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HFEPX Metric Hub

Efficiency & Cost Metric Papers In CS.AI

Updated from current HFEPX corpus (Mar 1, 2026). 33 papers are grouped in this metric page.

Read Full Context

Updated from current HFEPX corpus (Mar 1, 2026). 33 papers are grouped in this metric page. Common evaluation modes: Automatic Metrics, Simulation Env. Most common rater population: Domain Experts. Common annotation unit: Trajectory. Frequent quality control: Inter Annotator Agreement Reported. Frequently cited benchmark: BrowseComp. Common metric signal: cost. 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 Sep 24, 2025.

Papers: 33 Last published: Sep 24, 2025 Global RSS

Researcher Quick Triage

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

Metric Coverage

81.8%

27 sampled papers include metric names.

Benchmark Anchoring

18.2%

Papers with explicit dataset/benchmark anchors for fair comparison.

Quality Controls

3.0%

1 papers report calibration/adjudication/IAA controls.

  • 33 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

  • 37% of papers report explicit human-feedback signals, led by pairwise preferences.
  • automatic metrics appears in 60.6% of papers in this hub.
  • BrowseComp is a recurring benchmark anchor for cross-paper comparisons in this page.
Metric Notes (Expanded)

Metric-Driven Protocol Takeaways

  • Most common quality-control signal is inter-annotator agreement reporting (3% of papers).
  • 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

  • cost is reported in 66.7% of hub papers (18/33); compare with a secondary metric before ranking methods.
  • latency is reported in 44.4% of hub papers (12/33); compare with a secondary metric before ranking methods.

Benchmark Context

  • BrowseComp appears in 3.7% of hub papers (1/33); use this cohort for benchmark-matched comparisons.
  • ContentBench appears in 3.7% of hub papers (1/33); 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
Can Large Language Models Replace Human Coders? Introducing ContentBench

Feb 23, 2026

Agreement, Cost ContentBench Automatic Metrics Not reported
Step 3.5 Flash: Open Frontier-Level Intelligence with 11B Active Parameters

Feb 11, 2026

Latency, Cost LiveCodeBench, BrowseComp Not reported Not reported
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
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
A Scalable Framework for Evaluating Health Language Models

Mar 30, 2025

Accuracy, Agreement Not reported Automatic Metrics Inter Annotator Agreement Reported
SparkMe: Adaptive Semi-Structured Interviewing for Qualitative Insight Discovery

Feb 24, 2026

Cost Not reported Automatic Metrics Not reported
From Pixels to Policies: Reinforcing Spatial Reasoning in Language Models for Content-Aware Layout Design

Feb 14, 2026

Latency Not reported Simulation Env Not reported
CAMEL: Confidence-Gated Reflection for Reward Modeling

Feb 24, 2026

Accuracy, Cost Not reported Automatic Metrics Not reported
RLHFless: Serverless Computing for Efficient RLHF

Feb 26, 2026

Cost Not reported Automatic Metrics Not reported
Researcher Workflow (Detailed)

Checklist

  • Moderate: Papers with explicit human feedback

    Coverage is usable but incomplete (37% vs 45% target).

  • Gap: Papers reporting quality controls

    Coverage is a replication risk (3.7% vs 30% target).

  • Moderate: Papers naming benchmarks/datasets

    Coverage is usable but incomplete (22.2% 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 (18.5% vs 35% target).

  • Moderate: Papers with known annotation unit

    Coverage is usable but incomplete (29.6% vs 35% target).

Strengths

  • Agentic evaluation appears in 74.1% of papers.

Known Gaps

  • Only 3.7% of papers report quality controls; prioritize calibration/adjudication evidence.
  • Rater population is under-specified (18.5% coverage).

Suggested Next Analyses

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

Recommended Queries

Known Limitations
  • Only 3.7% of papers report quality controls; prioritize calibration/adjudication evidence.
  • Rater population is under-specified (18.5% coverage).
  • Narrative synthesis is grounded in metadata and abstracts only; full-paper implementation details are not parsed.
Research Utility Snapshot (Detailed)

Top Metrics

  • Cost (18)
  • Latency (12)
  • Accuracy (9)
  • Throughput (4)

Evaluation Modes

  • Automatic Metrics (20)
  • Simulation Env (6)
  • Llm As Judge (2)

Top Benchmarks

  • BrowseComp (1)
  • ContentBench (1)
  • HotpotQA (1)
  • Imo Answerbench (1)

Agentic Mix

  • Long Horizon (12)
  • Multi Agent (4)
  • Tool Use (3)
  • Web Browsing (3)

Top Papers Reporting This Metric

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