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HFEPX Hub

Web Browsing + Simulation Env Papers

Updated from current HFEPX corpus (Apr 12, 2026). 16 papers are grouped in this hub page.

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Updated from current HFEPX corpus (Apr 12, 2026). 16 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: 16 Last published: Mar 22, 2026 Global RSS Tag RSS
Web BrowsingSimulation Env

Researcher Quick Triage

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

All Sampled Papers (16) Replication-Ready Only (5)

High-Signal Coverage

100.0%

16 / 16 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.
  • 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.

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

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Why This Matters For Eval Research

  • 31.3% 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 25% of hub papers (4/16); use this cohort for benchmark-matched comparisons.
  • OSWorld appears in 12.5% of hub papers (2/16); use this cohort for benchmark-matched comparisons.

Metric Interpretation

  • accuracy is reported in 25% of hub papers (4/16); compare with a secondary metric before ranking methods.
  • cost is reported in 18.8% of hub papers (3/16); compare with a secondary metric before ranking methods.
Researcher Checklist (Expanded)

Researcher Checklist

  • Moderate: Papers with explicit human feedback

    Coverage is usable but incomplete (31.3% 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 (56.3% vs 35% target).

  • Strong: Papers naming evaluation metrics

    Coverage is strong (62.5% vs 35% target).

  • Gap: Papers with known rater population

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

  • Strong: Papers with known annotation unit

    Coverage is strong (37.5% vs 35% target).

Strengths

  • Most papers provide measurable evaluation context (56.3% benchmarks, 62.5% 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 (6.3% 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 OSWorld) 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
Let's Think in Two Steps: Mitigating Agreement Bias in MLLMs with Self-Grounded Verification

Jul 15, 2025

 Yes
Pairwise Preference
Automatic Metrics , Simulation Env VisualWebArena , OSWorld Accuracy 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
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… Let's Think in Two Steps: Mitigating Agreement Bias… LUDOBENCH: Evaluating LLM Behavioural Decision-Maki…
Human Feedback DemonstrationsPairwise PreferenceNot reported
Evaluation Modes Human Eval, Llm As JudgeAutomatic Metrics, Simulation EnvSimulation Env
Benchmarks WebArena, ToolBenchVisualWebArena, OSWorldLudobench
Metrics Precision, Pass@1AccuracyDice
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. 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. Let's Think in Two Steps: Mitigating Agreement Bias in MLLMs with Self-Grounded Verification

    Include a human-eval paper to calibrate against judge-based evaluation settings. Signals: automatic metrics + pairwise preferences. Focus: VisualWebArena / accuracy. Abstract: Multimodal LLMs (MLLMs) offer a promising solution,.

  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. MoMaGen: Generating Demonstrations under Soft and Hard Constraints for Multi-Step Bimanual Mobile Manipulation

    Adds simulation environments with demonstration data for broader protocol coverage within this hub. Signals: simulation environments + demonstration data. Abstract: Imitation learning from large-scale, diverse human demonstrations has.

  8. Go-Browse: Training Web Agents with Structured Exploration

    Adds simulation environments for broader protocol coverage within this hub. Signals: simulation environments. Focus: WebArena / success rate. Abstract: One of the fundamental problems in digital agents is.

Known Limitations

Known Limitations

  • Only 0% 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 (3)
  • Critique Edit (1)
  • Pairwise Preference (1)

Evaluation Modes

  • Simulation Env (16)
  • Automatic Metrics (4)
  • Human Eval (1)
  • Llm As Judge (1)

Top Benchmarks

  • WebArena (4)
  • OSWorld (2)
  • BIRD (1)
  • BrowseComp (1)

Top Metrics

  • Accuracy (4)
  • Cost (3)
  • Success rate (2)
  • Agreement (1)

Rater Population Mix

  • Domain Experts (1)

Quality Controls

Coverage diagnostics (sample-based): human-feedback 31.3% · benchmarks 50.0% · metrics 62.5% · quality controls 0.0%.

Top Papers

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