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HFEPX Hub

Long Horizon + Simulation Env (Last 30 Days)

Updated from current HFEPX corpus (Mar 1, 2026). 10 papers are grouped in this hub page.

Read Full Context

Updated from current HFEPX corpus (Mar 1, 2026). 10 papers are grouped in this hub page. Common evaluation modes: Simulation Env, Human Eval. Most common rater population: Domain Experts. Common annotation unit: Trajectory. Frequent quality control: Adjudication. Frequently cited benchmark: ALFWorld. Common metric signal: success 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 15, 2026.

Papers: 10 Last published: Feb 15, 2026 Global RSS Tag RSS
Long HorizonSimulation EnvLast 30d

Researcher Quick Triage

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

High-Signal Coverage

100.0%

10 / 10 sampled papers are not low-signal flagged.

Replication-Ready Set

1

Benchmark + metric + eval mode explicitly present.

Judge/Human Comparability

0

Papers containing both `human_eval` and `llm_as_judge`.

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

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

Why This Matters (Expanded)

Why This Matters For Eval Research

  • 10% of papers report explicit human-feedback signals, led by expert verification.
  • simulation environments appears in 100% of papers in this hub.
  • ALFWorld is a recurring benchmark anchor for cross-paper comparisons in this page.
Protocol Notes (Expanded)

Protocol Takeaways

  • Most common quality-control signal is adjudication (10% of papers).
  • 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.

Benchmark Interpretation

  • ALFWorld appears in 20% of hub papers (2/10); use this cohort for benchmark-matched comparisons.
  • WebShop appears in 20% of hub papers (2/10); use this cohort for benchmark-matched comparisons.

Metric Interpretation

  • success rate is reported in 20% of hub papers (2/10); compare with a secondary metric before ranking methods.
  • pass@1 is reported in 10% of hub papers (1/10); compare with a secondary metric before ranking methods.
Researcher Checklist (Expanded)

Researcher Checklist

  • Gap: Papers with explicit human feedback

    Coverage is a replication risk (10% vs 45% target).

  • Gap: Papers reporting quality controls

    Coverage is a replication risk (10% vs 30% target).

  • Strong: Papers naming benchmarks/datasets

    Coverage is strong (60% vs 35% target).

  • Moderate: Papers naming evaluation metrics

    Coverage is usable but incomplete (30% vs 35% target).

  • Gap: Papers with known rater population

    Coverage is a replication risk (10% vs 35% target).

  • Strong: Papers with known annotation unit

    Coverage is strong (80% vs 35% target).

Strengths

  • Agentic evaluation appears in 100% of papers.

Known Gaps

  • Only 10% of papers report quality controls; prioritize calibration/adjudication evidence.
  • Rater population is under-specified (10% coverage).

Suggested Next Analyses

  • Pair this hub with llm_as_judge pages to benchmark automated-vs-human evaluation tradeoffs.
  • Stratify by benchmark (ALFWorld vs WebShop) before comparing methods.
  • Track metric sensitivity by reporting both success rate and pass@1.
  • Add inter-annotator agreement checks when reproducing these protocols.
Recommended Queries (Expanded)

Recommended Queries

Start Here (Best First 6)

Ranked for protocol completeness (human signal, benchmark + metric anchors, quality controls, and judge/human overlap).

Protocol Matrix (Top 12)

Use this to quickly compare protocol ingredients instead of scanning long prose.

Paper HF Signal Eval Modes Benchmarks Metrics QC
AD-Bench: A Real-World, Trajectory-Aware Advertising Analytics Benchmark for LLM Agents

Feb 15, 2026

Yes Simulation Env Ad Bench Pass@1 , Pass@3 Not Reported
The Trinity of Consistency as a Defining Principle for General World Models

Feb 26, 2026

No
Not Reported
Simulation Env Cow Bench Not Reported Not Reported
Hierarchy-of-Groups Policy Optimization for Long-Horizon Agentic Tasks

Feb 26, 2026

No
Not Reported
Simulation Env ALFWorld , WebShop Not Reported Not Reported
SELAUR: Self Evolving LLM Agent via Uncertainty-aware Rewards

Feb 24, 2026

No
Not Reported
Simulation Env ALFWorld , WebShop Not Reported Not Reported
Mobile-Agent-v3.5: Multi-platform Fundamental GUI Agents

Feb 15, 2026

No
Not Reported
Simulation Env WebArena , OSWorld Not Reported Not Reported
ARLArena: A Unified Framework for Stable Agentic Reinforcement Learning

Feb 25, 2026

No
Not Reported
Simulation Env Arlarena Not Reported Not Reported
FrameRef: A Framing Dataset and Simulation Testbed for Modeling Bounded Rational Information Health

Feb 17, 2026

No
Not Reported
Human Eval , Simulation Env Not Reported Not Reported Adjudication
Self-Correcting VLA: Online Action Refinement via Sparse World Imagination

Feb 25, 2026

No
Not Reported
Simulation Env Not Reported Success rate , Throughput Not Reported
LiLo-VLA: Compositional Long-Horizon Manipulation via Linked Object-Centric Policies

Feb 25, 2026

No
Not Reported
Simulation Env Not Reported Success rate Not Reported
Efficient Hierarchical Any-Angle Path Planning on Multi-Resolution 3D Grids

Feb 24, 2026

No
Not Reported
Simulation Env Not Reported Not Reported Not Reported

Protocol Diff (Top Papers)

Fast side-by-side comparison for the highest-ranked papers in this hub.

Signal AD-Bench: A Real-World, Trajectory-Aware Advertisin… The Trinity of Consistency as a Defining Principle… Hierarchy-of-Groups Policy Optimization for Long-Ho…
Human Feedback Expert VerificationNot reportedNot reported
Evaluation Modes Simulation EnvSimulation EnvSimulation Env
Benchmarks Ad BenchCow BenchALFWorld, WebShop
Metrics Pass@1, Pass@3Not reportedNot reported
Quality Controls Not reportedNot reportedNot reported
Rater Population Domain ExpertsUnknownUnknown
Annotation Unit TrajectoryTrajectoryTrajectory
Suggested Reading Order (Extended)

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

Suggested Reading Order

  1. The Trinity of Consistency as a Defining Principle for General World Models

    Start here for detailed protocol reporting and quality-control evidence. Signals: simulation environments. Focus: Cow-Bench. Abstract: CoW-Bench evaluates both video generation models and UMMs under a unified evaluation protocol.

  2. Hierarchy-of-Groups Policy Optimization for Long-Horizon Agentic Tasks

    Start here for detailed protocol reporting and quality-control evidence. Signals: simulation environments. Focus: ALFWorld. Abstract: Group-based reinforcement learning (RL), such as GRPO, has advanced the capabilities of large.

  3. Self-Correcting VLA: Online Action Refinement via Sparse World Imagination

    Start here for detailed protocol reporting and quality-control evidence. Signals: simulation environments. Focus: success rate. Abstract: Standard vision-language-action (VLA) models rely on fitting statistical data priors, limiting their.

  4. FrameRef: A Framing Dataset and Simulation Testbed for Modeling Bounded Rational Information Health

    Include a human-eval paper to calibrate against judge-based evaluation settings. Signals: human evaluation. Abstract: Human evaluation further confirms that FrameRef's generated framings measurably affect human judgment.

  5. AD-Bench: A Real-World, Trajectory-Aware Advertising Analytics Benchmark for LLM Agents

    Include a human-eval paper to calibrate against judge-based evaluation settings. Signals: simulation environments + expert verification. Focus: Ad-Bench / pass@1. Abstract: While Large Language Model (LLM) agents have.

  6. SELAUR: Self Evolving LLM Agent via Uncertainty-aware Rewards

    Adds simulation environments for broader protocol coverage within this hub. Signals: simulation environments. Focus: ALFWorld. Abstract: Large language models (LLMs) are increasingly deployed as multi-step decision-making agents, where.

  7. Mobile-Agent-v3.5: Multi-platform Fundamental GUI Agents

    Adds simulation environments for broader protocol coverage within this hub. Signals: simulation environments. Focus: WebArena. Abstract: The paper introduces GUI-Owl-1.5, the latest native GUI agent model that features.

  8. ARLArena: A Unified Framework for Stable Agentic Reinforcement Learning

    Adds simulation environments for broader protocol coverage within this hub. Signals: simulation environments. Focus: Arlarena. Abstract: Agentic reinforcement learning (ARL) has rapidly gained attention as a promising paradigm.

Known Limitations

Known Limitations

  • Only 10% of papers report quality controls; prioritize calibration/adjudication evidence.
  • Rater population is under-specified (10% 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

  • Expert Verification (1)

Evaluation Modes

  • Simulation Env (10)
  • Human Eval (1)

Top Benchmarks

  • ALFWorld (2)
  • WebShop (2)
  • Ad Bench (1)
  • Arlarena (1)

Top Metrics

  • Success rate (2)
  • Pass@1 (1)
  • Pass@3 (1)
  • Throughput (1)

Rater Population Mix

  • Domain Experts (1)

Quality Controls

  • Adjudication (1)
Coverage diagnostics (sample-based): human-feedback 10.0% · benchmarks 60.0% · metrics 30.0% · quality controls 10.0%.

Top Papers

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