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HFEPX Archive Slice

HFEPX Fortnight Archive: 2026-F04

Updated from current HFEPX corpus (Apr 12, 2026). 480 papers are grouped in this daily page.

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Updated from current HFEPX corpus (Apr 12, 2026). 480 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: Calibration. Frequently cited benchmark: BrowseComp. 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 22, 2026.

Papers: 480 Last published: Feb 22, 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 480 papers).

High-Signal Coverage

100.0%

60 / 60 papers are not low-signal flagged.

Benchmark Anchors

11.7%

Papers with benchmark/dataset mentions in extraction output.

Metric Anchors

48.3%

Papers with reported metric mentions in extraction output.

  • 4 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 for trend comparison: review top papers first, then validate shifts in the protocol matrix.

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Why This Time Slice Matters

  • 13.8% of papers report explicit human-feedback signals, led by pairwise preferences.
  • automatic metrics appears in 36% of papers in this hub.
  • BrowseComp 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 (2.7% of papers).
  • Rater context is mostly domain experts, and annotation is commonly trajectory-level annotation; use this to scope replication staffing.
  • Compare papers that report both human_eval and llm_as_judge to quantify judge-human agreement drift.

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
Yor-Sarc: A gold-standard dataset for sarcasm detection in a low-resource African language

Feb 21, 2026

Automatic Metrics Not reported Agreement Inter Annotator Agreement Reported, Adjudication
Why Agent Caching Fails and How to Fix It: Structured Intent Canonicalization with Few-Shot Learning

Feb 21, 2026

Automatic Metrics Nyayabench Accuracy, Precision Not reported
ArabicNumBench: Evaluating Arabic Number Reading in Large Language Models

Feb 21, 2026

Automatic Metrics Arabicnumbench Accuracy Not reported
SPQ: An Ensemble Technique for Large Language Model Compression

Feb 20, 2026

Automatic Metrics GSM8K, TruthfulQA Accuracy, Perplexity Not reported
Adaptive Data Augmentation with Multi-armed Bandit: Sample-Efficient Embedding Calibration for Implicit Pattern Recognition

Feb 22, 2026

Automatic Metrics Not reported Accuracy Calibration
Think$^{2}$: Grounded Metacognitive Reasoning in Large Language Models

Feb 21, 2026

Human Eval GSM8K, AIME Not reported Not reported
MANATEE: Inference-Time Lightweight Diffusion Based Safety Defense for LLMs

Feb 21, 2026

Automatic Metrics Not reported Success rate, Jailbreak success rate Not reported
PerSoMed: A Large-Scale Balanced Dataset for Persian Social Media Text Classification

Feb 22, 2026

Automatic Metrics Not reported F1, Precision Not reported
Anatomy of Agentic Memory: Taxonomy and Empirical Analysis of Evaluation and System Limitations

Feb 22, 2026

Automatic Metrics Not reported Accuracy, Latency Not reported
No Need For Real Anomaly: MLLM Empowered Zero-Shot Video Anomaly Detection

Feb 22, 2026

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

Checklist

  • Gap: Papers with explicit human feedback

    Coverage is a replication risk (13.8% vs 45% target).

  • Gap: Papers reporting quality controls

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

  • Gap: Papers naming benchmarks/datasets

    Coverage is a replication risk (5.4% vs 35% target).

  • Gap: Papers naming evaluation metrics

    Coverage is a replication risk (14.6% vs 35% target).

  • Gap: Papers with known rater population

    Coverage is a replication risk (9.8% vs 35% target).

  • Gap: Papers with known annotation unit

    Coverage is a replication risk (12.1% vs 35% target).

Strengths

  • Contains both human-eval and LLM-as-judge protocols for head-to-head methodology comparison.

Known Gaps

  • Only 5% of papers report quality controls; prioritize calibration/adjudication evidence.
  • Rater population is under-specified (9.8% coverage).
  • Annotation unit is under-specified (12.1% coverage).

Suggested Next Analyses

  • Compare papers that report both human_eval and llm_as_judge to quantify judge-human agreement drift.
  • Stratify by benchmark (BrowseComp vs LiveCodeBench) before comparing methods.
  • Track metric sensitivity by reporting both accuracy and cost.

Recommended Queries

Known Limitations
  • Only 5% of papers report quality controls; prioritize calibration/adjudication evidence.
  • Rater population is under-specified (9.8% 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 (173)
  • Simulation Env (17)
  • Llm As Judge (10)
  • Human Eval (8)

Top Metrics

  • Accuracy (33)
  • Cost (12)
  • Latency (8)
  • Recall (5)

Top Benchmarks

  • BrowseComp (2)
  • LiveCodeBench (2)
  • MT Bench (2)
  • Ad Bench (1)

Quality Controls

  • Calibration (13)
  • Inter Annotator Agreement Reported (7)
  • Adjudication (5)
  • Gold Questions (1)

Papers In This Archive Slice

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