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HFEPX Hub

Automatic Metrics Papers (Last 90 Days)

Updated from current HFEPX corpus (Mar 1, 2026). 350 papers are grouped in this hub page.

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Updated from current HFEPX corpus (Mar 1, 2026). 350 papers are grouped in this hub page. Common evaluation modes: Automatic Metrics, Llm As Judge. Most common rater population: Domain Experts. Common annotation unit: Trajectory. Frequent quality control: Calibration. Frequently cited benchmark: BrowseComp. 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 Feb 15, 2026.

Papers: 350 Last published: Feb 15, 2026 Global RSS Tag RSS
Automatic MetricsLast 90d

Researcher Quick Triage

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

Analysis blocks below are computed from the currently loaded sample (60 of 350 total papers in this hub).

High-Signal Coverage

100.0%

60 / 60 sampled papers are not low-signal flagged.

Replication-Ready Set

17

Benchmark + metric + eval mode explicitly present.

Judge/Human Comparability

0

Papers containing both `human_eval` and `llm_as_judge`.

  • 17 papers are replication-ready (benchmark + metric + explicit evaluation mode).
  • 0 papers support judge-vs-human agreement analysis.
  • 9 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 (17 papers).

Why This Matters (Expanded)

Why This Matters For Eval Research

  • 12.9% 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.
Protocol Notes (Expanded)

Protocol Takeaways

  • Most common quality-control signal is rater calibration (2.6% of papers).
  • Rater context is mostly domain experts, and annotation is commonly trajectory-level annotation; use this to scope replication staffing.
  • Compare papers that report both human_eval and llm_as_judge to quantify judge-human agreement drift.

Benchmark Interpretation

  • BrowseComp appears in 0.9% of hub papers (3/350); use this cohort for benchmark-matched comparisons.
  • MMLU appears in 0.6% of hub papers (2/350); use this cohort for benchmark-matched comparisons.

Metric Interpretation

  • accuracy is reported in 14.6% of hub papers (51/350); compare with a secondary metric before ranking methods.
  • cost is reported in 5.7% of hub papers (20/350); compare with a secondary metric before ranking methods.
Researcher Checklist (Expanded)

Researcher Checklist

  • Gap: Papers with explicit human feedback

    Coverage is a replication risk (12.9% vs 45% target).

  • Gap: Papers reporting quality controls

    Coverage is a replication risk (6.3% vs 30% target).

  • Gap: Papers naming benchmarks/datasets

    Coverage is a replication risk (6% vs 35% target).

  • Moderate: Papers naming evaluation metrics

    Coverage is usable but incomplete (27.4% vs 35% target).

  • Gap: Papers with known rater population

    Coverage is a replication risk (11.4% vs 35% target).

  • Gap: Papers with known annotation unit

    Coverage is a replication risk (11.4% vs 35% target).

Strengths

  • Contains both human-eval and LLM-as-judge protocols for head-to-head methodology comparison.

Known Gaps

  • Only 6.3% of papers report quality controls; prioritize calibration/adjudication evidence.
  • Rater population is under-specified (11.4% coverage).
  • Annotation unit is under-specified (11.4% coverage).

Suggested Next Analyses

  • Compare papers that report both human_eval and llm_as_judge to quantify judge-human agreement drift.
  • Stratify by benchmark (BrowseComp vs MMLU) before comparing methods.
  • Track metric sensitivity by reporting both accuracy and cost.
Recommended Queries (Expanded)

Recommended Queries

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
HLE-Verified: A Systematic Verification and Structured Revision of Humanity's Last Exam

Feb 15, 2026

Yes Automatic Metrics HLE Accuracy Adjudication
SCOPE: Selective Conformal Optimized Pairwise LLM Judging

Feb 13, 2026

Yes Automatic Metrics MT Bench , LMSYS Chatbot Arena Error rate Calibration
CricBench: A Multilingual Benchmark for Evaluating LLMs in Cricket Analytics

Dec 26, 2025

Yes Automatic Metrics DROP , BIRD Accuracy Gold Questions
Document Reconstruction Unlocks Scalable Long-Context RLVR

Feb 9, 2026

Yes Automatic Metrics LongBench Coherence Not Reported
MemoryArena: Benchmarking Agent Memory in Interdependent Multi-Session Agentic Tasks

Feb 18, 2026

Yes Automatic Metrics Memoryarena Recall Not Reported
Duel-Evolve: Reward-Free Test-Time Scaling via LLM Self-Preferences

Feb 25, 2026

Yes Automatic Metrics LiveCodeBench , Mathbench Accuracy Not Reported
Can Large Language Models Replace Human Coders? Introducing ContentBench

Feb 23, 2026

Yes Automatic Metrics ContentBench Agreement , Cost Not Reported
AMA-Bench: Evaluating Long-Horizon Memory for Agentic Applications

Feb 26, 2026

No
Not Reported
Automatic Metrics Ama Bench Accuracy Not Reported
SWE-Protégé: Learning to Selectively Collaborate With an Expert Unlocks Small Language Models as Software Engineering Agents

Feb 25, 2026

No
Not Reported
Automatic Metrics SWE Bench , SWE Bench Verified Pass@1 , Latency Not Reported
D-COT: Disciplined Chain-of-Thought Learning for Efficient Reasoning in Small Language Models

Feb 25, 2026

No
Not Reported
Automatic Metrics MMLU , MMLU Pro Accuracy Not Reported
Search More, Think Less: Rethinking Long-Horizon Agentic Search for Efficiency and Generalization

Feb 26, 2026

No
Not Reported
Automatic Metrics GAIA , BrowseComp Accuracy , Latency 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

Protocol Diff (Top Papers)

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

Signal HLE-Verified: A Systematic Verification and Structu… SCOPE: Selective Conformal Optimized Pairwise LLM J… CricBench: A Multilingual Benchmark for Evaluating…
Human Feedback Expert Verification, Critique EditPairwise PreferenceExpert Verification
Evaluation Modes Automatic MetricsAutomatic MetricsAutomatic Metrics
Benchmarks HLEMT Bench, LMSYS Chatbot ArenaDROP, BIRD
Metrics AccuracyError rateAccuracy
Quality Controls AdjudicationCalibrationGold Questions
Rater Population Domain ExpertsUnknownDomain Experts
Annotation Unit UnknownPairwiseUnknown
Suggested Reading Order (Extended)

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

Suggested Reading Order

  1. An artificial intelligence framework for end-to-end rare disease phenotyping from clinical notes using large language models

    Start here for detailed protocol reporting and quality-control evidence. Signals: automatic metrics + expert verification. Focus: f1. Abstract: Phenotyping is fundamental to rare disease diagnosis, but manual curation.

  2. HLE-Verified: A Systematic Verification and Structured Revision of Humanity's Last Exam

    Include a human-eval paper to calibrate against judge-based evaluation settings. Signals: automatic metrics + expert verification. Focus: HLE / accuracy. Abstract: Overall, HLE-Verified improves HLE-style evaluations by reducing.

  3. SCOPE: Selective Conformal Optimized Pairwise LLM Judging

    Include a human-eval paper to calibrate against judge-based evaluation settings. Signals: automatic metrics + pairwise preferences. Focus: MT-Bench / error rate. Abstract: Large language models (LLMs) are increasingly.

  4. CricBench: A Multilingual Benchmark for Evaluating LLMs in Cricket Analytics

    Adds automatic metrics with expert verification for broader protocol coverage within this hub. Signals: automatic metrics + expert verification. Focus: DROP / accuracy. Abstract: We evaluate six state-of-the-art.

  5. MemoryArena: Benchmarking Agent Memory in Interdependent Multi-Session Agentic Tasks

    Adds automatic metrics with pairwise preferences for broader protocol coverage within this hub. Signals: automatic metrics + pairwise preferences. Focus: Memoryarena / recall. Abstract: MemoryArena supports evaluation across.

Known Limitations

Known Limitations

  • Only 6.3% of papers report quality controls; prioritize calibration/adjudication evidence.
  • Rater population is under-specified (11.4% 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 (19)
  • Expert Verification (15)
  • Critique Edit (5)
  • Red Team (5)

Evaluation Modes

  • Automatic Metrics (350)
  • Llm As Judge (7)
  • Simulation Env (5)
  • Human Eval (4)

Top Benchmarks

  • BrowseComp (3)
  • MMLU (2)
  • SWE Bench (2)
  • SWE Bench Verified (2)

Top Metrics

  • Accuracy (51)
  • Cost (20)
  • Latency (12)
  • F1 (8)

Rater Population Mix

  • Domain Experts (40)

Quality Controls

  • Calibration (9)
  • Inter Annotator Agreement Reported (9)
  • Adjudication (4)
  • Gold Questions (2)
Coverage diagnostics (sample-based): human-feedback 68.3% · benchmarks 31.7% · metrics 90.0% · quality controls 15.0%.

Top Papers

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