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HFEPX Benchmark Hub

AIME Or BFCL Benchmark Papers

Updated from current HFEPX corpus (Apr 27, 2026). 29 papers are grouped in this benchmark page.

Read Full Context

Updated from current HFEPX corpus (Apr 27, 2026). 29 papers are grouped in this benchmark page. Common evaluation modes: Automatic Metrics, Human Eval. Common annotation unit: Freeform. 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: 29 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%

29 / 29 sampled papers are not low-signal flagged.

Replication-Ready Set

9

Papers with explicit benchmark + metric + eval mode fields.

Quality Controls

0.0%

0 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

  • 17.2% of papers report explicit human-feedback signals, led by pairwise preferences.
  • automatic metrics appears in 31% 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 Freeform; 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

  • AIME appears in 69% of hub papers (20/29); use this cohort for benchmark-matched comparisons.
  • BFCL appears in 34.5% of hub papers (10/29); use this cohort for benchmark-matched comparisons.

Metric Interpretation

  • accuracy is reported in 31% of hub papers (9/29); compare with a secondary metric before ranking methods.
  • cost is reported in 13.8% of hub papers (4/29); 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
GIFT: Group-Relative Implicit Fine-Tuning Integrates GRPO with DPO and UNA

Oct 27, 2025

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

Jun 3, 2025

 Automatic Metrics Critique Edit Pass@1 Not reported
Brief Is Better: Non-Monotonic Chain-of-Thought Budget Effects in Function-Calling Language Agents

Apr 2, 2026

 Automatic Metrics Not reported Accuracy Not reported
SkillX: Automatically Constructing Skill Knowledge Bases for Agents

Apr 6, 2026

 Automatic Metrics Not reported Task success Not reported
The Bitter Lesson of Diffusion Language Models for Agentic Workflows: A Comprehensive Reality Check

Jan 19, 2026

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

Feb 21, 2026

 Human Eval Pairwise Preference Not reported Not reported
DeepPrune: Parallel Scaling without Inter-trace Redundancy

Oct 9, 2025

 Llm As Judge, Automatic Metrics Not reported Accuracy, Auroc Not reported
Failure Makes the Agent Stronger: Enhancing Accuracy through Structured Reflection for Reliable Tool Interactions

Sep 23, 2025

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

Checklist

  • Gap: Papers with explicit human feedback

    Coverage is a replication risk (17.2% 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 (62.1% 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 (10.3% vs 35% target).

Strengths

  • Most papers provide measurable evaluation context (100% benchmarks, 62.1% metrics).
  • Contains both human-eval and LLM-as-judge protocols for head-to-head methodology comparison.

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 (10.3% coverage).

Suggested Next Analyses

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

Recommended Queries

Judge vs Human AgreementBenchmark 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 (9)
  • Human Eval (1)
  • Llm As Judge (1)
  • Simulation Env (1)

Human Feedback Mix

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

Top Benchmarks

  • AIME (20)
  • BFCL (10)
  • MATH 500 (7)
  • GPQA (6)

Top Metrics

  • Accuracy (9)
  • Cost (4)
  • Pass@1 (3)
  • Precision (2)

Top Papers On This Benchmark

Related Benchmark Hubs

GSM8K Or AIME Or BFCL Benchmark Papers MMLU Or AIME Or BFCL Benchmark Papers DROP Or AIME Or BFCL Benchmark Papers AIME Benchmark Papers AIME Benchmark Papers (Last 365 Days) AIME Benchmark Papers (Last 240 Days) GPQA Or BFCL Benchmark Papers (23) HumanEval+ Or BFCL Benchmark Papers (21) MATH-500 Or BFCL Benchmark Papers (25) GPQA Or HumanEval+ Benchmark Papers (22) MATH-500 Or GPQA Benchmark Papers (25) MATH-500 Or HumanEval+ Benchmark Papers (24) MATH-500 Benchmark Papers (15) MMLU Or AIME Or AlpacaEval Benchmark Papers (60) MMLU Or AIME Or BFCL Benchmark Papers (59) GSM8K Or MMLU Or AIME Benchmark Papers (72) MMLU Or AIME Or HotpotQA Benchmark Papers (62) MMLU Or AIME Or HumanEval+ Benchmark Papers (57) MMLU Or AIME Or LiveCodeBench Benchmark Papers (59) MMLU Or AIME Or LongBench Benchmark Papers (59) MMLU Or AIME Or MATH-500 Benchmark Papers (58) MMLU Or AIME Or SWE-bench Benchmark Papers (64) MMLU Or AIME Benchmark Papers (50) GSM8K Or MMLU Or AlpacaEval Benchmark Papers (65)
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