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HFEPX Benchmark Hub

AIME Benchmark Papers (Last 240 Days)

Updated from current HFEPX corpus (Mar 31, 2026). 11 papers are grouped in this benchmark page.

Read Full Context

Updated from current HFEPX corpus (Mar 31, 2026). 11 papers are grouped in this benchmark page. Common evaluation modes: Automatic Metrics, Human Eval. Common annotation unit: Multi Dim Rubric. Frequently cited benchmark: AIME. 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: 11 Last published: Oct 5, 2025 Global RSS

Researcher Quick Triage

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

High-Signal Coverage

100.0%

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

Replication-Ready Set

2

Papers with explicit benchmark + metric + eval mode fields.

Quality Controls

0.0%

0 papers report calibration/adjudication/IAA controls.

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

Primary action: Use this page to map benchmark mentions first; wait for stronger metric/QC coverage before strict comparisons.

Why This Matters (Expanded)

Why This Matters For Eval Research

  • 27.3% of papers report explicit human-feedback signals, led by pairwise preferences.
  • automatic metrics appears in 18.2% of papers in this hub.
  • AIME is a recurring benchmark anchor for cross-paper comparisons in this page.
Protocol Notes (Expanded)

Protocol Takeaways

  • Quality-control reporting is sparse in this slice; prioritize papers with explicit calibration or adjudication steps.
  • Rater context is mostly unspecified rater pools, 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

  • AIME appears in 100% of hub papers (11/11); use this cohort for benchmark-matched comparisons.
  • MATH-500 appears in 27.3% of hub papers (3/11); use this cohort for benchmark-matched comparisons.

Metric Interpretation

  • accuracy is reported in 9.1% of hub papers (1/11); compare with a secondary metric before ranking methods.
  • pass@1 is reported in 9.1% of hub papers (1/11); 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
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
Think$^{2}$: Grounded Metacognitive Reasoning in Large Language Models

Feb 21, 2026

 Human Eval Pairwise Preference Not reported Not reported
SortedRL: Accelerating RL Training for LLMs through Online Length-Aware Scheduling

Mar 24, 2026

 Not reported Not reported Not reported Not reported
TERMINATOR: Learning Optimal Exit Points for Early Stopping in Chain-of-Thought Reasoning

Mar 13, 2026

 Not reported Not reported Not reported Not reported
PostTrainBench: Can LLM Agents Automate LLM Post-Training?

Mar 9, 2026

 Not reported Not reported Not reported Not reported
Tool Verification for Test-Time Reinforcement Learning

Mar 2, 2026

 Not reported Not reported Not reported Not reported
CHIMERA: Compact Synthetic Data for Generalizable LLM Reasoning

Mar 1, 2026

 Not reported Not reported Not reported Not reported
Sparks of Cooperative Reasoning: LLMs as Strategic Hanabi Agents

Jan 26, 2026

 Not reported Not reported Not reported Not reported
Towards Self-Evolving Benchmarks: Synthesizing Agent Trajectories via Test-Time Exploration under Validate-by-Reproduce Paradigm

Oct 1, 2025

 Not reported Not reported Not reported Not reported
Researcher Workflow (Detailed)

Checklist

  • Moderate: Papers with explicit human feedback

    Coverage is usable but incomplete (27.3% 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 (100% vs 35% target).

  • Moderate: Papers naming evaluation metrics

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

  • Gap: Papers with known rater population

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

  • Gap: Papers with known annotation unit

    Coverage is a replication risk (18.2% vs 35% target).

Strengths

  • This hub still surfaces a concentrated paper set for protocol triage and replication planning.

Known Gaps

  • Only 0% of papers report quality controls; prioritize calibration/adjudication evidence.
  • Rater population is under-specified (0% coverage).
  • Annotation unit is under-specified (18.2% coverage).

Suggested Next Analyses

  • Pair this hub with llm_as_judge pages to benchmark automated-vs-human evaluation tradeoffs.
  • Stratify by benchmark (AIME vs MATH-500) before comparing methods.
  • Track metric sensitivity by reporting both accuracy and pass@1.

Recommended Queries

Human Eval ProtocolsBenchmark Slice: AIMEMetric Slice: accuracyRecent High-Signal Papers
Known Limitations
  • Only 0% 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 (2)
  • Human Eval (1)
  • Simulation Env (1)

Human Feedback Mix

  • Pairwise Preference (2)
  • Rubric Rating (1)

Top Benchmarks

  • AIME (11)
  • MATH 500 (3)
  • GPQA (2)
  • BFCL (1)

Top Metrics

  • Accuracy (1)
  • Pass@1 (1)
  • Pass@10 (1)
  • Pass@k (1)

Top Papers On This Benchmark

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