Skip to content
← Back to explorer

HFEPX Archive Slice

HFEPX Weekly Archive: 2026-W14

Updated from current HFEPX corpus (Apr 12, 2026). 371 papers are grouped in this daily page.

Read Full Context

Updated from current HFEPX corpus (Apr 12, 2026). 371 papers are grouped in this daily page. Common evaluation modes: Automatic Metrics, Human Eval. 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 Apr 5, 2026.

Papers: 371 Last published: Apr 5, 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 371 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

38.3%

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.

Get this digest every Friday →

Subscribe

Why This Time Slice Matters

  • 12.4% of papers report explicit human-feedback signals, led by pairwise preferences.
  • automatic metrics appears in 34% 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 (3.8% 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
Brief Is Better: Non-Monotonic Chain-of-Thought Budget Effects in Function-Calling Language Agents

Apr 2, 2026

Automatic Metrics BFCL Accuracy Not reported
GaelEval: Benchmarking LLM Performance for Scottish Gaelic

Apr 2, 2026

Automatic Metrics Gaeleval Accuracy Not reported
LLM-as-a-Judge for Time Series Explanations

Apr 2, 2026

Llm As Judge, Automatic Metrics DROP Accuracy, Faithfulness Not reported
Reliable Control-Point Selection for Steering Reasoning in Large Language Models

Apr 2, 2026

Automatic Metrics MATH 500 Accuracy Not reported
Blinded Radiologist and LLM-Based Evaluation of LLM-Generated Japanese Translations of Chest CT Reports: Comparative Study

Apr 2, 2026

Llm As Judge, Automatic Metrics Not reported Accuracy Not reported
SEAL: An Open, Auditable, and Fair Data Generation Framework for AI-Native 6G Networks

Apr 2, 2026

Automatic Metrics, Simulation Env Not reported Accuracy, Latency Calibration
Optimizing RAG Rerankers with LLM Feedback via Reinforcement Learning

Apr 2, 2026

Automatic Metrics Not reported Relevance Not reported
Diff-KD: Diffusion-based Knowledge Distillation for Collaborative Perception under Corruptions

Apr 2, 2026

Automatic Metrics Not reported Accuracy Calibration
StoryScope: Investigating idiosyncrasies in AI fiction

Apr 3, 2026

Automatic Metrics Not reported F1, F1 macro Not reported
JoyAI-LLM Flash: Advancing Mid-Scale LLMs with Token Efficiency

Apr 3, 2026

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

Checklist

  • Gap: Papers with explicit human feedback

    Coverage is a replication risk (12.4% vs 45% target).

  • Gap: Papers reporting quality controls

    Coverage is a replication risk (6.7% vs 30% target).

  • Gap: Papers naming benchmarks/datasets

    Coverage is a replication risk (5.7% vs 35% target).

  • Gap: Papers naming evaluation metrics

    Coverage is a replication risk (18.3% vs 35% target).

  • Gap: Papers with known rater population

    Coverage is a replication risk (11.3% vs 35% target).

  • Gap: Papers with known annotation unit

    Coverage is a replication risk (12.9% vs 35% target).

Strengths

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

Known Gaps

  • Only 6.7% of papers report quality controls; prioritize calibration/adjudication evidence.
  • Rater population is under-specified (11.3% coverage).
  • Annotation unit is under-specified (12.9% 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 GSM8K) before comparing methods.
  • Track metric sensitivity by reporting both accuracy and cost.

Recommended Queries

Known Limitations
  • Only 6.7% of papers report quality controls; prioritize calibration/adjudication evidence.
  • Rater population is under-specified (11.3% 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 (126)
  • Human Eval (7)
  • Llm As Judge (6)
  • Simulation Env (6)

Top Metrics

  • Accuracy (31)
  • Cost (16)
  • F1 (7)
  • Agreement (6)

Top Benchmarks

  • DROP (2)
  • GSM8K (2)
  • WebArena (2)
  • AgentSocialBench (1)

Quality Controls

  • Calibration (14)
  • Inter Annotator Agreement Reported (8)
  • Adjudication (3)
  • Gold Questions (3)

Papers In This Archive Slice

Recent Archive Slices

Need human evaluators for your AI research? Scale annotation with expert AI Trainers.