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HFEPX Metric Hub

Latency + Automatic Metrics Metric Papers (Last 90 Days)

Updated from current HFEPX corpus (Mar 1, 2026). 12 papers are grouped in this metric page.

Read Full Context

Updated from current HFEPX corpus (Mar 1, 2026). 12 papers are grouped in this metric page. Common evaluation modes: Automatic Metrics, Llm As Judge. Most common rater population: Domain Experts. Common annotation unit: Trajectory. Frequently cited benchmark: BrowseComp. Common metric signal: latency. 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 Feb 25, 2026.

Papers: 12 Last published: Feb 25, 2026 Global RSS

Researcher Quick Triage

Use this page to compare metric behavior across protocols and benchmarks before selecting your reporting stack. Quality band: Medium .

Metric Coverage

100.0%

12 sampled papers include metric names.

Benchmark Anchoring

41.7%

Papers with explicit dataset/benchmark anchors for fair comparison.

Quality Controls

0.0%

0 papers report calibration/adjudication/IAA controls.

  • 12 papers are not low-signal flagged in this sample.
  • Use the protocol matrix below to avoid comparing metrics across incompatible eval setups.

Primary action: Use the top metric-reliable papers first, then compare benchmark context in the matrix before drawing conclusions.

Why This Matters (Expanded)

Why This Matters For Eval Research

  • automatic metrics appears in 100% of papers in this hub.
  • BrowseComp is a recurring benchmark anchor for cross-paper comparisons in this page.
  • long-horizon tasks appears in 58.3% of papers, indicating agentic evaluation demand.
Metric Notes (Expanded)

Metric-Driven 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.
  • Pair this hub with a human_eval-heavy hub to validate judge-model calibration.

Metric Interpretation

  • latency is reported in 100% of hub papers (12/12); compare with a secondary metric before ranking methods.
  • accuracy is reported in 50% of hub papers (6/12); compare with a secondary metric before ranking methods.

Benchmark Context

  • BrowseComp appears in 16.7% of hub papers (2/12); use this cohort for benchmark-matched comparisons.
  • GAIA appears in 8.3% of hub papers (1/12); use this cohort for benchmark-matched comparisons.

Start Here (Metric-Reliable First 6)

Ranked for metric reporting completeness and comparability.

Metric Protocol Matrix (Top 10)

Compare metric, benchmark, and evaluation context side by side.

Paper Metrics Benchmarks Eval Modes Quality Controls
SWE-Protégé: Learning to Selectively Collaborate With an Expert Unlocks Small Language Models as Software Engineering Agents

Feb 25, 2026

Pass@1, Latency SWE Bench, SWE Bench Verified Automatic Metrics Not reported
Search More, Think Less: Rethinking Long-Horizon Agentic Search for Efficiency and Generalization

Feb 26, 2026

Accuracy, Latency GAIA, BrowseComp Automatic Metrics Not reported
Zooming without Zooming: Region-to-Image Distillation for Fine-Grained Multimodal Perception

Feb 12, 2026

Latency Zoombench Automatic Metrics Not reported
AceGRPO: Adaptive Curriculum Enhanced Group Relative Policy Optimization for Autonomous Machine Learning Engineering

Feb 8, 2026

Latency MLE Bench Automatic Metrics Not reported
Towards Efficient Agents: A Co-Design of Inference Architecture and System

Dec 20, 2025

Accuracy, Latency BrowseComp Automatic Metrics Not reported
Test-Time Scaling with Diffusion Language Models via Reward-Guided Stitching

Feb 26, 2026

Accuracy, Latency Not reported Automatic Metrics Not reported
A Multi-Agent Framework for Medical AI: Leveraging Fine-Tuned GPT, LLaMA, and DeepSeek R1 for Evidence-Based and Bias-Aware Clinical Query Processing

Feb 15, 2026

Accuracy, Bleu Not reported Automatic Metrics Not reported
Luna-2: Scalable Single-Token Evaluation with Small Language Models

Feb 20, 2026

Accuracy, Latency Not reported Llm As Judge, Automatic Metrics Not reported
Spatio-Temporal Token Pruning for Efficient High-Resolution GUI Agents

Feb 26, 2026

Precision, Latency Not reported Automatic Metrics Not reported
Anatomy of Agentic Memory: Taxonomy and Empirical Analysis of Evaluation and System Limitations

Feb 22, 2026

Accuracy, Latency Not reported Automatic Metrics Not reported
Researcher Workflow (Detailed)

Checklist

  • Gap: Papers with explicit human feedback

    Coverage is a replication risk (0% vs 45% target).

  • Gap: Papers reporting quality controls

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

  • Strong: Papers naming benchmarks/datasets

    Coverage is strong (41.7% 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 (8.3% vs 35% target).

  • Gap: Papers with known annotation unit

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

Strengths

  • Most papers provide measurable evaluation context (41.7% benchmarks, 100% metrics).
  • Agentic evaluation appears in 91.7% of papers.

Known Gaps

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

Suggested Next Analyses

  • Pair this hub with a human_eval-heavy hub to validate judge-model calibration.
  • Stratify by benchmark (BrowseComp vs GAIA) before comparing methods.
  • Track metric sensitivity by reporting both latency and accuracy.

Recommended Queries

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)

Top Metrics

  • Latency (12)
  • Accuracy (6)
  • Cost (5)
  • Inference cost (3)

Evaluation Modes

  • Automatic Metrics (12)
  • Llm As Judge (1)

Top Benchmarks

  • BrowseComp (2)
  • GAIA (1)
  • MLE Bench (1)
  • SWE Bench (1)

Agentic Mix

  • Long Horizon (7)
  • Web Browsing (2)
  • Multi Agent (1)
  • Tool Use (1)

Top Papers Reporting This Metric

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