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HFEPX Archive Slice

HFEPX Fortnight Archive: 2025-F16

Updated from current HFEPX corpus (Apr 17, 2026). 55 papers are grouped in this daily page.

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Updated from current HFEPX corpus (Apr 17, 2026). 55 papers are grouped in this daily page. Common evaluation modes: Automatic Metrics, Llm As Judge. Most common rater population: Domain Experts. Common annotation unit: Ranking. Frequent quality control: Adjudication. Frequently cited benchmark: DocBench. 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 Aug 10, 2025.

Papers: 55 Last published: Aug 10, 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 .

High-Signal Coverage

100.0%

55 / 55 papers are not low-signal flagged.

Benchmark Anchors

14.5%

Papers with benchmark/dataset mentions in extraction output.

Metric Anchors

29.1%

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

  • 5.5% of papers report explicit human-feedback signals, led by red-team protocols.
  • automatic metrics appears in 23.6% of papers in this hub.
  • DocBench is a recurring benchmark anchor for cross-paper comparisons in this page.

Protocol Takeaways For This Period

  • Most common quality-control signal is adjudication (1.8% 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
Not All Errors Are Created Equal: ASCoT Addresses Late-Stage Fragility in Efficient LLM Reasoning

Aug 7, 2025

Automatic Metrics MATH 500, GSM8K Accuracy Not reported
Share Your Attention: Transformer Weight Sharing via Matrix-based Dictionary Learning

Aug 6, 2025

Automatic Metrics DROP Accuracy, Perplexity Not reported
CoAct-1: Computer-using Multi-Agent System with Coding Actions

Aug 5, 2025

Automatic Metrics OSWorld Success rate Not reported
LiveMCPBench: Can Agents Navigate an Ocean of MCP Tools?

Aug 3, 2025

Llm As Judge Livemcpbench Task success Not reported
SEVADE: Self-Evolving Multi-Agent Analysis with Decoupled Evaluation for Hallucination-Resistant Irony Detection

Aug 9, 2025

Automatic Metrics Not reported Accuracy, F1 Adjudication
Activation-Guided Local Editing for Jailbreaking Attacks

Aug 1, 2025

Automatic Metrics Not reported Success rate, Jailbreak success rate Not reported
Memp: Exploring Agent Procedural Memory

Aug 8, 2025

Simulation Env ALFWorld Not reported Not reported
Mixed-Initiative Dialog for Human-Robot Collaborative Manipulation

Aug 7, 2025

Simulation Env Not reported Task success Not reported
LLMEval-Fair: A Large-Scale Longitudinal Study on Robust and Fair Evaluation of Large Language Models

Aug 7, 2025

Llm As Judge Llmeval Not reported Not reported
QA-Dragon: Query-Aware Dynamic RAG System for Knowledge-Intensive Visual Question Answering

Aug 7, 2025

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

Checklist

  • Gap: Papers with explicit human feedback

    Coverage is a replication risk (5.5% vs 45% target).

  • Gap: Papers reporting quality controls

    Coverage is a replication risk (1.8% vs 30% target).

  • Gap: Papers naming benchmarks/datasets

    Coverage is a replication risk (9.1% vs 35% target).

  • Moderate: Papers naming evaluation metrics

    Coverage is usable but incomplete (23.6% vs 35% target).

  • Gap: Papers with known rater population

    Coverage is a replication risk (3.6% vs 35% target).

  • Gap: Papers with known annotation unit

    Coverage is a replication risk (7.3% vs 35% target).

Strengths

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

Known Gaps

  • Only 1.8% of papers report quality controls; prioritize calibration/adjudication evidence.
  • Rater population is under-specified (3.6% coverage).
  • Annotation unit is under-specified (7.3% coverage).

Suggested Next Analyses

  • Compare papers that report both human_eval and llm_as_judge to quantify judge-human agreement drift.
  • Stratify by benchmark (DocBench vs EmbodiedBench) before comparing methods.
  • Track metric sensitivity by reporting both accuracy and success rate.
  • Add inter-annotator agreement checks when reproducing these protocols.

Recommended Queries

Known Limitations
  • Only 1.8% of papers report quality controls; prioritize calibration/adjudication evidence.
  • Rater population is under-specified (3.6% 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 (13)
  • Llm As Judge (4)
  • Simulation Env (2)
  • Human Eval (1)

Top Metrics

  • Accuracy (5)
  • Success rate (4)
  • Precision (2)
  • Task success (2)

Top Benchmarks

  • DocBench (1)
  • EmbodiedBench (1)
  • HaluEval (1)
  • Livemcpbench (1)

Quality Controls

  • Adjudication (1)

Papers In This Archive Slice

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