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HFEPX Hub

Automatic Metrics + Math (Last 30 Days)

Updated from current HFEPX corpus (Apr 19, 2026). 14 papers are grouped in this hub page.

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Updated from current HFEPX corpus (Apr 19, 2026). 14 papers are grouped in this hub page. Common evaluation modes: Automatic Metrics, Human Eval. Most common rater population: Domain Experts. Common annotation unit: Trajectory. Frequently cited benchmark: GSM8K. 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 Apr 2, 2026.

Papers: 14 Last published: Apr 2, 2026 Global RSS Tag RSS
Automatic MetricsMathLast 30d

Researcher Quick Triage

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

All Sampled Papers (14) Replication-Ready Only (5)

High-Signal Coverage

100.0%

14 / 14 sampled papers are not low-signal flagged.

Replication-Ready Set

5

Benchmark + metric + eval mode explicitly present.

Judge/Human Comparability

0

Papers containing both `human_eval` and `llm_as_judge`.

  • 5 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 (5 papers).

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Why This Matters For Eval Research

  • 35.7% of papers report explicit human-feedback signals, led by pairwise preferences.
  • automatic metrics appears in 100% of papers in this hub.
  • GSM8K 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 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

  • GSM8K appears in 21.4% of hub papers (3/14); use this cohort for benchmark-matched comparisons.
  • HLE appears in 7.1% of hub papers (1/14); use this cohort for benchmark-matched comparisons.

Metric Interpretation

  • accuracy is reported in 78.6% of hub papers (11/14); compare with a secondary metric before ranking methods.
  • cost is reported in 28.6% of hub papers (4/14); compare with a secondary metric before ranking methods.
Researcher Checklist (Expanded)

Researcher Checklist

  • Moderate: Papers with explicit human feedback

    Coverage is usable but incomplete (35.7% 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 (35.7% vs 35% target).

  • Strong: Papers naming evaluation metrics

    Coverage is strong (100% vs 35% target).

  • Gap: Papers with known rater population

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

  • Strong: Papers with known annotation unit

    Coverage is strong (42.9% vs 35% target).

Strengths

  • Most papers provide measurable evaluation context (35.7% benchmarks, 100% metrics).
  • Contains both human-eval and LLM-as-judge protocols for head-to-head methodology comparison.
  • Agentic evaluation appears in 50% of papers.

Known Gaps

  • Only 0% of papers report quality controls; prioritize calibration/adjudication evidence.
  • Rater population is under-specified (14.3% coverage).
  • LLM-as-judge appears without enough inter-annotator agreement reporting.

Suggested Next Analyses

  • Compare papers that report both human_eval and llm_as_judge to quantify judge-human agreement drift.
  • Stratify by benchmark (GSM8K vs HLE) before comparing methods.
  • Track metric sensitivity by reporting both accuracy and cost.
Recommended Queries (Expanded)

Recommended Queries

Judge vs Human Agreement Benchmark Slice: GSM8K 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
Stabilizing Iterative Self-Training with Verified Reasoning via Symbolic Recursive Self-Alignment

Mar 23, 2026

 Yes
Pairwise Preference
Automatic Metrics GSM8K Accuracy Not Reported
PAVE: Premise-Aware Validation and Editing for Retrieval-Augmented LLMs

Mar 21, 2026

 Yes
Critique Edit
Automatic Metrics Post Retrieval Accuracy Not Reported
Don't Overthink It: Inter-Rollout Action Agreement as a Free Adaptive-Compute Signal for LLM Agents

Apr 9, 2026

 No
Not Reported
Automatic Metrics GSM8K Accuracy Not Reported
S0 Tuning: Zero-Overhead Adaptation of Hybrid Recurrent-Attention Models

Apr 1, 2026

 No
Not Reported
Automatic Metrics MATH 500 , GSM8K Pass@1 , Inference cost Not Reported
Agent Q-Mix: Selecting the Right Action for LLM Multi-Agent Systems through Reinforcement Learning

Apr 1, 2026

 No
Not Reported
Automatic Metrics HLE Accuracy , Token cost Not Reported

Protocol Diff (Top Papers)

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

Signal Stabilizing Iterative Self-Training with Verified R… PAVE: Premise-Aware Validation and Editing for Retr… Don't Overthink It: Inter-Rollout Action Agreement…
Human Feedback Pairwise PreferenceCritique EditNot reported
Evaluation Modes Automatic MetricsAutomatic MetricsAutomatic Metrics
Benchmarks GSM8KPost RetrievalGSM8K
Metrics AccuracyAccuracyAccuracy
Quality Controls Not reportedNot reportedNot reported
Rater Population UnknownUnknownUnknown
Annotation Unit UnknownUnknownTrajectory
Suggested Reading Order (Extended)

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

Suggested Reading Order

  1. Don't Overthink It: Inter-Rollout Action Agreement as a Free Adaptive-Compute Signal for LLM Agents

    Start here for detailed protocol reporting and quality-control evidence. Signals: automatic metrics. Focus: GSM8K / accuracy. Abstract: Inference-time compute scaling has emerged as a powerful technique for improving.

  2. Cognitive Loop of Thought: Reversible Hierarchical Markov Chain for Efficient Mathematical Reasoning

    Start here for detailed protocol reporting and quality-control evidence. Signals: automatic metrics. Focus: accuracy. Abstract: Multi-step Chain-of-Thought (CoT) has significantly advanced the mathematical reasoning capabilities of LLMs by.

  3. How Long Reasoning Chains Influence LLMs' Judgment of Answer Factuality

    Start here for detailed protocol reporting and quality-control evidence. Signals: human evaluation. Focus: accuracy. Abstract: Large language models (LLMs) has been widely adopted as a scalable surrogate for.

  4. Learning to Predict Future-Aligned Research Proposals with Language Models

    Include a human-eval paper to calibrate against judge-based evaluation settings. Signals: human evaluation. Focus: accuracy. Abstract: Large language models (LLMs) are increasingly used to assist ideation in research,.

  5. RuleForge: Automated Generation and Validation for Web Vulnerability Detection at Scale

    Include an LLM-as-judge paper to test judge design and agreement assumptions. Signals: LLM-as-judge + expert verification. Focus: auroc. Abstract: This paper focuses on RuleForge's architecture and operational deployment.

  6. Stabilizing Iterative Self-Training with Verified Reasoning via Symbolic Recursive Self-Alignment

    Include an LLM-as-judge paper to test judge design and agreement assumptions. Signals: automatic metrics + pairwise preferences. Focus: GSM8K / accuracy. Abstract: We further demonstrate that constructing DPO.

  7. PAVE: Premise-Aware Validation and Editing for Retrieval-Augmented LLMs

    Adds automatic metrics with critique/edit feedback for broader protocol coverage within this hub. Signals: automatic metrics + critique/edit feedback. Focus: post-retrieval / accuracy. Abstract: Retrieval-augmented language models can.

  8. QED-Nano: Teaching a Tiny Model to Prove Hard Theorems

    Adds automatic metrics with rubric ratings for broader protocol coverage within this hub. Signals: automatic metrics + rubric ratings. Focus: cost. Abstract: Our training recipe has three stages:.

Known Limitations

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 Snapshot (Detailed)

Research Utility Snapshot

Human Feedback Mix

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

Evaluation Modes

  • Automatic Metrics (14)
  • Human Eval (2)
  • Llm As Judge (1)

Top Benchmarks

  • GSM8K (3)
  • HLE (1)
  • HumanEval+ (1)
  • MATH 500 (1)

Top Metrics

  • Accuracy (11)
  • Cost (4)
  • Coherence (2)
  • Inference cost (2)

Rater Population Mix

  • Domain Experts (2)

Quality Controls

Coverage diagnostics (sample-based): human-feedback 35.7% · benchmarks 35.7% · metrics 100.0% · quality controls 0.0%.

Top Papers

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