Updated from current HFEPX corpus (Apr 17, 2026). 28 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: 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: 28 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 (28) Replication-Ready Only (7)

High-Signal Coverage

100.0%

28 / 28 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).

Need evaluators for this research workflow?

Post a Job →

Why This Matters For Eval Research

  • 21.4% 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 (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

  • HLE appears in 3.6% of hub papers (1/28); use this cohort for benchmark-matched comparisons.
  • Kernelbench appears in 3.6% of hub papers (1/28); use this cohort for benchmark-matched comparisons.

Metric Interpretation

  • accuracy is reported in 71.4% of hub papers (20/28); compare with a secondary metric before ranking methods.
  • cost is reported in 14.3% of hub papers (4/28); compare with a secondary metric before ranking methods.
Researcher Checklist (Expanded)

Researcher Checklist

  • Gap: Papers with explicit human feedback

    Coverage is a replication risk (21.4% 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 (25% vs 35% target).

  • Strong: Papers naming evaluation metrics

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

  • Moderate: Papers with known rater population

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

  • Gap: Papers with known annotation unit

    Coverage is a replication risk (7.1% 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 7.1% of papers report quality controls; prioritize calibration/adjudication evidence.
  • Annotation unit is under-specified (7.1% 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 Kernelbench) 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
StitchCUDA: An Automated Multi-Agents End-to-End GPU Programing Framework with Rubric-based Agentic Reinforcement Learning

Mar 3, 2026

 Yes
Rubric Rating
Automatic Metrics Kernelbench 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
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
QChunker: Learning Question-Aware Text Chunking for Domain RAG via Multi-Agent Debate

Mar 12, 2026

 No
Not Reported
Automatic Metrics Understanding Retrieval Coherence 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 StitchCUDA: An Automated Multi-Agents End-to-End GP… Agent Q-Mix: Selecting the Right Action for LLM Mul…
Human Feedback Expert VerificationRubric RatingNot reported
Evaluation Modes Automatic MetricsAutomatic MetricsAutomatic Metrics
Benchmarks Sodium BenchKernelbenchHLE
Metrics AccuracySuccess rateAccuracy, Token cost
Quality Controls Not reportedNot reportedNot reported
Rater Population Domain ExpertsUnknownUnknown
Annotation Unit UnknownMulti Dim RubricUnknown
Suggested Reading Order (Extended)

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

Suggested Reading Order

  1. Learning to Interrupt in Language-based Multi-agent Communication

    Start here for detailed protocol reporting and quality-control evidence. Signals: automatic metrics. Focus: cost. Abstract: Multi-agent systems using large language models (LLMs) have demonstrated impressive capabilities across various.

  2. Paper Circle: An Open-source Multi-agent Research Discovery and Analysis Framework

    Start here for detailed protocol reporting and quality-control evidence. Signals: automatic metrics. Focus: recall. Abstract: The rapid growth of scientific literature has made it increasingly difficult for researchers.

  3. Social Dynamics as Critical Vulnerabilities that Undermine Objective Decision-Making in LLM Collectives

    Start here for detailed protocol reporting and quality-control evidence. Signals: automatic metrics. Focus: accuracy. Abstract: Large language model (LLM) agents are increasingly acting as human delegates in multi-agent.

  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. StitchCUDA: An Automated Multi-Agents End-to-End GPU Programing Framework with Rubric-based Agentic Reinforcement Learning

    Adds automatic metrics with rubric ratings for broader protocol coverage within this hub. Signals: automatic metrics + rubric ratings. Focus: Kernelbench / success rate. Abstract: To fundamentally improve.

  8. 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.

Known Limitations

Known Limitations

  • Only 7.1% of papers report quality controls; prioritize calibration/adjudication evidence.
  • Annotation unit is under-specified (7.1% 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)
  • Red Team (1)
  • Rubric Rating (1)

Evaluation Modes

  • Automatic Metrics (28)
  • Simulation Env (2)
  • Human Eval (1)
  • Llm As Judge (1)

Top Benchmarks

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

Top Metrics

  • Accuracy (20)
  • Cost (4)
  • Recall (3)
  • Precision (2)

Rater Population Mix

  • Domain Experts (8)

Quality Controls

  • Calibration (2)
Coverage diagnostics (sample-based): human-feedback 21.4% · benchmarks 25.0% · metrics 100.0% · quality controls 7.1%.

Top Papers

Related Hubs

Get Started

Join the #1 Platform for AI Training Talent

Where top AI builders and expert AI Trainers connect to build the future of AI.
Self-Service
Post a Job
Post your project and get a shortlist of qualified AI Trainers and Data Labelers. Hire and manage your team in the tools you already use.
Create Account & Post a Job
Managed Service
For Large Projects
Done-for-You
We recruit, onboard, and manage a dedicated team inside your tools. End-to-end operations for large or complex projects.
Learn About Managed Service
For Freelancers
Join as an AI Trainer
Find AI training and data labeling projects across platforms, all in one place. One profile, one application process, more opportunities.
Join Now