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HFEPX Fortnight Archive: 2025-F06

Updated from current HFEPX corpus (Feb 27, 2026). 6 papers are grouped in this daily page. Common evaluation modes: Automatic Metrics, Simulation Env. Most common rater population: Domain Experts. Common annotation unit: Trajectory. Frequently cited benchmark: Retrieval. 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 23, 2025.

Papers: 6 Last published: Mar 23, 2025 Global RSS

Research Narrative

Grounded narrative Model: deterministic-grounded Source: persisted

Updated from current HFEPX corpus (Feb 27, 2026). This page tracks 6 papers for HFEPX Fortnight Archive: 2025-F06. Dominant protocol signals include automatic metrics, simulation environments, with frequent benchmark focus on Retrieval, Re-Bench and metric focus on accuracy, f1. Use the grounded sections below to prioritize reproducible protocol choices, benchmark-matched comparisons, and judge-vs-human evaluation checks.

Why This Matters For Eval Research

Protocol Takeaways

Benchmark Interpretation

  • Retrieval appears in 50% of hub papers (3/6); use this cohort for benchmark-matched comparisons.
  • Re-Bench appears in 16.7% of hub papers (1/6); use this cohort for benchmark-matched comparisons.

Metric Interpretation

  • accuracy is reported in 33.3% of hub papers (2/6); compare with a secondary metric before ranking methods.
  • f1 is reported in 16.7% of hub papers (1/6); compare with a secondary metric before ranking methods.

Researcher Checklist

  • Tighten coverage on Papers with explicit human feedback. Coverage is usable but incomplete (33.3% vs 45% target).
  • Close gap on Papers reporting quality controls. Coverage is a replication risk (0% vs 30% target).
  • Maintain strength on Papers naming benchmarks/datasets. Coverage is strong (66.7% vs 35% target).
  • Maintain strength on Papers naming evaluation metrics. Coverage is strong (66.7% vs 35% target).
  • Maintain strength on Papers with known rater population. Coverage is strong (50% vs 35% target).
  • Close gap on Papers with known annotation unit. Coverage is a replication risk (16.7% vs 35% target).

Papers with explicit human feedback

Coverage is usable but incomplete (33.3% vs 45% target).

Papers reporting quality controls

Coverage is a replication risk (0% vs 30% target).

Papers naming benchmarks/datasets

Coverage is strong (66.7% vs 35% target).

Papers naming evaluation metrics

Coverage is strong (66.7% vs 35% target).

Papers with known rater population

Coverage is strong (50% vs 35% target).

Papers with known annotation unit

Coverage is a replication risk (16.7% vs 35% target).

Suggested Reading Order

  1. 1. MedPlan: A Two-Stage RAG-Based System for Personalized Medical Plan Generation

    Start here for detailed protocol reporting, including rater and quality-control evidence.

  2. 2. Imitating AI agents increase diversity in homogeneous information environments but can reduce it in heterogeneous ones

    Start here for detailed protocol reporting, including rater and quality-control evidence.

  3. 3. EmoGRACE: Aspect-based emotion analysis for social media data

    Start here for detailed protocol reporting, including rater and quality-control evidence.

  4. 4. Measuring AI Ability to Complete Long Software Tasks

    Adds automatic metrics with expert verification for broader coverage within this hub.

  5. 5. A Survey on the Optimization of Large Language Model-based Agents

    Adds simulation environments for broader coverage within this hub.

  6. 6. Integrating Chain-of-Thought and Retrieval Augmented Generation Enhances Rare Disease Diagnosis from Clinical Notes

    Adds automatic metrics for broader coverage within this hub.

Known Limitations

  • Only 0% of papers report quality controls; prioritize calibration/adjudication evidence.
  • Annotation unit is under-specified (16.7% coverage).
  • Narrative synthesis is grounded in metadata and abstracts only; full-paper implementation details are not parsed.

Research Utility Links

automatic_metrics vs simulation_env

both=0, left_only=4, right_only=2

0 papers use both Automatic Metrics and Simulation Env.

Benchmark Brief

Re-Bench

Coverage: 1 papers (16.7%)

1 papers (16.7%) mention Re-Bench.

Examples: Measuring AI Ability to Complete Long Software Tasks

Metric Brief

f1

Coverage: 1 papers (16.7%)

1 papers (16.7%) mention f1.

Examples: EmoGRACE: Aspect-based emotion analysis for social media data

Metric Brief

success rate

Coverage: 1 papers (16.7%)

1 papers (16.7%) mention success rate.

Examples: Measuring AI Ability to Complete Long Software Tasks

Papers Published On This Date

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