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HFEPX Benchmark Hub

APPS + Coding Benchmark Papers

Updated from current HFEPX corpus (Mar 17, 2026). 2 papers are grouped in this benchmark page.

Read Full Context

Updated from current HFEPX corpus (Mar 17, 2026). 2 papers are grouped in this benchmark page. Common evaluation modes: Human Eval, Llm As Judge. Frequently cited benchmark: APPS. 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 Jan 5, 2026.

Papers: 2 Last published: Jan 5, 2026 Global RSS

Researcher Quick Triage

Use this page for benchmark-matched method comparisons and eval protocol selection. Quality band: Developing .

High-Signal Coverage

100.0%

2 / 2 sampled papers are not low-signal flagged.

Replication-Ready Set

0

Papers with explicit benchmark + metric + eval mode fields.

Quality Controls

0.0%

0 papers report calibration/adjudication/IAA controls.

  • 2 papers explicitly name benchmark datasets in the sampled set.
  • 0 papers report at least one metric term in metadata extraction.
  • Start with the ranked shortlist below before reading all papers.

Primary action: Use this page to map benchmark mentions first; wait for stronger metric/QC coverage before strict comparisons.

Why This Matters (Expanded)

Why This Matters For Eval Research

  • 100% of papers report explicit human-feedback signals, led by pairwise preferences.
  • human evaluation appears in 50% of papers in this hub.
  • APPS is a recurring benchmark anchor for cross-paper comparisons in this page.
Protocol Notes (Expanded)

Protocol Takeaways

  • Quality-control reporting is sparse in this slice; prioritize papers with explicit calibration or adjudication steps.
  • Compare papers that report both human_eval and llm_as_judge to quantify judge-human agreement drift.
  • Stratify by benchmark (APPS vs LMSYS Chatbot Arena) before comparing methods.

Benchmark Interpretation

  • APPS appears in 100% of hub papers (2/2); use this cohort for benchmark-matched comparisons.
  • LMSYS Chatbot Arena appears in 50% of hub papers (1/2); use this cohort for benchmark-matched comparisons.

Start Here (Benchmark-Matched First 6)

Ranked by protocol completeness so you can quickly find papers suitable for comparison studies.

Protocol Matrix (Top 10)

Compare protocol ingredients quickly before deep-reading full papers.

Paper Eval Modes Human Feedback Metrics Quality Controls
WebCoderBench: Benchmarking Web Application Generation with Comprehensive and Interpretable Evaluation Metrics

Jan 5, 2026

 Llm As Judge Pairwise Preference Not reported Not reported
EasyAnimate: High-Performance Video Generation Framework with Hybrid Windows Attention and Reward Backpropagation

May 29, 2024

 Human Eval Pairwise Preference Not reported Not reported
Researcher Workflow (Detailed)

Checklist

  • Strong: Papers with explicit human feedback

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

  • Gap: Papers reporting quality controls

    Coverage is a replication risk (0% vs 30% target).

  • Strong: Papers naming benchmarks/datasets

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

  • Gap: Papers naming evaluation metrics

    Coverage is a replication risk (0% vs 35% target).

  • Gap: Papers with known rater population

    Coverage is a replication risk (0% vs 35% target).

  • Gap: Papers with known annotation unit

    Coverage is a replication risk (0% vs 35% target).

Strengths

  • Strong human-feedback signal (100% of papers).
  • Contains both human-eval and LLM-as-judge protocols for head-to-head methodology comparison.

Known Gaps

  • Only 0% of papers report quality controls; prioritize calibration/adjudication evidence.
  • Rater population is under-specified (0% coverage).
  • Annotation unit is under-specified (0% coverage).

Suggested Next Analyses

  • Compare papers that report both human_eval and llm_as_judge to quantify judge-human agreement drift.
  • Stratify by benchmark (APPS vs LMSYS Chatbot Arena) before comparing methods.

Recommended Queries

Judge vs Human AgreementBenchmark Slice: APPSRecent High-Signal Papers
Known Limitations
  • Only 0% of papers report quality controls; prioritize calibration/adjudication evidence.
  • Rater population is under-specified (0% coverage).
  • Narrative synthesis is grounded in metadata and abstracts only; full-paper implementation details are not parsed.
Research Utility Snapshot (Detailed)

Evaluation Modes

  • Human Eval (1)
  • Llm As Judge (1)

Human Feedback Mix

  • Pairwise Preference (2)

Top Benchmarks

  • APPS (2)
  • LMSYS Chatbot Arena (1)
  • Webcoderbench (1)

Top Metrics

Top Papers On This Benchmark

Related Benchmark Hubs

APPS + Pairwise Preference Benchmark Papers APPS Benchmark Papers Coding Evaluation Suite Benchmark Papers Coding + Pairwise Preference Papers Human Eval Or Llm As Judge Papers Coding Papers DROP Benchmark Papers (Last 45 Days) (11) DROP Benchmark Papers (Last 60 Days) (11) DROP Benchmark Papers (Last 75 Days) (11) Reasoning & Math Suite Benchmark Papers + Automatic Metrics (10) Reasoning & Math Suite Benchmark Papers (23) Reasoning & Math Suite Benchmark Papers In CS.CL (21) Reasoning & Math Suite Benchmark Papers In CS.AI (15) Coding Evaluation Suite Benchmark Papers (11) Coding Evaluation Suite Benchmark Papers In CS.CL (10) SWE-Bench Ecosystem Benchmark Papers (13) SWE-Bench Ecosystem Benchmark Papers In CS.CL (11)

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