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HFEPX Archive Slice

HFEPX Weekly Archive: 2026-W11

Updated from current HFEPX corpus (Apr 17, 2026). 703 papers are grouped in this daily page.

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Updated from current HFEPX corpus (Apr 17, 2026). 703 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: DROP. 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 Mar 15, 2026.

Papers: 703 Last published: Mar 15, 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 .

Analysis blocks are computed from the loaded sample (60 of 703 papers).

High-Signal Coverage

100.0%

60 / 60 papers are not low-signal flagged.

Benchmark Anchors

18.3%

Papers with benchmark/dataset mentions in extraction output.

Metric Anchors

46.7%

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

  • 7% of papers report explicit human-feedback signals, led by pairwise preferences.
  • automatic metrics appears in 18.2% of papers in this hub.
  • DROP 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% 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
SemEval-2026 Task 6: CLARITY -- Unmasking Political Question Evasions

Mar 14, 2026

Automatic Metrics Semeval F1, F1 macro Not reported
Top-b: Entropic Regulation of Relative Probability Bands in Autoregressive Language Processes

Mar 15, 2026

Automatic Metrics GSM8K, GPQA Accuracy Not reported
CangjieBench: Benchmarking LLMs on a Low-Resource General-Purpose Programming Language

Mar 15, 2026

Automatic Metrics HumanEval+, Cangjiebench Accuracy, Cost Not reported
MedPriv-Bench: Benchmarking the Privacy-Utility Trade-off of Large Language Models in Medical Open-End Question Answering

Mar 15, 2026

Automatic Metrics Medpriv Bench Accuracy Not reported
The Reasoning Bottleneck in Graph-RAG: Structured Prompting and Context Compression for Multi-Hop QA

Mar 14, 2026

Automatic Metrics HotpotQA Accuracy, Cost Not reported
FLUX: Data Worth Training On

Mar 14, 2026

Automatic Metrics MMLU Accuracy, Cost Not reported
ToolFlood: Beyond Selection -- Hiding Valid Tools from LLM Agents via Semantic Covering

Mar 14, 2026

Automatic Metrics ToolBench Success rate, Jailbreak success rate Not reported
An Industrial-Scale Insurance LLM Achieving Verifiable Domain Mastery and Hallucination Control without Competence Trade-offs

Mar 15, 2026

Automatic Metrics Not reported Hallucination rate Not reported
Exposing Long-Tail Safety Failures in Large Language Models through Efficient Diverse Response Sampling

Mar 15, 2026

Automatic Metrics Not reported Cost Not reported
Step-CoT: Stepwise Visual Chain-of-Thought for Medical Visual Question Answering

Mar 14, 2026

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

Checklist

  • Gap: Papers with explicit human feedback

    Coverage is a replication risk (7% vs 45% target).

  • Gap: Papers reporting quality controls

    Coverage is a replication risk (2% vs 30% target).

  • Gap: Papers naming benchmarks/datasets

    Coverage is a replication risk (8.8% vs 35% target).

  • Moderate: Papers naming evaluation metrics

    Coverage is usable but incomplete (30.4% vs 35% target).

  • Gap: Papers with known rater population

    Coverage is a replication risk (6% vs 35% target).

  • Gap: Papers with known annotation unit

    Coverage is a replication risk (4.8% vs 35% target).

Strengths

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

Known Gaps

  • Only 2% of papers report quality controls; prioritize calibration/adjudication evidence.
  • Rater population is under-specified (6% coverage).
  • Annotation unit is under-specified (4.8% coverage).

Suggested Next Analyses

  • Compare papers that report both human_eval and llm_as_judge to quantify judge-human agreement drift.
  • Stratify by benchmark (DROP vs AIME) 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% of papers report quality controls; prioritize calibration/adjudication evidence.
  • Rater population is under-specified (6% 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 (128)
  • Simulation Env (13)
  • Llm As Judge (8)
  • Human Eval (6)

Top Metrics

  • Accuracy (98)
  • Cost (46)
  • Latency (22)
  • Precision (15)

Top Benchmarks

  • DROP (3)
  • AIME (2)
  • GPQA (2)
  • GSM8K (2)

Quality Controls

  • Calibration (7)
  • Gold Questions (4)
  • Adjudication (3)

Papers In This Archive Slice

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