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HFEPX Archive Slice

HFEPX Weekly Archive: 2026-W03

Updated from current HFEPX corpus (Apr 12, 2026). 61 papers are grouped in this daily page.

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Updated from current HFEPX corpus (Apr 12, 2026). 61 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: BFCL. 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 Jan 18, 2026.

Papers: 61 Last published: Jan 18, 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 61 papers).

High-Signal Coverage

100.0%

60 / 60 papers are not low-signal flagged.

Benchmark Anchors

15.0%

Papers with benchmark/dataset mentions in extraction output.

Metric Anchors

31.7%

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

  • 13.1% of papers report explicit human-feedback signals, led by pairwise preferences.
  • automatic metrics appears in 27.9% of papers in this hub.
  • BFCL 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 (1.6% 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
PEARL: Self-Evolving Assistant for Time Management with Reinforcement Learning

Jan 17, 2026

Automatic Metrics Calconflictbench Error rate Not reported
AJAR: Adaptive Jailbreak Architecture for Red-teaming

Jan 16, 2026

Simulation Env Harmbench Success rate, Jailbreak success rate Not reported
Legal Experts Disagree With Rationale Extraction Techniques for Explaining ECtHR Case Outcome Classification

Jan 18, 2026

Llm As Judge Inteval Faithfulness Not reported
Vision-as-Inverse-Graphics Agent via Interleaved Multimodal Reasoning

Jan 16, 2026

Automatic Metrics Blenderbench, Slidebench Accuracy Not reported
Fast-ThinkAct: Efficient Vision-Language-Action Reasoning via Verbalizable Latent Planning

Jan 14, 2026

Simulation Env Not reported Latency Not reported
DYCP: Dynamic Context Pruning for Long-Form Dialogue with LLMs

Jan 12, 2026

Automatic Metrics MT Bench Latency, Cost Not reported
PosIR: Position-Aware Heterogeneous Information Retrieval Benchmark

Jan 13, 2026

Automatic Metrics Not reported Relevance Not reported
Improving Low-Resource Machine Translation via Round-Trip Reinforcement Learning

Jan 18, 2026

Automatic Metrics Not reported Bleu Not reported
Multi-Task Instruction Tuning via Data Scheduling for Low-Resource Arabic AudioLLMs

Jan 18, 2026

Automatic Metrics Not reported Jailbreak success rate Not reported
Orthogonalized Policy Optimization:Policy Optimization as Orthogonal Projection in Hilbert Space

Jan 18, 2026

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

Checklist

  • Gap: Papers with explicit human feedback

    Coverage is a replication risk (13.1% vs 45% target).

  • Gap: Papers reporting quality controls

    Coverage is a replication risk (1.6% vs 30% target).

  • Gap: Papers naming benchmarks/datasets

    Coverage is a replication risk (13.1% vs 35% target).

  • Moderate: Papers naming evaluation metrics

    Coverage is usable but incomplete (23% vs 35% target).

  • Gap: Papers with known rater population

    Coverage is a replication risk (11.5% vs 35% target).

  • Gap: Papers with known annotation unit

    Coverage is a replication risk (11.5% vs 35% target).

Strengths

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

Known Gaps

  • Only 1.6% of papers report quality controls; prioritize calibration/adjudication evidence.
  • Rater population is under-specified (11.5% coverage).
  • Annotation unit is under-specified (11.5% coverage).

Suggested Next Analyses

  • Pair this hub with a human_eval-heavy hub to validate judge-model calibration.
  • Stratify by benchmark (BFCL vs Blenderbench) 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 1.6% of papers report quality controls; prioritize calibration/adjudication evidence.
  • Rater population is under-specified (11.5% 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 (17)
  • Simulation Env (4)
  • Llm As Judge (2)

Top Metrics

  • Accuracy (7)
  • Cost (2)
  • Relevance (2)
  • Coherence (1)

Top Benchmarks

  • BFCL (1)
  • Blenderbench (1)
  • Calconflictbench (1)
  • Harmbench (1)

Quality Controls

  • Calibration (1)

Papers In This Archive Slice

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