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HFEPX Weekly Archive: 2026-W03

Updated from current HFEPX corpus (Feb 27, 2026). 15 papers are grouped in this daily page. Common evaluation modes: Automatic Metrics, Simulation Env. Most common rater population: Domain Experts. Common annotation unit: Multi Dim Rubric. Frequent quality control: Calibration. Frequently cited benchmark: Retrieval. Common metric signal: f1. 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 Jan 18, 2026.

Papers: 15 Last published: Jan 18, 2026 Global RSS

Research Narrative

Grounded narrative Model: deterministic-grounded Source: persisted

Updated from current HFEPX corpus (Feb 27, 2026). This page tracks 15 papers for HFEPX Weekly Archive: 2026-W03. Dominant protocol signals include automatic metrics, simulation environments, with frequent benchmark focus on Retrieval, GAIA and metric focus on f1, coherence. 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 13.3% of hub papers (2/15); use this cohort for benchmark-matched comparisons.
  • GAIA appears in 6.7% of hub papers (1/15); use this cohort for benchmark-matched comparisons.

Metric Interpretation

  • f1 is reported in 13.3% of hub papers (2/15); compare with a secondary metric before ranking methods.
  • coherence is reported in 6.7% of hub papers (1/15); 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 (6.7% vs 30% target).
  • Maintain strength on Papers naming benchmarks/datasets. Coverage is strong (40% vs 35% target).
  • Tighten coverage on Papers naming evaluation metrics. Coverage is usable but incomplete (26.7% vs 35% target).
  • Tighten coverage on Papers with known rater population. Coverage is usable but incomplete (26.7% vs 35% target).
  • Close gap on Papers with known annotation unit. Coverage is a replication risk (13.3% 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 (6.7% vs 30% target).

Papers naming benchmarks/datasets

Coverage is strong (40% vs 35% target).

Papers naming evaluation metrics

Coverage is usable but incomplete (26.7% vs 35% target).

Papers with known rater population

Coverage is usable but incomplete (26.7% vs 35% target).

Papers with known annotation unit

Coverage is a replication risk (13.3% vs 35% target).

Suggested Reading Order

  1. 1. Cross-Cultural Expert-Level Art Critique Evaluation with Vision-Language Models

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

  2. 2. Orthogonalized Policy Optimization:Policy Optimization as Orthogonal Projection in Hilbert Space

    High citation traction makes this a useful baseline for method and protocol context.

  3. 3. Event Detection with a Context-Aware Encoder and LoRA for Improved Performance on Long-Tailed Classes

    High citation traction makes this a useful baseline for method and protocol context.

  4. 4. Generating metamers of human scene understanding

    High citation traction makes this a useful baseline for method and protocol context.

  5. 5. Toward Ultra-Long-Horizon Agentic Science: Cognitive Accumulation for Machine Learning Engineering

    Adds simulation environments for broader coverage within this hub.

  6. 6. AWED-FiNER: Agents, Web applications, and Expert Detectors for Fine-grained Named Entity Recognition across 36 Languages for 6.6 Billion Speakers

    Adds automatic metrics for broader coverage within this hub.

  7. 7. Alignment Pretraining: AI Discourse Causes Self-Fulfilling (Mis)alignment

    Adds automatic metrics for broader coverage within this hub.

  8. 8. Context Volume Drives Performance: Tackling Domain Shift in Extremely Low-Resource Translation via RAG

    Adds automatic metrics for broader coverage within this hub.

Known Limitations

  • Only 6.7% of papers report quality controls; prioritize calibration/adjudication evidence.
  • Annotation unit is under-specified (13.3% 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=13, right_only=2

0 papers use both Automatic Metrics and Simulation Env.

Benchmark Brief

GAIA

Coverage: 1 papers (6.7%)

1 papers (6.7%) mention GAIA.

Examples: CLiMB: A Domain-Informed Novelty Detection Clustering Framework for Galactic Archaeology and Scientific Discovery

Benchmark Brief

MATH

Coverage: 1 papers (6.7%)

1 papers (6.7%) mention MATH.

Examples: Orthogonalized Policy Optimization:Policy Optimization as Orthogonal Projection in Hilbert Space

Metric Brief

coherence

Coverage: 1 papers (6.7%)

1 papers (6.7%) mention coherence.

Examples: Toward Ultra-Long-Horizon Agentic Science: Cognitive Accumulation for Machine Learning Engineering

Metric Brief

latency

Coverage: 1 papers (6.7%)

1 papers (6.7%) mention latency.

Examples: Fast-ThinkAct: Efficient Vision-Language-Action Reasoning via Verbalizable Latent Planning

Papers Published On This Date

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