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HFEPX Hub

Web Browsing + Simulation Env (Last 60 Days)

Updated from current HFEPX corpus (Apr 9, 2026). 11 papers are grouped in this hub page.

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Updated from current HFEPX corpus (Apr 9, 2026). 11 papers are grouped in this hub page. Common evaluation modes: Simulation Env, Automatic Metrics. Most common rater population: Domain Experts. Common annotation unit: Trajectory. Frequently cited benchmark: WebArena. 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: 11 Last published: Mar 22, 2026 Global RSS Tag RSS
Web BrowsingSimulation EnvLast 60d

Researcher Quick Triage

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

All Sampled Papers (11) Replication-Ready Only (3)

High-Signal Coverage

100.0%

11 / 11 sampled papers are not low-signal flagged.

Replication-Ready Set

3

Benchmark + metric + eval mode explicitly present.

Judge/Human Comparability

1

Papers containing both `human_eval` and `llm_as_judge`.

  • 3 papers are replication-ready (benchmark + metric + explicit evaluation mode).
  • 1 papers support judge-vs-human agreement analysis.
  • 0 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

  • 27.3% of papers report explicit human-feedback signals, led by demonstration data.
  • simulation environments appears in 100% of papers in this hub.
  • WebArena 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.
  • Quality-control reporting is sparse in this slice; prioritize papers with explicit calibration or adjudication steps.
  • Rater context is mostly domain experts, and annotation is commonly trajectory-level annotation; use this to scope replication staffing.

Benchmark Interpretation

  • WebArena appears in 27.3% of hub papers (3/11); use this cohort for benchmark-matched comparisons.
  • BIRD appears in 9.1% of hub papers (1/11); use this cohort for benchmark-matched comparisons.

Metric Interpretation

  • accuracy is reported in 27.3% of hub papers (3/11); compare with a secondary metric before ranking methods.
  • cost is reported in 9.1% of hub papers (1/11); compare with a secondary metric before ranking methods.
Researcher Checklist (Expanded)

Researcher Checklist

  • Moderate: Papers with explicit human feedback

    Coverage is usable but incomplete (27.3% vs 45% target).

  • Gap: Papers reporting quality controls

    Coverage is a replication risk (0% vs 30% target).

  • Strong: Papers naming benchmarks/datasets

    Coverage is strong (63.6% vs 35% target).

  • Strong: Papers naming evaluation metrics

    Coverage is strong (54.5% vs 35% target).

  • Gap: Papers with known rater population

    Coverage is a replication risk (9.1% vs 35% target).

  • Moderate: Papers with known annotation unit

    Coverage is usable but incomplete (27.3% vs 35% target).

Strengths

  • Most papers provide measurable evaluation context (63.6% benchmarks, 54.5% metrics).
  • Contains both human-eval and LLM-as-judge protocols for head-to-head methodology comparison.
  • Agentic evaluation appears in 100% of papers.

Known Gaps

  • Only 0% of papers report quality controls; prioritize calibration/adjudication evidence.
  • Rater population is under-specified (9.1% 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 (WebArena vs BIRD) before comparing methods.
  • Track metric sensitivity by reporting both accuracy and cost.
Recommended Queries (Expanded)

Recommended Queries

Judge vs Human Agreement Benchmark Slice: WebArena 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
When Users Change Their Mind: Evaluating Interruptible Agents in Long-Horizon Web Navigation

Apr 1, 2026

 Yes
Critique Edit
Simulation Env WebArena , Interruptbench Not Reported Not Reported
Meanings and Measurements: Multi-Agent Probabilistic Grounding for Vision-Language Navigation

Mar 19, 2026

 Yes
Demonstrations
Simulation Env Mapg Bench Not Reported 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
BEACON: Language-Conditioned Navigation Affordance Prediction under Occlusion

Mar 10, 2026

 No
Not Reported
Automatic Metrics , Simulation Env BIRD Accuracy 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
Mind over Space: Can Multimodal Large Language Models Mentally Navigate?

Mar 23, 2026

 No
Not Reported
Automatic Metrics , Simulation Env Not Reported Accuracy Not Reported
Integrating Deep RL and Bayesian Inference for ObjectNav in Mobile Robotics

Mar 26, 2026

 No
Not Reported
Simulation Env Not Reported Success rate Not Reported
BrowseComp-$V^3$: A Visual, Vertical, and Verifiable Benchmark for Multimodal Browsing Agents

Feb 13, 2026

 No
Not Reported
Automatic Metrics , Simulation Env Not Reported Accuracy Not Reported
Efficient Hierarchical Any-Angle Path Planning on Multi-Resolution 3D Grids

Feb 24, 2026

 No
Not Reported
Simulation Env Not Reported Not Reported Not Reported
Contextual Safety Reasoning and Grounding for Open-World Robots

Feb 23, 2026

 No
Not Reported
Simulation Env Not Reported Not Reported 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… When Users Change Their Mind: Evaluating Interrupti… Meanings and Measurements: Multi-Agent Probabilisti…
Human Feedback DemonstrationsCritique EditDemonstrations
Evaluation Modes Human Eval, Llm As JudgeSimulation EnvSimulation Env
Benchmarks WebArena, ToolBenchWebArena, InterruptbenchMapg Bench
Metrics Precision, Pass@1Not reportedNot reported
Quality Controls Not reportedNot reportedNot reported
Rater Population UnknownUnknownUnknown
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. 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. Integrating Deep RL and Bayesian Inference for ObjectNav in Mobile Robotics

    Start here for detailed protocol reporting and quality-control evidence. Signals: simulation environments. Focus: success rate. Abstract: Autonomous object search is challenging for mobile robots operating in indoor environments.

  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

    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.

  6. BEACON: Language-Conditioned Navigation Affordance Prediction under Occlusion

    Adds automatic metrics for broader protocol coverage within this hub. Signals: automatic metrics. Focus: BIRD / accuracy. Abstract: Language-conditioned local navigation requires a robot to infer a nearby.

  7. Mobile-Agent-v3.5: Multi-platform Fundamental GUI Agents

    Adds simulation environments for broader protocol coverage within this hub. Signals: simulation environments. Focus: WebArena. Abstract: The paper introduces GUI-Owl-1.5, the latest native GUI agent model that features.

  8. BrowseComp-$V^3$: A Visual, Vertical, and Verifiable Benchmark for Multimodal Browsing Agents

    Adds automatic metrics for broader protocol coverage within this hub. Signals: automatic metrics. Focus: accuracy. Abstract: Multimodal large language models (MLLMs), equipped with increasingly advanced planning and tool-use.

Known Limitations

Known Limitations

  • Only 0% of papers report quality controls; prioritize calibration/adjudication evidence.
  • Rater population is under-specified (9.1% 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 (2)
  • Critique Edit (1)

Evaluation Modes

  • Simulation Env (11)
  • Automatic Metrics (3)
  • Human Eval (1)
  • Llm As Judge (1)

Top Benchmarks

  • WebArena (3)
  • BIRD (1)
  • BrowseComp (1)
  • Interruptbench (1)

Top Metrics

  • Accuracy (3)
  • Cost (1)
  • Dice (1)
  • Pass@1 (1)

Rater Population Mix

  • Domain Experts (1)

Quality Controls

Coverage diagnostics (sample-based): human-feedback 27.3% · benchmarks 54.5% · metrics 54.5% · quality controls 0.0%.

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…

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

    Henry Peng Zou, Chunyu Miao, Wei-Chieh Huang, Yankai Chen, Yue Zhou · Apr 1, 2026 · Citations: 0

    Critique Edit Simulation Env Long Horizon

    As LLM agents transition from short, static problem solving to executing complex, long-horizon tasks in dynamic environments, the ability to handle user interruptions, such as adding requirement or revising goals, during mid-task execution…

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

    Swagat Padhan, Lakshya Jain, Bhavya Minesh Shah, Omkar Patil, Thao Nguyen · Mar 19, 2026 · Citations: 0

    Demonstrations Simulation Env Multi Agent

    To address this limitation, we propose MAPG (Multi-Agent Probabilistic Grounding), an agentic framework that decomposes language queries into structured subcomponents and queries a VLM to ground each component.

  • BrowseComp-$V^3$: A Visual, Vertical, and Verifiable Benchmark for Multimodal Browsing Agents

    Huanyao Zhang, Jiepeng Zhou, Bo Li, Bowen Zhou, Yanzhe Shan · Feb 13, 2026 · Citations: 0

    Automatic MetricsSimulation Env Web Browsing

    Multimodal large language models (MLLMs), equipped with increasingly advanced planning and tool-use capabilities, are evolving into autonomous agents capable of performing multimodal web browsing and deep search in open-world environments.

  • 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.

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

    Ojas Jain, Dhruv Kumar · Apr 7, 2026 · Citations: 0

    Simulation Env Multi Agent

    We introduce LudoBench, a benchmark for evaluating LLM strategic reasoning in Ludo, a stochastic multi-agent board game whose dice mechanics, piece capture, safe-square navigation, and home-path progression introduce meaningful planning…

  • Mobile-Agent-v3.5: Multi-platform Fundamental GUI Agents

    Haiyang Xu, Xi Zhang, Haowei Liu, Junyang Wang, Zhaozai Zhu · Feb 15, 2026 · Citations: 0

    Simulation Env Long Horizon

    The paper introduces GUI-Owl-1.5, the latest native GUI agent model that features instruct/thinking variants in multiple sizes (2B/4B/8B/32B/235B) and supports a range of platforms (desktop, mobile, browser, and more) to enable cloud-edge…

  • Mind over Space: Can Multimodal Large Language Models Mentally Navigate?

    Qihui Zhu, Shouwei Ruan, Xiao Yang, Hao Jiang, Yao Huang · Mar 23, 2026 · Citations: 0

    Automatic MetricsSimulation Env Web Browsing

    Despite the widespread adoption of MLLMs in embodied agents, their capabilities remain largely confined to reactive planning from immediate observations, consistently failing in spatial reasoning across extensive spatiotemporal scales.

  • Integrating Deep RL and Bayesian Inference for ObjectNav in Mobile Robotics

    João Castelo-Branco, José Santos-Victor, Alexandre Bernardino · Mar 26, 2026 · Citations: 0

    Simulation Env Web Browsing

    Autonomous object search is challenging for mobile robots operating in indoor environments due to partial observability, perceptual uncertainty, and the need to trade off exploration and navigation efficiency.

  • Efficient Hierarchical Any-Angle Path Planning on Multi-Resolution 3D Grids

    Victor Reijgwart, Cesar Cadena, Roland Siegwart, Lionel Ott · Feb 24, 2026 · Citations: 0

    Simulation Env Long Horizon

    Hierarchical, multi-resolution volumetric mapping approaches are widely used to represent large and complex environments as they can efficiently capture their occupancy and connectivity information.

  • Contextual Safety Reasoning and Grounding for Open-World Robots

    Zachary Ravichandran, David Snyder, Alexander Robey, Hamed Hassani, Vijay Kumar · Feb 23, 2026 · Citations: 0

    Simulation Env Web Browsing

    Traditional safety approaches enforce fixed constraints in user-specified contexts, limiting their ability to handle the open-ended contextual variability of real-world deployment.

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