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HFEPX Hub

Automatic Metrics + Coding (Last 30 Days)

Updated from current HFEPX corpus (Mar 1, 2026). 25 papers are grouped in this hub page.

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Updated from current HFEPX corpus (Mar 1, 2026). 25 papers are grouped in this hub page. Common evaluation modes: Automatic Metrics, Simulation Env. Most common rater population: Domain Experts. Common annotation unit: Trajectory. Frequently cited benchmark: SWE-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 24, 2026.

Papers: 25 Last published: Feb 24, 2026 Global RSS Tag RSS
Automatic MetricsCodingLast 30d

Researcher Quick Triage

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

All Sampled Papers (25) Replication-Ready Only (5)

High-Signal Coverage

100.0%

25 / 25 sampled papers are not low-signal flagged.

Replication-Ready Set

5

Benchmark + metric + eval mode explicitly present.

Judge/Human Comparability

0

Papers containing both `human_eval` and `llm_as_judge`.

  • 5 papers are replication-ready (benchmark + metric + explicit evaluation mode).
  • 0 papers support judge-vs-human agreement analysis.
  • 0 papers report explicit quality controls (calibration/adjudication/IAA).

Primary action: Start with the top 2 papers in “Start Here”, then validate assumptions in the protocol matrix.

Currently showing only replication-ready papers in ranking and matrix sections (5 papers).

Why This Matters (Expanded)

Why This Matters For Eval Research

  • 36% of papers report explicit human-feedback signals, led by pairwise preferences.
  • automatic metrics appears in 100% of papers in this hub.
  • SWE-bench 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.
  • Rater context is mostly domain experts, and annotation is commonly trajectory-level annotation; use this to scope replication staffing.
  • Stratify by benchmark (SWE-bench vs SWE-bench Verified) before comparing methods.

Benchmark Interpretation

  • SWE-bench appears in 8% of hub papers (2/25); use this cohort for benchmark-matched comparisons.
  • SWE-bench Verified appears in 8% of hub papers (2/25); use this cohort for benchmark-matched comparisons.

Metric Interpretation

  • accuracy is reported in 40% of hub papers (10/25); compare with a secondary metric before ranking methods.
  • cost is reported in 28% of hub papers (7/25); compare with a secondary metric before ranking methods.
Researcher Checklist (Expanded)

Researcher Checklist

  • Moderate: Papers with explicit human feedback

    Coverage is usable but incomplete (36% vs 45% target).

  • Gap: Papers reporting quality controls

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

  • Moderate: Papers naming benchmarks/datasets

    Coverage is usable but incomplete (28% vs 35% target).

  • Strong: Papers naming evaluation metrics

    Coverage is strong (88% vs 35% target).

  • Moderate: Papers with known rater population

    Coverage is usable but incomplete (24% vs 35% target).

  • Moderate: Papers with known annotation unit

    Coverage is usable but incomplete (28% vs 35% target).

Strengths

  • Agentic evaluation appears in 64% of papers.

Known Gaps

  • Only 0% of papers report quality controls; prioritize calibration/adjudication evidence.
  • Rater population is under-specified (24% coverage).

Suggested Next Analyses

  • Stratify by benchmark (SWE-bench vs SWE-bench Verified) before comparing methods.
  • Track metric sensitivity by reporting both accuracy and cost.
Recommended Queries (Expanded)

Recommended Queries

Benchmark Slice: SWE-bench Metric Slice: accuracy Recent High-Signal Papers

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
Document Reconstruction Unlocks Scalable Long-Context RLVR

Feb 9, 2026

 Yes
Rubric Rating
Automatic Metrics LongBench Coherence Not Reported
Can Large Language Models Replace Human Coders? Introducing ContentBench

Feb 23, 2026

 Yes
Critique Edit
Automatic Metrics ContentBench Agreement , Cost Not Reported
SWE-Protégé: Learning to Selectively Collaborate With an Expert Unlocks Small Language Models as Software Engineering Agents

Feb 25, 2026

 No
Not Reported
Automatic Metrics SWE Bench , SWE Bench Verified Pass@1 , Latency Not Reported
Zooming without Zooming: Region-to-Image Distillation for Fine-Grained Multimodal Perception

Feb 12, 2026

 No
Not Reported
Automatic Metrics Zoombench Latency Not Reported
AceGRPO: Adaptive Curriculum Enhanced Group Relative Policy Optimization for Autonomous Machine Learning Engineering

Feb 8, 2026

 No
Not Reported
Automatic Metrics MLE Bench Latency Not Reported

Protocol Diff (Top Papers)

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

Signal Document Reconstruction Unlocks Scalable Long-Conte… Can Large Language Models Replace Human Coders? Int… SWE-Protégé: Learning to Selectively Collaborate Wi…
Human Feedback Rubric RatingCritique EditNot reported
Evaluation Modes Automatic MetricsAutomatic MetricsAutomatic Metrics
Benchmarks LongBenchContentBenchSWE Bench, SWE Bench Verified
Metrics CoherenceAgreement, CostPass@1, Latency
Quality Controls Not reportedNot reportedNot reported
Rater Population Domain ExpertsUnknownDomain Experts
Annotation Unit Multi Dim RubricUnknownUnknown
Suggested Reading Order (Extended)

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

Suggested Reading Order

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

    Start here for detailed protocol reporting and quality-control evidence. Signals: automatic metrics. Focus: accuracy. Abstract: While Multi-Agent Systems (MAS) excel in complex reasoning, they suffer from the cascading.

  2. Test-Time Scaling with Diffusion Language Models via Reward-Guided Stitching

    Start here for detailed protocol reporting and quality-control evidence. Signals: automatic metrics. Focus: accuracy. Abstract: Reasoning with large language models often benefits from generating multiple chains-of-thought, but existing.

  3. GUI-Libra: Training Native GUI Agents to Reason and Act with Action-aware Supervision and Partially Verifiable RL

    Start here for detailed protocol reporting and quality-control evidence. Signals: automatic metrics. Focus: accuracy. Abstract: Open-source native GUI agents still lag behind closed-source systems on long-horizon navigation tasks.

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

    Include a human-eval paper to calibrate against judge-based evaluation settings. Signals: automatic metrics + expert verification. Focus: cost. Abstract: The code, datasets, and evaluation protocols for SparkMe are.

  5. Document Reconstruction Unlocks Scalable Long-Context RLVR

    Include a human-eval paper to calibrate against judge-based evaluation settings. Signals: automatic metrics + rubric ratings. Focus: LongBench / coherence. Abstract: However, it often relies on gold-standard answers.

  6. 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.

  7. Can Large Language Models Replace Human Coders? Introducing ContentBench

    Adds automatic metrics with critique/edit feedback for broader protocol coverage within this hub. Signals: automatic metrics + critique/edit feedback. Focus: ContentBench / agreement. Abstract: Among the 59 evaluated.

  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 0% of papers report quality controls; prioritize calibration/adjudication evidence.
  • Rater population is under-specified (24% 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

  • Pairwise Preference (4)
  • Expert Verification (3)
  • Critique Edit (1)
  • Rubric Rating (1)

Evaluation Modes

  • Automatic Metrics (25)
  • Simulation Env (2)

Top Benchmarks

  • SWE Bench (2)
  • SWE Bench Verified (2)
  • Charteditbench (1)
  • ContentBench (1)

Top Metrics

  • Accuracy (10)
  • Cost (7)
  • Latency (4)
  • Pass@1 (2)

Rater Population Mix

  • Domain Experts (6)

Quality Controls

Coverage diagnostics (sample-based): human-feedback 36.0% · benchmarks 28.0% · metrics 84.0% · quality controls 0.0%.

Top Papers

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