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HFEPX Hub

Automatic Metrics + Law (Last 120 Days)

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

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Updated from current HFEPX corpus (Mar 10, 2026). 10 papers are grouped in this hub page. Common evaluation modes: Automatic Metrics, Human Eval. Most common rater population: Domain Experts. Common annotation unit: Multi Dim Rubric. Frequent quality control: Adjudication. Frequently cited benchmark: HLE. 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: 10 Last published: Mar 9, 2026 Global RSS Tag RSS
Automatic MetricsLawLast 120d

Researcher Quick Triage

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

High-Signal Coverage

100.0%

10 / 10 sampled papers are not low-signal flagged.

Replication-Ready Set

2

Benchmark + metric + eval mode explicitly present.

Judge/Human Comparability

0

Papers containing both `human_eval` and `llm_as_judge`.

  • 2 papers are replication-ready (benchmark + metric + explicit evaluation mode).
  • 0 papers support judge-vs-human agreement analysis.
  • 1 papers report explicit quality controls (calibration/adjudication/IAA).

Primary action: Use this page for scouting only; collect additional papers before attempting replication-critical comparisons.

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Why This Matters For Eval Research

  • 40% of papers report explicit human-feedback signals, led by expert verification.
  • automatic metrics appears in 100% of papers in this hub.
  • HLE is a recurring benchmark anchor for cross-paper comparisons in this page.

Protocol Takeaways

  • Most common quality-control signal is adjudication (10% of papers).
  • Rater context is mostly domain experts, and annotation is commonly multi-dimensional rubrics; use this to scope replication staffing.
  • Pair this hub with llm_as_judge pages to benchmark automated-vs-human evaluation tradeoffs.

Benchmark Interpretation

  • HLE appears in 10% of hub papers (1/10); use this cohort for benchmark-matched comparisons.
  • MATH appears in 10% of hub papers (1/10); use this cohort for benchmark-matched comparisons.

Metric Interpretation

  • accuracy is reported in 40% of hub papers (4/10); compare with a secondary metric before ranking methods.
  • coherence is reported in 10% of hub papers (1/10); compare with a secondary metric before ranking methods.
Researcher Checklist (Expanded)

Researcher Checklist

  • Moderate: Papers with explicit human feedback

    Coverage is usable but incomplete (40% vs 45% target).

  • Gap: Papers reporting quality controls

    Coverage is a replication risk (10% vs 30% target).

  • Moderate: Papers naming benchmarks/datasets

    Coverage is usable but incomplete (30% vs 35% target).

  • Strong: Papers naming evaluation metrics

    Coverage is strong (90% vs 35% target).

  • Strong: Papers with known rater population

    Coverage is strong (40% vs 35% target).

  • Moderate: Papers with known annotation unit

    Coverage is usable but incomplete (30% vs 35% target).

Strengths

  • Agentic evaluation appears in 80% of papers.

Known Gaps

  • Only 10% of papers report quality controls; prioritize calibration/adjudication evidence.

Suggested Next Analyses

  • Pair this hub with llm_as_judge pages to benchmark automated-vs-human evaluation tradeoffs.
  • Stratify by benchmark (HLE vs MATH) before comparing methods.
  • Track metric sensitivity by reporting both accuracy and coherence.
  • Add inter-annotator agreement checks when reproducing these protocols.
Recommended Queries (Expanded)

Recommended Queries

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.

Strongest recent paper

APEX-Agents

Useful for current practice scanning; published Jan 20, 2026.

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

Mar 9, 2026

Yes Automatic Metrics Onemillion Bench Accuracy , Coherence Not Reported
APEX-Agents

Jan 20, 2026

Yes Automatic Metrics Not Reported Pass@1 Not Reported
Adaptation of Agentic AI: A Survey of Post-Training, Memory, and Skills

Dec 18, 2025

Yes Automatic Metrics Not Reported Cost Not Reported
Vichara: Appellate Judgment Prediction and Explanation for the Indian Judicial System

Feb 20, 2026

No
Not Reported
Human Eval , Automatic Metrics Not Reported F1 Not Reported
TableMind++: An Uncertainty-Aware Programmatic Agent for Tool-Augmented Table Reasoning

Mar 8, 2026

No
Not Reported
Automatic Metrics Not Reported Precision Not Reported
Orthogonalized Policy Optimization:Policy Optimization as Orthogonal Projection in Hilbert Space

Jan 18, 2026

No
Not Reported
Automatic Metrics MATH Not Reported Not Reported
MAWARITH: A Dataset and Benchmark for Legal Inheritance Reasoning with LLMs

Mar 8, 2026

No
Not Reported
Automatic Metrics Not Reported Accuracy Not Reported
Whisper: Courtside Edition Enhancing ASR Performance Through LLM-Driven Context Generation

Feb 21, 2026

No
Not Reported
Automatic Metrics Not Reported Error rate , Wer Not Reported
Conflict-Aware Fusion: Resolving Logic Inertia in Large Language Models via Structured Cognitive Priors

Dec 6, 2025

No
Not Reported
Automatic Metrics Not Reported Accuracy 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… \$OneMillion-Bench: How Far are Language Agents fro… APEX-Agents
Human Feedback Expert Verification, Critique EditRubric RatingRubric Rating, Expert Verification
Evaluation Modes Automatic MetricsAutomatic MetricsAutomatic Metrics
Benchmarks HLEOnemillion BenchNot reported
Metrics AccuracyAccuracy, CoherencePass@1
Quality Controls AdjudicationNot reportedNot reported
Rater Population Domain ExpertsDomain ExpertsDomain Experts
Annotation Unit UnknownMulti Dim RubricMulti Dim Rubric
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. MAWARITH: A Dataset and Benchmark for Legal Inheritance Reasoning with LLMs

    Start here for detailed protocol reporting and quality-control evidence. Signals: automatic metrics. Focus: accuracy. Abstract: Islamic inheritance law ('ilm al-mawarith) is challenging for large language models because solving.

  3. TableMind++: An Uncertainty-Aware Programmatic Agent for Tool-Augmented Table Reasoning

    Start here for detailed protocol reporting and quality-control evidence. Signals: automatic metrics. Focus: precision. Abstract: Table reasoning requires models to jointly perform semantic understanding and precise numerical operations.

  4. Vichara: Appellate Judgment Prediction and Explanation for the Indian Judicial System

    Include a human-eval paper to calibrate against judge-based evaluation settings. Signals: human evaluation. Focus: f1. Abstract: Human evaluation of the generated explanations across Clarity, Linking, and Usefulness metrics.

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

    Include an LLM-as-judge paper to test judge design and agreement assumptions. Signals: automatic metrics + expert verification. Focus: HLE / accuracy. Abstract: Overall, HLE-Verified improves HLE-style evaluations by.

  6. APEX-Agents

    Adds automatic metrics with rubric ratings for broader protocol coverage within this hub. Signals: automatic metrics + rubric ratings. Focus: pass@1. Abstract: We open source the APEX-Agents benchmark.

  7. 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).

  8. Orthogonalized Policy Optimization:Policy Optimization as Orthogonal Projection in Hilbert Space

    Adds automatic metrics for broader protocol coverage within this hub. Signals: automatic metrics. Focus: MATH. Abstract: We propose Orthogonalized Policy Optimization (OPO), a principled framework for large language.

Known Limitations

Known Limitations

  • Only 10% of papers report quality controls; prioritize calibration/adjudication evidence.
  • Narrative synthesis is grounded in metadata and abstracts only; full-paper implementation details are not parsed.
  • Cross-page comparisons should be benchmark- and metric-matched to avoid protocol confounding.
Research Utility Snapshot (Detailed)

Research Utility Snapshot

Human Feedback Mix

  • Expert Verification (2)
  • Rubric Rating (2)
  • Critique Edit (1)
  • Pairwise Preference (1)

Evaluation Modes

  • Automatic Metrics (10)
  • Human Eval (1)

Top Benchmarks

  • HLE (1)
  • MATH (1)
  • Onemillion Bench (1)

Top Metrics

  • Accuracy (4)
  • Coherence (1)
  • Cost (1)
  • Error rate (1)

Rater Population Mix

  • Domain Experts (4)

Quality Controls

  • Adjudication (1)
Coverage diagnostics (sample-based): human-feedback 40.0% · benchmarks 30.0% · metrics 90.0% · quality controls 10.0%.

Top Papers

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