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HFEPX Archive Slice

HFEPX Daily Archive: 2025-10-09

Updated from current HFEPX corpus (Apr 17, 2026). 22 papers are grouped in this daily page.

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Updated from current HFEPX corpus (Apr 17, 2026). 22 papers are grouped in this daily page. Common evaluation modes: Automatic Metrics, Llm As Judge. Most common rater population: Domain Experts. Common annotation unit: Trajectory. 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 9, 2025.

Papers: 22 Last published: Oct 9, 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 .

High-Signal Coverage

100.0%

22 / 22 papers are not low-signal flagged.

Benchmark Anchors

13.6%

Papers with benchmark/dataset mentions in extraction output.

Metric Anchors

36.4%

Papers with reported metric mentions in extraction output.

  • 0 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

  • 18.2% of papers report explicit human-feedback signals, led by red-team protocols.
  • automatic metrics appears in 36.4% of papers in this hub.
  • AIME is a recurring benchmark anchor for cross-paper comparisons in this page.

Protocol Takeaways For This Period

  • Quality-control reporting is sparse in this slice; prioritize papers with explicit calibration or adjudication steps.
  • 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
How Reliable is Language Model Micro-Benchmarking?

Oct 9, 2025

Automatic Metrics MMLU, MMLU Pro Accuracy, Cost Not reported
DeepPrune: Parallel Scaling without Inter-trace Redundancy

Oct 9, 2025

Llm As Judge, Automatic Metrics MATH 500, GPQA Accuracy, Auroc Not reported
Augmenting Rating-Scale Measures with Text-Derived Items Using the Information-Determined Scoring (IDS) Framework

Oct 9, 2025

Automatic Metrics, Simulation Env Not reported Accuracy, Precision Not reported
MOSAIC: Multi-agent Orchestration for Task-Intelligent Scientific Coding

Oct 9, 2025

Automatic Metrics Not reported Accuracy Not reported
If Probable, Then Acceptable? Understanding Conditional Acceptability Judgments in Large Language Models

Oct 9, 2025

Automatic Metrics Not reported Relevance Not reported
Emotionally Charged, Logically Blurred: AI-driven Emotional Framing Impairs Human Fallacy Detection

Oct 9, 2025

Automatic Metrics Not reported F1 Not reported
Lossless Vocabulary Reduction for Auto-Regressive Language Models

Oct 9, 2025

Automatic Metrics Not reported Accuracy Not reported
AdaSwitch: Balancing Exploration and Guidance in Knowledge Distillation via Adaptive Switching

Oct 9, 2025

Automatic Metrics Not reported Accuracy, Latency Not reported
How Many Code and Test Cases Are Enough? Evaluating Test Cases Generation from a Binary-Matrix Perspective

Oct 9, 2025

Not reported Tc Bench Not reported Not reported
Fewer Weights, More Problems: A Practical Attack on LLM Pruning

Oct 9, 2025

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

Checklist

  • Gap: Papers with explicit human feedback

    Coverage is a replication risk (18.2% vs 45% target).

  • Gap: Papers reporting quality controls

    Coverage is a replication risk (0% vs 30% target).

  • Gap: Papers naming benchmarks/datasets

    Coverage is a replication risk (13.6% vs 35% target).

  • Moderate: Papers naming evaluation metrics

    Coverage is usable but incomplete (31.8% vs 35% target).

  • Gap: Papers with known rater population

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

  • Gap: Papers with known annotation unit

    Coverage is a replication risk (18.2% vs 35% target).

Strengths

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

Known Gaps

  • Only 0% of papers report quality controls; prioritize calibration/adjudication evidence.
  • Rater population is under-specified (9.1% coverage).
  • Annotation unit is under-specified (18.2% coverage).

Suggested Next Analyses

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

Recommended Queries

Known Limitations
  • Only 0% 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 (8)
  • Llm As Judge (1)
  • Simulation Env (1)

Top Metrics

  • Accuracy (6)
  • Auroc (1)
  • Cost (1)
  • Perplexity (1)

Top Benchmarks

  • AIME (1)
  • BBH (1)
  • BIG Bench (1)
  • GPQA (1)

Quality Controls

Papers In This Archive Slice

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