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HFEPX Benchmark Hub

MATH-500 Benchmark Papers (Last 120 Days)

Updated from current HFEPX corpus (Apr 25, 2026). 10 papers are grouped in this benchmark page.

Read Full Context

Updated from current HFEPX corpus (Apr 25, 2026). 10 papers are grouped in this benchmark page. Common evaluation modes: Automatic Metrics. Common annotation unit: Trajectory. Frequently cited benchmark: MATH-500. 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: 10 Last published: Apr 1, 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

1

Papers with explicit benchmark + metric + eval mode fields.

Quality Controls

0.0%

0 papers report calibration/adjudication/IAA controls.

  • 1 papers explicitly name benchmark datasets in the sampled set.
  • 1 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

  • automatic metrics appears in 10% of papers in this hub.
  • MATH-500 is a recurring benchmark anchor for cross-paper comparisons in this page.
  • long-horizon tasks appears in 10% of papers, indicating agentic evaluation demand.
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.
  • Stratify by benchmark (MATH-500 vs AIME) before comparing methods.

Benchmark Interpretation

  • MATH-500 appears in 100% of hub papers (10/10); use this cohort for benchmark-matched comparisons.
  • AIME appears in 60% of hub papers (6/10); use this cohort for benchmark-matched comparisons.

Metric Interpretation

  • accuracy is reported in 40% of hub papers (4/10); compare with a secondary metric before ranking methods.
  • cost is reported in 40% of hub papers (4/10); 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
S0 Tuning: Zero-Overhead Adaptation of Hybrid Recurrent-Attention Models

Apr 1, 2026

 Automatic Metrics Not reported Pass@1, Cost Not reported
TRACES: Tagging Reasoning Steps for Adaptive Cost-Efficient Early-Stopping

Apr 22, 2026

 Not reported Not reported Not reported Not reported
MoE-nD: Per-Layer Mixture-of-Experts Routing for Multi-Axis KV Cache Compression

Apr 20, 2026

 Not reported Not reported Not reported Not reported
Peer-Predictive Self-Training for Language Model Reasoning

Apr 14, 2026

 Not reported Not reported Not reported Not reported
SortedRL: Accelerating RL Training for LLMs through Online Length-Aware Scheduling

Mar 24, 2026

 Not reported Not reported Not reported Not reported
TERMINATOR: Learning Optimal Exit Points for Early Stopping in Chain-of-Thought Reasoning

Mar 13, 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
Draft-Thinking: Learning Efficient Reasoning in Long Chain-of-Thought LLMs

Feb 28, 2026

 Not reported Not reported Not reported Not reported
TRIM: Hybrid Inference via Targeted Stepwise Routing in Multi-Step Reasoning Tasks

Jan 15, 2026

 Not reported Not reported Not reported Not reported
PILOT: Planning via Internalized Latent Optimization Trajectories for Large Language Models

Jan 7, 2026

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

Checklist

  • Gap: Papers with explicit human feedback

    Coverage is a replication risk (0% 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 (70% 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% vs 35% target).

Strengths

  • Most papers provide measurable evaluation context (100% benchmarks, 70% 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 (10% coverage).

Suggested Next Analyses

  • Stratify by benchmark (MATH-500 vs AIME) before comparing methods.
  • Track metric sensitivity by reporting both accuracy and cost.

Recommended Queries

Benchmark Slice: MATH-500Metric 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 (1)

Human Feedback Mix

Top Benchmarks

  • MATH 500 (10)
  • AIME (6)
  • GPQA (2)
  • GSM8K (2)

Top Metrics

  • Accuracy (4)
  • Cost (4)
  • Exact match (1)
  • Inference cost (1)

Top Papers On This Benchmark

Related Benchmark Hubs

MATH-500 Benchmark Papers (Last 150 Days) MATH-500 Benchmark Papers (Last 180 Days) MATH-500 Benchmark Papers (Last 240 Days) MATH-500 Benchmark Papers (Last 300 Days) MATH-500 Benchmark Papers (Last 365 Days) MATH-500 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)
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