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HFEPX Archive Slice

HFEPX Daily Archive: 2025-06-04

Updated from current HFEPX corpus (Mar 8, 2026). 9 papers are grouped in this daily page.

Read Full Context

Updated from current HFEPX corpus (Mar 8, 2026). 9 papers are grouped in this daily page. Common evaluation modes: Automatic Metrics, Simulation Env. Most common rater population: Domain Experts. Common annotation unit: Trajectory. Frequently cited benchmark: Hssbench. 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 Jun 4, 2025.

Papers: 9 Last published: Jun 4, 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: Medium .

High-Signal Coverage

100.0%

9 / 9 papers are not low-signal flagged.

Benchmark Anchors

44.4%

Papers with benchmark/dataset mentions in extraction output.

Metric Anchors

66.7%

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 as early signal only; benchmark/metric anchoring is limited for rigorous period-over-period claims.

Why This Time Slice Matters

  • 11.1% of papers report explicit human-feedback signals, led by expert verification.
  • automatic metrics appears in 22.2% of papers in this hub.
  • Hssbench is a recurring benchmark anchor for cross-paper comparisons in this page.

Protocol Takeaways For This Period

  • Quality-control reporting is sparse in this slice; prioritize papers with explicit calibration or adjudication steps.
  • Rater context is mostly domain experts, and annotation is commonly trajectory-level annotation; use this to scope replication staffing.
  • Pair this hub with llm_as_judge pages to benchmark automated-vs-human evaluation tradeoffs.

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
Go-Browse: Training Web Agents with Structured Exploration

Jun 4, 2025

Simulation Env WebArena Success rate Not reported
CyclicReflex: Improving Reasoning Models via Cyclical Reflection Token Scheduling

Jun 4, 2025

Not reported MATH 500, GPQA Cost Not reported
"Don't Do That!": Guiding Embodied Systems through Large Language Model-based Constraint Generation

Jun 4, 2025

Simulation Env Not reported Cost Not reported
High Accuracy, Less Talk (HALT): Reliable LLMs through Capability-Aligned Finetuning

Jun 4, 2025

Automatic Metrics Not reported Accuracy, F1 Not reported
HSSBench: Benchmarking Humanities and Social Sciences Ability for Multimodal Large Language Models

Jun 4, 2025

Not reported Hssbench Not reported Not reported
EuroGEST: Investigating gender stereotypes in multilingual language models

Jun 4, 2025

Human Eval, Automatic Metrics Not reported Accuracy Not reported
Watermarking Degrades Alignment in Language Models: Analysis and Mitigation

Jun 4, 2025

Not reported Not reported Perplexity, Helpfulness Not reported
Beyond Memorization: A Rigorous Evaluation Framework for Medical Knowledge Editing

Jun 4, 2025

Not reported Mededitbench Not reported Not reported
Toward Beginner-Friendly LLMs for Language Learning: Controlling Difficulty in Conversation

Jun 4, 2025

Not reported Not reported Not reported Not reported
Researcher Workflow (Detailed)

Checklist

  • Gap: Papers with explicit human feedback

    Coverage is a replication risk (11.1% vs 45% target).

  • Gap: Papers reporting quality controls

    Coverage is a replication risk (0% vs 30% target).

  • Moderate: Papers naming benchmarks/datasets

    Coverage is usable but incomplete (22.2% vs 35% target).

  • Moderate: Papers naming evaluation metrics

    Coverage is usable but incomplete (33.3% vs 35% target).

  • Moderate: Papers with known rater population

    Coverage is usable but incomplete (22.2% vs 35% target).

  • Gap: Papers with known annotation unit

    Coverage is a replication risk (11.1% vs 35% target).

Strengths

  • Agentic evaluation appears in 33.3% of papers.

Known Gaps

  • Only 0% of papers report quality controls; prioritize calibration/adjudication evidence.
  • Rater population is under-specified (22.2% coverage).
  • Annotation unit is under-specified (11.1% coverage).

Suggested Next Analyses

  • Pair this hub with llm_as_judge pages to benchmark automated-vs-human evaluation tradeoffs.
  • Stratify by benchmark (Hssbench vs WebArena) before comparing methods.
  • Track metric sensitivity by reporting both accuracy and cost.

Recommended Queries

Known Limitations
  • Only 0% of papers report quality controls; prioritize calibration/adjudication evidence.
  • Rater population is under-specified (22.2% 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 (2)
  • Simulation Env (2)
  • Human Eval (1)

Top Metrics

  • Accuracy (1)
  • Cost (1)
  • Inference cost (1)
  • Success rate (1)

Top Benchmarks

  • Hssbench (1)
  • WebArena (1)

Quality Controls

Papers In This Archive Slice

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