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HFEPX Hub

General + Demonstrations Papers

Updated from current HFEPX corpus (Mar 8, 2026). 20 papers are grouped in this hub page.

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Updated from current HFEPX corpus (Mar 8, 2026). 20 papers are grouped in this hub page. Common evaluation modes: Automatic Metrics, Simulation Env. Most common rater population: Domain Experts. Common annotation unit: Multi Dim Rubric. Frequently cited benchmark: Auditbench. Common metric signal: win rate. 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 Feb 4, 2025.

Papers: 20 Last published: Feb 4, 2025 Global RSS Tag RSS
GeneralDemonstrations

Researcher Quick Triage

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

All Sampled Papers (20) Replication-Ready Only (0)

High-Signal Coverage

100.0%

20 / 20 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 25% 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 multi-dimensional rubrics; use this to scope replication staffing.
  • Stratify by benchmark (Auditbench vs Fewmmbench) before comparing methods.

Benchmark Interpretation

  • Auditbench appears in 5% of hub papers (1/20); use this cohort for benchmark-matched comparisons.
  • Fewmmbench appears in 5% of hub papers (1/20); use this cohort for benchmark-matched comparisons.

Metric Interpretation

  • win rate is reported in 10% of hub papers (2/20); compare with a secondary metric before ranking methods.
  • cost is reported in 5% of hub papers (1/20); 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 (10% vs 35% target).

  • Gap: Papers naming evaluation metrics

    Coverage is a replication risk (20% vs 35% target).

  • Moderate: Papers with known rater population

    Coverage is usable but incomplete (25% vs 35% target).

  • Gap: Papers with known annotation unit

    Coverage is a replication risk (15% vs 35% target).

Strengths

  • Strong human-feedback signal (100% of papers).
  • Agentic evaluation appears in 30% of papers.

Known Gaps

  • Only 0% of papers report quality controls; prioritize calibration/adjudication evidence.
  • Annotation unit is under-specified (15% coverage).
  • Benchmark coverage is thin (10% of papers mention benchmarks/datasets).

Suggested Next Analyses

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

Recommended Queries

Benchmark Slice: Auditbench Metric Slice: win rate 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. 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.

  2. 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.

  3. ArgLLM-App: An Interactive System for Argumentative Reasoning with Large Language Models

    Start here for detailed protocol reporting and quality-control evidence. Signals: demonstration data. Abstract: Argumentative LLMs (ArgLLMs) are an existing approach leveraging Large Language Models (LLMs) and computational argumentation.

  4. VolleyBots: A Testbed for Multi-Drone Volleyball Game Combining Motion Control and Strategic Play

    Include a human-eval paper to calibrate against judge-based evaluation settings. Signals: automatic metrics + demonstration data. Focus: win rate. Abstract: Robot sports, characterized by well-defined objectives, explicit rules,.

  5. MoMaGen: Generating Demonstrations under Soft and Hard Constraints for Multi-Step Bimanual Mobile Manipulation

    Include a human-eval paper to calibrate against judge-based evaluation settings. Signals: simulation environments + demonstration data. Abstract: Imitation learning from large-scale, diverse human demonstrations has been shown to.

  6. SPACeR: Self-Play Anchoring with Centralized Reference Models

    Adds simulation environments with demonstration data for broader protocol coverage within this hub. Signals: simulation environments + demonstration data. Abstract: Developing autonomous vehicles (AVs) requires not only safety.

  7. Mastering Multi-Drone Volleyball through Hierarchical Co-Self-Play Reinforcement Learning

    Adds automatic metrics with demonstration data for broader protocol coverage within this hub. Signals: automatic metrics + demonstration data. Focus: win rate. Abstract: In this paper, we tackle.

  8. CowPilot: A Framework for Autonomous and Human-Agent Collaborative Web Navigation

    Adds automatic metrics with pairwise preferences for broader protocol coverage within this hub. Signals: automatic metrics + pairwise preferences. Focus: success rate. Abstract: While much work on web.

Known Limitations

Known Limitations

  • Only 0% of papers report quality controls; prioritize calibration/adjudication evidence.
  • Annotation unit is under-specified (15% 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 (20)
  • Pairwise Preference (2)
  • Rubric Rating (1)

Evaluation Modes

  • Automatic Metrics (5)
  • Simulation Env (3)

Top Benchmarks

  • Auditbench (1)
  • Fewmmbench (1)

Top Metrics

  • Win rate (2)
  • Cost (1)
  • Success rate (1)
  • Task success (1)

Rater Population Mix

  • Domain Experts (5)

Quality Controls

Coverage diagnostics (sample-based): human-feedback 100.0% · benchmarks 10.0% · metrics 20.0% · quality controls 0.0%.

Top Papers

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

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