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HFEPX Hub

CS.CV Papers (Last 30 Days)

Updated from current HFEPX corpus (Apr 12, 2026). 331 papers are grouped in this hub page.

Read Full Context

Updated from current HFEPX corpus (Apr 12, 2026). 331 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: 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: 331 Last published: Mar 19, 2026 Global RSS
Cs.CVLast 30d

Researcher Quick Triage

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

Analysis blocks below are computed from the currently loaded sample (60 of 331 total papers in this hub).

All Sampled Papers (60) Replication-Ready Only (3)

High-Signal Coverage

100.0%

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

Replication-Ready Set

3

Benchmark + metric + eval mode explicitly present.

Judge/Human Comparability

0

Papers containing both `human_eval` and `llm_as_judge`.

  • 3 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 (3 papers).

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Why This Matters For Eval Research

  • 3.6% of papers report explicit human-feedback signals, led by expert verification.
  • automatic metrics appears in 16.9% 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 adjudication (0.3% 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 0.9% of hub papers (3/331); use this cohort for benchmark-matched comparisons.
  • BIRD appears in 0.6% of hub papers (2/331); use this cohort for benchmark-matched comparisons.

Metric Interpretation

  • accuracy is reported in 17.5% of hub papers (58/331); compare with a secondary metric before ranking methods.
  • cost is reported in 5.1% of hub papers (17/331); compare with a secondary metric before ranking methods.
Researcher Checklist (Expanded)

Researcher Checklist

  • Gap: Papers with explicit human feedback

    Coverage is a replication risk (3.6% vs 45% target).

  • Gap: Papers reporting quality controls

    Coverage is a replication risk (0.6% vs 30% target).

  • Gap: Papers naming benchmarks/datasets

    Coverage is a replication risk (10% vs 35% target).

  • Moderate: Papers naming evaluation metrics

    Coverage is usable but incomplete (33.5% vs 35% target).

  • Gap: Papers with known rater population

    Coverage is a replication risk (4.2% vs 35% target).

  • Gap: Papers with known annotation unit

    Coverage is a replication risk (5.1% vs 35% target).

Strengths

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

Known Gaps

  • Only 0.6% of papers report quality controls; prioritize calibration/adjudication evidence.
  • Rater population is under-specified (4.2% coverage).
  • Annotation unit is under-specified (5.1% 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

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. Less Is More? Selective Visual Attention to High-Importance Regions for Multimodal Radiology Summarization

    Include a human-eval paper to calibrate against judge-based evaluation settings. Signals: human evaluation. Focus: bleu. Abstract: ViTAS achieves SOTA results with 29.25% BLEU-4 and 69.83% ROUGE-L, improved factual.

  5. 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.

  6. Appear2Meaning: A Cross-Cultural Benchmark for Structured Cultural Metadata Inference from Images

    Include an LLM-as-judge paper to test judge design and agreement assumptions. Signals: LLM-as-judge. Focus: accuracy. Abstract: We introduce a multi-category, cross-cultural benchmark for this task and evaluate VLMs.

  7. SODIUM: From Open Web Data to Queryable Databases

    Adds automatic metrics with expert verification for broader protocol coverage within this hub. Signals: automatic metrics + expert verification. Focus: Sodium-Bench / accuracy. Abstract: During research, domain experts.

  8. 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.

Known Limitations

Known Limitations

  • Only 0.6% of papers report quality controls; prioritize calibration/adjudication evidence.
  • Rater population is under-specified (4.2% 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

  • Expert Verification (5)
  • Pairwise Preference (3)
  • Demonstrations (2)
  • Red Team (2)

Evaluation Modes

  • Automatic Metrics (56)
  • Simulation Env (9)
  • Human Eval (2)
  • Llm As Judge (2)

Top Benchmarks

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

Top Metrics

  • Accuracy (58)
  • Cost (17)
  • Coherence (12)
  • Latency (8)

Rater Population Mix

  • Domain Experts (14)

Quality Controls

  • Adjudication (1)
  • Inter Annotator Agreement Reported (1)
Coverage diagnostics (sample-based): human-feedback 20.0% · benchmarks 6.7% · metrics 33.3% · quality controls 1.7%.

Top Papers

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