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HFEPX Hub

Simulation Env + Coding Papers

Updated from current HFEPX corpus (Apr 12, 2026). 25 papers are grouped in this hub page.

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Updated from current HFEPX corpus (Apr 12, 2026). 25 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: Ad-Bench. 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: 25 Last published: Feb 15, 2026 Global RSS Tag RSS
Simulation EnvCoding

Researcher Quick Triage

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

High-Signal Coverage

100.0%

25 / 25 sampled papers are not low-signal flagged.

Replication-Ready Set

5

Benchmark + metric + eval mode explicitly present.

Judge/Human Comparability

0

Papers containing both `human_eval` and `llm_as_judge`.

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

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Why This Matters For Eval Research

  • 36% of papers report explicit human-feedback signals, led by critique/edit feedback.
  • simulation environments appears in 100% of papers in this hub.
  • Ad-Bench is a recurring benchmark anchor for cross-paper comparisons in this page.

Protocol Takeaways

  • Most common quality-control signal is adjudication (4% 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

  • Ad-Bench appears in 4% of hub papers (1/25); use this cohort for benchmark-matched comparisons.
  • AIME appears in 4% of hub papers (1/25); use this cohort for benchmark-matched comparisons.

Metric Interpretation

  • accuracy is reported in 28% of hub papers (7/25); compare with a secondary metric before ranking methods.
  • success rate is reported in 12% of hub papers (3/25); compare with a secondary metric before ranking methods.
Researcher Checklist (Expanded)

Researcher Checklist

  • Moderate: Papers with explicit human feedback

    Coverage is usable but incomplete (36% vs 45% target).

  • Gap: Papers reporting quality controls

    Coverage is a replication risk (4% vs 30% target).

  • Moderate: Papers naming benchmarks/datasets

    Coverage is usable but incomplete (32% vs 35% target).

  • Strong: Papers naming evaluation metrics

    Coverage is strong (68% vs 35% target).

  • Gap: Papers with known rater population

    Coverage is a replication risk (16% vs 35% target).

  • Strong: Papers with known annotation unit

    Coverage is strong (36% vs 35% target).

Strengths

  • Agentic evaluation appears in 64% of papers.

Known Gaps

  • Only 4% of papers report quality controls; prioritize calibration/adjudication evidence.
  • Rater population is under-specified (16% coverage).

Suggested Next Analyses

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

Recommended Queries

Start with These 3

Use these when you need one protocol anchor, one benchmark anchor, and one recent comparison point before reading the wider hub.

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
AJAR: Adaptive Jailbreak Architecture for Red-teaming

Jan 16, 2026

Yes Simulation Env Harmbench Success rate , Jailbreak success rate Not Reported
Let's Think in Two Steps: Mitigating Agreement Bias in MLLMs with Self-Grounded Verification

Jul 15, 2025

Yes Automatic Metrics , Simulation Env VisualWebArena , OSWorld Accuracy Not Reported
Don't Pass@k: A Bayesian Framework for Large Language Model Evaluation

Oct 5, 2025

Yes Automatic Metrics , Simulation Env AIME Accuracy , Pass@k Not Reported
When Users Change Their Mind: Evaluating Interruptible Agents in Long-Horizon Web Navigation

Apr 1, 2026

Yes Simulation Env WebArena , Interruptbench Not Reported Not Reported
VehicleMemBench: An Executable Benchmark for Multi-User Long-Term Memory in In-Vehicle Agents

Mar 25, 2026

Yes Simulation Env Vehiclemembench Not Reported Not Reported
PRBench: End-to-end Paper Reproduction in Physics Research

Mar 29, 2026

Yes Automatic Metrics , Simulation Env Not Reported Accuracy , Success rate Not Reported
LUDOBENCH: Evaluating LLM Behavioural Decision-Making Through Spot-Based Board Game Scenarios in Ludo

Apr 7, 2026

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

Feb 26, 2026

No
Not Reported
Simulation Env ALFWorld , WebShop 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
JAWS: Enhancing Long-term Rollout of Neural PDE Solvers via Spatially-Adaptive Jacobian Regularization

Mar 4, 2026

No
Not Reported
Automatic Metrics , Simulation Env Not Reported Accuracy 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… AJAR: Adaptive Jailbreak Architecture for Red-teami… Let's Think in Two Steps: Mitigating Agreement Bias…
Human Feedback Expert VerificationRed TeamPairwise Preference
Evaluation Modes Simulation EnvSimulation EnvAutomatic Metrics, Simulation Env
Benchmarks Ad BenchHarmbenchVisualWebArena, OSWorld
Metrics Pass@1, Pass@3Success rate, Jailbreak success rateAccuracy
Quality Controls Not reportedNot reportedNot reported
Rater Population Domain ExpertsUnknownUnknown
Annotation Unit TrajectoryUnknownTrajectory
Suggested Reading Order (Extended)

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

Suggested Reading Order

  1. LUDOBENCH: Evaluating LLM Behavioural Decision-Making Through Spot-Based Board Game Scenarios in Ludo

    Start here for detailed protocol reporting and quality-control evidence. Signals: simulation environments. Focus: Ludobench / dice. Abstract: We introduce LudoBench, a benchmark for evaluating LLM strategic reasoning in.

  2. When Users Change Their Mind: Evaluating Interruptible Agents in Long-Horizon Web Navigation

    Start here for detailed protocol reporting and quality-control evidence. Signals: simulation environments + critique/edit feedback. Focus: WebArena. Abstract: As LLM agents transition from short, static problem solving to.

  3. PRBench: End-to-end Paper Reproduction in Physics Research

    Start here for detailed protocol reporting and quality-control evidence. Signals: automatic metrics + rubric ratings. Focus: accuracy. Abstract: All tasks are contributed by domain experts from over 20.

  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. Let's Think in Two Steps: Mitigating Agreement Bias in MLLMs with Self-Grounded Verification

    Include an LLM-as-judge paper to test judge design and agreement assumptions. Signals: automatic metrics + pairwise preferences. Focus: VisualWebArena / accuracy. Abstract: Multimodal LLMs (MLLMs) offer a promising.

  7. Don't Pass@k: A Bayesian Framework for Large Language Model Evaluation

    Adds automatic metrics with rubric ratings for broader protocol coverage within this hub. Signals: automatic metrics + rubric ratings. Focus: AIME / accuracy. Abstract: Evaluation outcomes are modeled.

  8. VehicleMemBench: An Executable Benchmark for Multi-User Long-Term Memory in In-Vehicle Agents

    Adds simulation environments with pairwise preferences for broader protocol coverage within this hub. Signals: simulation environments + pairwise preferences. Focus: Vehiclemembench. Abstract: This evolution requires agents to continuously.

Known Limitations

Known Limitations

  • Only 4% of papers report quality controls; prioritize calibration/adjudication evidence.
  • Rater population is under-specified (16% 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 (2)
  • Rubric Rating (2)

Evaluation Modes

  • Simulation Env (25)
  • Automatic Metrics (8)
  • Human Eval (1)

Top Benchmarks

  • Ad Bench (1)
  • AIME (1)
  • ALFWorld (1)
  • Harmbench (1)

Top Metrics

  • Accuracy (7)
  • Success rate (3)
  • Pass@1 (2)
  • Agreement (1)

Rater Population Mix

  • Domain Experts (4)

Quality Controls

  • Adjudication (1)
Coverage diagnostics (sample-based): human-feedback 36.0% · benchmarks 32.0% · metrics 68.0% · quality controls 4.0%.

Top Papers

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