Skip to content
← Back to explorer

HFEPX Hub

CS.CV Papers (Last 60 Days)

Updated from current HFEPX corpus (Apr 12, 2026). 533 papers are grouped in this hub page.

Read Full Context

Updated from current HFEPX corpus (Apr 12, 2026). 533 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: Calibration. Frequently cited benchmark: DROP. 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: 533 Last published: Mar 19, 2026 Global RSS
Cs.CVLast 60d

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 533 total papers in this hub).

All Sampled Papers (60) Replication-Ready Only (5)

High-Signal Coverage

100.0%

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

Replication-Ready Set

5

Benchmark + metric + eval mode explicitly present.

Judge/Human Comparability

0

Papers containing both `human_eval` and `llm_as_judge`.

  • 5 papers are replication-ready (benchmark + metric + explicit evaluation mode).
  • 0 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 (5 papers).

Need evaluators for this research workflow?

Post a Job →

Why This Matters For Eval Research

  • 5.1% of papers report explicit human-feedback signals, led by pairwise preferences.
  • automatic metrics appears in 19.9% of papers in this hub.
  • DROP is a recurring benchmark anchor for cross-paper comparisons in this page.

Protocol Takeaways

  • Most common quality-control signal is rater calibration (0.6% of papers).
  • Rater context is mostly domain experts, and annotation is commonly trajectory-level annotation; use this to scope replication staffing.
  • Compare papers that report both human_eval and llm_as_judge to quantify judge-human agreement drift.

Benchmark Interpretation

  • DROP appears in 0.8% of hub papers (4/533); use this cohort for benchmark-matched comparisons.
  • BIRD appears in 0.6% of hub papers (3/533); use this cohort for benchmark-matched comparisons.

Metric Interpretation

  • accuracy is reported in 16.5% of hub papers (88/533); compare with a secondary metric before ranking methods.
  • cost is reported in 4.9% of hub papers (26/533); compare with a secondary metric before ranking methods.
Researcher Checklist (Expanded)

Researcher Checklist

  • Gap: Papers with explicit human feedback

    Coverage is a replication risk (5.1% vs 45% target).

  • Gap: Papers reporting quality controls

    Coverage is a replication risk (0.9% vs 30% target).

  • Gap: Papers naming benchmarks/datasets

    Coverage is a replication risk (8.3% vs 35% target).

  • Moderate: Papers naming evaluation metrics

    Coverage is usable but incomplete (32.1% vs 35% target).

  • Gap: Papers with known rater population

    Coverage is a replication risk (4.7% vs 35% target).

  • Gap: Papers with known annotation unit

    Coverage is a replication risk (5.4% vs 35% target).

Strengths

  • Contains both human-eval and LLM-as-judge protocols for head-to-head methodology comparison.

Known Gaps

  • Only 0.9% of papers report quality controls; prioritize calibration/adjudication evidence.
  • Rater population is under-specified (4.7% coverage).
  • Annotation unit is under-specified (5.4% coverage).

Suggested Next Analyses

  • Compare papers that report both human_eval and llm_as_judge to quantify judge-human agreement drift.
  • Stratify by benchmark (DROP 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: DROP Metric Slice: accuracy IAA-Reported Evaluations 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
SODIUM: From Open Web Data to Queryable Databases

Mar 19, 2026

 Yes
Expert Verification
Automatic Metrics Sodium Bench Accuracy Not Reported
PASK: Toward Intent-Aware Proactive Agents with Long-Term Memory

Apr 9, 2026

 No
Not Reported
Automatic Metrics Latentneeds Bench Precision , Latency 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
BEACON: Language-Conditioned Navigation Affordance Prediction under Occlusion

Mar 10, 2026

 No
Not Reported
Automatic Metrics , Simulation Env BIRD Accuracy Not Reported
Zooming without Zooming: Region-to-Image Distillation for Fine-Grained Multimodal Perception

Feb 12, 2026

 No
Not Reported
Automatic Metrics Zoombench Latency 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 PASK: Toward Intent-Aware Proactive Agents with Lon… LRC-WeatherNet: LiDAR, RADAR, and Camera Fusion Net…
Human Feedback Expert VerificationNot reportedNot reported
Evaluation Modes Automatic MetricsAutomatic MetricsAutomatic Metrics
Benchmarks Sodium BenchLatentneeds BenchBIRD
Metrics AccuracyPrecision, LatencyPrecision
Quality Controls Not reportedNot reportedNot reported
Rater Population Domain ExpertsUnknownUnknown
Annotation Unit UnknownUnknownUnknown
Suggested Reading Order (Extended)

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

Suggested Reading Order

  1. Act Wisely: Cultivating Meta-Cognitive Tool Use in Agentic Multimodal Models

    Start here for detailed protocol reporting and quality-control evidence. Signals: automatic metrics. Focus: accuracy. Abstract: The advent of agentic multimodal models has empowered systems to actively interact with.

  2. SIM1: Physics-Aligned Simulator as Zero-Shot Data Scaler in Deformable Worlds

    Start here for detailed protocol reporting and quality-control evidence. Abstract: Robotic manipulation with deformable objects represents a data-intensive regime in embodied learning, where shape, contact, and topology co-evolve.

  3. Seeing but Not Thinking: Routing Distraction in Multimodal Mixture-of-Experts

    Start here for detailed protocol reporting and quality-control evidence. Signals: expert verification. Abstract: Multimodal Mixture-of-Experts (MoE) models have achieved remarkable performance on vision-language tasks.

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

    Include a human-eval paper to calibrate against judge-based evaluation settings. Signals: simulation environments + demonstration data. Focus: Mapg-Bench. Abstract: Robots collaborating with humans must convert natural language goals.

  5. 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.

  6. SOLE-R1: Video-Language Reasoning as the Sole Reward for On-Robot Reinforcement Learning

    Adds simulation environments with demonstration data for broader protocol coverage within this hub. Signals: simulation environments + demonstration data. Abstract: Vision-language models (VLMs) have shown impressive capabilities across.

  7. Step-CoT: Stepwise Visual Chain-of-Thought for Medical Visual Question Answering

    Adds automatic metrics with expert verification for broader protocol coverage within this hub. Signals: automatic metrics + expert verification. Focus: accuracy. Abstract: Chain-of-thought (CoT) reasoning has advanced medical.

  8. SleepVLM: Explainable and Rule-Grounded Sleep Staging via a Vision-Language Model

    Adds automatic metrics with expert verification for broader protocol coverage within this hub. Signals: automatic metrics + expert verification. Focus: accuracy. Abstract: While automated sleep staging has achieved.

Known Limitations

Known Limitations

  • Only 0.9% of papers report quality controls; prioritize calibration/adjudication evidence.
  • Rater population is under-specified (4.7% 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 (13)
  • Expert Verification (7)
  • Demonstrations (4)
  • Red Team (3)

Evaluation Modes

  • Automatic Metrics (106)
  • Simulation Env (14)
  • Human Eval (4)
  • Llm As Judge (2)

Top Benchmarks

  • DROP (4)
  • BIRD (3)
  • GenEval (2)
  • MMBench (2)

Top Metrics

  • Accuracy (88)
  • Cost (26)
  • Precision (17)
  • Coherence (16)

Rater Population Mix

  • Domain Experts (25)

Quality Controls

  • Calibration (3)
  • Adjudication (1)
  • Inter Annotator Agreement Reported (1)
Coverage diagnostics (sample-based): human-feedback 45.0% · benchmarks 10.0% · metrics 58.3% · quality controls 1.7%.

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.
Create Account & Post a Job
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.
Learn About Managed Service
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.
Join Now