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HFEPX Hub

CS.CV + Long Horizon Papers

Updated from current HFEPX corpus (Apr 12, 2026). 40 papers are grouped in this hub page.

Read Full Context

Updated from current HFEPX corpus (Apr 12, 2026). 40 papers are grouped in this hub page. Common evaluation modes: Automatic Metrics, Simulation Env. Most common rater population: Domain Experts. Common annotation unit: Trajectory. Frequently cited benchmark: Blenderbench. 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 14, 2026.

Papers: 40 Last published: Mar 14, 2026 Global RSS Tag RSS
Cs.CVLong Horizon

Researcher Quick Triage

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

All Sampled Papers (40) Replication-Ready Only (2)

High-Signal Coverage

100.0%

40 / 40 sampled papers are not low-signal flagged.

Replication-Ready Set

2

Benchmark + metric + eval mode explicitly present.

Judge/Human Comparability

0

Papers containing both `human_eval` and `llm_as_judge`.

  • 2 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: Use this page for scouting only; collect additional papers before attempting replication-critical comparisons.

Currently showing only replication-ready papers in ranking and matrix sections (2 papers).

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Why This Matters For Eval Research

  • 25% of papers report explicit human-feedback signals, led by pairwise preferences.
  • automatic metrics appears in 40% of papers in this hub.
  • Blenderbench 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 trajectory-level annotation; use this to scope replication staffing.
  • Stratify by benchmark (Blenderbench vs Latentneeds-Bench) before comparing methods.

Benchmark Interpretation

  • Blenderbench appears in 4.2% of hub papers (1/40); use this cohort for benchmark-matched comparisons.
  • Latentneeds-Bench appears in 4.2% of hub papers (1/40); use this cohort for benchmark-matched comparisons.

Metric Interpretation

  • accuracy is reported in 41.7% of hub papers (10/40); compare with a secondary metric before ranking methods.
  • latency is reported in 12.5% of hub papers (3/40); compare with a secondary metric before ranking methods.
Researcher Checklist (Expanded)

Researcher Checklist

  • Gap: Papers with explicit human feedback

    Coverage is a replication risk (25% vs 45% target).

  • Gap: Papers reporting quality controls

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

  • Gap: Papers naming benchmarks/datasets

    Coverage is a replication risk (12.5% vs 35% target).

  • Strong: Papers naming evaluation metrics

    Coverage is strong (83.3% vs 35% target).

  • Gap: Papers with known rater population

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

  • Strong: Papers with known annotation unit

    Coverage is strong (50% 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 (8.3% coverage).
  • Benchmark coverage is thin (12.5% of papers mention benchmarks/datasets).

Suggested Next Analyses

  • Stratify by benchmark (Blenderbench vs Latentneeds-Bench) before comparing methods.
  • Track metric sensitivity by reporting both accuracy and latency.
Recommended Queries (Expanded)

Recommended Queries

Benchmark Slice: Blenderbench 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
PASK: Toward Intent-Aware Proactive Agents with Long-Term Memory

Apr 9, 2026

 No
Not Reported
Automatic Metrics Latentneeds Bench Precision , Latency Not Reported
Vision-as-Inverse-Graphics Agent via Interleaved Multimodal Reasoning

Jan 16, 2026

 No
Not Reported
Automatic Metrics Blenderbench , Slidebench Accuracy Not Reported

Protocol Diff (Top Papers)

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

Signal PASK: Toward Intent-Aware Proactive Agents with Lon… Vision-as-Inverse-Graphics Agent via Interleaved Mu…
Human Feedback Not reportedNot reported
Evaluation Modes Automatic MetricsAutomatic Metrics
Benchmarks Latentneeds BenchBlenderbench, Slidebench
Metrics Precision, LatencyAccuracy
Quality Controls Not reportedNot reported
Rater Population UnknownUnknown
Annotation Unit UnknownUnknown
Suggested Reading Order (Extended)

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

Suggested Reading Order

  1. PASK: Toward Intent-Aware Proactive Agents with Long-Term Memory

    Start here for detailed protocol reporting and quality-control evidence. Signals: automatic metrics. Focus: Latentneeds-Bench / precision. Abstract: Proactivity is a core expectation for AGI.

  2. Novel Memory Forgetting Techniques for Autonomous AI Agents: Balancing Relevance and Efficiency

    Start here for detailed protocol reporting and quality-control evidence. Signals: automatic metrics. Focus: accuracy. Abstract: Long-horizon conversational agents require persistent memory for coherent reasoning, yet uncontrolled accumulation causes.

  3. HippoCamp: Benchmarking Contextual Agents on Personal Computers

    Start here for detailed protocol reporting and quality-control evidence. Signals: automatic metrics. Focus: accuracy. Abstract: We present HippoCamp, a new benchmark designed to evaluate agents' capabilities on multimodal.

  4. Fast-ThinkAct: Efficient Vision-Language-Action Reasoning via Verbalizable Latent Planning

    Include a human-eval paper to calibrate against judge-based evaluation settings. Signals: simulation environments + pairwise preferences. Focus: latency. Abstract: Fast-ThinkAct learns to reason efficiently with latent CoTs by.

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

    Include a human-eval paper to calibrate against judge-based evaluation settings. Signals: simulation environments + demonstration data. Abstract: Vision-language models (VLMs) have shown impressive capabilities across diverse tasks, motivating.

  6. 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.

  7. Watch and Learn: Learning to Use Computers from Online Videos

    Adds evaluation protocol evidence with demonstration data for broader protocol coverage within this hub. Signals: demonstration data. Focus: OSWorld. Abstract: Instead of directly generating actions or relying on.

  8. BEAT: Visual Backdoor Attacks on VLM-based Embodied Agents via Contrastive Trigger Learning

    Adds automatic metrics with pairwise preferences for broader protocol coverage within this hub. Signals: automatic metrics + pairwise preferences. Focus: accuracy. Abstract: CTL formulates trigger discrimination as preference.

Known Limitations

Known Limitations

  • Only 0% of papers report quality controls; prioritize calibration/adjudication evidence.
  • Rater population is under-specified (8.3% 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

  • Pairwise Preference (3)
  • Demonstrations (2)
  • Expert Verification (1)
  • Rubric Rating (1)

Evaluation Modes

  • Automatic Metrics (16)
  • Simulation Env (6)

Top Benchmarks

  • Blenderbench (1)
  • Latentneeds Bench (1)
  • OSWorld (1)
  • Slidebench (1)

Top Metrics

  • Accuracy (10)
  • Latency (3)
  • Cost (2)
  • F1 (2)

Rater Population Mix

  • Domain Experts (2)

Quality Controls

Coverage diagnostics (sample-based): human-feedback 15.0% · benchmarks 15.0% · metrics 52.5% · quality controls 0.0%.

Top Papers

  • PASK: Toward Intent-Aware Proactive Agents with Long-Term Memory

    Zhifei Xie, Zongzheng Hu, Fangda Ye, Xin Zhang, Haobo Chai · Apr 9, 2026 · Citations: 0

    Automatic Metrics Long Horizon

    Prior work remains largely confined to laboratory settings, leaving a clear gap in real-world proactive agent: depth, complexity, ambiguity, precision and real-time constraints.

  • Vision-as-Inverse-Graphics Agent via Interleaved Multimodal Reasoning

    Shaofeng Yin, Jiaxin Ge, Zora Zhiruo Wang, Chenyang Wang, Xiuyu Li · Jan 16, 2026 · Citations: 0

    Automatic Metrics Long Horizon

    To address this, we introduce VIGA (Vision-as-Inverse-Graphics Agent), an interleaved multimodal reasoning framework where symbolic logic and visual perception actively cross-verify each other.

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