Skip to content
← Back to explorer

HFEPX Hub

CS.AI + Law Papers

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

Read Full Context

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

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%

11 / 11 sampled papers are not low-signal flagged.

Replication-Ready Set

1

Benchmark + metric + eval mode explicitly present.

Judge/Human Comparability

0

Papers containing both `human_eval` and `llm_as_judge`.

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

Need evaluators for this research workflow?

Post a Job →

Why This Matters For Eval Research

  • 54.5% of papers report explicit human-feedback signals, led by pairwise preferences.
  • automatic metrics appears in 63.6% of papers in this hub.
  • Cow-Bench 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.
  • Pair this hub with llm_as_judge pages to benchmark automated-vs-human evaluation tradeoffs.

Benchmark Interpretation

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

Metric Interpretation

  • accuracy is reported in 18.2% of hub papers (2/11); compare with a secondary metric before ranking methods.
  • coherence is reported in 9.1% of hub papers (1/11); compare with a secondary metric before ranking methods.
Researcher Checklist (Expanded)

Researcher Checklist

  • Strong: Papers with explicit human feedback

    Coverage is strong (54.5% vs 45% target).

  • Gap: Papers reporting quality controls

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

  • Strong: Papers naming benchmarks/datasets

    Coverage is strong (36.4% vs 35% target).

  • Strong: Papers naming evaluation metrics

    Coverage is strong (45.5% vs 35% target).

  • Moderate: Papers with known rater population

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

  • Strong: Papers with known annotation unit

    Coverage is strong (54.5% vs 35% target).

Strengths

  • Strong human-feedback signal (54.5% of papers).
  • Most papers provide measurable evaluation context (36.4% benchmarks, 45.5% metrics).
  • Agentic evaluation appears in 81.8% of papers.

Known Gaps

  • Only 0% 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 (Cow-Bench vs Lawbench) before comparing methods.
  • Track metric sensitivity by reporting both accuracy and coherence.
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 benchmark reference

APEX-Agents

Reported benchmark with pass@1 gives a fast comparison anchor.

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 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
The Trinity of Consistency as a Defining Principle for General World Models

Feb 26, 2026

No
Not Reported
Simulation Env Cow Bench Not Reported Not Reported
RoboPocket: Improve Robot Policies Instantly with Your Phone

Mar 5, 2026

Yes Not Reported Not Reported Not Reported Not Reported
Multimodal Multi-Agent Empowered Legal Judgment Prediction

Jan 19, 2026

No
Not Reported
Simulation Env Lawbench Not Reported 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
Orthogonalized Policy Optimization:Policy Optimization as Orthogonal Projection in Hilbert Space

Jan 18, 2026

No
Not Reported
Automatic Metrics MATH Not Reported Not Reported
CORE: Measuring Multi-Agent LLM Interaction Quality under Game-Theoretic Pressures

Aug 16, 2025

Yes Not Reported Not Reported Not Reported 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
On the Complexity of Neural Computation in Superposition

Sep 5, 2024

Yes Automatic Metrics Not Reported Not Reported 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… APEX-Agents Adaptation of Agentic AI: A Survey of Post-Training…
Human Feedback Rubric RatingRubric Rating, Expert VerificationPairwise Preference
Evaluation Modes Automatic MetricsAutomatic MetricsAutomatic Metrics
Benchmarks Onemillion BenchNot reportedNot reported
Metrics Accuracy, CoherencePass@1Cost
Quality Controls Not reportedNot reportedNot reported
Rater Population Domain ExpertsDomain ExpertsUnknown
Annotation Unit Multi Dim RubricMulti Dim RubricUnknown
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. RoboPocket: Improve Robot Policies Instantly with Your Phone

    Start here for detailed protocol reporting and quality-control evidence. Signals: demonstration data. Abstract: Scaling imitation learning is fundamentally constrained by the efficiency of data collection.

  3. The Trinity of Consistency as a Defining Principle for General World Models

    Start here for detailed protocol reporting and quality-control evidence. Signals: simulation environments. Focus: Cow-Bench. Abstract: CoW-Bench evaluates both video generation models and UMMs under a unified evaluation protocol.

  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. APEX-Agents

    Include an LLM-as-judge paper to test judge design and agreement assumptions. Signals: automatic metrics + rubric ratings. Focus: pass@1. Abstract: We open source the APEX-Agents benchmark (n=480) with.

  6. 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).

  7. CORE: Measuring Multi-Agent LLM Interaction Quality under Game-Theoretic Pressures

    Adds evaluation protocol evidence with pairwise preferences for broader protocol coverage within this hub. Signals: pairwise preferences. Abstract: We apply CORE to pairwise LLM dialogs across competitive, cooperative,.

  8. Multimodal Multi-Agent Empowered Legal Judgment Prediction

    Adds simulation environments for broader protocol coverage within this hub. Signals: simulation environments. Focus: Lawbench. Abstract: Legal Judgment Prediction (LJP) aims to predict the outcomes of legal cases.

Known Limitations

Known Limitations

  • Only 0% 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

  • Pairwise Preference (3)
  • Rubric Rating (2)
  • Demonstrations (1)
  • Expert Verification (1)

Evaluation Modes

  • Automatic Metrics (7)
  • Simulation Env (2)
  • Human Eval (1)

Top Benchmarks

  • Cow Bench (1)
  • Lawbench (1)
  • MATH (1)
  • Onemillion Bench (1)

Top Metrics

  • Accuracy (2)
  • Coherence (1)
  • Cost (1)
  • F1 (1)

Rater Population Mix

  • Domain Experts (3)

Quality Controls

Coverage diagnostics (sample-based): human-feedback 54.5% · benchmarks 36.4% · metrics 45.5% · quality controls 0.0%.

Top Papers

Related Hubs

Need human evaluators for your AI research? Scale annotation with expert AI Trainers.