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HFEPX Benchmark Hub

MATH-500 Or LongBench Benchmark Papers

Updated from current HFEPX corpus (Apr 25, 2026). 24 papers are grouped in this benchmark page.

Read Full Context

Updated from current HFEPX corpus (Apr 25, 2026). 24 papers are grouped in this benchmark page. Common evaluation modes: Automatic Metrics, Llm As Judge. Most common rater population: Domain Experts. Common annotation unit: Multi Dim Rubric. 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 Feb 9, 2026.

Papers: 24 Last published: Feb 9, 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%

24 / 24 sampled papers are not low-signal flagged.

Replication-Ready Set

5

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.
  • 5 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

  • 4.2% of papers report explicit human-feedback signals, led by rubric ratings.
  • automatic metrics appears in 20.8% of papers in this hub.
  • MATH-500 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 multi-dimensional rubrics; use this to scope replication staffing.
  • Pair this hub with a human_eval-heavy hub to validate judge-model calibration.

Benchmark Interpretation

  • MATH-500 appears in 62.5% of hub papers (15/24); use this cohort for benchmark-matched comparisons.
  • LongBench appears in 41.7% of hub papers (10/24); use this cohort for benchmark-matched comparisons.

Metric Interpretation

  • accuracy is reported in 37.5% of hub papers (9/24); compare with a secondary metric before ranking methods.
  • cost is reported in 29.2% of hub papers (7/24); 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
Document Reconstruction Unlocks Scalable Long-Context RLVR

Feb 9, 2026

 Automatic Metrics Rubric Rating Coherence Not reported
S0 Tuning: Zero-Overhead Adaptation of Hybrid Recurrent-Attention Models

Apr 1, 2026

 Automatic Metrics Not reported Pass@1, Cost 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
Towards Hierarchical Multi-Step Reward Models for Enhanced Reasoning in Large Language Models

Mar 16, 2025

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

Apr 22, 2026

 Not reported Not reported Not reported Not reported
DASH-KV: Accelerating Long-Context LLM Inference via Asymmetric KV Cache Hashing

Apr 21, 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
LongAct: Harnessing Intrinsic Activation Patterns for Long-Context Reinforcement Learning

Apr 16, 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
Researcher Workflow (Detailed)

Checklist

  • Gap: Papers with explicit human feedback

    Coverage is a replication risk (4.2% 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 (75% vs 35% target).

  • Gap: Papers with known rater population

    Coverage is a replication risk (4.2% vs 35% target).

  • Gap: Papers with known annotation unit

    Coverage is a replication risk (12.5% vs 35% target).

Strengths

  • Most papers provide measurable evaluation context (100% benchmarks, 75% metrics).

Known Gaps

  • Only 0% of papers report quality controls; prioritize calibration/adjudication evidence.
  • Rater population is under-specified (4.2% coverage).
  • Annotation unit is under-specified (12.5% coverage).

Suggested Next Analyses

  • Pair this hub with a human_eval-heavy hub to validate judge-model calibration.
  • Stratify by benchmark (MATH-500 vs LongBench) before comparing methods.
  • Track metric sensitivity by reporting both accuracy and cost.

Recommended Queries

LLM-as-Judge ProtocolsBenchmark 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 (4.2% 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 (5)
  • Llm As Judge (1)

Human Feedback Mix

  • Rubric Rating (1)

Top Benchmarks

  • MATH 500 (15)
  • LongBench (10)
  • AIME (7)
  • GSM8K (5)

Top Metrics

  • Accuracy (9)
  • Cost (7)
  • Precision (3)
  • Coherence (2)

Top Papers On This Benchmark

Related Benchmark Hubs

DROP Or MATH-500 Or LongBench Benchmark Papers MATH-500 Benchmark Papers MATH-500 Benchmark Papers (Last 365 Days) GSM8K Or AIME Or LongBench Benchmark Papers MATH-500 In CS.CL Papers MATH-500 Benchmark Papers (Last 240 Days) 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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