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HFEPX Hub

Multi Agent Papers (Last 60 Days)

Updated from current HFEPX corpus (Apr 12, 2026). 119 papers are grouped in this hub page.

Read Full Context

Updated from current HFEPX corpus (Apr 12, 2026). 119 papers are grouped in this hub page. Common evaluation modes: Automatic Metrics, Simulation Env. Most common rater population: Domain Experts. Common annotation unit: Ranking. Frequent quality control: Calibration. Frequently cited benchmark: AdvBench. 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 27, 2026.

Papers: 119 Last published: Feb 27, 2026 Global RSS Tag RSS
Multi AgentLast 60d

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 119 total papers in this hub).

High-Signal Coverage

100.0%

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

Replication-Ready Set

13

Benchmark + metric + eval mode explicitly present.

Judge/Human Comparability

0

Papers containing both `human_eval` and `llm_as_judge`.

  • 13 papers are replication-ready (benchmark + metric + explicit evaluation mode).
  • 0 papers support judge-vs-human agreement analysis.
  • 3 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

  • 30% of papers report explicit human-feedback signals, led by expert verification.
  • automatic metrics appears in 39.5% of papers in this hub.
  • AdvBench is a recurring benchmark anchor for cross-paper comparisons in this page.

Protocol Takeaways

  • Most common quality-control signal is rater calibration (2.5% 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

  • AdvBench appears in 1.3% of hub papers (1/119); use this cohort for benchmark-matched comparisons.
  • AlpacaEval appears in 1.3% of hub papers (1/119); use this cohort for benchmark-matched comparisons.

Metric Interpretation

  • accuracy is reported in 36.3% of hub papers (29/119); compare with a secondary metric before ranking methods.
  • cost is reported in 10% of hub papers (8/119); compare with a secondary metric before ranking methods.
Researcher Checklist (Expanded)

Researcher Checklist

  • Moderate: Papers with explicit human feedback

    Coverage is usable but incomplete (30% vs 45% target).

  • Gap: Papers reporting quality controls

    Coverage is a replication risk (3.8% vs 30% target).

  • Gap: Papers naming benchmarks/datasets

    Coverage is a replication risk (20% vs 35% target).

  • Strong: Papers naming evaluation metrics

    Coverage is strong (61.3% vs 35% target).

  • Moderate: Papers with known rater population

    Coverage is usable but incomplete (25% vs 35% target).

  • Gap: Papers with known annotation unit

    Coverage is a replication risk (16.3% 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 3.8% of papers report quality controls; prioritize calibration/adjudication evidence.
  • Annotation unit is under-specified (16.3% 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 (AdvBench vs AlpacaEval) 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
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
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
Elo-Evolve: A Co-evolutionary Framework for Language Model Alignment

Feb 14, 2026

Yes Automatic Metrics MT Bench , AlpacaEval Elo Not Reported
Team of Thoughts: Efficient Test-time Scaling of Agentic Systems through Orchestrated Tool Calling

Feb 18, 2026

No
Not Reported
Automatic Metrics LiveCodeBench Accuracy Calibration
Meanings and Measurements: Multi-Agent Probabilistic Grounding for Vision-Language Navigation

Mar 19, 2026

Yes Simulation Env Mapg Bench Not Reported Not Reported
Improving Clinical Diagnosis with Counterfactual Multi-Agent Reasoning

Mar 29, 2026

Yes Human Eval , Automatic Metrics Not Reported Accuracy Not Reported
ClinicalAgents: Multi-Agent Orchestration for Clinical Decision Making with Dual-Memory

Mar 27, 2026

Yes Automatic Metrics Not Reported Accuracy Not Reported
A Multidisciplinary AI Board for Multimodal Dementia Characterization and Risk Assessment

Mar 23, 2026

Yes Automatic Metrics Not Reported Accuracy , Auroc Not Reported
LUDOBENCH: Evaluating LLM Behavioural Decision-Making Through Spot-Based Board Game Scenarios in Ludo

Apr 7, 2026

No
Not Reported
Simulation Env Ludobench Dice 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
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 , Cost 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 Jailbreak Foundry: From Papers to Runnable Attacks… StitchCUDA: An Automated Multi-Agents End-to-End GP…
Human Feedback Expert VerificationRed TeamRubric Rating
Evaluation Modes Automatic MetricsLlm As JudgeAutomatic Metrics
Benchmarks Sodium BenchAdvBench, Jbf EvalKernelbench
Metrics AccuracySuccess rate, Jailbreak success rateSuccess rate
Quality Controls Not reportedNot reportedNot reported
Rater Population Domain ExpertsUnknownUnknown
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. Strategic Persuasion with Trait-Conditioned Multi-Agent Systems for Iterative Legal Argumentation

    Start here for detailed protocol reporting and quality-control evidence. Signals: simulation environments. Abstract: Strategic interaction in adversarial domains such as law, diplomacy, and negotiation is mediated by language,.

  2. 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).

  3. 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.

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

    Include a human-eval paper to calibrate against judge-based evaluation settings. Signals: LLM-as-judge + red-team protocols. Focus: AdvBench / success rate. Abstract: This system enables a standardized AdvBench evaluation.

  5. 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.

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

    Adds simulation environments with demonstration data for broader protocol coverage within this hub. Signals: simulation environments + demonstration data. Focus: Mapg-Bench. Abstract: Robots collaborating with humans must convert.

  7. SODIUM: From Open Web Data to Queryable Databases

    Adds automatic metrics with expert verification for broader protocol coverage within this hub. Signals: automatic metrics + expert verification. Focus: Sodium-Bench / accuracy. Abstract: During research, domain experts.

Known Limitations

Known Limitations

  • Only 3.8% of papers report quality controls; prioritize calibration/adjudication evidence.
  • Annotation unit is under-specified (16.3% 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 (9)
  • Pairwise Preference (9)
  • Rubric Rating (4)
  • Red Team (2)

Evaluation Modes

  • Automatic Metrics (47)
  • Simulation Env (23)
  • Llm As Judge (5)
  • Human Eval (2)

Top Benchmarks

  • AdvBench (1)
  • AlpacaEval (1)
  • AlpacaEval 2.0 (1)
  • APPS (1)

Top Metrics

  • Accuracy (29)
  • Cost (8)
  • Recall (4)
  • Relevance (4)

Rater Population Mix

  • Domain Experts (20)

Quality Controls

  • Calibration (3)
Coverage diagnostics (sample-based): human-feedback 40.0% · benchmarks 26.7% · metrics 53.3% · quality controls 5.0%.

Top Papers

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