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HFEPX Hub

Multi Agent + Coding (Last 45 Days)

Updated from current HFEPX corpus (Mar 8, 2026). 13 papers are grouped in this hub page.

Read Full Context

Updated from current HFEPX corpus (Mar 8, 2026). 13 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: AdvBench. Common metric signal: cost. 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: 13 Last published: Feb 27, 2026 Global RSS Tag RSS
Multi AgentCodingLast 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%

13 / 13 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.

Why This Matters For Eval Research

  • 53.8% of papers report explicit human-feedback signals, led by expert verification.
  • automatic metrics appears in 53.8% 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 (7.7% 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

  • AdvBench appears in 7.7% of hub papers (1/13); use this cohort for benchmark-matched comparisons.
  • Jbf-Eval appears in 7.7% of hub papers (1/13); use this cohort for benchmark-matched comparisons.

Metric Interpretation

  • cost is reported in 15.4% of hub papers (2/13); compare with a secondary metric before ranking methods.
  • success rate is reported in 15.4% of hub papers (2/13); compare with a secondary metric before ranking methods.
Researcher Checklist (Expanded)

Researcher Checklist

  • Strong: Papers with explicit human feedback

    Coverage is strong (53.8% vs 45% target).

  • Gap: Papers reporting quality controls

    Coverage is a replication risk (7.7% vs 30% target).

  • Moderate: Papers naming benchmarks/datasets

    Coverage is usable but incomplete (23.1% vs 35% target).

  • Strong: Papers naming evaluation metrics

    Coverage is strong (53.8% vs 35% target).

  • Moderate: Papers with known rater population

    Coverage is usable but incomplete (30.8% vs 35% target).

  • Moderate: Papers with known annotation unit

    Coverage is usable but incomplete (23.1% vs 35% target).

Strengths

  • Strong human-feedback signal (53.8% of papers).
  • Agentic evaluation appears in 100% of papers.

Known Gaps

  • Only 7.7% of papers report quality controls; prioritize calibration/adjudication evidence.
  • Annotation unit is under-specified (23.1% coverage).
  • LLM-as-judge appears without enough inter-annotator agreement reporting.

Suggested Next Analyses

  • Pair this hub with a human_eval-heavy hub to validate judge-model calibration.
  • Stratify by benchmark (AdvBench vs Jbf-Eval) before comparing methods.
  • Track metric sensitivity by reporting both cost and success rate.
  • 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
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
Team of Thoughts: Efficient Test-time Scaling of Agentic Systems through Orchestrated Tool Calling

Feb 18, 2026

Yes Not Reported LiveCodeBench Not Reported Calibration
SparkMe: Adaptive Semi-Structured Interviewing for Qualitative Insight Discovery

Feb 24, 2026

Yes Automatic Metrics Not Reported Cost Not Reported
PrivAct: Internalizing Contextual Privacy Preservation via Multi-Agent Preference Training

Feb 14, 2026

Yes Automatic Metrics Not Reported Helpfulness Not Reported
Demonstrating ViviDoc: Generating Interactive Documents through Human-Agent Collaboration

Mar 2, 2026

Yes Not Reported Not Reported Not Reported Not Reported
The Vision Wormhole: Latent-Space Communication in Heterogeneous Multi-Agent Systems

Feb 17, 2026

Yes Not Reported Not Reported Not Reported Not Reported
GenDB: The Next Generation of Query Processing -- Synthesized, Not Engineered

Mar 2, 2026

No
Not Reported
Automatic Metrics Not Reported Cost Not Reported
AgentDropoutV2: Optimizing Information Flow in Multi-Agent Systems via Test-Time Rectify-or-Reject Pruning

Feb 26, 2026

No
Not Reported
Automatic Metrics Not Reported Accuracy Not Reported
BLUFF: Benchmarking the Detection of False and Synthetic Content across 58 Low-Resource Languages

Feb 28, 2026

No
Not Reported
Automatic Metrics Not Reported F1 Not Reported
MALLVI: A Multi-Agent Framework for Integrated Generalized Robotics Manipulation

Feb 18, 2026

No
Not Reported
Simulation Env Not Reported Not Reported Not Reported
OR-Agent: Bridging Evolutionary Search and Structured Research for Automated Algorithm Discovery

Feb 14, 2026

No
Not Reported
Simulation Env Not Reported Not Reported Not Reported

Protocol Diff (Top Papers)

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

Signal Jailbreak Foundry: From Papers to Runnable Attacks… StitchCUDA: An Automated Multi-Agents End-to-End GP… Team of Thoughts: Efficient Test-time Scaling of Ag…
Human Feedback Red TeamRubric RatingExpert Verification
Evaluation Modes Llm As JudgeAutomatic MetricsNot reported
Benchmarks AdvBench, Jbf EvalKernelbenchLiveCodeBench
Metrics Success rate, Jailbreak success rateSuccess rateNot reported
Quality Controls Not reportedNot reportedCalibration
Rater Population UnknownUnknownDomain Experts
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. StitchCUDA: An Automated Multi-Agents End-to-End GPU Programing Framework with Rubric-based Agentic Reinforcement Learning

    Start here for detailed protocol reporting and quality-control evidence. Signals: automatic metrics + rubric ratings. Focus: Kernelbench / success rate. Abstract: To fundamentally improve the Coder's ability in.

  2. GenDB: The Next Generation of Query Processing -- Synthesized, Not Engineered

    Start here for detailed protocol reporting and quality-control evidence. Signals: automatic metrics. Focus: cost. Abstract: Traditional query processing relies on engines that are carefully optimized and engineered by.

  3. Demonstrating ViviDoc: Generating Interactive Documents through Human-Agent Collaboration

    Start here for detailed protocol reporting and quality-control evidence. Signals: expert verification. Abstract: Interactive articles help readers engage with complex ideas through exploration, yet creating them remains costly,.

  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. Team of Thoughts: Efficient Test-time Scaling of Agentic Systems through Orchestrated Tool Calling

    Adds evaluation protocol evidence with expert verification for broader protocol coverage within this hub. Signals: expert verification. Focus: LiveCodeBench. Abstract: Existing Multi-Agent Systems (MAS) typically rely on static,.

  6. SparkMe: Adaptive Semi-Structured Interviewing for Qualitative Insight Discovery

    Adds automatic metrics with expert verification for broader protocol coverage within this hub. Signals: automatic metrics + expert verification. Focus: cost. Abstract: The code, datasets, and evaluation protocols.

  7. PrivAct: Internalizing Contextual Privacy Preservation via Multi-Agent Preference Training

    Adds automatic metrics with pairwise preferences for broader protocol coverage within this hub. Signals: automatic metrics + pairwise preferences. Focus: helpfulness. Abstract: By embedding privacy preferences into each.

  8. The Vision Wormhole: Latent-Space Communication in Heterogeneous Multi-Agent Systems

    Adds evaluation protocol evidence with pairwise preferences for broader protocol coverage within this hub. Signals: pairwise preferences. Abstract: Our framework adopts a hub-and-spoke topology to reduce pairwise alignment.

Known Limitations

Known Limitations

  • Only 7.7% of papers report quality controls; prioritize calibration/adjudication evidence.
  • Annotation unit is under-specified (23.1% 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 (3)
  • Pairwise Preference (2)
  • Red Team (1)
  • Rubric Rating (1)

Evaluation Modes

  • Automatic Metrics (7)
  • Simulation Env (2)
  • Llm As Judge (1)

Top Benchmarks

  • AdvBench (1)
  • Jbf Eval (1)
  • Kernelbench (1)
  • LiveCodeBench (1)

Top Metrics

  • Cost (2)
  • Success rate (2)
  • Accuracy (1)
  • F1 (1)

Rater Population Mix

  • Domain Experts (4)

Quality Controls

  • Calibration (1)
Coverage diagnostics (sample-based): human-feedback 53.8% · benchmarks 23.1% · metrics 53.8% · quality controls 7.7%.

Top Papers

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