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HFEPX Hub

Simulation Env + General Papers

Updated from current HFEPX corpus (Apr 12, 2026). 80 papers are grouped in this hub page.

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Updated from current HFEPX corpus (Apr 12, 2026). 80 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: 80 Last published: Mar 22, 2026 Global RSS Tag RSS
Simulation EnvGeneral

Researcher Quick Triage

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

Analysis blocks below are computed from the currently loaded sample (60 of 80 total papers in this hub).

All Sampled Papers (60) Replication-Ready Only (7)

High-Signal Coverage

100.0%

60 / 60 sampled papers are not low-signal flagged.

Replication-Ready Set

7

Benchmark + metric + eval mode explicitly present.

Judge/Human Comparability

1

Papers containing both `human_eval` and `llm_as_judge`.

  • 7 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 (7 papers).

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Why This Matters For Eval Research

  • 27.5% 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 (1.3% 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 (5/80); use this cohort for benchmark-matched comparisons.
  • WebArena appears in 5% of hub papers (4/80); use this cohort for benchmark-matched comparisons.

Metric Interpretation

  • accuracy is reported in 23.8% of hub papers (19/80); compare with a secondary metric before ranking methods.
  • cost is reported in 11.3% of hub papers (9/80); compare with a secondary metric before ranking methods.
Researcher Checklist (Expanded)

Researcher Checklist

  • Moderate: Papers with explicit human feedback

    Coverage is usable but incomplete (27.5% vs 45% target).

  • Gap: Papers reporting quality controls

    Coverage is a replication risk (1.3% vs 30% target).

  • Moderate: Papers naming benchmarks/datasets

    Coverage is usable but incomplete (25% vs 35% target).

  • Strong: Papers naming evaluation metrics

    Coverage is strong (50% vs 35% target).

  • Gap: Papers with known rater population

    Coverage is a replication risk (8.8% vs 35% target).

  • Moderate: Papers with known annotation unit

    Coverage is usable but incomplete (25% vs 35% target).

Strengths

  • Contains both human-eval and LLM-as-judge protocols for head-to-head methodology comparison.
  • Agentic evaluation appears in 66.3% of papers.

Known Gaps

  • Only 1.3% of papers report quality controls; prioritize calibration/adjudication evidence.
  • Rater population is under-specified (8.8% 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 WebArena) 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 Cost , Token cost 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
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
Go-Browse: Training Web Agents with Structured Exploration

Jun 4, 2025

 No
Not Reported
Simulation Env WebArena 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 Embodied Task Planning via Graph-Informed Action Ge…
Human Feedback DemonstrationsNot reportedNot reported
Evaluation Modes Human Eval, Llm As JudgeSimulation EnvSimulation Env
Benchmarks WebArena, ToolBenchALFWorldALFWorld
Metrics Precision, Pass@1Cost, Token costPass@1, Cost
Quality Controls Not reportedNot reportedNot reported
Rater Population UnknownUnknownUnknown
Annotation Unit TrajectoryTrajectoryFreeform
Suggested Reading Order (Extended)

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

Suggested Reading Order

  1. 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.

  2. ReDAct: Uncertainty-Aware Deferral for LLM Agents

    Start here for detailed protocol reporting and quality-control evidence. Signals: simulation environments. Focus: ALFWorld / cost. Abstract: Recently, LLM-based agents have become increasingly popular across many applications, including.

  3. 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.

  4. EpidemIQs: Prompt-to-Paper LLM Agents for Epidemic Modeling and Analysis

    Include a human-eval paper to calibrate against judge-based evaluation settings. Signals: LLM-as-judge + expert verification. Focus: success rate. Abstract: We evaluate EpidemIQs across several different epidemic scenarios, measuring.

  5. VolleyBots: A Testbed for Multi-Drone Volleyball Game Combining Motion Control and Strategic Play

    Adds automatic metrics with demonstration data for broader protocol coverage within this hub. Signals: automatic metrics + demonstration data. Focus: win rate. Abstract: Robot sports, characterized by well-defined.

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

    Adds simulation environments with demonstration data for broader protocol coverage within this hub. Signals: simulation environments + demonstration data. Focus: Mapg-Bench. Abstract: Robots collaborating with humans must convert.

  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,.

Known Limitations

Known Limitations

  • Only 1.3% of papers report quality controls; prioritize calibration/adjudication evidence.
  • Rater population is under-specified (8.8% 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 (11)
  • Pairwise Preference (7)
  • Rubric Rating (2)
  • Critique Edit (1)

Evaluation Modes

  • Simulation Env (80)
  • Automatic Metrics (22)
  • Llm As Judge (7)
  • Human Eval (2)

Top Benchmarks

  • ALFWorld (5)
  • WebArena (4)
  • HotpotQA (2)
  • OSWorld (2)

Top Metrics

  • Accuracy (19)
  • Cost (9)
  • Success rate (5)
  • Coherence (3)

Rater Population Mix

  • Domain Experts (6)
  • Mixed (1)

Quality Controls

  • Calibration (1)
Coverage diagnostics (sample-based): human-feedback 36.7% · benchmarks 31.7% · metrics 45.0% · quality controls 1.7%.

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.

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

    Xiang Li, Ning Yan, Masood Mortazavi · Jan 29, 2026 · Citations: 0

    Simulation Env Long Horizon

    We propose GiG, a novel planning framework that structures embodied agents' memory using a Graph-in-Graph architecture.

  • Go-Browse: Training Web Agents with Structured Exploration

    Apurva Gandhi, Graham Neubig · Jun 4, 2025 · Citations: 0

    Simulation Env Web Browsing

    To address this, we propose Go-Browse, a method for automatically collecting diverse and realistic web agent data at scale through structured exploration of web environments.

  • 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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