Skip to content
← Back to explorer

HFEPX Benchmark Hub

MMLU Or SWE-bench Or HotpotQA Benchmark Papers

Updated from current HFEPX corpus (Apr 25, 2026). 57 papers are grouped in this benchmark page.

Read Full Context

Updated from current HFEPX corpus (Apr 25, 2026). 57 papers are grouped in this benchmark page. Common evaluation modes: Automatic Metrics, Simulation Env. Most common rater population: Domain Experts. Common annotation unit: Trajectory. Frequently cited benchmark: MMLU. 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 Mar 28, 2026.

Papers: 57 Last published: Mar 28, 2026 Global RSS

Researcher Quick Triage

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

High-Signal Coverage

100.0%

57 / 57 sampled papers are not low-signal flagged.

Replication-Ready Set

13

Papers with explicit benchmark + metric + eval mode fields.

Quality Controls

0.0%

0 papers report calibration/adjudication/IAA controls.

  • 19 papers explicitly name benchmark datasets in the sampled set.
  • 13 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

  • 14% of papers report explicit human-feedback signals, led by pairwise preferences.
  • automatic metrics appears in 24.6% of papers in this hub.
  • MMLU 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 trajectory-level annotation; use this to scope replication staffing.
  • Pair this hub with a human_eval-heavy hub to validate judge-model calibration.

Benchmark Interpretation

  • MMLU appears in 52.6% of hub papers (30/57); use this cohort for benchmark-matched comparisons.
  • SWE-bench appears in 26.3% of hub papers (15/57); use this cohort for benchmark-matched comparisons.

Metric Interpretation

  • accuracy is reported in 33.3% of hub papers (19/57); compare with a secondary metric before ranking methods.
  • cost is reported in 24.6% of hub papers (14/57); 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
PubMed Reasoner: Dynamic Reasoning-based Retrieval for Evidence-Grounded Biomedical Question Answering

Mar 28, 2026

 Llm As Judge, Automatic Metrics Expert Verification Accuracy, Relevance Not reported
$V_1$: Unifying Generation and Self-Verification for Parallel Reasoners

Mar 4, 2026

 Automatic Metrics Pairwise Preference Pass@1 Not reported
How Reliable is Language Model Micro-Benchmarking?

Oct 9, 2025

 Automatic Metrics Pairwise Preference Accuracy, Cost Not reported
MemMachine: A Ground-Truth-Preserving Memory System for Personalized AI Agents

Apr 6, 2026

 Automatic Metrics Not reported Accuracy, Recall Not reported
OSCAR: Orchestrated Self-verification and Cross-path Refinement

Apr 2, 2026

 Automatic Metrics Not reported Accuracy Not reported
Learning When to Sample: Confidence-Aware Self-Consistency for Efficient LLM Chain-of-Thought Reasoning

Mar 9, 2026

 Automatic Metrics Not reported Accuracy, Cost 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
D-COT: Disciplined Chain-of-Thought Learning for Efficient Reasoning in Small Language Models

Feb 25, 2026

 Automatic Metrics Not reported Accuracy Not reported
Cross-Context Verification: Hierarchical Detection of Benchmark Contamination through Session-Isolated Analysis

Mar 23, 2026

 Automatic Metrics Not reported Accuracy, Recall Not reported
Confidence-Driven Multi-Scale Model Selection for Cost-Efficient Inference

Feb 25, 2026

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

Checklist

  • Gap: Papers with explicit human feedback

    Coverage is a replication risk (14% 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 (59.6% vs 35% target).

  • Gap: Papers with known rater population

    Coverage is a replication risk (3.5% vs 35% target).

  • Gap: Papers with known annotation unit

    Coverage is a replication risk (14% vs 35% target).

Strengths

  • Most papers provide measurable evaluation context (100% benchmarks, 59.6% metrics).

Known Gaps

  • Only 0% of papers report quality controls; prioritize calibration/adjudication evidence.
  • Rater population is under-specified (3.5% coverage).
  • Annotation unit is under-specified (14% coverage).

Suggested Next Analyses

  • Pair this hub with a human_eval-heavy hub to validate judge-model calibration.
  • Stratify by benchmark (MMLU vs SWE-bench) before comparing methods.
  • Track metric sensitivity by reporting both accuracy and cost.

Recommended Queries

LLM-as-Judge ProtocolsBenchmark Slice: MMLUMetric Slice: accuracyRecent High-Signal Papers
Known Limitations
  • Only 0% of papers report quality controls; prioritize calibration/adjudication evidence.
  • Rater population is under-specified (3.5% 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 (14)
  • Simulation Env (2)
  • Llm As Judge (1)

Human Feedback Mix

  • Pairwise Preference (4)
  • Demonstrations (2)
  • Expert Verification (1)
  • Rubric Rating (1)

Top Benchmarks

  • MMLU (30)
  • SWE Bench (15)
  • HotpotQA (12)
  • GSM8K (8)

Top Metrics

  • Accuracy (19)
  • Cost (14)
  • Recall (5)
  • F1 (3)

Top Papers On This Benchmark

Related Benchmark Hubs

MMLU Or SWE-bench Benchmark Papers MMLU Or HotpotQA Benchmark Papers MMLU Or SWE-bench Or GPQA Benchmark Papers MMLU Or SWE-bench Or WebArena Benchmark Papers MMLU Or SWE-bench Or LongBench Benchmark Papers MMLU Or SWE-bench Or AlpacaEval Benchmark Papers DROP Benchmark Papers (Last 30 Days) (19) GPQA Or HumanEval+ Benchmark Papers (21) MATH-500 Or GPQA Benchmark Papers (25) MATH-500 Or HumanEval+ Benchmark Papers (23) MATH-500 Benchmark Papers (15) MMLU Or AIME Or AlpacaEval Benchmark Papers (59) MMLU Or AIME Or GPQA Benchmark Papers (53) GSM8K Or MMLU Or AIME Benchmark Papers (71) MMLU Or AIME Or HotpotQA Benchmark Papers (61) MMLU Or AIME Or HumanEval+ Benchmark Papers (56) MMLU Or AIME Or LiveCodeBench Benchmark Papers (58) MMLU Or AIME Or LongBench Benchmark Papers (58) MMLU Or AIME Or MATH-500 Benchmark Papers (57) MMLU Or AIME Or SWE-bench Benchmark Papers (63) MMLU Or AIME Benchmark Papers (49) GSM8K Or MMLU Or AlpacaEval Benchmark Papers (64) MMLU Or MATH-500 Or AlpacaEval Benchmark Papers (55) MMLU Or SWE-bench Or AlpacaEval Benchmark Papers (56)
Get Started

Join the #1 Platform for AI Training Talent

Where top AI builders and expert AI Trainers connect to build the future of AI.
Self-Service
Post a Job
Post your project and get a shortlist of qualified AI Trainers and Data Labelers. Hire and manage your team in the tools you already use.
Create Account & Post a Job
Managed Service
For Large Projects
Done-for-You
We recruit, onboard, and manage a dedicated team inside your tools. End-to-end operations for large or complex projects.
Learn About Managed Service
For Freelancers
Join as an AI Trainer
Find AI training and data labeling projects across platforms, all in one place. One profile, one application process, more opportunities.
Join Now