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HFEPX Archive Slice

HFEPX Daily Archive: 2026-02-14

Updated from current HFEPX corpus (Apr 12, 2026). 22 papers are grouped in this daily page.

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Updated from current HFEPX corpus (Apr 12, 2026). 22 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: Inter Annotator Agreement Reported. 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 Feb 14, 2026.

Papers: 22 Last published: Feb 14, 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%

22 / 22 papers are not low-signal flagged.

Benchmark Anchors

18.2%

Papers with benchmark/dataset mentions in extraction output.

Metric Anchors

63.6%

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

  • 31.8% of papers report explicit human-feedback signals, led by pairwise preferences.
  • automatic metrics appears in 45.5% 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 inter-annotator agreement reporting (4.5% 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
Elo-Evolve: A Co-evolutionary Framework for Language Model Alignment

Feb 14, 2026

Automatic Metrics MT Bench, AlpacaEval Elo Not reported
Pre-Editorial Normalization for Automatically Transcribed Medieval Manuscripts in Old French and Latin

Feb 14, 2026

Automatic Metrics Medieval Cer Not reported
Do Mixed-Vendor Multi-Agent LLMs Improve Clinical Diagnosis?

Feb 14, 2026

Automatic Metrics Rarebench, Diagnosisarena Accuracy, Recall Not reported
From Pixels to Policies: Reinforcing Spatial Reasoning in Language Models for Content-Aware Layout Design

Feb 14, 2026

Simulation Env Not reported Latency Not reported
ADAB: Arabic Dataset for Automated Politeness Benchmarking -- A Large-Scale Resource for Computational Sociopragmatics

Feb 14, 2026

Automatic Metrics Not reported Kappa, Agreement Inter Annotator Agreement Reported
Bridging the Multilingual Safety Divide: Efficient, Culturally-Aware Alignment for Global South Languages

Feb 14, 2026

Automatic Metrics Not reported Toxicity Not reported
PrivAct: Internalizing Contextual Privacy Preservation via Multi-Agent Preference Training

Feb 14, 2026

Automatic Metrics Not reported Helpfulness Not reported
Evaluating Adjective-Noun Compositionality in LLMs: Functional vs Representational Perspectives

Feb 14, 2026

Automatic Metrics Not reported Task success Not reported
Tutoring Large Language Models to be Domain-adaptive, Precise, and Safe

Feb 14, 2026

Not reported Not reported Precision Not reported
Beyond Words: Evaluating and Bridging Epistemic Divergence in User-Agent Interaction via Theory of Mind

Feb 14, 2026

Automatic Metrics Not reported Task success Not reported
Researcher Workflow (Detailed)

Checklist

  • Moderate: Papers with explicit human feedback

    Coverage is usable but incomplete (31.8% vs 45% target).

  • Gap: Papers reporting quality controls

    Coverage is a replication risk (4.5% vs 30% target).

  • Gap: Papers naming benchmarks/datasets

    Coverage is a replication risk (9.1% vs 35% target).

  • Strong: Papers naming evaluation metrics

    Coverage is strong (36.4% vs 35% target).

  • Gap: Papers with known rater population

    Coverage is a replication risk (9.1% vs 35% target).

  • Moderate: Papers with known annotation unit

    Coverage is usable but incomplete (22.7% vs 35% target).

Strengths

  • Agentic evaluation appears in 31.8% of papers.

Known Gaps

  • Only 4.5% of papers report quality controls; prioritize calibration/adjudication evidence.
  • Rater population is under-specified (9.1% coverage).
  • Annotation unit is under-specified (22.7% coverage).

Suggested Next Analyses

  • Pair this hub with a human_eval-heavy hub to validate judge-model calibration.
  • Stratify by benchmark (AlpacaEval vs AlpacaEval 2.0) before comparing methods.
  • Track metric sensitivity by reporting both accuracy and coherence.

Recommended Queries

Known Limitations
  • Only 4.5% of papers report quality controls; prioritize calibration/adjudication evidence.
  • Rater population is under-specified (9.1% 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 (10)
  • Simulation Env (3)
  • Llm As Judge (1)

Top Metrics

  • Accuracy (1)
  • Coherence (1)
  • Elo (1)
  • F1 (1)

Top Benchmarks

  • AlpacaEval (1)
  • AlpacaEval 2.0 (1)
  • Diagnosisarena (1)
  • MT Bench (1)

Quality Controls

  • Inter Annotator Agreement Reported (1)

Papers In This Archive Slice

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