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HFEPX Archive Slice

HFEPX Weekly Archive: 2026-W15

Updated from current HFEPX corpus (Apr 9, 2026). 315 papers are grouped in this daily page.

Read Full Context

Updated from current HFEPX corpus (Apr 9, 2026). 315 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: 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 Apr 8, 2026.

Papers: 315 Last published: Apr 8, 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 315 papers).

High-Signal Coverage

100.0%

60 / 60 papers are not low-signal flagged.

Benchmark Anchors

16.7%

Papers with benchmark/dataset mentions in extraction output.

Metric Anchors

50.0%

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

  • 7.9% of papers report explicit human-feedback signals, led by pairwise preferences.
  • automatic metrics appears in 28.9% 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 rater calibration (2.2% 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
Personalized RewardBench: Evaluating Reward Models with Human Aligned Personalization

Apr 8, 2026

Human Eval, Automatic Metrics Rewardbench Accuracy, Helpfulness Not reported
TraceSafe: A Systematic Assessment of LLM Guardrails on Multi-Step Tool-Calling Trajectories

Apr 8, 2026

Automatic Metrics Tracesafe Bench Accuracy Not reported
SemEval-2026 Task 3: Dimensional Aspect-Based Sentiment Analysis (DimABSA)

Apr 8, 2026

Automatic Metrics Semeval F1 Not reported
ReDAct: Uncertainty-Aware Deferral for LLM Agents

Apr 8, 2026

Simulation Env ALFWorld Cost, Token cost Not reported
Gemma 4, Phi-4, and Qwen3: Accuracy-Efficiency Tradeoffs in Dense and MoE Reasoning Language Models

Apr 8, 2026

Automatic Metrics GSM8K, TruthfulQA Accuracy, Latency Not reported
MedDialBench: Benchmarking LLM Diagnostic Robustness under Parametric Adversarial Patient Behaviors

Apr 8, 2026

Automatic Metrics Meddialbench Accuracy Not reported
How Much LLM Does a Self-Revising Agent Actually Need?

Apr 8, 2026

Automatic Metrics Not reported F1, Win rate Not reported
Yale-DM-Lab at ArchEHR-QA 2026: Deterministic Grounding and Multi-Pass Evidence Alignment for EHR Question Answering

Apr 8, 2026

Automatic Metrics Not reported Accuracy, F1 Not reported
Self-Preference Bias in Rubric-Based Evaluation of Large Language Models

Apr 8, 2026

Llm As Judge IFEval, Healthbench Not reported Not reported
Appear2Meaning: A Cross-Cultural Benchmark for Structured Cultural Metadata Inference from Images

Apr 8, 2026

Llm As Judge, Automatic Metrics Not reported Accuracy, Exact match 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 (3.5% vs 30% target).

  • Gap: Papers naming benchmarks/datasets

    Coverage is a replication risk (3.8% vs 35% target).

  • Gap: Papers naming evaluation metrics

    Coverage is a replication risk (20% vs 35% target).

  • Gap: Papers with known rater population

    Coverage is a replication risk (7.9% vs 35% target).

  • Gap: Papers with known annotation unit

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

Strengths

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

Known Gaps

  • Only 3.5% of papers report quality controls; prioritize calibration/adjudication evidence.
  • Rater population is under-specified (7.9% coverage).
  • Annotation unit is under-specified (9.5% 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 BFCL) before comparing methods.
  • Track metric sensitivity by reporting both accuracy and cost.

Recommended Queries

Known Limitations
  • Only 3.5% of papers report quality controls; prioritize calibration/adjudication evidence.
  • Rater population is under-specified (7.9% 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 (91)
  • Simulation Env (11)
  • Human Eval (6)
  • Llm As Judge (6)

Top Metrics

  • Accuracy (30)
  • Cost (16)
  • Recall (8)
  • F1 (6)

Top Benchmarks

  • ALFWorld (1)
  • BFCL (1)
  • Full Duplex Bench (1)
  • Healthbench (1)

Quality Controls

  • Calibration (7)
  • Inter Annotator Agreement Reported (3)
  • Adjudication (2)
  • Gold Questions (2)

Papers In This Archive Slice

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