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HFEPX Hub

CS.AI + Web Browsing Papers

Updated from current HFEPX corpus (Apr 12, 2026). 57 papers are grouped in this hub page.

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Updated from current HFEPX corpus (Apr 12, 2026). 57 papers are grouped in this hub page. Common evaluation modes: Automatic Metrics, Simulation Env. Most common rater population: Domain Experts. Common annotation unit: Trajectory. Frequent quality control: Adjudication. Frequently cited benchmark: BIRD. 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: 57 Last published: Mar 22, 2026 Global RSS Tag RSS
Cs.AIWeb Browsing

Researcher Quick Triage

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

All Sampled Papers (57) Replication-Ready Only (7)

High-Signal Coverage

100.0%

57 / 57 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.
  • 2 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

  • 32.4% of papers report explicit human-feedback signals, led by demonstration data.
  • automatic metrics appears in 40.4% of papers in this hub.
  • BIRD 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 adjudication (3.5% of papers).
  • Rater context is mostly domain experts, and annotation is commonly trajectory-level annotation; use this to scope replication staffing.

Benchmark Interpretation

  • BIRD appears in 5.4% of hub papers (2/57); use this cohort for benchmark-matched comparisons.
  • OSWorld appears in 5.4% of hub papers (2/57); use this cohort for benchmark-matched comparisons.

Metric Interpretation

  • accuracy is reported in 35.1% of hub papers (13/57); compare with a secondary metric before ranking methods.
  • cost is reported in 13.5% of hub papers (5/57); compare with a secondary metric before ranking methods.
Researcher Checklist (Expanded)

Researcher Checklist

  • Moderate: Papers with explicit human feedback

    Coverage is usable but incomplete (32.4% vs 45% target).

  • Gap: Papers reporting quality controls

    Coverage is a replication risk (5.4% vs 30% target).

  • Moderate: Papers naming benchmarks/datasets

    Coverage is usable but incomplete (32.4% vs 35% target).

  • Strong: Papers naming evaluation metrics

    Coverage is strong (70.3% vs 35% target).

  • Gap: Papers with known rater population

    Coverage is a replication risk (10.8% vs 35% target).

  • Moderate: Papers with known annotation unit

    Coverage is usable but incomplete (29.7% vs 35% target).

Strengths

  • 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 5.4% of papers report quality controls; prioritize calibration/adjudication evidence.
  • Rater population is under-specified (10.8% 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 (BIRD vs OSWorld) 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: BIRD 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
SODIUM: From Open Web Data to Queryable Databases

Mar 19, 2026

 Yes
Expert Verification
Automatic Metrics Sodium Bench Accuracy Not Reported
Vision-DeepResearch Benchmark: Rethinking Visual and Textual Search for Multimodal Large Language Models

Feb 2, 2026

 Yes
Expert Verification
Automatic Metrics Vdr Bench Not Reported Adjudication
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
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
Don't Overthink It: Inter-Rollout Action Agreement as a Free Adaptive-Compute Signal for LLM Agents

Apr 9, 2026

 No
Not Reported
Automatic Metrics GSM8K Accuracy Not Reported
LRC-WeatherNet: LiDAR, RADAR, and Camera Fusion Network for Real-time Weather-type Classification in Autonomous Driving

Mar 23, 2026

 No
Not Reported
Automatic Metrics BIRD Precision Not Reported
InnoEval: On Research Idea Evaluation as a Knowledge-Grounded, Multi-Perspective Reasoning Problem

Feb 16, 2026

 No
Not Reported
Llm As Judge Innoeval Not Reported Adjudication
Vibe Code Bench: Evaluating AI Models on End-to-End Web Application Development

Mar 4, 2026

 Yes
Pairwise Preference
Automatic Metrics Not Reported Accuracy , Agreement Not Reported
BEACON: Language-Conditioned Navigation Affordance Prediction under Occlusion

Mar 10, 2026

 No
Not Reported
Automatic Metrics , Simulation Env BIRD Accuracy Not Reported
Role-Augmented Intent-Driven Generative Search Engine Optimization

Aug 15, 2025

 Yes
Rubric Rating
Automatic Metrics Not Reported Perplexity 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… SODIUM: From Open Web Data to Queryable Databases Vision-DeepResearch Benchmark: Rethinking Visual an…
Human Feedback DemonstrationsExpert VerificationExpert Verification
Evaluation Modes Human Eval, Llm As JudgeAutomatic MetricsAutomatic Metrics
Benchmarks WebArena, ToolBenchSodium BenchVdr Bench
Metrics Precision, Pass@1AccuracyNot reported
Quality Controls Not reportedNot reportedAdjudication
Rater Population UnknownDomain ExpertsDomain Experts
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. Don't Overthink It: Inter-Rollout Action Agreement as a Free Adaptive-Compute Signal for LLM Agents

    Start here for detailed protocol reporting and quality-control evidence. Signals: automatic metrics. Focus: GSM8K / accuracy. Abstract: Inference-time compute scaling has emerged as a powerful technique for improving.

  2. 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.

  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. InnoEval: On Research Idea Evaluation as a Knowledge-Grounded, Multi-Perspective Reasoning Problem

    Include an LLM-as-judge paper to test judge design and agreement assumptions. Signals: LLM-as-judge. Focus: Innoeval. Abstract: However, existing idea evaluation methods often suffer from narrow knowledge horizons, flattened.

  7. Vision-DeepResearch Benchmark: Rethinking Visual and Textual Search for Multimodal Large Language Models

    Adds automatic metrics with expert verification for broader protocol coverage within this hub. Signals: automatic metrics + expert verification. Focus: Vdr-Bench. Abstract: Multimodal Large Language Models (MLLMs) have.

  8. 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.

Known Limitations

Known Limitations

  • Only 5.4% of papers report quality controls; prioritize calibration/adjudication evidence.
  • Rater population is under-specified (10.8% 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 (6)
  • Pairwise Preference (4)
  • Expert Verification (2)
  • Rubric Rating (1)

Evaluation Modes

  • Automatic Metrics (23)
  • Simulation Env (14)
  • Llm As Judge (2)
  • Human Eval (1)

Top Benchmarks

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

Top Metrics

  • Accuracy (13)
  • Cost (5)
  • Precision (5)
  • Agreement (3)

Rater Population Mix

  • Domain Experts (4)

Quality Controls

  • Adjudication (2)
Coverage diagnostics (sample-based): human-feedback 21.1% · benchmarks 21.1% · metrics 49.1% · quality controls 3.5%.

Top Papers

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