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HFEPX Archive Slice

HFEPX Daily Archive: 2025-10-08

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

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Updated from current HFEPX corpus (Apr 12, 2026). 12 papers are grouped in this daily page. Common evaluation modes: Automatic Metrics, Human Eval. Most common rater population: Domain Experts. Frequent quality control: Adjudication. 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 Oct 8, 2025.

Papers: 12 Last published: Oct 8, 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: Medium .

High-Signal Coverage

100.0%

12 / 12 papers are not low-signal flagged.

Benchmark Anchors

16.7%

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

  • 8.3% of papers report explicit human-feedback signals, led by pairwise preferences.
  • automatic metrics appears in 25% 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 adjudication (8.3% of papers).
  • Rater context is mostly domain experts, and annotation is commonly mixed annotation units; 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
EconCausal: A Context-Aware Causal Reasoning Benchmark for Large Language Models in Social Science

Oct 8, 2025

Automatic Metrics Not reported Accuracy, Cost Adjudication
Open ASR Leaderboard: Towards Reproducible and Transparent Multilingual and Long-Form Speech Recognition Evaluation

Oct 8, 2025

Automatic Metrics Not reported Accuracy, Error rate Not reported
Multi-hop Deep Joint Source-Channel Coding with Deep Hash Distillation for Semantically Aligned Image Recovery

Oct 8, 2025

Automatic Metrics Not reported Mse Not reported
FURINA: A Fully Customizable Role-Playing Benchmark via Scalable Multi-Agent Collaboration Pipeline

Oct 8, 2025

Human Eval Furina Bench Not reported Not reported
PIKA: Expert-Level Synthetic Datasets for Post-Training Alignment from Scratch

Oct 8, 2025

Not reported LMSYS Chatbot Arena, AlpacaEval Not reported Not reported
PATCH: Mitigating PII Leakage in Language Models with Privacy-Aware Targeted Circuit PatcHing

Oct 8, 2025

Not reported Not reported Recall Not reported
Biasless Language Models Learn Unnaturally: How LLMs Fail to Distinguish the Possible from the Impossible

Oct 8, 2025

Not reported Not reported Not reported Not reported
Search-R3: Unifying Reasoning and Embedding in Large Language Models

Oct 8, 2025

Not reported Not reported Not reported Not reported
LAD-RAG: Layout-aware Dynamic RAG for Visually-Rich Document Understanding

Oct 8, 2025

Not reported Not reported Not reported Not reported
Exposing Citation Vulnerabilities in Generative Engines

Oct 8, 2025

Not reported Not reported Not reported Not reported
Researcher Workflow (Detailed)

Checklist

  • Gap: Papers with explicit human feedback

    Coverage is a replication risk (8.3% vs 45% target).

  • Gap: Papers reporting quality controls

    Coverage is a replication risk (8.3% vs 30% target).

  • Moderate: Papers naming benchmarks/datasets

    Coverage is usable but incomplete (25% vs 35% target).

  • Moderate: Papers naming evaluation metrics

    Coverage is usable but incomplete (25% vs 35% target).

  • Gap: Papers with known rater population

    Coverage is a replication risk (8.3% vs 35% target).

  • Gap: Papers with known annotation unit

    Coverage is a replication risk (0% vs 35% target).

Strengths

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

Known Gaps

  • Only 8.3% of papers report quality controls; prioritize calibration/adjudication evidence.
  • Rater population is under-specified (8.3% coverage).
  • Annotation unit is under-specified (0% coverage).

Suggested Next Analyses

  • Pair this hub with llm_as_judge pages to benchmark automated-vs-human evaluation tradeoffs.
  • Stratify by benchmark (AlpacaEval vs AlpacaEval 2.0) 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 8.3% of papers report quality controls; prioritize calibration/adjudication evidence.
  • Rater population is under-specified (8.3% 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 (3)
  • Human Eval (1)

Top Metrics

  • Accuracy (2)
  • Cost (1)
  • Latency (1)
  • Recall (1)

Top Benchmarks

  • AlpacaEval (1)
  • AlpacaEval 2.0 (1)
  • Arena Hard (1)
  • DocVQA (1)

Quality Controls

  • Adjudication (1)

Papers In This Archive Slice

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