Skip to content
← Back to explorer

HFEPX Hub

Simulation Env + General (Last 30 Days)

Updated from current HFEPX corpus (Apr 17, 2026). 21 papers are grouped in this hub page.

Read Full Context

Updated from current HFEPX corpus (Apr 17, 2026). 21 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: Calibration. 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 Mar 22, 2026.

Papers: 21 Last published: Mar 22, 2026 Global RSS Tag RSS
Simulation EnvGeneralLast 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%

21 / 21 sampled papers are not low-signal flagged.

Replication-Ready Set

2

Benchmark + metric + eval mode explicitly present.

Judge/Human Comparability

1

Papers containing both `human_eval` and `llm_as_judge`.

  • 2 papers are replication-ready (benchmark + metric + explicit evaluation mode).
  • 1 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.

Need evaluators for this research workflow?

Post a Job →

Why This Matters For Eval Research

  • 19% of papers report explicit human-feedback signals, led by demonstration data.
  • simulation environments appears in 100% of papers in this hub.
  • ALFWorld is a recurring benchmark anchor for cross-paper comparisons in this page.

Protocol Takeaways

  • 1 sampled papers report both human evaluation and LLM-as-judge, supporting direct agreement checks.
  • Most common quality-control signal is rater calibration (4.8% of papers).
  • Rater context is mostly domain experts, and annotation is commonly trajectory-level annotation; use this to scope replication staffing.

Benchmark Interpretation

  • ALFWorld appears in 4.8% of hub papers (1/21); use this cohort for benchmark-matched comparisons.
  • Mapg-Bench appears in 4.8% of hub papers (1/21); use this cohort for benchmark-matched comparisons.

Metric Interpretation

  • accuracy is reported in 33.3% of hub papers (7/21); compare with a secondary metric before ranking methods.
  • cost is reported in 9.5% of hub papers (2/21); compare with a secondary metric before ranking methods.
Researcher Checklist (Expanded)

Researcher Checklist

  • Gap: Papers with explicit human feedback

    Coverage is a replication risk (19% vs 45% target).

  • Gap: Papers reporting quality controls

    Coverage is a replication risk (4.8% vs 30% target).

  • Gap: Papers naming benchmarks/datasets

    Coverage is a replication risk (19% vs 35% target).

  • Strong: Papers naming evaluation metrics

    Coverage is strong (57.1% vs 35% target).

  • Gap: Papers with known rater population

    Coverage is a replication risk (9.5% vs 35% target).

  • Moderate: Papers with known annotation unit

    Coverage is usable but incomplete (28.6% vs 35% target).

Strengths

  • Contains both human-eval and LLM-as-judge protocols for head-to-head methodology comparison.
  • Agentic evaluation appears in 71.4% of papers.

Known Gaps

  • Only 4.8% of papers report quality controls; prioritize calibration/adjudication evidence.
  • Rater population is under-specified (9.5% coverage).
  • Benchmark coverage is thin (19% of papers mention benchmarks/datasets).

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 Mapg-Bench) before comparing methods.
  • Track metric sensitivity by reporting both accuracy and cost.
  • 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
AgentHER: Hindsight Experience Replay for LLM Agent Trajectory Relabeling

Mar 22, 2026

Yes Human Eval , Llm As Judge WebArena , ToolBench Precision , Pass@1 Not Reported
Meanings and Measurements: Multi-Agent Probabilistic Grounding for Vision-Language Navigation

Mar 19, 2026

Yes Simulation Env Mapg Bench Not Reported Not Reported
ReDAct: Uncertainty-Aware Deferral for LLM Agents

Apr 8, 2026

No
Not Reported
Simulation Env ALFWorld Token cost Not Reported
SEAL: An Open, Auditable, and Fair Data Generation Framework for AI-Native 6G Networks

Apr 2, 2026

No
Not Reported
Automatic Metrics , Simulation Env Not Reported Accuracy Calibration
SOLE-R1: Video-Language Reasoning as the Sole Reward for On-Robot Reinforcement Learning

Mar 30, 2026

Yes Simulation Env Not Reported Not Reported Not Reported
I Can't Believe It's Corrupt: Evaluating Corruption in Multi-Agent Governance Systems

Mar 19, 2026

Yes Simulation Env Not Reported Not Reported Not Reported
OccuBench: Evaluating AI Agents on Real-World Professional Tasks via Language Environment Simulation

Apr 13, 2026

No
Not Reported
Simulation Env Occubench Not Reported Not Reported
Box Maze: A Process-Control Architecture for Reliable LLM Reasoning

Mar 19, 2026

Yes
Not Reported
Simulation Env Not Reported Not Reported Not Reported
Social Dynamics as Critical Vulnerabilities that Undermine Objective Decision-Making in LLM Collectives

Apr 7, 2026

No
Not Reported
Automatic Metrics , Simulation Env Not Reported Accuracy Not Reported
Spatio-Temporal Attention Enhanced Multi-Agent DRL for UAV-Assisted Wireless Networks with Limited Communications

Mar 23, 2026

No
Not Reported
Simulation Env Not Reported Not Reported Not Reported
ActionParty: Multi-Subject Action Binding in Generative Video Games

Apr 2, 2026

No
Not Reported
Automatic Metrics , Simulation Env Not Reported Accuracy Not Reported
MolQuest: A Benchmark for Agentic Evaluation of Abductive Reasoning in Chemical Structure Elucidation

Mar 26, 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 AgentHER: Hindsight Experience Replay for LLM Agent… Meanings and Measurements: Multi-Agent Probabilisti… ReDAct: Uncertainty-Aware Deferral for LLM Agents
Human Feedback DemonstrationsDemonstrationsNot reported
Evaluation Modes Human Eval, Llm As JudgeSimulation EnvSimulation Env
Benchmarks WebArena, ToolBenchMapg BenchALFWorld
Metrics Precision, Pass@1Not reportedToken cost
Quality Controls Not reportedNot reportedNot reported
Rater Population UnknownUnknownUnknown
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. OccuBench: Evaluating AI Agents on Real-World Professional Tasks via Language Environment Simulation

    Start here for detailed protocol reporting and quality-control evidence. Signals: simulation environments. Focus: Occubench. Abstract: AI agents are expected to perform professional work across hundreds of occupational domains.

  2. Towards Real-world Human Behavior Simulation: Benchmarking Large Language Models on Long-horizon, Cross-scenario, Heterogeneous Behavior Traces

    Start here for detailed protocol reporting and quality-control evidence. Signals: simulation environments. Abstract: The emergence of Large Language Models (LLMs) has illuminated the potential for a general-purpose user.

  3. Sell More, Play Less: Benchmarking LLM Realistic Selling Skill

    Start here for detailed protocol reporting and quality-control evidence. Signals: human evaluation. Abstract: We propose a fully automatic evaluation pipeline that combines (i) an LLM-based rater for sales-process.

  4. AgentHER: Hindsight Experience Replay for LLM Agent Trajectory Relabeling

    Include a human-eval paper to calibrate against judge-based evaluation settings. Signals: human evaluation + demonstration data. Focus: WebArena / precision. Abstract: AgentHER realises this idea through a four-stage.

  5. Meanings and Measurements: Multi-Agent Probabilistic Grounding for Vision-Language Navigation

    Include a human-eval paper to calibrate against judge-based evaluation settings. Signals: simulation environments + demonstration data. Focus: Mapg-Bench. Abstract: Robots collaborating with humans must convert natural language goals.

  6. SOLE-R1: Video-Language Reasoning as the Sole Reward for On-Robot Reinforcement Learning

    Adds simulation environments with demonstration data for broader protocol coverage within this hub. Signals: simulation environments + demonstration data. Abstract: Vision-language models (VLMs) have shown impressive capabilities across.

  7. I Can't Believe It's Corrupt: Evaluating Corruption in Multi-Agent Governance Systems

    Adds simulation environments with rubric ratings for broader protocol coverage within this hub. Signals: simulation environments + rubric ratings. Abstract: We evaluate multi-agent governance simulations in which agents.

  8. ReDAct: Uncertainty-Aware Deferral for LLM Agents

    Adds simulation environments for broader protocol coverage within this hub. Signals: simulation environments. Focus: ALFWorld / cost. Abstract: Recently, LLM-based agents have become increasingly popular across many applications,.

Known Limitations

Known Limitations

  • Only 4.8% of papers report quality controls; prioritize calibration/adjudication evidence.
  • Rater population is under-specified (9.5% 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 (3)
  • Rubric Rating (1)

Evaluation Modes

  • Simulation Env (21)
  • Automatic Metrics (8)
  • Human Eval (2)
  • Llm As Judge (1)

Top Benchmarks

  • ALFWorld (1)
  • Mapg Bench (1)
  • Occubench (1)
  • ToolBench (1)

Top Metrics

  • Accuracy (7)
  • Cost (2)
  • Latency (1)
  • Pass@1 (1)

Rater Population Mix

  • Domain Experts (1)
  • Mixed (1)

Quality Controls

  • Calibration (1)
Coverage diagnostics (sample-based): human-feedback 23.8% · benchmarks 19.0% · metrics 57.1% · quality controls 4.8%.

Top Papers

Related Hubs

Get Started

Join the #1 Platform for AI Training Talent

Where top AI builders and expert AI Trainers connect to build the future of AI.
Self-Service
Post a Job
Post your project and get a shortlist of qualified AI Trainers and Data Labelers. Hire and manage your team in the tools you already use.
Managed Service
For Large Projects
Done-for-You
We recruit, onboard, and manage a dedicated team inside your tools. End-to-end operations for large or complex projects.
For Freelancers
Join as an AI Trainer
Find AI training and data labeling projects across platforms, all in one place. One profile, one application process, more opportunities.