Skip to content
← Back to explorer

HFEPX Hub

Demonstrations Papers (Last 60 Days)

Updated from current HFEPX corpus (Mar 8, 2026). 14 papers are grouped in this hub page.

Read Full Context

Updated from current HFEPX corpus (Mar 8, 2026). 14 papers are grouped in this hub page. Common evaluation modes: Automatic Metrics. Most common rater population: Domain Experts. Common annotation unit: Trajectory. Frequently cited benchmark: Auditbench. 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 5, 2026.

Papers: 14 Last published: Mar 5, 2026 Global RSS Tag RSS
DemonstrationsLast 60d

Researcher Quick Triage

This hub is best used for protocol triage and replication planning from abstract-level evidence. Quality band: Developing .

All Sampled Papers (14) Replication-Ready Only (0)

High-Signal Coverage

100.0%

14 / 14 sampled papers are not low-signal flagged.

Replication-Ready Set

0

Benchmark + metric + eval mode explicitly present.

Judge/Human Comparability

0

Papers containing both `human_eval` and `llm_as_judge`.

  • 0 papers are replication-ready (benchmark + metric + explicit evaluation mode).
  • 0 papers support judge-vs-human agreement analysis.
  • 0 papers report explicit quality controls (calibration/adjudication/IAA).

Primary action: Use this page for scouting only; collect additional papers before attempting replication-critical comparisons.

Currently showing only replication-ready papers in ranking and matrix sections (0 papers).

Why This Matters For Eval Research

  • 100% of papers report explicit human-feedback signals, led by demonstration data.
  • automatic metrics appears in 14.3% of papers in this hub.
  • Auditbench is a recurring benchmark anchor for cross-paper comparisons in this page.

Protocol Takeaways

  • 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.
  • Stratify by benchmark (Auditbench vs Fewmmbench) before comparing methods.

Benchmark Interpretation

  • Auditbench appears in 7.1% of hub papers (1/14); use this cohort for benchmark-matched comparisons.
  • Fewmmbench appears in 7.1% of hub papers (1/14); use this cohort for benchmark-matched comparisons.

Metric Interpretation

  • accuracy is reported in 7.1% of hub papers (1/14); compare with a secondary metric before ranking methods.
  • cost is reported in 7.1% of hub papers (1/14); compare with a secondary metric before ranking methods.
Researcher Checklist (Expanded)

Researcher Checklist

  • Strong: Papers with explicit human feedback

    Coverage is strong (100% vs 45% target).

  • Gap: Papers reporting quality controls

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

  • Gap: Papers naming benchmarks/datasets

    Coverage is a replication risk (14.3% vs 35% target).

  • Gap: Papers naming evaluation metrics

    Coverage is a replication risk (14.3% vs 35% target).

  • Gap: Papers with known rater population

    Coverage is a replication risk (14.3% vs 35% target).

  • Moderate: Papers with known annotation unit

    Coverage is usable but incomplete (21.4% vs 35% target).

Strengths

  • Strong human-feedback signal (100% of papers).

Known Gaps

  • Only 0% of papers report quality controls; prioritize calibration/adjudication evidence.
  • Rater population is under-specified (14.3% coverage).
  • Annotation unit is under-specified (21.4% coverage).

Suggested Next Analyses

  • Stratify by benchmark (Auditbench vs Fewmmbench) before comparing methods.
  • Track metric sensitivity by reporting both accuracy and cost.
Recommended Queries (Expanded)

Recommended Queries

Benchmark Slice: Auditbench Metric Slice: accuracy Recent High-Signal Papers
Suggested Reading Order (Extended)

This section is intentionally expanded only when needed; use “Start Here” above for a faster pass.

Suggested Reading Order

  1. RoboPocket: Improve Robot Policies Instantly with Your Phone

    Start here for detailed protocol reporting and quality-control evidence. Signals: demonstration data. Abstract: Scaling imitation learning is fundamentally constrained by the efficiency of data collection.

  2. TimeWarp: Evaluating Web Agents by Revisiting the Past

    Start here for detailed protocol reporting and quality-control evidence. Signals: demonstration data. Abstract: The improvement of web agents on current benchmarks raises the question: Do today's agents perform.

  3. Optimizing In-Context Demonstrations for LLM-based Automated Grading

    Start here for detailed protocol reporting and quality-control evidence. Signals: rubric ratings. Abstract: Standard retrieval methods typically select examples based on semantic similarity, which often fails to capture.

  4. IDP Accelerator: Agentic Document Intelligence from Extraction to Compliance Validation

    Adds automatic metrics with demonstration data for broader protocol coverage within this hub. Signals: automatic metrics + demonstration data. Focus: accuracy. Abstract: Understanding and extracting structured insights from.

  5. Orchestration-Free Customer Service Automation: A Privacy-Preserving and Flowchart-Guided Framework

    Adds automatic metrics with demonstration data for broader protocol coverage within this hub. Signals: automatic metrics + demonstration data. Focus: cost. Abstract: Customer service automation has seen growing.

  6. AuditBench: Evaluating Alignment Auditing Techniques on Models with Hidden Behaviors

    Adds evaluation protocol evidence with demonstration data for broader protocol coverage within this hub. Signals: demonstration data. Focus: Auditbench. Abstract: We introduce AuditBench, an alignment auditing benchmark.

  7. FewMMBench: A Benchmark for Multimodal Few-Shot Learning

    Adds evaluation protocol evidence with demonstration data for broader protocol coverage within this hub. Signals: demonstration data. Focus: Fewmmbench. Abstract: As multimodal large language models (MLLMs) advance in.

  8. Risk-Aware World Model Predictive Control for Generalizable End-to-End Autonomous Driving

    Adds evaluation protocol evidence with demonstration data for broader protocol coverage within this hub. Signals: demonstration data. Abstract: With advances in imitation learning (IL) and large-scale driving datasets,.

Known Limitations

Known Limitations

  • Only 0% of papers report quality controls; prioritize calibration/adjudication evidence.
  • Rater population is under-specified (14.3% coverage).
  • Narrative synthesis is grounded in metadata and abstracts only; full-paper implementation details are not parsed.
Research Utility Snapshot (Detailed)

Research Utility Snapshot

Human Feedback Mix

  • Demonstrations (14)
  • Rubric Rating (1)

Evaluation Modes

  • Automatic Metrics (2)

Top Benchmarks

  • Auditbench (1)
  • Fewmmbench (1)

Top Metrics

  • Accuracy (1)
  • Cost (1)
  • Latency (1)

Rater Population Mix

  • Domain Experts (2)

Quality Controls

Coverage diagnostics (sample-based): human-feedback 100.0% · benchmarks 14.3% · metrics 14.3% · quality controls 0.0%.

Top Papers

No replication-ready papers in the loaded sample. Switch to “All Sampled Papers” for broader coverage.

Related Hubs

Need human evaluators for your AI research? Scale annotation with expert AI Trainers.

Post a Job Get a Quote