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HFEPX Archive Slice

HFEPX Daily Archive: 2026-03-03

Updated from current HFEPX corpus (Apr 17, 2026). 54 papers are grouped in this daily page.

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Updated from current HFEPX corpus (Apr 17, 2026). 54 papers are grouped in this daily page. Common evaluation modes: Automatic Metrics, Simulation Env. Most common rater population: Domain Experts. Common annotation unit: Ranking. Frequent quality control: Calibration. Frequently cited benchmark: Kernelbench. 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 3, 2026.

Papers: 54 Last published: Mar 3, 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 .

High-Signal Coverage

100.0%

54 / 54 papers are not low-signal flagged.

Benchmark Anchors

13.0%

Papers with benchmark/dataset mentions in extraction output.

Metric Anchors

35.2%

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

  • 18.5% of papers report explicit human-feedback signals, led by pairwise preferences.
  • automatic metrics appears in 27.8% of papers in this hub.
  • Kernelbench 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 (1.9% of papers).
  • Rater context is mostly domain experts, and annotation is commonly ranking 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
StitchCUDA: An Automated Multi-Agents End-to-End GPU Programing Framework with Rubric-based Agentic Reinforcement Learning

Mar 3, 2026

Automatic Metrics Kernelbench Success rate Not reported
Guideline-Grounded Evidence Accumulation for High-Stakes Agent Verification

Mar 3, 2026

Automatic Metrics Not reported Brier score, Auroc Calibration
Think, But Don't Overthink: Reproducing Recursive Language Models

Mar 3, 2026

Automatic Metrics Needle In A Haystack Accuracy Not reported
MoD-DPO: Towards Mitigating Cross-modal Hallucinations in Omni LLMs using Modality Decoupled Preference Optimization

Mar 3, 2026

Automatic Metrics Not reported Accuracy Not reported
MUSE: A Run-Centric Platform for Multimodal Unified Safety Evaluation of Large Language Models

Mar 3, 2026

Automatic Metrics Not reported Success rate, Jailbreak success rate Not reported
No Memorization, No Detection: Output Distribution-Based Contamination Detection in Small Language Models

Mar 3, 2026

Not reported GSM8K, HumanEval+ Perplexity Not reported
Belief-Sim: Towards Belief-Driven Simulation of Demographic Misinformation Susceptibility

Mar 3, 2026

Automatic Metrics, Simulation Env Not reported Accuracy Not reported
Evaluating Performance Drift from Model Switching in Multi-Turn LLM Systems

Mar 3, 2026

Automatic Metrics Not reported F1, Success rate Not reported
MaBERT:A Padding Safe Interleaved Transformer Mamba Hybrid Encoder for Efficient Extended Context Masked Language Modeling

Mar 3, 2026

Automatic Metrics Not reported Latency Not reported
Contextualized Privacy Defense for LLM Agents

Mar 3, 2026

Simulation Env Not reported Helpfulness Not reported
Researcher Workflow (Detailed)

Checklist

  • Gap: Papers with explicit human feedback

    Coverage is a replication risk (18.5% vs 45% target).

  • Gap: Papers reporting quality controls

    Coverage is a replication risk (1.9% vs 30% target).

  • Gap: Papers naming benchmarks/datasets

    Coverage is a replication risk (1.9% vs 35% target).

  • Gap: Papers naming evaluation metrics

    Coverage is a replication risk (13% vs 35% target).

  • Gap: Papers with known rater population

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

  • Gap: Papers with known annotation unit

    Coverage is a replication risk (14.8% vs 35% target).

Strengths

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

Known Gaps

  • Only 1.9% of papers report quality controls; prioritize calibration/adjudication evidence.
  • Rater population is under-specified (9.3% coverage).
  • Annotation unit is under-specified (14.8% coverage).

Suggested Next Analyses

  • Compare papers that report both human_eval and llm_as_judge to quantify judge-human agreement drift.
  • Track metric sensitivity by reporting both accuracy and success rate.
  • Add inter-annotator agreement checks when reproducing these protocols.

Recommended Queries

Known Limitations
  • Only 1.9% of papers report quality controls; prioritize calibration/adjudication evidence.
  • Rater population is under-specified (9.3% 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 (15)
  • Simulation Env (5)
  • Llm As Judge (2)
  • Human Eval (1)

Top Metrics

  • Accuracy (3)
  • Success rate (2)
  • Auroc (1)
  • Brier score (1)

Top Benchmarks

  • Kernelbench (1)

Quality Controls

  • Calibration (1)

Papers In This Archive Slice

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