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HFEPX Benchmark Hub

Reasoning & Math Suite Benchmark Papers In CS.AI

Updated from current HFEPX corpus (Mar 8, 2026). 13 papers are grouped in this benchmark page.

Read Full Context

Updated from current HFEPX corpus (Mar 8, 2026). 13 papers are grouped in this benchmark page. Common evaluation modes: Automatic Metrics, Human Eval. Common annotation unit: Ranking. 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 Feb 21, 2026.

Papers: 13 Last published: Feb 21, 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%

13 / 13 sampled papers are not low-signal flagged.

Replication-Ready Set

3

Papers with explicit benchmark + metric + eval mode fields.

Quality Controls

0.0%

0 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

  • 23.1% of papers report explicit human-feedback signals, led by pairwise preferences.
  • automatic metrics appears in 23.1% 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 ranking annotation; 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 38.5% of hub papers (5/13); use this cohort for benchmark-matched comparisons.
  • GSM8K appears in 38.5% of hub papers (5/13); use this cohort for benchmark-matched comparisons.

Metric Interpretation

  • accuracy is reported in 30.8% of hub papers (4/13); compare with a secondary metric before ranking methods.
  • cost is reported in 15.4% of hub papers (2/13); 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
Critique-GRPO: Advancing LLM Reasoning with Natural Language and Numerical Feedback

Jun 3, 2025

 Automatic Metrics Critique Edit Pass@1 Not reported
Think$^{2}$: Grounded Metacognitive Reasoning in Large Language Models

Feb 21, 2026

 Human Eval Pairwise Preference Not reported Not reported
Cache What Lasts: Token Retention for Memory-Bounded KV Cache in LLMs

Dec 3, 2025

 Automatic Metrics Not reported Cost Not reported
SPARE: Single-Pass Annotation with Reference-Guided Evaluation for Automatic Process Supervision and Reward Modelling

Jun 18, 2025

 Automatic Metrics Not reported Accuracy, Precision Not reported
Long Grounded Thoughts: Synthesizing Visual Problems and Reasoning Chains at Scale

Nov 7, 2025

 Not reported Pairwise Preference Not reported Not reported
Reasoning Theater: Disentangling Model Beliefs from Chain-of-Thought

Mar 5, 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
Tool Verification for Test-Time Reinforcement Learning

Mar 2, 2026

 Not reported Not reported Not reported Not reported
CHIMERA: Compact Synthetic Data for Generalizable LLM Reasoning

Mar 1, 2026

 Not reported Not reported Not reported Not reported
Draft-Thinking: Learning Efficient Reasoning in Long Chain-of-Thought LLMs

Feb 28, 2026

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

Checklist

  • Gap: Papers with explicit human feedback

    Coverage is a replication risk (23.1% 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 (53.8% 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 (15.4% vs 35% target).

Strengths

  • Most papers provide measurable evaluation context (100% benchmarks, 53.8% metrics).

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 (15.4% coverage).

Suggested Next Analyses

  • Pair this hub with llm_as_judge pages to benchmark automated-vs-human evaluation tradeoffs.
  • Stratify by benchmark (AIME vs GSM8K) before comparing methods.
  • Track metric sensitivity by reporting both accuracy and cost.

Recommended Queries

Human Eval ProtocolsBenchmark 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 (3)
  • Human Eval (1)

Human Feedback Mix

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

Top Benchmarks

  • AIME (5)
  • GSM8K (5)
  • MMLU (4)
  • MATH 500 (3)

Top Metrics

  • Accuracy (4)
  • Cost (2)
  • Recall (2)
  • Calibration error (1)

Top Papers On This Benchmark

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