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HFEPX Archive Slice

HFEPX Daily Archive: 2026-03-23

Updated from current HFEPX corpus (Apr 9, 2026). 138 papers are grouped in this daily page.

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Updated from current HFEPX corpus (Apr 9, 2026). 138 papers are grouped in this daily page. Common evaluation modes: Automatic Metrics, Simulation Env. Most common rater population: Domain Experts. Common annotation unit: Trajectory. Frequent quality control: Gold Questions. Frequently cited benchmark: AlpacaEval. 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 23, 2026.

Papers: 138 Last published: Mar 23, 2026 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 .

Analysis blocks are computed from the loaded sample (60 of 138 papers).

High-Signal Coverage

100.0%

60 / 60 papers are not low-signal flagged.

Benchmark Anchors

5.0%

Papers with benchmark/dataset mentions in extraction output.

Metric Anchors

28.3%

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

  • 9.4% of papers report explicit human-feedback signals, led by pairwise preferences.
  • automatic metrics appears in 24.6% of papers in this hub.
  • AlpacaEval is a recurring benchmark anchor for cross-paper comparisons in this page.

Protocol Takeaways For This Period

  • Most common quality-control signal is gold-question checks (0.7% of papers).
  • Rater context is mostly domain experts, and annotation is commonly trajectory-level annotation; use this to scope replication staffing.
  • Pair this hub with a human_eval-heavy hub to validate judge-model calibration.

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
LRC-WeatherNet: LiDAR, RADAR, and Camera Fusion Network for Real-time Weather-type Classification in Autonomous Driving

Mar 23, 2026

Automatic Metrics BIRD Precision Not reported
Guideline-grounded retrieval-augmented generation for ophthalmic clinical decision support

Mar 23, 2026

Automatic Metrics Healthbench Accuracy Not reported
Learning When to Act: Interval-Aware Reinforcement Learning with Predictive Temporal Structure

Mar 23, 2026

Automatic Metrics Not reported Kappa Not reported
Functional Component Ablation Reveals Specialization Patterns in Hybrid Language Model Architectures

Mar 23, 2026

Automatic Metrics Not reported Perplexity Not reported
Towards Automated Community Notes Generation with Large Vision Language Models for Combating Contextual Deception

Mar 23, 2026

Automatic Metrics Not reported Helpfulness Not reported
Adapting Self-Supervised Speech Representations for Cross-lingual Dysarthria Detection in Parkinson's Disease

Mar 23, 2026

Automatic Metrics Not reported F1 Not reported
ROM: Real-time Overthinking Mitigation via Streaming Detection and Intervention

Mar 23, 2026

Automatic Metrics Not reported Accuracy, Latency Not reported
SegMaFormer: A Hybrid State-Space and Transformer Model for Efficient Segmentation

Mar 23, 2026

Automatic Metrics Not reported Dice Not reported
Parameter-Efficient Fine-Tuning for Medical Text Summarization: A Comparative Study of Lora, Prompt Tuning, and Full Fine-Tuning

Mar 23, 2026

Automatic Metrics Not reported Rouge Not reported
BHDD: A Burmese Handwritten Digit Dataset

Mar 23, 2026

Automatic Metrics Not reported Accuracy Not reported
Researcher Workflow (Detailed)

Checklist

  • Gap: Papers with explicit human feedback

    Coverage is a replication risk (9.4% vs 45% target).

  • Gap: Papers reporting quality controls

    Coverage is a replication risk (0.7% vs 30% target).

  • Gap: Papers naming benchmarks/datasets

    Coverage is a replication risk (8% vs 35% target).

  • Moderate: Papers naming evaluation metrics

    Coverage is usable but incomplete (22.5% vs 35% target).

  • Gap: Papers with known rater population

    Coverage is a replication risk (8.7% vs 35% target).

  • Gap: Papers with known annotation unit

    Coverage is a replication risk (11.6% vs 35% target).

Strengths

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

Known Gaps

  • Only 0.7% of papers report quality controls; prioritize calibration/adjudication evidence.
  • Rater population is under-specified (8.7% coverage).
  • Annotation unit is under-specified (11.6% coverage).

Suggested Next Analyses

  • Pair this hub with a human_eval-heavy hub to validate judge-model calibration.
  • Stratify by benchmark (AlpacaEval vs BIRD) before comparing methods.
  • Track metric sensitivity by reporting both accuracy and cost.
  • Add inter-annotator agreement checks when reproducing these protocols.

Recommended Queries

Known Limitations
  • Only 0.7% of papers report quality controls; prioritize calibration/adjudication evidence.
  • Rater population is under-specified (8.7% 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 (34)
  • Simulation Env (6)
  • Llm As Judge (4)

Top Metrics

  • Accuracy (14)
  • Cost (8)
  • Precision (3)
  • Throughput (3)

Top Benchmarks

  • AlpacaEval (1)
  • BIRD (1)
  • Crmarena (1)
  • Enterprisearena (1)

Quality Controls

  • Gold Questions (1)

Papers In This Archive Slice

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