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HFEPX Archive Slice

HFEPX Fortnight Archive: 2025-F11

Updated from current HFEPX corpus (Apr 12, 2026). 97 papers are grouped in this daily page.

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Updated from current HFEPX corpus (Apr 12, 2026). 97 papers are grouped in this daily page. Common evaluation modes: Automatic Metrics, Llm As Judge. Most common rater population: Domain Experts. Common annotation unit: Trajectory. Frequent quality control: Calibration. Frequently cited benchmark: AdvBench. 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 Jun 1, 2025.

Papers: 97 Last published: Jun 1, 2025 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 97 papers).

High-Signal Coverage

100.0%

60 / 60 papers are not low-signal flagged.

Benchmark Anchors

10.0%

Papers with benchmark/dataset mentions in extraction output.

Metric Anchors

25.0%

Papers with reported metric mentions in extraction output.

  • 0 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

  • 21.6% of papers report explicit human-feedback signals, led by pairwise preferences.
  • automatic metrics appears in 24.7% of papers in this hub.
  • AdvBench 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 (2.1% of papers).
  • Rater context is mostly domain experts, and annotation is commonly trajectory-level annotation; use this to scope replication staffing.
  • Pair this hub with a human_eval-heavy hub to validate judge-model calibration.

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
RedTeamCUA: Realistic Adversarial Testing of Computer-Use Agents in Hybrid Web-OS Environments

May 28, 2025

Automatic Metrics Rtc Bench Jailbreak success rate Not reported
Just as Humans Need Vaccines, So Do Models: Model Immunization to Combat Falsehoods

May 23, 2025

Automatic Metrics TruthfulQA Accuracy Not reported
SealQA: Raising the Bar for Reasoning in Search-Augmented Language Models

Jun 1, 2025

Automatic Metrics Needle In A Haystack Accuracy Not reported
Reviewing Scientific Papers for Critical Problems With Reasoning LLMs: Baseline Approaches and Automatic Evaluation

May 28, 2025

Automatic Metrics Not reported Cost Not reported
Incentivizing Strong Reasoning from Weak Supervision

May 26, 2025

Automatic Metrics Not reported Cost Not reported
Bayesian Attention Mechanism: A Probabilistic Framework for Positional Encoding and Context Length Extrapolation

May 28, 2025

Automatic Metrics Not reported Accuracy, Perplexity Not reported
Flying Pigs, FaR and Beyond: Evaluating LLM Reasoning in Counterfactual Worlds

May 28, 2025

Automatic Metrics Not reported Accuracy Not reported
RefTool: Reference-Guided Tool Creation for Knowledge-Intensive Reasoning

May 27, 2025

Automatic Metrics Not reported Accuracy, Cost Not reported
VLM-3R: Vision-Language Models Augmented with Instruction-Aligned 3D Reconstruction

May 26, 2025

Automatic Metrics Not reported Accuracy Not reported
Inference-time Alignment in Continuous Space

May 26, 2025

Not reported AdvBench Not reported Not reported
Researcher Workflow (Detailed)

Checklist

  • Gap: Papers with explicit human feedback

    Coverage is a replication risk (21.6% vs 45% target).

  • Gap: Papers reporting quality controls

    Coverage is a replication risk (2.1% vs 30% target).

  • Gap: Papers naming benchmarks/datasets

    Coverage is a replication risk (9.3% vs 35% target).

  • Moderate: Papers naming evaluation metrics

    Coverage is usable but incomplete (23.7% vs 35% target).

  • Gap: Papers with known rater population

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

  • Gap: Papers with known annotation unit

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

Strengths

  • This hub still surfaces a concentrated paper set for protocol triage and replication planning.

Known Gaps

  • Only 2.1% of papers report quality controls; prioritize calibration/adjudication evidence.
  • Rater population is under-specified (7.2% coverage).
  • Annotation unit is under-specified (7.2% coverage).

Suggested Next Analyses

  • Pair this hub with a human_eval-heavy hub to validate judge-model calibration.
  • Stratify by benchmark (AdvBench vs ALFWorld) 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 2.1% of papers report quality controls; prioritize calibration/adjudication evidence.
  • Rater population is under-specified (7.2% 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)
  • Llm As Judge (2)
  • Simulation Env (2)

Top Metrics

  • Accuracy (16)
  • Cost (6)
  • Recall (3)
  • Jailbreak success rate (2)

Top Benchmarks

  • AdvBench (1)
  • ALFWorld (1)
  • DROP (1)
  • HotpotQA (1)

Quality Controls

  • Calibration (2)

Papers In This Archive Slice

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