Skip to content
← Back to explorer

HFEPX Hub

Web Browsing + Automatic Metrics (Last 45 Days)

Updated from current HFEPX corpus (Apr 17, 2026). 15 papers are grouped in this hub page.

Read Full Context

Updated from current HFEPX corpus (Apr 17, 2026). 15 papers are grouped in this hub page. Common evaluation modes: Automatic Metrics, Simulation Env. Most common rater population: Domain Experts. Common annotation unit: Pairwise. 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 19, 2026.

Papers: 15 Last published: Mar 19, 2026 Global RSS Tag RSS
Web BrowsingAutomatic MetricsLast 45d

Researcher Quick Triage

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

High-Signal Coverage

100.0%

15 / 15 sampled papers are not low-signal flagged.

Replication-Ready Set

4

Benchmark + metric + eval mode explicitly present.

Judge/Human Comparability

0

Papers containing both `human_eval` and `llm_as_judge`.

  • 4 papers are replication-ready (benchmark + metric + explicit evaluation mode).
  • 0 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.

Need evaluators for this research workflow?

Post a Job →

Why This Matters For Eval Research

  • 20% of papers report explicit human-feedback signals, led by pairwise preferences.
  • automatic metrics appears in 100% of papers in this hub.
  • BIRD is a recurring benchmark anchor for cross-paper comparisons in this page.

Protocol Takeaways

  • 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 pairwise annotation; use this to scope replication staffing.
  • Stratify by benchmark (BIRD vs GSM8K) before comparing methods.

Benchmark Interpretation

  • BIRD appears in 13.3% of hub papers (2/15); use this cohort for benchmark-matched comparisons.
  • GSM8K appears in 6.7% of hub papers (1/15); use this cohort for benchmark-matched comparisons.

Metric Interpretation

  • accuracy is reported in 86.7% of hub papers (13/15); compare with a secondary metric before ranking methods.
  • cost is reported in 26.7% of hub papers (4/15); compare with a secondary metric before ranking methods.
Researcher Checklist (Expanded)

Researcher Checklist

  • Gap: Papers with explicit human feedback

    Coverage is a replication risk (20% vs 45% target).

  • Gap: Papers reporting quality controls

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

  • Moderate: Papers naming benchmarks/datasets

    Coverage is usable but incomplete (26.7% vs 35% target).

  • Strong: Papers naming evaluation metrics

    Coverage is strong (100% vs 35% target).

  • Gap: Papers with known rater population

    Coverage is a replication risk (6.7% vs 35% target).

  • Gap: Papers with known annotation unit

    Coverage is a replication risk (13.3% vs 35% target).

Strengths

  • 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 (6.7% coverage).
  • Annotation unit is under-specified (13.3% coverage).

Suggested Next Analyses

  • Stratify by benchmark (BIRD vs GSM8K) before comparing methods.
  • Track metric sensitivity by reporting both accuracy and cost.
Recommended Queries (Expanded)

Recommended Queries

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
SODIUM: From Open Web Data to Queryable Databases

Mar 19, 2026

Yes Automatic Metrics Sodium Bench Accuracy 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
Vibe Code Bench: Evaluating AI Models on End-to-End Web Application Development

Mar 4, 2026

Yes Automatic Metrics Not Reported Accuracy , Agreement Not Reported
Sabiá-4 Technical Report

Mar 10, 2026

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

Mar 10, 2026

No
Not Reported
Automatic Metrics , Simulation Env BIRD Accuracy 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
AgentGL: Towards Agentic Graph Learning with LLMs via Reinforcement Learning

Apr 7, 2026

No
Not Reported
Automatic Metrics Not Reported Accuracy Not Reported
From Guessing to Placeholding: A Cost-Theoretic Framework for Uncertainty-Aware Code Completion

Apr 2, 2026

No
Not Reported
Automatic Metrics Not Reported Not Reported Not Reported
Rethinking SAR ATR: A Target-Aware Frequency-Spatial Enhancement Framework with Noise-Resilient Knowledge Guidance

Mar 23, 2026

No
Not Reported
Automatic Metrics Not Reported Accuracy , Precision Not Reported
Beyond Explicit Edges: Robust Reasoning over Noisy and Sparse Knowledge Graphs

Mar 14, 2026

No
Not Reported
Automatic Metrics Not Reported Accuracy Not Reported
Strategic Navigation or Stochastic Search? How Agents and Humans Reason Over Document Collections

Mar 12, 2026

No
Not Reported
Automatic Metrics Not Reported Accuracy Not Reported

Protocol Diff (Top Papers)

Fast side-by-side comparison for the highest-ranked papers in this hub.

Signal SODIUM: From Open Web Data to Queryable Databases Don't Overthink It: Inter-Rollout Action Agreement… LRC-WeatherNet: LiDAR, RADAR, and Camera Fusion Net…
Human Feedback Expert VerificationNot reportedNot reported
Evaluation Modes Automatic MetricsAutomatic MetricsAutomatic Metrics
Benchmarks Sodium BenchGSM8KBIRD
Metrics AccuracyAccuracyPrecision
Quality Controls Not reportedNot reportedNot reported
Rater Population Domain ExpertsUnknownUnknown
Annotation Unit UnknownTrajectoryUnknown
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. AgentGL: Towards Agentic Graph Learning with LLMs via Reinforcement Learning

    Start here for detailed protocol reporting and quality-control evidence. Signals: automatic metrics. Focus: accuracy. Abstract: Large Language Models (LLMs) increasingly rely on agentic capabilities-iterative retrieval, tool use, and.

  3. From Guessing to Placeholding: A Cost-Theoretic Framework for Uncertainty-Aware Code Completion

    Start here for detailed protocol reporting and quality-control evidence. Signals: automatic metrics. Focus: cost. Abstract: While Large Language Models (LLMs) have demonstrated exceptional proficiency in code completion, they.

  4. SODIUM: From Open Web Data to Queryable Databases

    Include a human-eval paper to calibrate against judge-based evaluation settings. Signals: automatic metrics + expert verification. Focus: Sodium-Bench / accuracy. Abstract: During research, domain experts often ask analytical.

  5. Vibe Code Bench: Evaluating AI Models on End-to-End Web Application Development

    Include a human-eval paper to calibrate against judge-based evaluation settings. Signals: automatic metrics + pairwise preferences. Focus: accuracy. Abstract: We identify self-testing during generation as a strong performance.

  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. Sabiá-4 Technical Report

    Adds automatic metrics with pairwise preferences for broader protocol coverage within this hub. Signals: automatic metrics + pairwise preferences. Focus: accuracy. Abstract: The models were developed through a.

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

    Adds automatic metrics for broader protocol coverage within this hub. Signals: automatic metrics. Focus: accuracy. Abstract: Despite the widespread adoption of MLLMs in embodied agents, their capabilities remain.

Known Limitations

Known Limitations

  • Only 0% of papers report quality controls; prioritize calibration/adjudication evidence.
  • Rater population is under-specified (6.7% 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

  • Pairwise Preference (2)
  • Expert Verification (1)

Evaluation Modes

  • Automatic Metrics (15)
  • Simulation Env (2)

Top Benchmarks

  • BIRD (2)
  • GSM8K (1)
  • Sodium Bench (1)

Top Metrics

  • Accuracy (13)
  • Cost (4)
  • Agreement (2)
  • Precision (2)

Rater Population Mix

  • Domain Experts (1)

Quality Controls

Coverage diagnostics (sample-based): human-feedback 20.0% · benchmarks 26.7% · metrics 100.0% · quality controls 0.0%.

Top Papers

Related Hubs

Get Started

Join the #1 Platform for AI Training Talent

Where top AI builders and expert AI Trainers connect to build the future of AI.
Self-Service
Post a Job
Post your project and get a shortlist of qualified AI Trainers and Data Labelers. Hire and manage your team in the tools you already use.
Managed Service
For Large Projects
Done-for-You
We recruit, onboard, and manage a dedicated team inside your tools. End-to-end operations for large or complex projects.
For Freelancers
Join as an AI Trainer
Find AI training and data labeling projects across platforms, all in one place. One profile, one application process, more opportunities.