Skip to content
← Back to explorer

HFEPX Archive Slice

HFEPX Daily Archive: 2026-03-10

Updated from current HFEPX corpus (Apr 12, 2026). 119 papers are grouped in this daily page.

Read Full Context

Updated from current HFEPX corpus (Apr 12, 2026). 119 papers are grouped in this daily page. Common evaluation modes: Automatic Metrics, Simulation Env. Most common rater population: Domain Experts. Common annotation unit: Ranking. Frequent quality control: Adjudication. Frequently cited benchmark: ALFWorld. 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 10, 2026.

Papers: 119 Last published: Mar 10, 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 119 papers).

High-Signal Coverage

100.0%

60 / 60 papers are not low-signal flagged.

Benchmark Anchors

5.0%

Papers with benchmark/dataset mentions in extraction output.

Metric Anchors

18.3%

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.

Get this digest every Friday →

Subscribe

Why This Time Slice Matters

  • 5.9% of papers report explicit human-feedback signals, led by expert verification.
  • automatic metrics appears in 20.2% of papers in this hub.
  • ALFWorld is a recurring benchmark anchor for cross-paper comparisons in this page.

Protocol Takeaways For This Period

  • Most common quality-control signal is adjudication (0.8% of papers).
  • Rater context is mostly domain experts, and annotation is commonly ranking 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
Adaptive Activation Cancellation for Hallucination Mitigation in Large Language Models

Mar 10, 2026

Automatic Metrics MMLU, TruthfulQA Accuracy, F1 Not reported
BEACON: Language-Conditioned Navigation Affordance Prediction under Occlusion

Mar 10, 2026

Automatic Metrics, Simulation Env BIRD Accuracy Not reported
Sabiá-4 Technical Report

Mar 10, 2026

Automatic Metrics Not reported Accuracy, Cost Not reported
Calibration-Reasoning Framework for Descriptive Speech Quality Assessment

Mar 10, 2026

Automatic Metrics Not reported Accuracy Calibration
Reason and Verify: A Framework for Faithful Retrieval-Augmented Generation

Mar 10, 2026

Automatic Metrics Not reported Accuracy, Faithfulness Not reported
S-GRADES -- Studying Generalization of Student Response Assessments in Diverse Evaluative Settings

Mar 10, 2026

Automatic Metrics Not reported Coherence Not reported
ViDia2Std: A Parallel Corpus and Methods for Low-Resource Vietnamese Dialect-to-Standard Translation

Mar 10, 2026

Automatic Metrics Not reported Bleu, Rouge Not reported
Video-Based Reward Modeling for Computer-Use Agents

Mar 10, 2026

Automatic Metrics Not reported Accuracy, Recall Not reported
Model Merging in the Era of Large Language Models: Methods, Applications, and Future Directions

Mar 10, 2026

Automatic Metrics Not reported Cost Not reported
MedMASLab: A Unified Orchestration Framework for Benchmarking Multimodal Medical Multi-Agent Systems

Mar 10, 2026

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

Checklist

  • Gap: Papers with explicit human feedback

    Coverage is a replication risk (5.9% vs 45% target).

  • Gap: Papers reporting quality controls

    Coverage is a replication risk (2.5% vs 30% target).

  • Gap: Papers naming benchmarks/datasets

    Coverage is a replication risk (8.4% vs 35% target).

  • Moderate: Papers naming evaluation metrics

    Coverage is usable but incomplete (30.3% vs 35% target).

  • Gap: Papers with known rater population

    Coverage is a replication risk (5.9% vs 35% target).

  • Gap: Papers with known annotation unit

    Coverage is a replication risk (5% vs 35% target).

Strengths

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

Known Gaps

  • Only 2.5% of papers report quality controls; prioritize calibration/adjudication evidence.
  • Rater population is under-specified (5.9% coverage).
  • Annotation unit is under-specified (5% coverage).

Suggested Next Analyses

  • Compare papers that report both human_eval and llm_as_judge to quantify judge-human agreement drift.
  • Stratify by benchmark (ALFWorld vs BIRD) 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 2.5% of papers report quality controls; prioritize calibration/adjudication evidence.
  • Rater population is under-specified (5.9% 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 (24)
  • Simulation Env (4)
  • Human Eval (1)
  • Llm As Judge (1)

Top Metrics

  • Accuracy (18)
  • Cost (7)
  • Recall (5)
  • Precision (4)

Top Benchmarks

  • ALFWorld (1)
  • BIRD (1)
  • CruxEval (1)
  • EsoLang Bench (1)

Quality Controls

  • Adjudication (1)
  • Calibration (1)
  • Gold Questions (1)

Papers In This Archive Slice

Recent Archive Slices

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