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HFEPX Archive Slice

HFEPX Fortnight Archive: 2025-F20

Updated from current HFEPX corpus (Apr 12, 2026). 184 papers are grouped in this daily page.

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Updated from current HFEPX corpus (Apr 12, 2026). 184 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: AIME. 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 5, 2025.

Papers: 184 Last published: Oct 5, 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: High .

Analysis blocks are computed from the loaded sample (60 of 184 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

31.7%

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

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Why This Time Slice Matters

  • 13.6% of papers report explicit human-feedback signals, led by pairwise preferences.
  • automatic metrics appears in 30.4% of papers in this hub.
  • AIME 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 (1.6% 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
Don't Pass@k: A Bayesian Framework for Large Language Model Evaluation

Oct 5, 2025

Automatic Metrics, Simulation Env AIME Accuracy, Pass@k Not reported
FOR-Prompting: From Objection to Revision via an Asymmetric Prompting Protocol

Oct 2, 2025

Automatic Metrics GSM8K Accuracy Not reported
MENLO: From Preferences to Proficiency -- Evaluating and Modeling Native-like Quality Across 47 Languages

Sep 30, 2025

Automatic Metrics Not reported Agreement Inter Annotator Agreement Reported
PoLi-RL: A Point-to-List Reinforcement Learning Framework for Conditional Semantic Textual Similarity

Oct 5, 2025

Automatic Metrics Not reported Spearman Not reported
PrefDisco: Benchmarking Proactive Personalized Reasoning

Sep 30, 2025

Automatic Metrics Not reported Accuracy Not reported
EditReward: A Human-Aligned Reward Model for Instruction-Guided Image Editing

Sep 30, 2025

Llm As Judge Genai Bench, Aurora Bench Not reported Not reported
Large Language Models Hallucination: A Comprehensive Survey

Oct 5, 2025

Automatic Metrics Not reported Accuracy, Cost Not reported
Slow-Fast Policy Optimization: Reposition-Before-Update for LLM Reasoning

Oct 5, 2025

Automatic Metrics Not reported Accuracy Not reported
Token Hidden Reward: Steering Exploration-Exploitation in Group Relative Deep Reinforcement Learning

Oct 4, 2025

Automatic Metrics Not reported Accuracy, Pass@k Not reported
Cache-to-Cache: Direct Semantic Communication Between Large Language Models

Oct 3, 2025

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

Checklist

  • Gap: Papers with explicit human feedback

    Coverage is a replication risk (13.6% vs 45% target).

  • Gap: Papers reporting quality controls

    Coverage is a replication risk (2.7% vs 30% target).

  • Gap: Papers naming benchmarks/datasets

    Coverage is a replication risk (7.6% vs 35% target).

  • Moderate: Papers naming evaluation metrics

    Coverage is usable but incomplete (21.7% vs 35% target).

  • Gap: Papers with known rater population

    Coverage is a replication risk (6.5% vs 35% target).

  • Gap: Papers with known annotation unit

    Coverage is a replication risk (11.4% vs 35% target).

Strengths

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

Known Gaps

  • Only 2.7% of papers report quality controls; prioritize calibration/adjudication evidence.
  • Rater population is under-specified (6.5% coverage).
  • Annotation unit is under-specified (11.4% coverage).

Suggested Next Analyses

  • Compare papers that report both human_eval and llm_as_judge to quantify judge-human agreement drift.
  • Stratify by benchmark (AIME vs GSM8K) before comparing methods.
  • Track metric sensitivity by reporting both accuracy and recall.

Recommended Queries

Known Limitations
  • Only 2.7% of papers report quality controls; prioritize calibration/adjudication evidence.
  • Rater population is under-specified (6.5% 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 (56)
  • Simulation Env (6)
  • Llm As Judge (5)
  • Human Eval (1)

Top Metrics

  • Accuracy (25)
  • Recall (6)
  • Precision (4)
  • Cost (3)

Top Benchmarks

  • AIME (3)
  • GSM8K (2)
  • MT Bench (2)
  • AlpacaEval (1)

Quality Controls

  • Calibration (3)
  • Inter Annotator Agreement Reported (2)

Papers In This Archive Slice

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