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HFEPX Hub

Tool Use + Automatic Metrics (Last 90 Days)

Updated from current HFEPX corpus (Mar 10, 2026). 11 papers are grouped in this hub page.

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Updated from current HFEPX corpus (Mar 10, 2026). 11 papers are grouped in this hub page. Common evaluation modes: Automatic Metrics. Most common rater population: Domain Experts. Common annotation unit: Multi Dim Rubric. Frequently cited benchmark: MMLU. 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 9, 2026.

Papers: 11 Last published: Mar 9, 2026 Global RSS Tag RSS
Tool UseAutomatic MetricsLast 90d

Researcher Quick Triage

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

All Sampled Papers (11) Replication-Ready Only (3)

High-Signal Coverage

100.0%

11 / 11 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.
  • 0 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

  • 27.3% of papers report explicit human-feedback signals, led by pairwise preferences.
  • automatic metrics appears in 100% of papers in this hub.
  • MMLU 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 multi-dimensional rubrics; use this to scope replication staffing.
  • Stratify by benchmark (MMLU vs Onemillion-Bench) before comparing methods.

Benchmark Interpretation

  • MMLU appears in 9.1% of hub papers (1/11); use this cohort for benchmark-matched comparisons.
  • Onemillion-Bench appears in 9.1% of hub papers (1/11); use this cohort for benchmark-matched comparisons.

Metric Interpretation

  • accuracy is reported in 36.4% of hub papers (4/11); compare with a secondary metric before ranking methods.
  • cost is reported in 36.4% of hub papers (4/11); compare with a secondary metric before ranking methods.
Researcher Checklist (Expanded)

Researcher Checklist

  • Moderate: Papers with explicit human feedback

    Coverage is usable but incomplete (27.3% 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 (27.3% vs 35% target).

  • Strong: Papers naming evaluation metrics

    Coverage is strong (81.8% vs 35% target).

  • Gap: Papers with known rater population

    Coverage is a replication risk (9.1% vs 35% target).

  • Moderate: Papers with known annotation unit

    Coverage is usable but incomplete (27.3% 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 (9.1% coverage).

Suggested Next Analyses

  • Stratify by benchmark (MMLU vs Onemillion-Bench) before comparing methods.
  • Track metric sensitivity by reporting both accuracy and cost.
Recommended Queries (Expanded)

Recommended Queries

Benchmark Slice: MMLU 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
\$OneMillion-Bench: How Far are Language Agents from Human Experts?

Mar 9, 2026

 Yes
Rubric Rating
Automatic Metrics Onemillion Bench Accuracy , Coherence Not Reported
Confidence-Driven Multi-Scale Model Selection for Cost-Efficient Inference

Feb 25, 2026

 No
Not Reported
Automatic Metrics MMLU Accuracy , Cost Not Reported
Zooming without Zooming: Region-to-Image Distillation for Fine-Grained Multimodal Perception

Feb 12, 2026

 No
Not Reported
Automatic Metrics Zoombench Latency Not Reported

Protocol Diff (Top Papers)

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

Signal \$OneMillion-Bench: How Far are Language Agents fro… Confidence-Driven Multi-Scale Model Selection for C… Zooming without Zooming: Region-to-Image Distillati…
Human Feedback Rubric RatingNot reportedNot reported
Evaluation Modes Automatic MetricsAutomatic MetricsAutomatic Metrics
Benchmarks Onemillion BenchMMLUZoombench
Metrics Accuracy, CoherenceAccuracy, CostLatency
Quality Controls Not reportedNot reportedNot reported
Rater Population Domain ExpertsUnknownUnknown
Annotation Unit Multi Dim RubricUnknownUnknown
Suggested Reading Order (Extended)

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

Suggested Reading Order

  1. \$OneMillion-Bench: How Far are Language Agents from Human Experts?

    Start here for detailed protocol reporting and quality-control evidence. Signals: automatic metrics + rubric ratings. Focus: Onemillion-Bench / accuracy. Abstract: We adopt a rubric-based evaluation protocol scoring factual.

  2. Confidence-Driven Multi-Scale Model Selection for Cost-Efficient Inference

    Start here for detailed protocol reporting and quality-control evidence. Signals: automatic metrics. Focus: MMLU / accuracy. Abstract: Large Language Models (LLMs) have revolutionized inference across diverse natural language.

  3. A Benchmark for Deep Information Synthesis

    Start here for detailed protocol reporting and quality-control evidence. Signals: automatic metrics. Focus: f1. Abstract: When evaluated on DEEPSYNTH, 11 state-of-the-art LLMs and deep research agents achieve a.

  4. What Matters For Safety Alignment?

    Include a human-eval paper to calibrate against judge-based evaluation settings. Signals: automatic metrics + red-team protocols. Focus: success rate. Abstract: This paper presents a comprehensive empirical study on.

  5. Adaptation of Agentic AI: A Survey of Post-Training, Memory, and Skills

    Adds automatic metrics with pairwise preferences for broader protocol coverage within this hub. Signals: automatic metrics + pairwise preferences. Focus: cost. Abstract: On the agent side, A1 (tool-execution-signaled).

  6. Zooming without Zooming: Region-to-Image Distillation for Fine-Grained Multimodal Perception

    Adds automatic metrics for broader protocol coverage within this hub. Signals: automatic metrics. Focus: Zoombench / latency. Abstract: Multimodal Large Language Models (MLLMs) excel at broad visual understanding.

  7. REDSearcher: A Scalable and Cost-Efficient Framework for Long-Horizon Search Agents

    Adds automatic metrics for broader protocol coverage within this hub. Signals: automatic metrics. Focus: recall. Abstract: Large language models are transitioning from generalpurpose knowledge engines to realworld problem.

  8. EnsembleLink: Accurate Record Linkage Without Training Data

    Adds automatic metrics for broader protocol coverage within this hub. Signals: automatic metrics. Focus: accuracy. Abstract: Record linkage, the process of matching records that refer to the same.

Known Limitations

Known Limitations

  • Only 0% of papers report quality controls; prioritize calibration/adjudication evidence.
  • Rater population is under-specified (9.1% 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 (1)
  • Red Team (1)
  • Rubric Rating (1)

Evaluation Modes

  • Automatic Metrics (11)

Top Benchmarks

  • MMLU (1)
  • Onemillion Bench (1)
  • Zoombench (1)

Top Metrics

  • Accuracy (4)
  • Cost (4)
  • Latency (2)
  • Coherence (1)

Rater Population Mix

  • Domain Experts (1)

Quality Controls

Coverage diagnostics (sample-based): human-feedback 27.3% · benchmarks 27.3% · metrics 81.8% · quality controls 0.0%.

Top Papers

  • \$OneMillion-Bench: How Far are Language Agents from Human Experts?

    Qianyu Yang, Yang Liu, Jiaqi Li, Jun Bai, Hao Chen · Mar 9, 2026 · Citations: 0

    Rubric Rating Automatic Metrics Tool Use

    To this end, we introduce \OneMillion-Bench \OneMillion-Bench, a benchmark of 400 expert-curated tasks spanning Law, Finance, Industry, Healthcare, and Natural Science, built to evaluate agents across economically consequential scenarios.

  • Confidence-Driven Multi-Scale Model Selection for Cost-Efficient Inference

    Bo-Wei Chen, Chung-Chi Chen, An-Zi Yen · Feb 25, 2026 · Citations: 0

    Automatic Metrics Tool Use

    Experiments on the Massive Multitask Language Understanding (MMLU) benchmark show that our approach achieves accuracy comparable to the largest model while reducing computational costs by 20\% to 40\%.

  • Zooming without Zooming: Region-to-Image Distillation for Fine-Grained Multimodal Perception

    Lai Wei, Liangbo He, Jun Lan, Lingzhong Dong, Yutong Cai · Feb 12, 2026 · Citations: 0

    Automatic Metrics Tool Use

    To address this, we propose Region-to-Image Distillation, which transforms zooming from an inference-time tool into a training-time primitive, thereby internalizing the benefits of agentic zooming into a single forward pass of an MLLM.

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