Skip to content
← Back to explorer

HFEPX Benchmark Hub

MATH-500 Or GPQA Benchmark Papers

Updated from current HFEPX corpus (Apr 17, 2026). 20 papers are grouped in this benchmark page.

Read Full Context

Updated from current HFEPX corpus (Apr 17, 2026). 20 papers are grouped in this benchmark page. Common evaluation modes: Automatic Metrics, Llm As Judge. Common annotation unit: Trajectory. Frequently cited benchmark: GPQA. 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 Apr 1, 2026.

Papers: 20 Last published: Apr 1, 2026 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%

20 / 20 sampled papers are not low-signal flagged.

Replication-Ready Set

6

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.
  • 6 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

  • 14.3% of papers report explicit human-feedback signals, led by demonstration data.
  • automatic metrics appears in 30% of papers in this hub.
  • GPQA 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 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

  • GPQA appears in 57.1% of hub papers (4/20); use this cohort for benchmark-matched comparisons.
  • GSM8K appears in 57.1% of hub papers (4/20); use this cohort for benchmark-matched comparisons.

Metric Interpretation

  • accuracy is reported in 42.9% of hub papers (3/20); compare with a secondary metric before ranking methods.
  • cost is reported in 42.9% of hub papers (3/20); 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
S0 Tuning: Zero-Overhead Adaptation of Hybrid Recurrent-Attention Models

Apr 1, 2026

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

Mar 15, 2026

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

Dec 3, 2025

 Automatic Metrics Not reported Cost Not reported
DeepPrune: Parallel Scaling without Inter-trace Redundancy

Oct 9, 2025

 Llm As Judge, Automatic Metrics Not reported Accuracy, Auroc Not reported
Schema for In-Context Learning

Oct 14, 2025

 Not reported Demonstrations Not reported Not reported
Towards Hierarchical Multi-Step Reward Models for Enhanced Reasoning in Large Language Models

Mar 16, 2025

 Automatic Metrics Not reported Cost, Coherence Not reported
Peer-Predictive Self-Training for Language Model Reasoning

Apr 14, 2026

 Not reported Not reported Not reported Not reported
Sensitivity-Positional Co-Localization in GQA Transformers

Apr 9, 2026

 Not reported Not reported Not reported Not reported
Squeeze Evolve: Unified Multi-Model Orchestration for Verifier-Free Evolution

Apr 9, 2026

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

Checklist

  • Gap: Papers with explicit human feedback

    Coverage is a replication risk (14.3% 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).

  • Strong: Papers naming evaluation metrics

    Coverage is strong (85.7% 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 (57.1% vs 35% target).

Strengths

  • Most papers provide measurable evaluation context (100% benchmarks, 85.7% metrics).
  • Agentic evaluation appears in 71.4% of papers.

Known Gaps

  • Only 0% of papers report quality controls; prioritize calibration/adjudication evidence.
  • Rater population is under-specified (0% coverage).
  • LLM-as-judge appears without enough inter-annotator agreement reporting.

Suggested Next Analyses

  • Pair this hub with a human_eval-heavy hub to validate judge-model calibration.
  • Stratify by benchmark (GPQA vs GSM8K) before comparing methods.
  • Track metric sensitivity by reporting both accuracy and cost.

Recommended Queries

LLM-as-Judge ProtocolsBenchmark Slice: GPQAMetric 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 (6)
  • Llm As Judge (1)

Human Feedback Mix

  • Demonstrations (1)

Top Benchmarks

  • GPQA (4)
  • GSM8K (4)
  • MATH 500 (4)
  • AIME (1)

Top Metrics

  • Accuracy (3)
  • Cost (3)
  • Auroc (1)
  • Coherence (1)

Top Papers On This Benchmark

Related Benchmark Hubs

AIME Or MATH-500 Or GPQA Benchmark Papers MATH-500 Or GPQA Or WebArena Benchmark Papers MATH-500 Or AlpacaEval Or GPQA Benchmark Papers SWE-bench Or MATH-500 Or GPQA Benchmark Papers LMSYS Chatbot Arena Or MATH-500 Or GPQA Benchmark Papers GSM8K Or MATH-500 Or GPQA Benchmark Papers MMLU Or AIME Or AlpacaEval Benchmark Papers (48) MMLU Or AIME Or GPQA Benchmark Papers (42) GSM8K Or MMLU Or AIME Benchmark Papers (57) MMLU Or AIME Or MATH-500 Benchmark Papers (46) MMLU Or AIME Or SWE-bench Benchmark Papers (51) MMLU Or AIME Benchmark Papers (39) MMLU Or AlpacaEval Or GPQA Benchmark Papers (39) GSM8K Or MMLU Or AlpacaEval Benchmark Papers (51) MMLU Or MATH-500 Or AlpacaEval Benchmark Papers (44) MMLU Or SWE-bench Or AlpacaEval Benchmark Papers (45) MMLU Or AlpacaEval Benchmark Papers (32) GSM8K Or MMLU Or GPQA Benchmark Papers (48) MMLU Or MATH-500 Or GPQA Benchmark Papers (39) MMLU Or SWE-bench Or GPQA Benchmark Papers (42) MMLU Or GPQA Benchmark Papers (29) GSM8K Or MMLU Or MATH-500 Benchmark Papers (50) GSM8K Or MMLU Or SWE-bench Benchmark Papers (55) GSM8K Or MMLU Benchmark Papers (42)
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