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HFEPX Hub

Automatic Metrics + Coding (Last 30 Days)

Updated from current HFEPX corpus (Apr 27, 2026). 20 papers are grouped in this hub page.

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Updated from current HFEPX corpus (Apr 27, 2026). 20 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: APPS. 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 Mar 29, 2026.

Papers: 20 Last published: Mar 29, 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 (20) Replication-Ready Only (6)

High-Signal Coverage

100.0%

20 / 20 sampled papers are not low-signal flagged.

Replication-Ready Set

6

Benchmark + metric + eval mode explicitly present.

Judge/Human Comparability

0

Papers containing both `human_eval` and `llm_as_judge`.

  • 6 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: 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 (6 papers).

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Why This Matters For Eval Research

  • 30% of papers report explicit human-feedback signals, led by rubric ratings.
  • automatic metrics appears in 100% of papers in this hub.
  • APPS is a recurring benchmark anchor for cross-paper comparisons in this page.

Protocol Takeaways

  • Most common quality-control signal is rater calibration (5% 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

  • APPS appears in 5% of hub papers (1/20); use this cohort for benchmark-matched comparisons.
  • BFCL appears in 5% of hub papers (1/20); use this cohort for benchmark-matched comparisons.

Metric Interpretation

  • accuracy is reported in 50% of hub papers (10/20); compare with a secondary metric before ranking methods.
  • cost is reported in 35% of hub papers (7/20); compare with a secondary metric before ranking methods.
Researcher Checklist (Expanded)

Researcher Checklist

  • Moderate: Papers with explicit human feedback

    Coverage is usable but incomplete (30% vs 45% target).

  • Gap: Papers reporting quality controls

    Coverage is a replication risk (5% vs 30% target).

  • Moderate: Papers naming benchmarks/datasets

    Coverage is usable but incomplete (30% vs 35% target).

  • Strong: Papers naming evaluation metrics

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

  • Gap: Papers with known rater population

    Coverage is a replication risk (20% vs 35% target).

  • Strong: Papers with known annotation unit

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

Strengths

  • Agentic evaluation appears in 65% of papers.

Known Gaps

  • Only 5% of papers report quality controls; prioritize calibration/adjudication evidence.
  • Rater population is under-specified (20% coverage).

Suggested Next Analyses

  • Pair this hub with llm_as_judge pages to benchmark automated-vs-human evaluation tradeoffs.
  • Stratify by benchmark (APPS vs BFCL) 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

Human Eval Protocols Benchmark Slice: APPS 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
Paper Reconstruction Evaluation: Evaluating Presentation and Hallucination in AI-written Papers

Apr 1, 2026

 Yes
Rubric Rating
Automatic Metrics Paperwrite Bench Not Reported Not Reported
Do Phone-Use Agents Respect Your Privacy?

Apr 1, 2026

 Yes
Pairwise Preference
Automatic Metrics APPS , Myphonebench Task success Not Reported
Navigating Large-Scale Document Collections: MuDABench for Multi-Document Analytical QA

Apr 24, 2026

 No
Not Reported
Automatic Metrics Mudabench Accuracy Not Reported
SkillX: Automatically Constructing Skill Knowledge Bases for Agents

Apr 6, 2026

 No
Not Reported
Automatic Metrics BFCL Task success Not Reported
S0 Tuning: Zero-Overhead Adaptation of Hybrid Recurrent-Attention Models

Apr 1, 2026

 No
Not Reported
Automatic Metrics MATH 500 , GSM8K Pass@1 , Inference cost Not Reported
Agent Q-Mix: Selecting the Right Action for LLM Multi-Agent Systems through Reinforcement Learning

Apr 1, 2026

 No
Not Reported
Automatic Metrics HLE Accuracy , Token cost Not Reported

Protocol Diff (Top Papers)

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

Signal Paper Reconstruction Evaluation: Evaluating Present… Do Phone-Use Agents Respect Your Privacy? Navigating Large-Scale Document Collections: MuDABe…
Human Feedback Rubric RatingPairwise PreferenceNot reported
Evaluation Modes Automatic MetricsAutomatic MetricsAutomatic Metrics
Benchmarks Paperwrite BenchAPPS, MyphonebenchMudabench
Metrics Not reportedTask successAccuracy
Quality Controls Not reportedNot reportedNot reported
Rater Population UnknownUnknownDomain 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. Dynamically Acquiring Text Content to Enable the Classification of Lesser-known Entities for Real-world Tasks

    Start here for detailed protocol reporting and quality-control evidence. Signals: automatic metrics + expert verification. Focus: f1. Abstract: Existing Natural Language Processing (NLP) resources often lack the task-specific.

  2. Navigating Large-Scale Document Collections: MuDABench for Multi-Document Analytical QA

    Start here for detailed protocol reporting and quality-control evidence. Signals: automatic metrics. Focus: Mudabench / accuracy. Abstract: We also propose an evaluation protocol that measures final answer accuracy.

  3. SHAPE: Unifying Safety, Helpfulness and Pedagogy for Educational LLMs

    Start here for detailed protocol reporting and quality-control evidence. Signals: automatic metrics + red-team protocols. Focus: helpfulness. Abstract: Experiments across multiple LLMs show that our method yields significantly.

  4. Learning to Predict Future-Aligned Research Proposals with Language Models

    Include a human-eval paper to calibrate against judge-based evaluation settings. Signals: human evaluation. Focus: accuracy. Abstract: Large language models (LLMs) are increasingly used to assist ideation in research,.

  5. PRBench: End-to-end Paper Reproduction in Physics Research

    Include a human-eval paper to calibrate against judge-based evaluation settings. Signals: automatic metrics + rubric ratings. Focus: accuracy. Abstract: All tasks are contributed by domain experts from over.

  6. Paper Reconstruction Evaluation: Evaluating Presentation and Hallucination in AI-written Papers

    Include an LLM-as-judge paper to test judge design and agreement assumptions. Signals: automatic metrics + rubric ratings. Focus: Paperwrite-Bench / cost. Abstract: PaperRecon disentangles the evaluation of the.

  7. Do Phone-Use Agents Respect Your Privacy?

    Adds automatic metrics with pairwise preferences for broader protocol coverage within this hub. Signals: automatic metrics + pairwise preferences. Focus: APPS / task success. Abstract: Across five frontier.

  8. QED-Nano: Teaching a Tiny Model to Prove Hard Theorems

    Adds automatic metrics with rubric ratings for broader protocol coverage within this hub. Signals: automatic metrics + rubric ratings. Focus: cost. Abstract: Our training recipe has three stages:.

Known Limitations

Known Limitations

  • Only 5% of papers report quality controls; prioritize calibration/adjudication evidence.
  • Rater population is under-specified (20% 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

  • Rubric Rating (3)
  • Expert Verification (2)
  • Pairwise Preference (1)
  • Red Team (1)

Evaluation Modes

  • Automatic Metrics (20)
  • Human Eval (1)
  • Simulation Env (1)

Top Benchmarks

  • APPS (1)
  • BFCL (1)
  • GSM8K (1)
  • HLE (1)

Top Metrics

  • Accuracy (10)
  • Cost (7)
  • Inference cost (2)
  • Latency (2)

Rater Population Mix

  • Domain Experts (4)

Quality Controls

  • Calibration (1)
Coverage diagnostics (sample-based): human-feedback 30.0% · benchmarks 30.0% · metrics 100.0% · quality controls 5.0%.

Top Papers

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