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HFEPX Archive Slice

HFEPX Quarterly Archive: 2026-Q2

Updated from current HFEPX corpus (Apr 12, 2026). 711 papers are grouped in this daily page.

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Updated from current HFEPX corpus (Apr 12, 2026). 711 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: GSM8K. 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 Apr 9, 2026.

Papers: 711 Last published: Apr 9, 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 711 papers).

High-Signal Coverage

100.0%

60 / 60 papers are not low-signal flagged.

Benchmark Anchors

6.7%

Papers with benchmark/dataset mentions in extraction output.

Metric Anchors

20.0%

Papers with reported metric mentions in extraction output.

  • 2 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

  • 7.9% of papers report explicit human-feedback signals, led by pairwise preferences.
  • automatic metrics appears in 25.5% of papers in this hub.
  • GSM8K 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 (2.3% 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
AVGen-Bench: A Task-Driven Benchmark for Multi-Granular Evaluation of Text-to-Audio-Video Generation

Apr 9, 2026

Automatic Metrics Avgen Bench, Avgenbench Coherence Not reported
Don't Overthink It: Inter-Rollout Action Agreement as a Free Adaptive-Compute Signal for LLM Agents

Apr 9, 2026

Automatic Metrics GSM8K Accuracy Not reported
AfriVoices-KE: A Multilingual Speech Dataset for Kenyan Languages

Apr 9, 2026

Automatic Metrics Not reported Accuracy Calibration
Act Wisely: Cultivating Meta-Cognitive Tool Use in Agentic Multimodal Models

Apr 9, 2026

Automatic Metrics Not reported Accuracy, Latency Not reported
Cram Less to Fit More: Training Data Pruning Improves Memorization of Facts

Apr 9, 2026

Automatic Metrics Not reported Accuracy Not reported
What do Language Models Learn and When? The Implicit Curriculum Hypothesis

Apr 9, 2026

Automatic Metrics Not reported Accuracy Not reported
Entropy-Gradient Grounding: Training-Free Evidence Retrieval in Vision-Language Models

Apr 9, 2026

Automatic Metrics Not reported Relevance Not reported
KV Cache Offloading for Context-Intensive Tasks

Apr 9, 2026

Automatic Metrics Not reported Accuracy, Latency Not reported
Verify Before You Commit: Towards Faithful Reasoning in LLM Agents via Self-Auditing

Apr 9, 2026

Automatic Metrics Not reported Faithfulness Not reported
A GAN and LLM-Driven Data Augmentation Framework for Dynamic Linguistic Pattern Modeling in Chinese Sarcasm Detection

Apr 9, 2026

Automatic Metrics Not reported F1 Not reported
Researcher Workflow (Detailed)

Checklist

  • Gap: Papers with explicit human feedback

    Coverage is a replication risk (7.9% vs 45% target).

  • Gap: Papers reporting quality controls

    Coverage is a replication risk (3.4% vs 30% target).

  • Gap: Papers naming benchmarks/datasets

    Coverage is a replication risk (7.2% vs 35% target).

  • Moderate: Papers naming evaluation metrics

    Coverage is usable but incomplete (23.6% vs 35% target).

  • Gap: Papers with known rater population

    Coverage is a replication risk (7.3% vs 35% target).

  • Gap: Papers with known annotation unit

    Coverage is a replication risk (9.6% vs 35% target).

Strengths

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

Known Gaps

  • Only 3.4% of papers report quality controls; prioritize calibration/adjudication evidence.
  • Rater population is under-specified (7.3% coverage).
  • Annotation unit is under-specified (9.6% coverage).

Suggested Next Analyses

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

Recommended Queries

Known Limitations
  • Only 3.4% of papers report quality controls; prioritize calibration/adjudication evidence.
  • Rater population is under-specified (7.3% 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 (181)
  • Simulation Env (16)
  • Llm As Judge (12)
  • Human Eval (9)

Top Metrics

  • Accuracy (76)
  • Cost (37)
  • F1 (15)
  • Latency (14)

Top Benchmarks

  • GSM8K (5)
  • MMLU (5)
  • BFCL (3)
  • DROP (3)

Quality Controls

  • Calibration (16)
  • Inter Annotator Agreement Reported (6)
  • Adjudication (4)
  • Gold Questions (3)

Papers In This Archive Slice

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