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HFEPX Hub

Multi Agent + General (Last 45 Days)

Updated from current HFEPX corpus (Apr 17, 2026). 25 papers are grouped in this hub page.

Read Full Context

Updated from current HFEPX corpus (Apr 17, 2026). 25 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: Mapg-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 19, 2026.

Papers: 25 Last published: Mar 19, 2026 Global RSS Tag RSS
Multi AgentGeneralLast 45d

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%

25 / 25 sampled papers are not low-signal flagged.

Replication-Ready Set

2

Benchmark + metric + eval mode explicitly present.

Judge/Human Comparability

0

Papers containing both `human_eval` and `llm_as_judge`.

  • 2 papers are replication-ready (benchmark + metric + explicit evaluation mode).
  • 0 papers support judge-vs-human agreement analysis.
  • 1 papers report explicit quality controls (calibration/adjudication/IAA).

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

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Why This Matters For Eval Research

  • 28% of papers report explicit human-feedback signals, led by pairwise preferences.
  • automatic metrics appears in 56% of papers in this hub.
  • Mapg-Bench is a recurring benchmark anchor for cross-paper comparisons in this page.

Protocol Takeaways

  • Most common quality-control signal is rater calibration (4% of papers).
  • Rater context is mostly domain experts, and annotation is commonly multi-dimensional rubrics; use this to scope replication staffing.
  • Pair this hub with a human_eval-heavy hub to validate judge-model calibration.

Benchmark Interpretation

  • Mapg-Bench appears in 4% of hub papers (1/25); use this cohort for benchmark-matched comparisons.
  • Occubench appears in 4% of hub papers (1/25); use this cohort for benchmark-matched comparisons.

Metric Interpretation

  • accuracy is reported in 36% of hub papers (9/25); compare with a secondary metric before ranking methods.
  • cost is reported in 8% of hub papers (2/25); compare with a secondary metric before ranking methods.
Researcher Checklist (Expanded)

Researcher Checklist

  • Moderate: Papers with explicit human feedback

    Coverage is usable but incomplete (28% vs 45% target).

  • Gap: Papers reporting quality controls

    Coverage is a replication risk (4% vs 30% target).

  • Gap: Papers naming benchmarks/datasets

    Coverage is a replication risk (16% vs 35% target).

  • Strong: Papers naming evaluation metrics

    Coverage is strong (60% vs 35% target).

  • Gap: Papers with known rater population

    Coverage is a replication risk (8% vs 35% target).

  • Gap: Papers with known annotation unit

    Coverage is a replication risk (12% vs 35% target).

Strengths

  • Agentic evaluation appears in 100% of papers.

Known Gaps

  • Only 4% of papers report quality controls; prioritize calibration/adjudication evidence.
  • Rater population is under-specified (8% coverage).
  • Annotation unit is under-specified (12% coverage).

Suggested Next Analyses

  • Pair this hub with a human_eval-heavy hub to validate judge-model calibration.
  • Stratify by benchmark (Mapg-Bench vs Occubench) 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

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 Automatic Metrics Sodium Bench Accuracy Not Reported
Meanings and Measurements: Multi-Agent Probabilistic Grounding for Vision-Language Navigation

Mar 19, 2026

Yes Simulation Env Mapg Bench Not Reported Not Reported
WebWeaver: Breaking Topology Confidentiality in LLM Multi-Agent Systems with Stealthy Context-Based Inference

Mar 11, 2026

Yes Automatic Metrics Not Reported Accuracy Not Reported
Diff-KD: Diffusion-based Knowledge Distillation for Collaborative Perception under Corruptions

Apr 2, 2026

No
Not Reported
Automatic Metrics Not Reported Accuracy Calibration
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
I Can't Believe It's Corrupt: Evaluating Corruption in Multi-Agent Governance Systems

Mar 19, 2026

Yes Simulation Env Not Reported Not Reported Not Reported
OccuBench: Evaluating AI Agents on Real-World Professional Tasks via Language Environment Simulation

Apr 13, 2026

No
Not Reported
Simulation Env Occubench Not Reported Not Reported
Build, Judge, Optimize: A Blueprint for Continuous Improvement of Multi-Agent Consumer Assistants

Mar 3, 2026

Yes Llm As Judge , Simulation Env Not Reported Not Reported Not Reported
Social Dynamics as Critical Vulnerabilities that Undermine Objective Decision-Making in LLM Collectives

Apr 7, 2026

No
Not Reported
Automatic Metrics , Simulation Env Not Reported Accuracy Not Reported
Spatio-Temporal Attention Enhanced Multi-Agent DRL for UAV-Assisted Wireless Networks with Limited Communications

Mar 23, 2026

No
Not Reported
Simulation Env Not Reported Not Reported Not Reported
ActionParty: Multi-Subject Action Binding in Generative Video Games

Apr 2, 2026

No
Not Reported
Automatic Metrics , Simulation Env Not Reported Accuracy Not Reported
Multi-Agent Dialectical Refinement for Enhanced Argument Classification

Mar 29, 2026

No
Not Reported
Llm As Judge , Automatic Metrics Not Reported F1 , F1 macro 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 Meanings and Measurements: Multi-Agent Probabilisti… WebWeaver: Breaking Topology Confidentiality in LLM…
Human Feedback Expert VerificationDemonstrationsRed Team
Evaluation Modes Automatic MetricsSimulation EnvAutomatic Metrics
Benchmarks Sodium BenchMapg BenchNot reported
Metrics AccuracyNot reportedAccuracy
Quality Controls Not reportedNot reportedNot reported
Rater Population Domain ExpertsUnknownUnknown
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. OccuBench: Evaluating AI Agents on Real-World Professional Tasks via Language Environment Simulation

    Start here for detailed protocol reporting and quality-control evidence. Signals: simulation environments. Focus: Occubench. Abstract: AI agents are expected to perform professional work across hundreds of occupational domains.

  2. 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.

  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. Meanings and Measurements: Multi-Agent Probabilistic Grounding for Vision-Language Navigation

    Include a human-eval paper to calibrate against judge-based evaluation settings. Signals: simulation environments + demonstration data. Focus: Mapg-Bench. Abstract: Robots collaborating with humans must convert natural language goals.

  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. Build, Judge, Optimize: A Blueprint for Continuous Improvement of Multi-Agent Consumer Assistants

    Include an LLM-as-judge paper to test judge design and agreement assumptions. Signals: LLM-as-judge + pairwise preferences. Abstract: Grocery shopping further amplifies these difficulties, as user requests are often.

  7. 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.

  8. I Can't Believe It's Corrupt: Evaluating Corruption in Multi-Agent Governance Systems

    Adds simulation environments with rubric ratings for broader protocol coverage within this hub. Signals: simulation environments + rubric ratings. Abstract: We evaluate multi-agent governance simulations in which agents.

Known Limitations

Known Limitations

  • Only 4% of papers report quality controls; prioritize calibration/adjudication evidence.
  • Rater population is under-specified (8% 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

  • Pairwise Preference (2)
  • Rubric Rating (2)
  • Demonstrations (1)
  • Expert Verification (1)

Evaluation Modes

  • Automatic Metrics (14)
  • Simulation Env (11)
  • Llm As Judge (2)

Top Benchmarks

  • Mapg Bench (1)
  • Occubench (1)
  • Sodium Bench (1)
  • Understanding Retrieval (1)

Top Metrics

  • Accuracy (9)
  • Cost (2)
  • Relevance (2)
  • Coherence (1)

Rater Population Mix

  • Domain Experts (2)

Quality Controls

  • Calibration (1)
Coverage diagnostics (sample-based): human-feedback 28.0% · benchmarks 16.0% · metrics 60.0% · quality controls 4.0%.

Top Papers

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