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HFEPX Hub

Coding + Expert Verification (Last 120 Days)

Updated from current HFEPX corpus (Mar 8, 2026). 11 papers are grouped in this hub page.

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Updated from current HFEPX corpus (Mar 8, 2026). 11 papers are grouped in this hub page. Common evaluation modes: Automatic Metrics, Simulation Env. Most common rater population: Domain Experts. Common annotation unit: Trajectory. Frequent quality control: Calibration. Frequently cited benchmark: Ad-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 Feb 15, 2026.

Papers: 11 Last published: Feb 15, 2026 Global RSS Tag RSS
CodingExpert VerificationLast 120d

Researcher Quick Triage

This hub is best used for protocol triage and replication planning from abstract-level evidence. Quality band: Developing .

All Sampled Papers (11) Replication-Ready Only (2)

High-Signal Coverage

100.0%

11 / 11 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.
  • 2 papers report explicit quality controls (calibration/adjudication/IAA).

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

Currently showing only replication-ready papers in ranking and matrix sections (2 papers).

Why This Matters For Eval Research

  • 100% of papers report explicit human-feedback signals, led by expert verification.
  • automatic metrics appears in 54.5% of papers in this hub.
  • Ad-Bench is a recurring benchmark anchor for cross-paper comparisons in this page.

Protocol Takeaways

  • Most common quality-control signal is rater calibration (9.1% of papers).
  • Rater context is mostly domain experts, and annotation is commonly trajectory-level annotation; use this to scope replication staffing.
  • Stratify by benchmark (Ad-Bench vs BIRD) before comparing methods.

Benchmark Interpretation

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

Metric Interpretation

  • accuracy is reported in 27.3% of hub papers (3/11); compare with a secondary metric before ranking methods.
  • cost is reported in 9.1% of hub papers (1/11); compare with a secondary metric before ranking methods.
Researcher Checklist (Expanded)

Researcher Checklist

  • Strong: Papers with explicit human feedback

    Coverage is strong (100% vs 45% target).

  • Moderate: Papers reporting quality controls

    Coverage is usable but incomplete (18.2% vs 30% target).

  • Moderate: Papers naming benchmarks/datasets

    Coverage is usable but incomplete (27.3% vs 35% target).

  • Strong: Papers naming evaluation metrics

    Coverage is strong (63.6% vs 35% target).

  • Strong: Papers with known rater population

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

  • Gap: Papers with known annotation unit

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

Strengths

  • Strong human-feedback signal (100% of papers).
  • Agentic evaluation appears in 36.4% of papers.

Known Gaps

  • Only 18.2% of papers report quality controls; prioritize calibration/adjudication evidence.
  • Annotation unit is under-specified (18.2% coverage).

Suggested Next Analyses

  • Stratify by benchmark (Ad-Bench vs BIRD) 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

Benchmark Slice: Ad-Bench 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
CricBench: A Multilingual Benchmark for Evaluating LLMs in Cricket Analytics

Dec 26, 2025

 Yes
Expert Verification
Automatic Metrics DROP , BIRD Accuracy Gold Questions
AD-Bench: A Real-World, Trajectory-Aware Advertising Analytics Benchmark for LLM Agents

Feb 15, 2026

 Yes
Expert Verification
Simulation Env Ad Bench Pass@1 , Pass@3 Not Reported

Protocol Diff (Top Papers)

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

Signal CricBench: A Multilingual Benchmark for Evaluating… AD-Bench: A Real-World, Trajectory-Aware Advertisin…
Human Feedback Expert VerificationExpert Verification
Evaluation Modes Automatic MetricsSimulation Env
Benchmarks DROP, BIRDAd Bench
Metrics AccuracyPass@1, Pass@3
Quality Controls Gold QuestionsNot reported
Rater Population Domain ExpertsDomain Experts
Annotation Unit UnknownTrajectory
Suggested Reading Order (Extended)

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

Suggested Reading Order

  1. PrivMedChat: End-to-End Differentially Private RLHF for Medical Dialogue Systems

    Start here for detailed protocol reporting and quality-control evidence. Signals: automatic metrics + pairwise preferences. Focus: rouge. Abstract: Our design enforces differential privacy at every training stage that.

  2. ExpGuard: LLM Content Moderation in Specialized Domains

    Start here for detailed protocol reporting and quality-control evidence. Signals: expert verification. Abstract: With the growing deployment of large language models (LLMs) in real-world applications, establishing robust safety.

  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. AD-Bench: A Real-World, Trajectory-Aware Advertising Analytics Benchmark for LLM Agents

    Include a human-eval paper to calibrate against judge-based evaluation settings. Signals: simulation environments + expert verification. Focus: Ad-Bench / pass@1. Abstract: While Large Language Model (LLM) agents have.

  5. CricBench: A Multilingual Benchmark for Evaluating LLMs in Cricket Analytics

    Include a human-eval paper to calibrate against judge-based evaluation settings. Signals: automatic metrics + expert verification. Focus: DROP / accuracy. Abstract: We evaluate six state-of-the-art models, including GPT-4o,.

  6. 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,.

  7. 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.

  8. MEDSYN: Benchmarking Multi-EviDence SYNthesis in Complex Clinical Cases for Multimodal Large Language Models

    Adds automatic metrics with expert verification for broader protocol coverage within this hub. Signals: automatic metrics + expert verification. Focus: accuracy. Abstract: Multimodal large language models (MLLMs) have.

Known Limitations

Known Limitations

  • Only 18.2% of papers report quality controls; prioritize calibration/adjudication evidence.
  • Annotation unit is under-specified (18.2% 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 (11)
  • Pairwise Preference (1)

Evaluation Modes

  • Automatic Metrics (6)
  • Simulation Env (1)

Top Benchmarks

  • Ad Bench (1)
  • BIRD (1)
  • Cricbench (1)
  • DROP (1)

Top Metrics

  • Accuracy (3)
  • Cost (1)
  • Dice (1)
  • Iou (1)

Rater Population Mix

  • Domain Experts (10)
  • Mixed (1)

Quality Controls

  • Calibration (1)
  • Gold Questions (1)
Coverage diagnostics (sample-based): human-feedback 100.0% · benchmarks 27.3% · metrics 63.6% · quality controls 18.2%.

Top Papers

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