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HFEPX Benchmark Hub

AIME Or SWE-bench Benchmark Papers

Updated from current HFEPX corpus (Mar 31, 2026). 21 papers are grouped in this benchmark page.

Read Full Context

Updated from current HFEPX corpus (Mar 31, 2026). 21 papers are grouped in this benchmark page. Common evaluation modes: Automatic Metrics, Human Eval. Most common rater population: Domain Experts. Common annotation unit: Multi Dim Rubric. Frequently cited benchmark: AIME. Common metric signal: pass@1. 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: 21 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%

21 / 21 sampled papers are not low-signal flagged.

Replication-Ready Set

4

Papers with explicit benchmark + metric + eval mode fields.

Quality Controls

0.0%

0 papers report calibration/adjudication/IAA controls.

  • 7 papers explicitly name benchmark datasets in the sampled set.
  • 4 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

  • 23.8% of papers report explicit human-feedback signals, led by pairwise preferences.
  • automatic metrics appears in 23.8% 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 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

  • AIME appears in 57.1% of hub papers (12/21); use this cohort for benchmark-matched comparisons.
  • SWE-bench appears in 47.6% of hub papers (10/21); use this cohort for benchmark-matched comparisons.

Metric Interpretation

  • pass@1 is reported in 19% of hub papers (4/21); compare with a secondary metric before ranking methods.
  • accuracy is reported in 4.8% of hub papers (1/21); 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
Critique-GRPO: Advancing LLM Reasoning with Natural Language and Numerical Feedback

Jun 3, 2025

 Automatic Metrics Critique Edit Pass@1 Not reported
SWE-Protégé: Learning to Selectively Collaborate With an Expert Unlocks Small Language Models as Software Engineering Agents

Feb 25, 2026

 Automatic Metrics Not reported Pass@1, Latency Not reported
KLong: Training LLM Agent for Extremely Long-horizon Tasks

Feb 19, 2026

 Not reported Rubric Rating Not reported Not reported
Think$^{2}$: Grounded Metacognitive Reasoning in Large Language Models

Feb 21, 2026

 Human Eval Pairwise Preference Not reported Not reported
Structurally Aligned Subtask-Level Memory for Software Engineering Agents

Feb 25, 2026

 Automatic Metrics Not reported 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
AdaRubric: Task-Adaptive Rubrics for LLM Agent Evaluation

Mar 22, 2026

 Not reported Not reported Not reported Not reported
FailureMem: A Failure-Aware Multimodal Framework for Autonomous Software Repair

Mar 18, 2026

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

Checklist

  • Gap: Papers with explicit human feedback

    Coverage is a replication risk (23.8% 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).

  • Strong: Papers naming evaluation metrics

    Coverage is strong (38.1% vs 35% target).

  • Gap: Papers with known rater population

    Coverage is a replication risk (4.8% vs 35% target).

  • Gap: Papers with known annotation unit

    Coverage is a replication risk (19% vs 35% target).

Strengths

  • Most papers provide measurable evaluation context (100% benchmarks, 38.1% metrics).

Known Gaps

  • Only 0% of papers report quality controls; prioritize calibration/adjudication evidence.
  • Rater population is under-specified (4.8% coverage).
  • Annotation unit is under-specified (19% coverage).

Suggested Next Analyses

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

Recommended Queries

Human Eval ProtocolsBenchmark Slice: AIMEMetric Slice: pass@1Recent High-Signal Papers
Known Limitations
  • Only 0% of papers report quality controls; prioritize calibration/adjudication evidence.
  • Rater population is under-specified (4.8% 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 (5)
  • Human Eval (1)
  • Simulation Env (1)

Human Feedback Mix

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

Top Benchmarks

  • AIME (12)
  • SWE Bench (10)
  • SWE Bench Verified (4)
  • MATH 500 (3)

Top Metrics

  • Pass@1 (4)
  • Accuracy (1)
  • Cost (1)
  • Latency (1)

Top Papers On This Benchmark

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