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HFEPX Benchmark Hub

AIME Or GSM8K Benchmark Papers

Updated from current HFEPX corpus (Mar 21, 2026). 19 papers are grouped in this benchmark page.

Read Full Context

Updated from current HFEPX corpus (Mar 21, 2026). 19 papers are grouped in this benchmark page. Common evaluation modes: Automatic Metrics, Human Eval. Common annotation unit: Scalar. Frequent quality control: Calibration. 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 Oct 5, 2025.

Papers: 19 Last published: Oct 5, 2025 Global RSS

Researcher Quick Triage

Use this page for benchmark-matched method comparisons and eval protocol selection. Quality band: Medium .

High-Signal Coverage

100.0%

19 / 19 sampled papers are not low-signal flagged.

Replication-Ready Set

9

Papers with explicit benchmark + metric + eval mode fields.

Quality Controls

5.3%

1 papers report calibration/adjudication/IAA controls.

  • 10 papers explicitly name benchmark datasets in the sampled set.
  • 9 papers report at least one metric term in metadata extraction.
  • Start with the ranked shortlist below before reading all papers.

Primary action: Start with the top 2 benchmark-matched papers, then compare evaluation modes in the protocol matrix.

Why This Matters (Expanded)

Why This Matters For Eval Research

  • 50% of papers report explicit human-feedback signals, led by pairwise preferences.
  • automatic metrics appears in 47.4% of papers in this hub.
  • GSM8K 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 (5.3% of papers).
  • Rater context is mostly unspecified rater pools, and annotation is commonly scalar scoring; use this to scope replication staffing.
  • Pair this hub with llm_as_judge pages to benchmark automated-vs-human evaluation tradeoffs.

Benchmark Interpretation

  • GSM8K appears in 70% of hub papers (7/19); use this cohort for benchmark-matched comparisons.
  • AIME appears in 40% of hub papers (4/19); use this cohort for benchmark-matched comparisons.

Metric Interpretation

  • accuracy is reported in 60% of hub papers (6/19); compare with a secondary metric before ranking methods.
  • cost is reported in 20% of hub papers (2/19); compare with a secondary metric before ranking methods.

Start Here (Benchmark-Matched First 6)

Ranked by protocol completeness so you can quickly find papers suitable for comparison studies.

Protocol Matrix (Top 10)

Compare protocol ingredients quickly before deep-reading full papers.

Paper Eval Modes Human Feedback Metrics Quality Controls
$V_1$: Unifying Generation and Self-Verification for Parallel Reasoners

Mar 4, 2026

 Automatic Metrics Pairwise Preference Pass@1 Not reported
Entropy trajectory shape predicts LLM reasoning reliability: A diagnostic study of uncertainty dynamics in chain-of-thought

Mar 19, 2026

 Automatic Metrics Not reported Accuracy, Calibration error Calibration
Don't Pass@k: A Bayesian Framework for Large Language Model Evaluation

Oct 5, 2025

 Automatic Metrics, Simulation Env Rubric Rating Accuracy, Pass@k Not reported
FOR-Prompting: From Objection to Revision via an Asymmetric Prompting Protocol

Oct 2, 2025

 Automatic Metrics Pairwise Preference, Critique Edit Accuracy Not reported
Critique-GRPO: Advancing LLM Reasoning with Natural Language and Numerical Feedback

Jun 3, 2025

 Automatic Metrics Critique Edit Pass@1 Not reported
Top-b: Entropic Regulation of Relative Probability Bands in Autoregressive Language Processes

Mar 15, 2026

 Automatic Metrics Not reported Accuracy Not reported
Think$^{2}$: Grounded Metacognitive Reasoning in Large Language Models

Feb 21, 2026

 Human Eval Pairwise Preference Not reported Not reported
The Flexibility Trap: Why Arbitrary Order Limits Reasoning Potential in Diffusion Language Models

Jan 21, 2026

 Automatic Metrics Not reported Accuracy Not reported
Cache What Lasts: Token Retention for Memory-Bounded KV Cache in LLMs

Dec 3, 2025

 Automatic Metrics Not reported Cost Not reported
SPARE: Single-Pass Annotation with Reference-Guided Evaluation for Automatic Process Supervision and Reward Modelling

Jun 18, 2025

 Automatic Metrics Not reported Accuracy, Precision Not reported
Researcher Workflow (Detailed)

Checklist

  • Strong: Papers with explicit human feedback

    Coverage is strong (50% vs 45% target).

  • Gap: Papers reporting quality controls

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

  • Strong: Papers naming benchmarks/datasets

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

  • Strong: Papers naming evaluation metrics

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

  • Gap: Papers with known rater population

    Coverage is a replication risk (0% vs 35% target).

  • Strong: Papers with known annotation unit

    Coverage is strong (70% vs 35% target).

Strengths

  • Strong human-feedback signal (50% of papers).
  • Most papers provide measurable evaluation context (100% benchmarks, 90% metrics).
  • Agentic evaluation appears in 50% of papers.

Known Gaps

  • Only 10% of papers report quality controls; prioritize calibration/adjudication evidence.
  • Rater population is under-specified (0% coverage).

Suggested Next Analyses

  • Pair this hub with llm_as_judge pages to benchmark automated-vs-human evaluation tradeoffs.
  • Stratify by benchmark (GSM8K vs AIME) before comparing methods.
  • Track metric sensitivity by reporting both accuracy and cost.
  • Add inter-annotator agreement checks when reproducing these protocols.

Recommended Queries

Human Eval ProtocolsBenchmark Slice: GSM8KMetric Slice: accuracyRecent High-Signal Papers
Known Limitations
  • Only 10% of papers report quality controls; prioritize calibration/adjudication evidence.
  • Rater population is under-specified (0% coverage).
  • Narrative synthesis is grounded in metadata and abstracts only; full-paper implementation details are not parsed.
Research Utility Snapshot (Detailed)

Evaluation Modes

  • Automatic Metrics (9)
  • Human Eval (1)
  • Simulation Env (1)

Human Feedback Mix

  • Pairwise Preference (3)
  • Critique Edit (2)
  • Rubric Rating (1)

Top Benchmarks

  • GSM8K (7)
  • AIME (4)
  • CodeContests (1)
  • Correctbench (1)

Top Metrics

  • Accuracy (6)
  • Cost (2)
  • Pass@1 (2)
  • Calibration error (1)

Top Papers On This Benchmark

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