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HFEPX Archive Slice

HFEPX Fortnight Archive: 2025-F23

Updated from current HFEPX corpus (Apr 12, 2026). 94 papers are grouped in this daily page.

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Updated from current HFEPX corpus (Apr 12, 2026). 94 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: CareMedEval. 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 Nov 16, 2025.

Papers: 94 Last published: Nov 16, 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 .

Analysis blocks are computed from the loaded sample (60 of 94 papers).

High-Signal Coverage

100.0%

60 / 60 papers are not low-signal flagged.

Benchmark Anchors

11.7%

Papers with benchmark/dataset mentions in extraction output.

Metric Anchors

45.0%

Papers with reported metric mentions in extraction output.

  • 0 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

  • 8.5% of papers report explicit human-feedback signals, led by pairwise preferences.
  • automatic metrics appears in 42.6% of papers in this hub.
  • CareMedEval is a recurring benchmark anchor for cross-paper comparisons in this page.

Protocol Takeaways For This Period

  • 1 sampled papers report both human evaluation and LLM-as-judge, supporting direct agreement checks.
  • Most common quality-control signal is rater calibration (1.1% of papers).
  • Rater context is mostly domain experts, and annotation is commonly ranking annotation; use this to scope replication staffing.

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
Towards Hyper-Efficient RAG Systems in VecDBs: Distributed Parallel Multi-Resolution Vector Search

Nov 12, 2025

Automatic Metrics NQ, MS MARCO F1, Latency Not reported
Unified Work Embeddings: Contrastive Learning of a Bidirectional Multi-task Ranker

Nov 11, 2025

Automatic Metrics Workbench Latency Not reported
More Agents Improve Math Problem Solving but Adversarial Robustness Gap Persists

Nov 10, 2025

Automatic Metrics MMLU, GSM8K Accuracy, Success rate Not reported
RPTS: Tree-Structured Reasoning Process Scoring for Faithful Multimodal Evaluation

Nov 10, 2025

Automatic Metrics Rpts Eval Faithfulness Not reported
Chain of Summaries: Summarization Through Iterative Questioning

Nov 12, 2025

Not reported TriviaQA, SQuAD Context length Not reported
From Passive to Persuasive: Localized Activation Injection for Empathy and Negotiation

Nov 16, 2025

Human Eval Not reported Toxicity Not reported
Context-Emotion Aware Therapeutic Dialogue Generation: A Multi-component Reinforcement Learning Approach to Language Models for Mental Health Support

Nov 14, 2025

Automatic Metrics Not reported Accuracy, Bleu Not reported
MedPT: A Massive Medical Question Answering Dataset for Brazilian-Portuguese Speakers

Nov 14, 2025

Automatic Metrics Not reported F1 Not reported
Conformal Constrained Policy Optimization for Cost-Effective LLM Agents

Nov 14, 2025

Automatic Metrics Not reported Accuracy, Cost Not reported
CLARITY: Contextual Linguistic Adaptation and Accent Retrieval for Dual-Bias Mitigation in Text-to-Speech Generation

Nov 14, 2025

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

Checklist

  • Gap: Papers with explicit human feedback

    Coverage is a replication risk (8.5% vs 45% target).

  • Gap: Papers reporting quality controls

    Coverage is a replication risk (1.1% vs 30% target).

  • Gap: Papers naming benchmarks/datasets

    Coverage is a replication risk (5.3% vs 35% target).

  • Gap: Papers naming evaluation metrics

    Coverage is a replication risk (14.9% vs 35% target).

  • Gap: Papers with known rater population

    Coverage is a replication risk (7.4% vs 35% target).

  • Gap: Papers with known annotation unit

    Coverage is a replication risk (7.4% vs 35% target).

Strengths

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

Known Gaps

  • Only 1.1% of papers report quality controls; prioritize calibration/adjudication evidence.
  • Rater population is under-specified (7.4% coverage).
  • Annotation unit is under-specified (7.4% coverage).

Suggested Next Analyses

  • Compare papers that report both human_eval and llm_as_judge to quantify judge-human agreement drift.
  • Stratify by benchmark (CareMedEval vs Cv-Bench) 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 1.1% of papers report quality controls; prioritize calibration/adjudication evidence.
  • Rater population is under-specified (7.4% 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 (40)
  • Simulation Env (4)
  • Human Eval (2)
  • Llm As Judge (1)

Top Metrics

  • Accuracy (6)
  • Cost (4)
  • Coherence (1)
  • Completion rate (1)

Top Benchmarks

  • CareMedEval (1)
  • Cv Bench (1)
  • Lexinstructeval (1)
  • MMLU (1)

Quality Controls

  • Calibration (1)

Papers In This Archive Slice

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