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HFEPX Benchmark Hub

HumanEval+ Or LongBench Benchmark Papers

Updated from current HFEPX corpus (Apr 27, 2026). 21 papers are grouped in this benchmark page.

Read Full Context

Updated from current HFEPX corpus (Apr 27, 2026). 21 papers are grouped in this benchmark page. Common evaluation modes: Automatic Metrics. Most common rater population: Domain Experts. Common annotation unit: Multi Dim Rubric. Frequently cited benchmark: HumanEval+. 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: 21 Last published: Feb 9, 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%

21 / 21 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.

  • 3 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

  • 4.8% of papers report explicit human-feedback signals, led by rubric ratings.
  • automatic metrics appears in 14.3% of papers in this hub.
  • HumanEval+ 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.
  • Stratify by benchmark (HumanEval+ vs LongBench) before comparing methods.

Benchmark Interpretation

  • HumanEval+ appears in 52.4% of hub papers (11/21); use this cohort for benchmark-matched comparisons.
  • LongBench appears in 47.6% of hub papers (10/21); use this cohort for benchmark-matched comparisons.

Metric Interpretation

  • accuracy is reported in 28.6% of hub papers (6/21); compare with a secondary metric before ranking methods.
  • cost is reported in 19% of hub papers (4/21); 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
Cost-Effective Communication: An Auction-based Method for Language Agent Interaction

Nov 17, 2025

Automatic Metrics Not reported Pass@1, Cost 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
RACER: Retrieval-Augmented Contextual Rapid Speculative Decoding

Apr 16, 2026

Not reported Not reported Not reported Not reported
StoryCoder: Narrative Reformulation for Structured Reasoning in LLM Code Generation

Apr 16, 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
HISA: Efficient Hierarchical Indexing for Fine-Grained Sparse Attention

Mar 30, 2026

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

Checklist

  • Gap: Papers with explicit human feedback

    Coverage is a replication risk (4.8% 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 (66.7% vs 35% target).

  • Gap: Papers with known rater population

    Coverage is a replication risk (4.8% vs 35% target).

  • Gap: Papers with known annotation unit

    Coverage is a replication risk (9.5% vs 35% target).

Strengths

  • Most papers provide measurable evaluation context (100% benchmarks, 66.7% metrics).

Known Gaps

  • Only 0% of papers report quality controls; prioritize calibration/adjudication evidence.
  • Rater population is under-specified (4.8% coverage).
  • Annotation unit is under-specified (9.5% coverage).

Suggested Next Analyses

  • Stratify by benchmark (HumanEval+ vs LongBench) before comparing methods.
  • Track metric sensitivity by reporting both accuracy and cost.

Recommended Queries

Known Limitations
  • Only 0% of papers report quality controls; prioritize calibration/adjudication evidence.
  • Rater population is under-specified (4.8% 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 Feedback Mix

  • Rubric Rating (1)

Top Benchmarks

  • HumanEval+ (11)
  • LongBench (10)
  • GSM8K (5)
  • MATH 500 (3)

Top Metrics

  • Accuracy (6)
  • Cost (4)
  • Precision (3)
  • Coherence (2)

Top Papers On This Benchmark

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