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HFEPX Archive Slice

HFEPX Fortnight Archive: 2025-F09

Updated from current HFEPX corpus (Apr 11, 2026). 21 papers are grouped in this daily page.

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Updated from current HFEPX corpus (Apr 11, 2026). 21 papers are grouped in this daily page. Common evaluation modes: Automatic Metrics, Human Eval. Most common rater population: Domain Experts. Common annotation unit: Freeform. Frequent quality control: Calibration. Frequently cited benchmark: Paperbench. 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 May 4, 2025.

Papers: 21 Last published: May 4, 2025 Global RSS

Researcher Quick Triage

Use this archive page for time-slice monitoring (what changed in evaluation methods, metrics, and protocol quality this period). Quality band: High .

High-Signal Coverage

100.0%

21 / 21 papers are not low-signal flagged.

Benchmark Anchors

9.5%

Papers with benchmark/dataset mentions in extraction output.

Metric Anchors

33.3%

Papers with reported metric mentions in extraction output.

  • 1 papers report explicit quality controls for this archive period.
  • Prioritize papers with both benchmark and metric anchors for reliable longitudinal comparisons.

Primary action: Use this slice as early signal only; benchmark/metric anchoring is limited for rigorous period-over-period claims.

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Why This Time Slice Matters

  • 4.8% of papers report explicit human-feedback signals, led by pairwise preferences.
  • automatic metrics appears in 33.3% of papers in this hub.
  • Paperbench is a recurring benchmark anchor for cross-paper comparisons in this page.

Protocol Takeaways For This Period

  • Most common quality-control signal is rater calibration (4.8% of papers).
  • Rater context is mostly domain experts, and annotation is commonly Freeform; use this to scope replication staffing.
  • Pair this hub with llm_as_judge pages to benchmark automated-vs-human evaluation tradeoffs.

Start Here (Highest-Signal Papers In This Slice)

Ranked by protocol completeness and evidence density for faster period-over-period review.

Protocol Matrix (Top 10)

Quickly compare method ingredients across this archive slice.

Paper Eval Modes Benchmarks Metrics Quality Controls
Toward Safe and Human-Aligned Game Conversational Recommendation via Multi-Agent Decomposition

Apr 26, 2025

Automatic Metrics Not reported Hit@5 Not reported
Comparing Uncertainty Measurement and Mitigation Methods for Large Language Models: A Systematic Review

Apr 25, 2025

Automatic Metrics Not reported Accuracy Calibration
Edge-Cloud Collaborative Computing on Distributed Intelligence and Model Optimization: A Survey

May 3, 2025

Automatic Metrics Not reported Latency Not reported
Reshaping MOFs text mining with a dynamic multi-agents framework of large language model

Apr 26, 2025

Automatic Metrics Not reported Accuracy, Precision Not reported
Reason Like a Radiologist: Chain-of-Thought and Reinforcement Learning for Verifiable Report Generation

Apr 25, 2025

Automatic Metrics Not reported Rouge Not reported
How much does context affect the accuracy of AI health advice?

Apr 25, 2025

Automatic Metrics Not reported Accuracy Not reported
Paper2Code: Automating Code Generation from Scientific Papers in Machine Learning

Apr 24, 2025

Human Eval Paperbench Not reported Not reported
ConformalNL2LTL: Translating Natural Language Instructions into Temporal Logic Formulas with Conformal Correctness Guarantees

Apr 22, 2025

Automatic Metrics Not reported Accuracy Not reported
TrustGeoGen: Formal-Verified Data Engine for Trustworthy Multi-modal Geometric Problem Solving

Apr 22, 2025

Not reported Olympiadbench Not reported Not reported
Adaptive Social Learning via Mode Policy Optimization for Language Agents

May 4, 2025

Simulation Env Not reported Not reported Not reported
Researcher Workflow (Detailed)

Checklist

  • Gap: Papers with explicit human feedback

    Coverage is a replication risk (4.8% vs 45% target).

  • Gap: Papers reporting quality controls

    Coverage is a replication risk (4.8% vs 30% target).

  • Gap: Papers naming benchmarks/datasets

    Coverage is a replication risk (4.8% vs 35% target).

  • Gap: Papers naming evaluation metrics

    Coverage is a replication risk (19% vs 35% target).

  • Gap: Papers with known rater population

    Coverage is a replication risk (9.5% vs 35% target).

  • Gap: Papers with known annotation unit

    Coverage is a replication risk (9.5% vs 35% target).

Strengths

  • This hub still surfaces a concentrated paper set for protocol triage and replication planning.

Known Gaps

  • Only 4.8% of papers report quality controls; prioritize calibration/adjudication evidence.
  • Rater population is under-specified (9.5% coverage).
  • Annotation unit is under-specified (9.5% coverage).

Suggested Next Analyses

  • Pair this hub with llm_as_judge pages to benchmark automated-vs-human evaluation tradeoffs.
  • Track metric sensitivity by reporting both accuracy and hit@5.
  • Add inter-annotator agreement checks when reproducing these protocols.

Recommended Queries

Known Limitations
  • Only 4.8% of papers report quality controls; prioritize calibration/adjudication evidence.
  • Rater population is under-specified (9.5% coverage).
  • Narrative synthesis is grounded in metadata and abstracts only; full-paper implementation details are not parsed.
Research Utility Snapshot (Detailed)

Evaluation Modes

  • Automatic Metrics (7)
  • Human Eval (2)
  • Simulation Env (1)

Top Metrics

  • Accuracy (2)
  • Hit@5 (1)
  • Perplexity (1)
  • Precision (1)

Top Benchmarks

  • Paperbench (1)

Quality Controls

  • Calibration (1)

Papers In This Archive Slice

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