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HFEPX Benchmark Hub

LongBench Benchmark Papers

Updated from current HFEPX corpus (Apr 27, 2026). 10 papers are grouped in this benchmark page.

Read Full Context

Updated from current HFEPX corpus (Apr 27, 2026). 10 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: LongBench. 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: 10 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%

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

  • 10% of papers report explicit human-feedback signals, led by rubric ratings.
  • automatic metrics appears in 10% of papers in this hub.
  • LongBench 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 (LongBench vs AIME) before comparing methods.

Benchmark Interpretation

  • LongBench appears in 100% of hub papers (10/10); use this cohort for benchmark-matched comparisons.
  • AIME appears in 10% of hub papers (1/10); use this cohort for benchmark-matched comparisons.

Metric Interpretation

  • accuracy is reported in 30% of hub papers (3/10); compare with a secondary metric before ranking methods.
  • precision is reported in 30% of hub papers (3/10); 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
Document Reconstruction Unlocks Scalable Long-Context RLVR

Feb 9, 2026

 Automatic Metrics Rubric Rating Coherence 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
HISA: Efficient Hierarchical Indexing for Fine-Grained Sparse Attention

Mar 30, 2026

 Not reported Not reported Not reported Not reported
EchoKV: Efficient KV Cache Compression via Similarity-Based Reconstruction

Mar 24, 2026

 Not reported Not reported Not reported Not reported
VQKV: High-Fidelity and High-Ratio Cache Compression via Vector-Quantization

Mar 17, 2026

 Not reported Not reported Not reported Not reported
LongRLVR: Long-Context Reinforcement Learning Requires Verifiable Context Rewards

Mar 2, 2026

 Not reported Not reported Not reported Not reported
FASA: Frequency-aware Sparse Attention

Feb 3, 2026

 Not reported Not reported Not reported Not reported
E2LLM: Encoder Elongated Large Language Models for Long-Context Understanding and Reasoning

Sep 10, 2024

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

Checklist

  • Gap: Papers with explicit human feedback

    Coverage is a replication risk (10% 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 (10% 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 (10% coverage).
  • Annotation unit is under-specified (10% coverage).

Suggested Next Analyses

  • Stratify by benchmark (LongBench vs AIME) before comparing methods.
  • Track metric sensitivity by reporting both accuracy and precision.

Recommended Queries

Benchmark Slice: LongBenchMetric Slice: accuracyRecent High-Signal Papers
Known Limitations
  • Only 0% of papers report quality controls; prioritize calibration/adjudication evidence.
  • Rater population is under-specified (10% 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

  • Rubric Rating (1)

Top Benchmarks

  • LongBench (10)
  • AIME (1)
  • MATH 500 (1)
  • Needle In A Haystack (1)

Top Metrics

  • Accuracy (3)
  • Precision (3)
  • Context length (2)
  • Agreement (1)

Top Papers On This Benchmark

Related Benchmark Hubs

LongBench In CS.CL Papers BFCL Or LongBench Benchmark Papers LongBench Or WebArena Benchmark Papers HumanEval+ Or LongBench Benchmark Papers AlpacaEval Or LongBench Benchmark Papers HotpotQA Or LongBench Benchmark Papers GPQA Or BFCL Benchmark Papers (23) HumanEval+ Or BFCL Benchmark Papers (21) MATH-500 Or BFCL Benchmark Papers (25) GPQA Or HumanEval+ Benchmark Papers (22) MATH-500 Or GPQA Benchmark Papers (25) MATH-500 Or HumanEval+ Benchmark Papers (24) MATH-500 Benchmark Papers (15) MMLU Or AIME Or AlpacaEval Benchmark Papers (60) MMLU Or AIME Or BFCL Benchmark Papers (59) GSM8K Or MMLU Or AIME Benchmark Papers (72) MMLU Or AIME Or HotpotQA Benchmark Papers (62) MMLU Or AIME Or HumanEval+ Benchmark Papers (57) MMLU Or AIME Or LiveCodeBench Benchmark Papers (59) MMLU Or AIME Or LongBench Benchmark Papers (59) MMLU Or AIME Or MATH-500 Benchmark Papers (58) MMLU Or AIME Or SWE-bench Benchmark Papers (64) MMLU Or AIME Benchmark Papers (50) GSM8K Or MMLU Or AlpacaEval Benchmark Papers (65)
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