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HFEPX Hub

Automatic Metrics + Coding + Multi Agent Papers

Updated from current HFEPX corpus (Feb 27, 2026). 11 papers are grouped in this hub page. Common evaluation modes: Automatic Metrics. Most common rater population: Domain Experts. Common annotation unit: Pairwise. Frequent quality control: Calibration. Frequently cited benchmark: LiveCodeBench. 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 26, 2026.

Papers: 11 Last published: Feb 26, 2026 Global RSS Tag RSS
Automatic MetricsCodingMulti Agent

Research Narrative

Grounded narrative Model: deterministic-grounded Source: persisted

Updated from current HFEPX corpus (Feb 27, 2026). This page tracks 11 papers for Automatic Metrics + Coding + Multi Agent Papers. Dominant protocol signals include automatic metrics, with frequent benchmark focus on LiveCodeBench, Retrieval and metric focus on accuracy, calibration. Use the grounded sections below to prioritize reproducible protocol choices, benchmark-matched comparisons, and judge-vs-human evaluation checks.

Why This Matters For Eval Research

Protocol Takeaways

Benchmark Interpretation

  • LiveCodeBench appears in 9.1% of hub papers (1/11); use this cohort for benchmark-matched comparisons.
  • Retrieval appears in 9.1% of hub papers (1/11); use this cohort for benchmark-matched comparisons.

Metric Interpretation

  • accuracy is reported in 18.2% of hub papers (2/11); compare with a secondary metric before ranking methods.
  • calibration is reported in 9.1% of hub papers (1/11); compare with a secondary metric before ranking methods.

Researcher Checklist

  • Maintain strength on Papers with explicit human feedback. Coverage is strong (45.5% vs 45% target).
  • Close gap on Papers reporting quality controls. Coverage is a replication risk (9.1% vs 30% target).
  • Close gap on Papers naming benchmarks/datasets. Coverage is a replication risk (18.2% vs 35% target).
  • Maintain strength on Papers naming evaluation metrics. Coverage is strong (45.5% vs 35% target).
  • Close gap on Papers with known rater population. Coverage is a replication risk (18.2% vs 35% target).
  • Maintain strength on Papers with known annotation unit. Coverage is strong (36.4% vs 35% target).

Papers with explicit human feedback

Coverage is strong (45.5% vs 45% target).

Papers reporting quality controls

Coverage is a replication risk (9.1% vs 30% target).

Papers naming benchmarks/datasets

Coverage is a replication risk (18.2% vs 35% target).

Papers naming evaluation metrics

Coverage is strong (45.5% vs 35% target).

Papers with known rater population

Coverage is a replication risk (18.2% vs 35% target).

Papers with known annotation unit

Coverage is strong (36.4% vs 35% target).

Suggested Reading Order

  1. 1. AgentDropoutV2: Optimizing Information Flow in Multi-Agent Systems via Test-Time Rectify-or-Reject Pruning

    Start here for detailed protocol reporting, including rater and quality-control evidence.

  2. 2. A Hierarchical Multi-Agent System for Autonomous Discovery in Geoscientific Data Archives

    Start here for detailed protocol reporting, including rater and quality-control evidence.

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

    Start here for detailed protocol reporting, including rater and quality-control evidence.

  4. 4. Inner Speech as Behavior Guides: Steerable Imitation of Diverse Behaviors for Human-AI coordination

    Adds automatic metrics with demonstration data for broader coverage within this hub.

  5. 5. Whisper: Courtside Edition Enhancing ASR Performance Through LLM-Driven Context Generation

    Adds automatic metrics for broader coverage within this hub.

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

    Adds automatic metrics with expert verification for broader coverage within this hub.

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

    Adds automatic metrics with pairwise preferences for broader coverage within this hub.

  8. 8. VILLAIN at AVerImaTeC: Verifying Image-Text Claims via Multi-Agent Collaboration

    Adds automatic metrics for broader coverage within this hub.

Known Limitations

  • Only 9.1% of papers report quality controls; prioritize calibration/adjudication evidence.
  • Rater population is under-specified (18.2% coverage).
  • Narrative synthesis is grounded in metadata and abstracts only; full-paper implementation details are not parsed.

Research Utility Links

Benchmark Brief

LiveCodeBench

Coverage: 1 papers (9.1%)

1 papers (9.1%) mention LiveCodeBench.

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

Benchmark Brief

Retrieval

Coverage: 1 papers (9.1%)

1 papers (9.1%) mention Retrieval.

Examples: AgentDropoutV2: Optimizing Information Flow in Multi-Agent Systems via Test-Time Rectify-or-Reject Pruning

Metric Brief

accuracy

Coverage: 2 papers (18.2%)

2 papers (18.2%) mention accuracy.

Examples: AgentDropoutV2: Optimizing Information Flow in Multi-Agent Systems via Test-Time Rectify-or-Reject Pruning , Reshaping MOFs text mining with a dynamic multi-agents framework of large language model

Metric Brief

calibration

Coverage: 1 papers (9.1%)

1 papers (9.1%) mention calibration.

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

Metric Brief

cost

Coverage: 1 papers (9.1%)

1 papers (9.1%) mention cost.

Examples: SparkMe: Adaptive Semi-Structured Interviewing for Qualitative Insight Discovery

Most Cited In This Hub

Fast path to methods with the strongest citation traction in this scope.

Papers: AgentDropoutV2: Optimizing Information Flow in Multi-Agent Systems via Test-Time Rectify-or-Reject Pruning , A Hierarchical Multi-Agent System for Autonomous Discovery in Geoscientific Data Archives , SparkMe: Adaptive Semi-Structured Interviewing for Qualitative Insight Discovery

Most Recent

Fast path to latest protocol changes and newly published evaluation setups.

Papers: AgentDropoutV2: Optimizing Information Flow in Multi-Agent Systems via Test-Time Rectify-or-Reject Pruning , A Hierarchical Multi-Agent System for Autonomous Discovery in Geoscientific Data Archives , SparkMe: Adaptive Semi-Structured Interviewing for Qualitative Insight Discovery

Best Protocol Detail

Papers with explicit rater/unit metadata and quality-control signals for reproducibility.

Papers: AgentDropoutV2: Optimizing Information Flow in Multi-Agent Systems via Test-Time Rectify-or-Reject Pruning , A Hierarchical Multi-Agent System for Autonomous Discovery in Geoscientific Data Archives , SparkMe: Adaptive Semi-Structured Interviewing for Qualitative Insight Discovery

Top Papers

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