Skip to content
← Back to explorer

HFEPX Archive Slice

HFEPX Daily Archive: 2026-03-27

Updated from current HFEPX corpus (Apr 9, 2026). 49 papers are grouped in this daily page.

Read Full Context

Updated from current HFEPX corpus (Apr 9, 2026). 49 papers are grouped in this daily page. Common evaluation modes: Automatic Metrics, Llm As Judge. Most common rater population: Domain Experts. Common annotation unit: Multi Dim Rubric. Frequent quality control: Calibration. Frequently cited benchmark: Codabench. 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 27, 2026.

Papers: 49 Last published: Mar 27, 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 .

High-Signal Coverage

100.0%

49 / 49 papers are not low-signal flagged.

Benchmark Anchors

22.4%

Papers with benchmark/dataset mentions in extraction output.

Metric Anchors

46.9%

Papers with reported metric mentions in extraction output.

  • 2 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.

Get this digest every Friday →

Subscribe

Why This Time Slice Matters

  • 12.2% of papers report explicit human-feedback signals, led by expert verification.
  • automatic metrics appears in 36.7% of papers in this hub.
  • Codabench 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 (4.1% 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 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
Stabilizing Rubric Integration Training via Decoupled Advantage Normalization

Mar 27, 2026

Automatic Metrics Olympiadbench Accuracy Not reported
ClimateCheck 2026: Scientific Fact-Checking and Disinformation Narrative Classification of Climate-related Claims

Mar 27, 2026

Automatic Metrics Codabench Recall, Recall@k Not reported
Xpertbench: Expert Level Tasks with Rubrics-Based Evaluation

Mar 27, 2026

Automatic Metrics Xpertbench Success rate Not reported
Automating Clinical Information Retrieval from Finnish Electronic Health Records Using Large Language Models

Mar 27, 2026

Automatic Metrics Not reported Accuracy, Precision Calibration
FormalProofBench: Can Models Write Graduate Level Math Proofs That Are Formally Verified?

Mar 27, 2026

Automatic Metrics Formalproofbench Accuracy, Latency Not reported
Analysing Calls to Order in German Parliamentary Debates

Mar 27, 2026

Automatic Metrics LMSYS Chatbot Arena Relevance Not reported
DataFlex: A Unified Framework for Data-Centric Dynamic Training of Large Language Models

Mar 27, 2026

Automatic Metrics MMLU, DROP Accuracy, Perplexity Not reported
GS-BrainText: A Multi-Site Brain Imaging Report Dataset from Generation Scotland for Clinical Natural Language Processing Development and Validation

Mar 27, 2026

Automatic Metrics Not reported F1 Calibration
ClinicalAgents: Multi-Agent Orchestration for Clinical Decision Making with Dual-Memory

Mar 27, 2026

Automatic Metrics Not reported Accuracy Not reported
Why Models Know But Don't Say: Chain-of-Thought Faithfulness Divergence Between Thinking Tokens and Answers in Open-Weight Reasoning Models

Mar 27, 2026

Not reported MMLU, GPQA Faithfulness Not reported
Researcher Workflow (Detailed)

Checklist

  • Gap: Papers with explicit human feedback

    Coverage is a replication risk (12.2% vs 45% target).

  • Gap: Papers reporting quality controls

    Coverage is a replication risk (4.1% vs 30% target).

  • Gap: Papers naming benchmarks/datasets

    Coverage is a replication risk (8.2% vs 35% target).

  • Gap: Papers naming evaluation metrics

    Coverage is a replication risk (12.2% vs 35% target).

  • Gap: Papers with known rater population

    Coverage is a replication risk (14.3% vs 35% target).

  • Gap: Papers with known annotation unit

    Coverage is a replication risk (8.2% vs 35% target).

Strengths

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

Known Gaps

  • Only 4.1% of papers report quality controls; prioritize calibration/adjudication evidence.
  • Rater population is under-specified (14.3% coverage).
  • Annotation unit is under-specified (8.2% coverage).

Suggested Next Analyses

  • Pair this hub with a human_eval-heavy hub to validate judge-model calibration.
  • Stratify by benchmark (Codabench vs GSM8K) before comparing methods.
  • Track metric sensitivity by reporting both accuracy and precision.
  • Add inter-annotator agreement checks when reproducing these protocols.

Recommended Queries

Known Limitations
  • Only 4.1% of papers report quality controls; prioritize calibration/adjudication evidence.
  • Rater population is under-specified (14.3% 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 (18)
  • Llm As Judge (1)

Top Metrics

  • Accuracy (4)
  • Precision (1)
  • Recall (1)
  • Recall@k (1)

Top Benchmarks

  • Codabench (1)
  • GSM8K (1)
  • Olympiadbench (1)
  • Xpertbench (1)

Quality Controls

  • Calibration (2)

Papers In This Archive Slice

Recent Archive Slices

Need human evaluators for your AI research? Scale annotation with expert AI Trainers.