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HFEPX Metric Hub

Cost + Automatic Metrics Metric Papers (Last 45 Days)

Updated from current HFEPX corpus (Mar 1, 2026). 19 papers are grouped in this metric page.

Read Full Context

Updated from current HFEPX corpus (Mar 1, 2026). 19 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: 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 Feb 24, 2026.

Papers: 19 Last published: Feb 24, 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%

19 sampled papers include metric names.

Benchmark Anchoring

21.1%

Papers with explicit dataset/benchmark anchors for fair comparison.

Quality Controls

0.0%

0 papers report calibration/adjudication/IAA controls.

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

Primary action: Treat this as directional signal only; metric reporting is present but benchmark anchoring is still thin.

Why This Matters (Expanded)

Why This Matters For Eval Research

  • 31.6% of papers report explicit human-feedback signals, led by pairwise preferences.
  • automatic metrics appears in 100% 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

  • 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

  • cost is reported in 100% of hub papers (19/19); compare with a secondary metric before ranking methods.
  • accuracy is reported in 42.1% of hub papers (8/19); compare with a secondary metric before ranking methods.

Benchmark Context

  • BrowseComp appears in 5.3% of hub papers (1/19); use this cohort for benchmark-matched comparisons.
  • ContentBench appears in 5.3% of hub papers (1/19); 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
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
Confidence-Driven Multi-Scale Model Selection for Cost-Efficient Inference

Feb 25, 2026

Accuracy, Cost MMLU Automatic Metrics Not reported
SparkMe: Adaptive Semi-Structured Interviewing for Qualitative Insight Discovery

Feb 24, 2026

Cost Not reported Automatic Metrics Not reported
Multi-Objective Alignment of Language Models for Personalized Psychotherapy

Feb 17, 2026

Agreement, Cost Not reported Automatic Metrics 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
Orchestration-Free Customer Service Automation: A Privacy-Preserving and Flowchart-Guided Framework

Feb 17, 2026

Cost Not reported 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
Researcher Workflow (Detailed)

Checklist

  • Moderate: Papers with explicit human feedback

    Coverage is usable but incomplete (31.6% vs 45% target).

  • Gap: Papers reporting quality controls

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

  • Moderate: Papers naming benchmarks/datasets

    Coverage is usable but incomplete (21.1% 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 (15.8% vs 35% target).

  • Moderate: Papers with known annotation unit

    Coverage is usable but incomplete (31.6% vs 35% target).

Strengths

  • Agentic evaluation appears in 68.4% of papers.

Known Gaps

  • Only 0% of papers report quality controls; prioritize calibration/adjudication evidence.
  • Rater population is under-specified (15.8% coverage).
  • LLM-as-judge appears without enough inter-annotator agreement reporting.

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 accuracy.

Recommended Queries

Known Limitations
  • Only 0% of papers report quality controls; prioritize calibration/adjudication evidence.
  • Rater population is under-specified (15.8% coverage).
  • Narrative synthesis is grounded in metadata and abstracts only; full-paper implementation details are not parsed.
Research Utility Snapshot (Detailed)

Top Metrics

  • Cost (19)
  • Accuracy (8)
  • Latency (5)
  • Inference cost (4)

Evaluation Modes

  • Automatic Metrics (19)
  • Llm As Judge (1)

Top Benchmarks

  • BrowseComp (1)
  • ContentBench (1)
  • GAIA (1)
  • MMLU (1)

Agentic Mix

  • Long Horizon (10)
  • Multi Agent (2)
  • Tool Use (2)

Top Papers Reporting This Metric

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