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HFEPX Hub

CS.LG + Law Papers

Updated from current HFEPX corpus (Feb 27, 2026). 14 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: MATH. 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 24, 2026.

Papers: 14 Last published: Feb 24, 2026 Global RSS Tag RSS
Cs.LGLaw

Research Narrative

Grounded narrative Model: deterministic-grounded Source: persisted

Updated from current HFEPX corpus (Feb 27, 2026). This page tracks 14 papers for CS.LG + Law Papers. Dominant protocol signals include automatic metrics, simulation environments, with frequent benchmark focus on MATH, GSM8K and metric focus on accuracy, cost. Use the grounded sections below to prioritize reproducible protocol choices, benchmark-matched comparisons, and judge-vs-human evaluation checks.

Why This Matters For Eval Research

Protocol Takeaways

Benchmark Interpretation

  • MATH appears in 14.3% of hub papers (2/14); use this cohort for benchmark-matched comparisons.
  • GSM8K appears in 7.1% of hub papers (1/14); use this cohort for benchmark-matched comparisons.

Metric Interpretation

  • accuracy is reported in 28.6% of hub papers (4/14); compare with a secondary metric before ranking methods.
  • cost is reported in 7.1% of hub papers (1/14); compare with a secondary metric before ranking methods.

Researcher Checklist

  • Close gap on Papers with explicit human feedback. Coverage is a replication risk (21.4% vs 45% target).
  • Close gap on Papers reporting quality controls. Coverage is a replication risk (0% vs 30% target).
  • Tighten coverage on Papers naming benchmarks/datasets. Coverage is usable but incomplete (28.6% vs 35% target).
  • Maintain strength on Papers naming evaluation metrics. Coverage is strong (50% vs 35% target).
  • Close gap on Papers with known rater population. Coverage is a replication risk (14.3% vs 35% target).
  • Close gap on Papers with known annotation unit. Coverage is a replication risk (14.3% vs 35% target).

Papers with explicit human feedback

Coverage is a replication risk (21.4% vs 45% target).

Papers reporting quality controls

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

Papers naming benchmarks/datasets

Coverage is usable but incomplete (28.6% vs 35% target).

Papers naming evaluation metrics

Coverage is strong (50% vs 35% target).

Papers with known rater population

Coverage is a replication risk (14.3% vs 35% target).

Papers with known annotation unit

Coverage is a replication risk (14.3% vs 35% target).

Suggested Reading Order

  1. 1. MrBERT: Modern Multilingual Encoders via Vocabulary, Domain, and Dimensional Adaptation

    Start here for detailed protocol reporting, including rater and quality-control evidence.

  2. 2. Agentic Adversarial QA for Improving Domain-Specific LLMs

    Start here for detailed protocol reporting, including rater and quality-control evidence.

  3. 3. Helpful to a Fault: Measuring Illicit Assistance in Multi-Turn, Multilingual LLM Agents

    Start here for detailed protocol reporting, including rater and quality-control evidence.

  4. 4. Prescriptive Scaling Reveals the Evolution of Language Model Capabilities

    Adds automatic metrics for broader coverage within this hub.

  5. 5. Scaling Beyond Masked Diffusion Language Models

    Adds automatic metrics for broader coverage within this hub.

  6. 6. APEX-Agents

    Adds simulation environments with rubric ratings for broader coverage within this hub.

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

    Adds automatic metrics for broader coverage within this hub.

  8. 8. Group Representational Position Encoding

    Adds automatic metrics for broader coverage within this hub.

Known Limitations

  • Only 0% of papers report quality controls; prioritize calibration/adjudication evidence.
  • Rater population is under-specified (14.3% coverage).
  • Narrative synthesis is grounded in metadata and abstracts only; full-paper implementation details are not parsed.

Research Utility Links

automatic_metrics vs simulation_env

both=0, left_only=13, right_only=1

0 papers use both Automatic Metrics and Simulation Env.

Benchmark Brief

MATH

Coverage: 2 papers (14.3%)

2 papers (14.3%) mention MATH.

Examples: Prescriptive Scaling Reveals the Evolution of Language Model Capabilities , Orthogonalized Policy Optimization:Policy Optimization as Orthogonal Projection in Hilbert Space

Benchmark Brief

GSM8K

Coverage: 1 papers (7.1%)

1 papers (7.1%) mention GSM8K.

Examples: Scaling Beyond Masked Diffusion Language Models

Benchmark Brief

Legalbench

Coverage: 1 papers (7.1%)

1 papers (7.1%) mention Legalbench.

Examples: Agentic Adversarial QA for Improving Domain-Specific LLMs

Metric Brief

accuracy

Coverage: 4 papers (28.6%)

4 papers (28.6%) mention accuracy.

Examples: Agentic Adversarial QA for Improving Domain-Specific LLMs , Prescriptive Scaling Reveals the Evolution of Language Model Capabilities , Conflict-Aware Fusion: Resolving Logic Inertia in Large Language Models via Structured Cognitive Priors

Metric Brief

cost

Coverage: 1 papers (7.1%)

1 papers (7.1%) mention cost.

Examples: Group Representational Position Encoding

Metric Brief

pass@1

Coverage: 1 papers (7.1%)

1 papers (7.1%) mention pass@1.

Examples: APEX-Agents

Most Cited In This Hub

Fast path to methods with the strongest citation traction in this scope.

Papers: MrBERT: Modern Multilingual Encoders via Vocabulary, Domain, and Dimensional Adaptation , Agentic Adversarial QA for Improving Domain-Specific LLMs , Helpful to a Fault: Measuring Illicit Assistance in Multi-Turn, Multilingual LLM Agents

Most Recent

Fast path to latest protocol changes and newly published evaluation setups.

Papers: MrBERT: Modern Multilingual Encoders via Vocabulary, Domain, and Dimensional Adaptation , Agentic Adversarial QA for Improving Domain-Specific LLMs , Helpful to a Fault: Measuring Illicit Assistance in Multi-Turn, Multilingual LLM Agents

Best Protocol Detail

Papers with explicit rater/unit metadata and quality-control signals for reproducibility.

Papers: MrBERT: Modern Multilingual Encoders via Vocabulary, Domain, and Dimensional Adaptation , Agentic Adversarial QA for Improving Domain-Specific LLMs , Helpful to a Fault: Measuring Illicit Assistance in Multi-Turn, Multilingual LLM Agents

Top Papers

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