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HFEPX Hub

Simulation Env + General (Last 45 Days)

Updated from current HFEPX corpus (Apr 18, 2026). 32 papers are grouped in this hub page.

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Updated from current HFEPX corpus (Apr 18, 2026). 32 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: 32 Last published: Mar 22, 2026 Global RSS Tag RSS
Simulation EnvGeneralLast 45d

Researcher Quick Triage

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

All Sampled Papers (32) Replication-Ready Only (5)

High-Signal Coverage

100.0%

32 / 32 sampled papers are not low-signal flagged.

Replication-Ready Set

5

Benchmark + metric + eval mode explicitly present.

Judge/Human Comparability

1

Papers containing both `human_eval` and `llm_as_judge`.

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

Currently showing only replication-ready papers in ranking and matrix sections (5 papers).

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Why This Matters For Eval Research

  • 25% 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 (3.1% 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 6.3% of hub papers (2/32); use this cohort for benchmark-matched comparisons.
  • BIRD appears in 3.1% of hub papers (1/32); use this cohort for benchmark-matched comparisons.

Metric Interpretation

  • accuracy is reported in 28.1% of hub papers (9/32); compare with a secondary metric before ranking methods.
  • cost is reported in 6.3% of hub papers (2/32); compare with a secondary metric before ranking methods.
Researcher Checklist (Expanded)

Researcher Checklist

  • Gap: Papers with explicit human feedback

    Coverage is a replication risk (25% vs 45% target).

  • Gap: Papers reporting quality controls

    Coverage is a replication risk (3.1% vs 30% target).

  • Moderate: Papers naming benchmarks/datasets

    Coverage is usable but incomplete (31.3% vs 35% target).

  • Strong: Papers naming evaluation metrics

    Coverage is strong (53.1% vs 35% target).

  • Gap: Papers with known rater population

    Coverage is a replication risk (6.3% vs 35% target).

  • Moderate: Papers with known annotation unit

    Coverage is usable but incomplete (21.9% vs 35% target).

Strengths

  • Contains both human-eval and LLM-as-judge protocols for head-to-head methodology comparison.
  • Agentic evaluation appears in 68.8% of papers.

Known Gaps

  • Only 3.1% of papers report quality controls; prioritize calibration/adjudication evidence.
  • Rater population is under-specified (6.3% coverage).
  • Annotation unit is under-specified (21.9% coverage).

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 BIRD) 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

Judge vs Human Agreement Benchmark Slice: ALFWorld Metric Slice: accuracy Recent High-Signal Papers

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
Demonstrations
Human Eval , Llm As Judge WebArena , ToolBench Precision , Pass@1 Not Reported
ReDAct: Uncertainty-Aware Deferral for LLM Agents

Apr 8, 2026

 No
Not Reported
Simulation Env ALFWorld Token cost Not Reported
DeceptGuard :A Constitutional Oversight Framework For Detecting Deception in LLM Agents

Mar 14, 2026

 No
Not Reported
Automatic Metrics , Simulation Env Deceptarena Faithfulness Not Reported
BEACON: Language-Conditioned Navigation Affordance Prediction under Occlusion

Mar 10, 2026

 No
Not Reported
Automatic Metrics , Simulation Env BIRD Accuracy Not Reported
Reward Prediction with Factorized World States

Mar 10, 2026

 No
Not Reported
Llm As Judge , Simulation Env ALFWorld Success rate 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… ReDAct: Uncertainty-Aware Deferral for LLM Agents DeceptGuard :A Constitutional Oversight Framework F…
Human Feedback DemonstrationsNot reportedNot reported
Evaluation Modes Human Eval, Llm As JudgeSimulation EnvAutomatic Metrics, Simulation Env
Benchmarks WebArena, ToolBenchALFWorldDeceptarena
Metrics Precision, Pass@1Token costFaithfulness
Quality Controls Not reportedNot reportedNot reported
Rater Population UnknownUnknownUnknown
Annotation Unit TrajectoryTrajectoryUnknown
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. MAPO: Mixed Advantage Policy Optimization for Long-Horizon Multi-Turn Dialogue

    Include an LLM-as-judge paper to test judge design and agreement assumptions. Signals: LLM-as-judge. Focus: Emobench. Abstract: We propose a critic-free and efficient RL algorithm named MAPO that leverages.

  7. LifeSim: Long-Horizon User Life Simulator for Personalized Assistant Evaluation

    Adds simulation environments with pairwise preferences for broader protocol coverage within this hub. Signals: simulation environments + pairwise preferences. Focus: Lifesim-Eval. Abstract: Under both single-scenario and long-horizon settings,.

  8. 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.

Known Limitations

Known Limitations

  • Only 3.1% of papers report quality controls; prioritize calibration/adjudication evidence.
  • Rater population is under-specified (6.3% 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

  • Demonstrations (4)
  • Pairwise Preference (2)
  • Red Team (1)
  • Rubric Rating (1)

Evaluation Modes

  • Simulation Env (32)
  • Automatic Metrics (11)
  • Llm As Judge (3)
  • Human Eval (2)

Top Benchmarks

  • ALFWorld (2)
  • BIRD (1)
  • Deceptarena (1)
  • Emobench (1)

Top Metrics

  • Accuracy (9)
  • Cost (2)
  • Success rate (2)
  • Faithfulness (1)

Rater Population Mix

  • Domain Experts (1)
  • Mixed (1)

Quality Controls

  • Calibration (1)
Coverage diagnostics (sample-based): human-feedback 28.1% · benchmarks 31.3% · metrics 53.1% · quality controls 3.1%.

Top Papers

  • AgentHER: Hindsight Experience Replay for LLM Agent Trajectory Relabeling

    Liang Ding · Mar 22, 2026 · Citations: 0

    Demonstrations Human EvalLlm As Judge Long Horizon

    LLM agents fail on the majority of real-world tasks -- GPT-4o succeeds on fewer than 15% of WebArena navigation tasks and below 55% pass@1 on ToolBench (Zhou et al., 2024; Qin et al., 2024) -- yet every failed trajectory is routinely…

  • ReDAct: Uncertainty-Aware Deferral for LLM Agents

    Dzianis Piatrashyn, Nikita Kotelevskii, Kirill Grishchenkov, Nikita Glazkov, Ivan Nasonov · Apr 8, 2026 · Citations: 0

    Simulation Env Long Horizon

    Recently, LLM-based agents have become increasingly popular across many applications, including complex sequential decision-making problems.

  • DeceptGuard :A Constitutional Oversight Framework For Detecting Deception in LLM Agents

    Snehasis Mukhopadhyay · Mar 14, 2026 · Citations: 0

    Automatic MetricsSimulation Env Long Horizon

    We introduce DECEPTGUARD, a unified framework that systematically compares three monitoring regimes: black-box monitors (actions and outputs only), CoT-aware monitors (additionally observing the agent's chain-of-thought reasoning trace),…

  • BEACON: Language-Conditioned Navigation Affordance Prediction under Occlusion

    Xinyu Gao, Gang Chen, Javier Alonso-Mora · Mar 10, 2026 · Citations: 0

    Automatic MetricsSimulation Env Web Browsing

    As a result, they struggle to infer target locations in occluded regions, typically caused by furniture or moving humans.

  • Reward Prediction with Factorized World States

    Yijun Shen, Delong Chen, Xianming Hu, Jiaming Mi, Hongbo Zhao · Mar 10, 2026 · Citations: 0

    Llm As JudgeSimulation Env

    We evaluate on RewardPrediction, a new benchmark dataset spanning five diverse domains and comprising 2,454 unique action-observation trajectories with step-wise ground-truth rewards.

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