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HFEPX Archive Slice

HFEPX Fortnight Archive: 2025-F18

Updated from current HFEPX corpus (Apr 17, 2026). 42 papers are grouped in this daily page.

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Updated from current HFEPX corpus (Apr 17, 2026). 42 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. Frequent quality control: Calibration. Frequently cited benchmark: BrowseComp. 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 Sep 7, 2025.

Papers: 42 Last published: Sep 7, 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%

42 / 42 papers are not low-signal flagged.

Benchmark Anchors

11.9%

Papers with benchmark/dataset mentions in extraction output.

Metric Anchors

40.5%

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

  • 11.9% of papers report explicit human-feedback signals, led by demonstration data.
  • automatic metrics appears in 42.9% of papers in this hub.
  • BrowseComp 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 (2.4% 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
Dyslexify: A Mechanistic Defense Against Typographic Attacks in CLIP

Aug 28, 2025

Automatic Metrics DROP Accuracy Not reported
AgentCoMa: A Compositional Benchmark Mixing Commonsense and Mathematical Reasoning in Real-World Scenarios

Aug 27, 2025

Automatic Metrics MATH Accuracy Not reported
Diffusion Language Models Know the Answer Before Decoding

Aug 27, 2025

Automatic Metrics MMLU, GSM8K Cost Not reported
Hybrid Deep Searcher: Scalable Parallel and Sequential Search Reasoning

Aug 26, 2025

Automatic Metrics Reasoning Query0retrieval F1 Not reported
Agri-Query: A Case Study on RAG vs. Long-Context LLMs for Cross-Lingual Technical Question Answering

Aug 25, 2025

Llm As Judge, Automatic Metrics Needle In A Haystack Accuracy Not reported
Error Notebook-Guided, Training-Free Part Retrieval in 3D CAD Assemblies via Vision-Language Models

Sep 1, 2025

Automatic Metrics Not reported Accuracy Not reported
LaTeXTrans: Structured LaTeX Translation with Multi-Agent Coordination

Aug 26, 2025

Automatic Metrics Not reported Accuracy Not reported
New Insights into Optimal Alignment of Acoustic and Linguistic Representations for Knowledge Transfer in ASR

Sep 6, 2025

Automatic Metrics Not reported Precision, Recall Not reported
No Text Needed: Forecasting MT Quality and Inequity from Fertility and Metadata

Sep 5, 2025

Automatic Metrics Not reported Accuracy Not reported
Do LLMs Adhere to Label Definitions? Examining Their Receptivity to External Label Definitions

Sep 2, 2025

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

Checklist

  • Gap: Papers with explicit human feedback

    Coverage is a replication risk (11.9% vs 45% target).

  • Gap: Papers reporting quality controls

    Coverage is a replication risk (2.4% vs 30% target).

  • Gap: Papers naming benchmarks/datasets

    Coverage is a replication risk (9.5% vs 35% target).

  • Moderate: Papers naming evaluation metrics

    Coverage is usable but incomplete (26.2% vs 35% target).

  • Gap: Papers with known rater population

    Coverage is a replication risk (11.9% vs 35% target).

  • Gap: Papers with known annotation unit

    Coverage is a replication risk (2.4% vs 35% target).

Strengths

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

Known Gaps

  • Only 2.4% of papers report quality controls; prioritize calibration/adjudication evidence.
  • Rater population is under-specified (11.9% coverage).
  • Annotation unit is under-specified (2.4% coverage).

Suggested Next Analyses

  • Pair this hub with a human_eval-heavy hub to validate judge-model calibration.
  • Stratify by benchmark (BrowseComp vs DROP) 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 2.4% of papers report quality controls; prioritize calibration/adjudication evidence.
  • Rater population is under-specified (11.9% 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 (2)
  • Simulation Env (2)

Top Metrics

  • Accuracy (6)
  • Cost (2)
  • F1 (2)
  • Calibration error (1)

Top Benchmarks

  • BrowseComp (1)
  • DROP (1)
  • MMLU (1)
  • Needle In A Haystack (1)

Quality Controls

  • Calibration (1)

Papers In This Archive Slice

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