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HFEPX Hub

Coding + Critique Edit Papers

Updated from current HFEPX corpus (Apr 27, 2026). 19 papers are grouped in this hub page.

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Updated from current HFEPX corpus (Apr 27, 2026). 19 papers are grouped in this hub page. Common evaluation modes: Automatic Metrics, Human Eval. Most common rater population: Domain Experts. Common annotation unit: Multi Dim Rubric. Frequent quality control: Calibration. Frequently cited benchmark: ContentBench. 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 Apr 1, 2026.

Papers: 19 Last published: Apr 1, 2026 Global RSS Tag RSS
CodingCritique Edit

Researcher Quick Triage

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

All Sampled Papers (19) Replication-Ready Only (1)

High-Signal Coverage

100.0%

19 / 19 sampled papers are not low-signal flagged.

Replication-Ready Set

1

Benchmark + metric + eval mode explicitly present.

Judge/Human Comparability

0

Papers containing both `human_eval` and `llm_as_judge`.

  • 1 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.

Currently showing only replication-ready papers in ranking and matrix sections (1 papers).

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Why This Matters For Eval Research

  • 100% of papers report explicit human-feedback signals, led by critique/edit feedback.
  • automatic metrics appears in 15.8% of papers in this hub.
  • ContentBench is a recurring benchmark anchor for cross-paper comparisons in this page.

Protocol Takeaways

  • Most common quality-control signal is rater calibration (5.3% 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 llm_as_judge pages to benchmark automated-vs-human evaluation tradeoffs.

Benchmark Interpretation

  • ContentBench appears in 5.3% of hub papers (1/19); use this cohort for benchmark-matched comparisons.
  • Interruptbench appears in 5.3% of hub papers (1/19); use this cohort for benchmark-matched comparisons.

Metric Interpretation

  • accuracy is reported in 10.5% of hub papers (2/19); compare with a secondary metric before ranking methods.
  • coherence is reported in 10.5% of hub papers (2/19); 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).

  • Gap: Papers reporting quality controls

    Coverage is a replication risk (5.3% vs 30% target).

  • Moderate: Papers naming benchmarks/datasets

    Coverage is usable but incomplete (21.1% vs 35% target).

  • Moderate: Papers naming evaluation metrics

    Coverage is usable but incomplete (26.3% vs 35% target).

  • Gap: Papers with known rater population

    Coverage is a replication risk (15.8% vs 35% target).

  • Gap: Papers with known annotation unit

    Coverage is a replication risk (10.5% vs 35% target).

Strengths

  • Strong human-feedback signal (100% of papers).
  • Agentic evaluation appears in 26.3% of papers.

Known Gaps

  • Only 5.3% of papers report quality controls; prioritize calibration/adjudication evidence.
  • Rater population is under-specified (15.8% coverage).
  • Annotation unit is under-specified (10.5% coverage).

Suggested Next Analyses

  • Pair this hub with llm_as_judge pages to benchmark automated-vs-human evaluation tradeoffs.
  • Stratify by benchmark (ContentBench vs Interruptbench) before comparing methods.
  • Track metric sensitivity by reporting both accuracy and coherence.
  • Add inter-annotator agreement checks when reproducing these protocols.
Recommended Queries (Expanded)

Recommended Queries

Human Eval Protocols Benchmark Slice: ContentBench 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
Can Large Language Models Replace Human Coders? Introducing ContentBench

Feb 23, 2026

 Yes
Critique Edit
Automatic Metrics ContentBench Agreement Not Reported
Suggested Reading Order (Extended)

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

Suggested Reading Order

  1. Cross-Cultural Expert-Level Art Critique Evaluation with Vision-Language Models

    Start here for detailed protocol reporting and quality-control evidence. Signals: rubric ratings. Abstract: Existing benchmarks assess perception without interpretation, and common evaluation proxies, such as automated metrics and.

  2. From Hallucination to Structure Snowballing: The Alignment Tax of Constrained Decoding in LLM Reflection

    High citation traction makes this a strong baseline for protocol comparison. Signals: critique/edit feedback. Abstract: Intrinsic self-correction in Large Language Models (LLMs) frequently fails in open-ended reasoning tasks.

  3. The Self Driving Portfolio: Agentic Architecture for Institutional Asset Management

    High citation traction makes this a strong baseline for protocol comparison. Signals: critique/edit feedback. Abstract: Agentic AI shifts the investor's role from analytical execution to oversight.

  4. Revision or Re-Solving? Decomposing Second-Pass Gains in Multi-LLM Pipelines

    High citation traction makes this a strong baseline for protocol comparison. Signals: critique/edit feedback. Abstract: Multi-LLM revision pipelines, in which a second model reviews and improves a draft.

  5. IntelliAsk: Learning to Ask High-Quality Research Questions via RLVR

    Include a human-eval paper to calibrate against judge-based evaluation settings. Signals: human evaluation + pairwise preferences. Focus: Writingbench. Abstract: To address this gap, we curate a high-quality dataset.

  6. XtraGPT: Context-Aware and Controllable Academic Paper Revision via Human-AI Collaboration

    Include a human-eval paper to calibrate against judge-based evaluation settings. Signals: human evaluation + pairwise preferences. Focus: coherence. Abstract: Both automated preference assessments and human evaluations confirm the.

  7. When Users Change Their Mind: Evaluating Interruptible Agents in Long-Horizon Web Navigation

    Include an LLM-as-judge paper to test judge design and agreement assumptions. Signals: simulation environments + critique/edit feedback. Focus: WebArena. Abstract: As LLM agents transition from short, static problem.

  8. From Pixels to Policies: Reinforcing Spatial Reasoning in Language Models for Content-Aware Layout Design

    Adds simulation environments with critique/edit feedback for broader protocol coverage within this hub. Signals: simulation environments + critique/edit feedback. Focus: latency. Abstract: We introduce LaySPA, a reinforcement learning.

Known Limitations

Known Limitations

  • Only 5.3% of papers report quality controls; prioritize calibration/adjudication evidence.
  • Rater population is under-specified (15.8% 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

  • Critique Edit (19)
  • Pairwise Preference (2)
  • Expert Verification (1)
  • Rlaif Or Synthetic Feedback (1)

Evaluation Modes

  • Automatic Metrics (3)
  • Human Eval (2)
  • Simulation Env (2)

Top Benchmarks

  • ContentBench (1)
  • Interruptbench (1)
  • LiveCodeBench (1)
  • WebArena (1)

Top Metrics

  • Accuracy (2)
  • Coherence (2)
  • Cost (2)
  • Agreement (1)

Rater Population Mix

  • Domain Experts (3)

Quality Controls

  • Calibration (1)
Coverage diagnostics (sample-based): human-feedback 100.0% · benchmarks 21.1% · metrics 26.3% · quality controls 5.3%.

Top Papers

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