Skip to content
← Back to explorer

HFEPX Benchmark Hub

GSM8K Benchmark Papers (Last 45 Days)

Updated from current HFEPX corpus (Mar 31, 2026). 10 papers are grouped in this benchmark page.

Read Full Context

Updated from current HFEPX corpus (Mar 31, 2026). 10 papers are grouped in this benchmark page. Common evaluation modes: Automatic Metrics, Human Eval. Common annotation unit: Trajectory. Frequent quality control: Calibration. Frequently cited benchmark: GSM8K. 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 19, 2026.

Papers: 10 Last published: Mar 19, 2026 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%

10 / 10 sampled papers are not low-signal flagged.

Replication-Ready Set

3

Papers with explicit benchmark + metric + eval mode fields.

Quality Controls

10.0%

1 papers report calibration/adjudication/IAA controls.

  • 5 papers explicitly name benchmark datasets in the sampled set.
  • 3 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

  • 30% of papers report explicit human-feedback signals, led by pairwise preferences.
  • automatic metrics appears in 30% of papers in this hub.
  • GSM8K is a recurring benchmark anchor for cross-paper comparisons in this page.
Protocol Notes (Expanded)

Protocol Takeaways

  • Most common quality-control signal is rater calibration (10% of papers).
  • Rater context is mostly unspecified rater pools, and annotation is commonly trajectory-level annotation; use this to scope replication staffing.
  • Pair this hub with llm_as_judge pages to benchmark automated-vs-human evaluation tradeoffs.

Benchmark Interpretation

  • GSM8K appears in 100% of hub papers (10/10); use this cohort for benchmark-matched comparisons.
  • MMLU appears in 40% of hub papers (4/10); use this cohort for benchmark-matched comparisons.

Metric Interpretation

  • accuracy is reported in 50% of hub papers (5/10); compare with a secondary metric before ranking methods.
  • auroc is reported in 10% of hub papers (1/10); 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
Entropy trajectory shape predicts LLM reasoning reliability: A diagnostic study of uncertainty dynamics in chain-of-thought

Mar 19, 2026

 Automatic Metrics Not reported Accuracy, Calibration error Calibration
Stabilizing Iterative Self-Training with Verified Reasoning via Symbolic Recursive Self-Alignment

Mar 23, 2026

 Automatic Metrics Pairwise Preference Accuracy Not reported
Top-b: Entropic Regulation of Relative Probability Bands in Autoregressive Language Processes

Mar 15, 2026

 Automatic Metrics Not reported Accuracy Not reported
RASPRef: Retrieval-Augmented Self-Supervised Prompt Refinement for Large Reasoning Models

Mar 27, 2026

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

Feb 21, 2026

 Human Eval Pairwise Preference Not reported Not reported
Is Mathematical Problem-Solving Expertise in Large Language Models Associated with Assessment Performance?

Mar 26, 2026

 Not reported Not reported Not reported Not reported
Cross-Model Disagreement as a Label-Free Correctness Signal

Mar 26, 2026

 Not reported Not reported Not reported Not reported
Efficient Detection of Bad Benchmark Items with Novel Scalability Coefficients

Mar 26, 2026

 Not reported Not reported Not reported Not reported
NeuroLoRA: Context-Aware Neuromodulation for Parameter-Efficient Multi-Task Adaptation

Mar 12, 2026

 Not reported Not reported Not reported Not reported
In-Context Environments Induce Evaluation-Awareness in Language Models

Mar 4, 2026

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

Checklist

  • Moderate: Papers with explicit human feedback

    Coverage is usable but incomplete (30% vs 45% target).

  • Gap: Papers reporting quality controls

    Coverage is a replication risk (10% vs 30% target).

  • Strong: Papers naming benchmarks/datasets

    Coverage is strong (100% vs 35% target).

  • Strong: Papers naming evaluation metrics

    Coverage is strong (60% vs 35% target).

  • Gap: Papers with known rater population

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

  • Moderate: Papers with known annotation unit

    Coverage is usable but incomplete (30% vs 35% target).

Strengths

  • Most papers provide measurable evaluation context (100% benchmarks, 60% metrics).
  • Agentic evaluation appears in 30% of papers.

Known Gaps

  • Only 10% of papers report quality controls; prioritize calibration/adjudication evidence.
  • Rater population is under-specified (0% coverage).

Suggested Next Analyses

  • Pair this hub with llm_as_judge pages to benchmark automated-vs-human evaluation tradeoffs.
  • Stratify by benchmark (GSM8K vs MMLU) before comparing methods.
  • Track metric sensitivity by reporting both accuracy and auroc.
  • Add inter-annotator agreement checks when reproducing these protocols.

Recommended Queries

Human Eval ProtocolsBenchmark Slice: GSM8KMetric Slice: accuracyRecent High-Signal Papers
Known Limitations
  • Only 10% 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 (3)
  • Human Eval (1)

Human Feedback Mix

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

Top Benchmarks

  • GSM8K (10)
  • MMLU (4)
  • AIME (1)
  • Correctbench (1)

Top Metrics

  • Accuracy (5)
  • Auroc (1)
  • Calibration error (1)
  • Cost (1)

Top Papers On This Benchmark

Related Benchmark Hubs

GSM8K Benchmark Papers (Last 60 Days) GSM8K Benchmark Papers (Last 75 Days) GSM8K Benchmark Papers (Last 90 Days) GSM8K Benchmark Papers (Last 120 Days) GSM8K Benchmark Papers (Last 150 Days) GSM8K Benchmark Papers (Last 180 Days) GSM8K Or MMLU Or AIME Benchmark Papers (36) MMLU Or AIME Or SWE-bench Benchmark Papers (35) MMLU Or AIME Benchmark Papers (26) GSM8K Or MMLU Or SWE-bench Benchmark Papers (35) GSM8K Or MMLU Benchmark Papers (25) MMLU Or SWE-bench Benchmark Papers (24) MMLU In CS.CL Papers (12) MMLU Benchmark Papers (Last 120 Days) (11) MMLU Benchmark Papers (Last 150 Days) (12) MMLU Benchmark Papers (Last 180 Days) (13) MMLU Benchmark Papers (Last 240 Days) (14) MMLU Benchmark Papers (Last 300 Days) (14) MMLU Benchmark Papers (Last 365 Days) (14) MMLU Benchmark Papers (Last 45 Days) (11) MMLU Benchmark Papers (Last 60 Days) (11) MMLU Benchmark Papers (Last 75 Days) (11) MMLU Benchmark Papers (Last 90 Days) (11) MMLU Benchmark Papers (14)

Need human evaluators for your AI research? Scale annotation with expert AI Trainers.

Post a Job Get a Quote