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HFEPX Hub

Simulation Env Papers (Last 120 Days)

Updated from current HFEPX corpus (Mar 1, 2026). 48 papers are grouped in this hub page.

Read Full Context

Updated from current HFEPX corpus (Mar 1, 2026). 48 papers are grouped in this hub page. Common evaluation modes: Simulation Env, Automatic Metrics. Most common rater population: Domain Experts. Common annotation unit: Trajectory. Frequent quality control: Adjudication. Frequently cited benchmark: ALFWorld. 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 Feb 15, 2026.

Papers: 48 Last published: Feb 15, 2026 Global RSS Tag RSS
Simulation EnvLast 120d

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%

48 / 48 sampled papers are not low-signal flagged.

Replication-Ready Set

3

Benchmark + metric + eval mode explicitly present.

Judge/Human Comparability

0

Papers containing both `human_eval` and `llm_as_judge`.

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

Primary action: Start with the top 2 papers in “Start Here”, then validate assumptions in the protocol matrix.

Why This Matters (Expanded)

Why This Matters For Eval Research

  • 20% of papers report explicit human-feedback signals, led by critique/edit feedback.
  • simulation environments appears in 62.5% 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 (2.1% of papers).
  • Rater context is mostly domain experts, and annotation is commonly trajectory-level annotation; use this to scope replication staffing.
  • Compare papers that report both human_eval and llm_as_judge to quantify judge-human agreement drift.

Benchmark Interpretation

  • ALFWorld appears in 10% of hub papers (3/48); use this cohort for benchmark-matched comparisons.
  • WebShop appears in 6.7% of hub papers (2/48); use this cohort for benchmark-matched comparisons.

Metric Interpretation

  • accuracy is reported in 16.7% of hub papers (5/48); compare with a secondary metric before ranking methods.
  • coherence is reported in 10% of hub papers (3/48); compare with a secondary metric before ranking methods.
Researcher Checklist (Expanded)

Researcher Checklist

  • Gap: Papers with explicit human feedback

    Coverage is a replication risk (20% vs 45% target).

  • Gap: Papers reporting quality controls

    Coverage is a replication risk (3.3% vs 30% target).

  • Strong: Papers naming benchmarks/datasets

    Coverage is strong (36.7% vs 35% target).

  • Strong: Papers naming evaluation metrics

    Coverage is strong (43.3% vs 35% target).

  • Moderate: Papers with known rater population

    Coverage is usable but incomplete (23.3% vs 35% target).

  • Moderate: Papers with known annotation unit

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

Strengths

  • Most papers provide measurable evaluation context (36.7% benchmarks, 43.3% metrics).
  • Contains both human-eval and LLM-as-judge protocols for head-to-head methodology comparison.
  • Agentic evaluation appears in 76.7% of papers.

Known Gaps

  • Only 3.3% of papers report quality controls; prioritize calibration/adjudication evidence.
  • Rater population is under-specified (23.3% coverage).
  • LLM-as-judge appears without enough inter-annotator agreement reporting.

Suggested Next Analyses

  • Compare papers that report both human_eval and llm_as_judge to quantify judge-human agreement drift.
  • Stratify by benchmark (ALFWorld vs WebShop) before comparing methods.
  • Track metric sensitivity by reporting both accuracy and coherence.
  • 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
From Pixels to Policies: Reinforcing Spatial Reasoning in Language Models for Content-Aware Layout Design

Feb 14, 2026

Yes Simulation Env Not Reported Latency Not Reported
Fast-ThinkAct: Efficient Vision-Language-Action Reasoning via Verbalizable Latent Planning

Jan 14, 2026

Yes Simulation Env Not Reported Latency Not Reported
Embodied Task Planning via Graph-Informed Action Generation with Large Language Model

Jan 29, 2026

No
Not Reported
Simulation Env ALFWorld Pass@1 , Cost Not Reported
TherapyProbe: Generating Design Knowledge for Relational Safety in Mental Health Chatbots Through Adversarial Simulation

Feb 26, 2026

Yes Simulation Env Not Reported Not Reported Not Reported
Assessing Risks of Large Language Models in Mental Health Support: A Framework for Automated Clinical AI Red Teaming

Feb 23, 2026

Yes Simulation Env Not Reported Not Reported Not Reported
World-Model-Augmented Web Agents with Action Correction

Feb 17, 2026

No
Not Reported
Llm As Judge , Simulation Env VisualWebArena , Mind2Web Not Reported 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

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… From Pixels to Policies: Reinforcing Spatial Reason… Fast-ThinkAct: Efficient Vision-Language-Action Rea…
Human Feedback Expert VerificationCritique EditPairwise Preference
Evaluation Modes Simulation EnvSimulation EnvSimulation Env
Benchmarks Ad BenchNot reportedNot reported
Metrics Pass@1, Pass@3LatencyLatency
Quality Controls Not reportedNot reportedNot reported
Rater Population Domain ExpertsUnknownUnknown
Annotation Unit TrajectoryUnknownUnknown
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. TherapyProbe: Generating Design Knowledge for Relational Safety in Mental Health Chatbots Through Adversarial Simulation

    Start here for detailed protocol reporting and quality-control evidence. Signals: simulation environments + expert verification. Abstract: As mental health chatbots proliferate to address the global treatment gap, a.

  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. World-Model-Augmented Web Agents with Action Correction

    Include an LLM-as-judge paper to test judge design and agreement assumptions. Signals: LLM-as-judge. Focus: VisualWebArena. Abstract: A world model, specialized in environmental state transitions, simulates action outcomes, which.

  7. Fast-ThinkAct: Efficient Vision-Language-Action Reasoning via Verbalizable Latent Planning

    Adds simulation environments with pairwise preferences for broader protocol coverage within this hub. Signals: simulation environments + pairwise preferences. Focus: latency. Abstract: Fast-ThinkAct learns to reason efficiently with.

  8. Embodied Task Planning via Graph-Informed Action Generation with Large Language Model

    Adds simulation environments for broader protocol coverage within this hub. Signals: simulation environments. Focus: ALFWorld / pass@1. Abstract: While Large Language Models (LLMs) have demonstrated strong zero-shot reasoning.

Known Limitations

Known Limitations

  • Only 3.3% of papers report quality controls; prioritize calibration/adjudication evidence.
  • Rater population is under-specified (23.3% 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

  • Critique Edit (2)
  • Expert Verification (2)
  • Pairwise Preference (1)
  • Red Team (1)

Evaluation Modes

  • Simulation Env (30)
  • Automatic Metrics (5)
  • Human Eval (1)
  • Llm As Judge (1)

Top Benchmarks

  • ALFWorld (3)
  • WebShop (2)
  • Ad Bench (1)
  • Arlarena (1)

Top Metrics

  • Accuracy (5)
  • Coherence (3)
  • Cost (3)
  • Latency (2)

Rater Population Mix

  • Domain Experts (7)

Quality Controls

  • Adjudication (1)
Coverage diagnostics (sample-based): human-feedback 12.5% · benchmarks 22.9% · metrics 31.3% · quality controls 6.3%.

Top Papers

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