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HFEPX Metric Hub

Throughput In CS.AI Papers

Updated from current HFEPX corpus (Apr 12, 2026). 32 papers are grouped in this metric page.

Read Full Context

Updated from current HFEPX corpus (Apr 12, 2026). 32 papers are grouped in this metric page. Common evaluation modes: Automatic Metrics, Llm As Judge. Most common rater population: Domain Experts. Common annotation unit: Trajectory. Frequently cited benchmark: AIME. Common metric signal: throughput. 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 Mar 23, 2026.

Papers: 32 Last published: Mar 23, 2026 Global RSS

When This Metric Page Is Useful

Useful for background comparison, but still validate benchmark and protocol details in the linked papers. Quality band: Medium .

Metric Coverage

21.9%

7 sampled papers include metric names.

Benchmark Anchoring

3.1%

Papers with explicit dataset/benchmark anchors for fair comparison.

Quality Controls

0.0%

0 papers report calibration/adjudication/IAA controls.

  • 32 papers are not low-signal flagged in this sample.
  • Use the protocol matrix below to avoid comparing metrics across incompatible eval setups.

Recommended next step: Treat this as directional signal only; metric reporting is present but benchmark anchoring is still thin.

Main limitation: Benchmark coverage is still thin, so avoid treating this page as a definitive guide to the metric.

What This Metric Page Tells You

What This Metric Page Tells You

  • 3.1% of papers report explicit human-feedback signals, led by pairwise preferences.
  • automatic metrics appears in 12.5% of papers in this hub.
  • AIME is a recurring benchmark anchor for cross-paper comparisons in this page.
Metric Notes (Expanded)

Metric-Driven 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 trajectory-level annotation; use this to scope replication staffing.
  • Pair this hub with a human_eval-heavy hub to validate judge-model calibration.

Metric Interpretation

  • throughput is reported in 100% of hub papers (32/32); compare with a secondary metric before ranking methods.
  • cost is reported in 37.5% of hub papers (12/32); compare with a secondary metric before ranking methods.

Benchmark Context

  • AIME appears in 3.1% of hub papers (1/32); use this cohort for benchmark-matched comparisons.
  • GPQA appears in 3.1% of hub papers (1/32); use this cohort for benchmark-matched comparisons.

Start Here (Metric-Reliable First 6)

Ranked for metric reporting completeness and comparability.

Metric Protocol Matrix (Top 10)

Compare metric, benchmark, and evaluation context side by side.

Paper Metrics Benchmarks Eval Modes Quality Controls
Weakly Supervised Distillation of Hallucination Signals into Transformer Representations

Apr 7, 2026

 F1, Latency SQuAD Llm As Judge, Automatic Metrics Not reported
Spatio-Temporal Attention Enhanced Multi-Agent DRL for UAV-Assisted Wireless Networks with Limited Communications

Mar 23, 2026

 Throughput Not reported Simulation Env Not reported
JoyAI-LLM Flash: Advancing Mid-Scale LLMs with Token Efficiency

Apr 3, 2026

 Throughput Not reported Not reported Not reported
Self-Correcting VLA: Online Action Refinement via Sparse World Imagination

Feb 25, 2026

 Success rate, Throughput Not reported Simulation Env Not reported
Luna-2: Scalable Single-Token Evaluation with Small Language Models

Feb 20, 2026

 Accuracy, Latency Not reported Llm As Judge, Automatic Metrics Not reported
The Headless Firm: How AI Reshapes Enterprise Boundaries

Feb 24, 2026

 Throughput, Cost Not reported Automatic Metrics Not reported
Anatomy of Agentic Memory: Taxonomy and Empirical Analysis of Evaluation and System Limitations

Feb 22, 2026

 Accuracy, Latency Not reported Automatic Metrics Not reported
Scalable Neural Decoders for Practical Fault-Tolerant Quantum Computation

Apr 9, 2026

 Not reported Not reported Not reported Not reported
Squeeze Evolve: Unified Multi-Model Orchestration for Verifier-Free Evolution

Apr 9, 2026

 Not reported Not reported Not reported Not reported
SM-Net: Learning a Continuous Spectral Manifold from Multiple Stellar Libraries

Mar 25, 2026

 Not reported Not reported Not reported Not reported
How To Use This Page

Checklist

  • Gap: Papers with explicit human feedback

    Coverage is a replication risk (3.1% vs 45% target).

  • Gap: Papers reporting quality controls

    Coverage is a replication risk (0% vs 30% target).

  • Gap: Papers naming benchmarks/datasets

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

  • Strong: Papers naming evaluation metrics

    Coverage is strong (100% vs 35% target).

  • Gap: Papers with known rater population

    Coverage is a replication risk (3.1% vs 35% target).

  • Gap: Papers with known annotation unit

    Coverage is a replication risk (6.3% vs 35% target).

Strengths

  • This hub still surfaces a concentrated paper set for protocol triage and replication planning.

Known Gaps

  • Only 0% of papers report quality controls; prioritize calibration/adjudication evidence.
  • Rater population is under-specified (3.1% coverage).
  • Annotation unit is under-specified (6.3% coverage).

Suggested Next Analyses

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

Recommended Queries

LLM-as-Judge ProtocolsBenchmark Slice: AIMEMetric Slice: throughputRecent High-Signal Papers
Known Limitations
  • Only 0% of papers report quality controls; prioritize calibration/adjudication evidence.
  • Rater population is under-specified (3.1% coverage).
  • Narrative synthesis is grounded in metadata and abstracts only; full-paper implementation details are not parsed.
Coverage Snapshot

Top Metrics

  • Throughput (32)
  • Cost (12)
  • Accuracy (11)
  • Latency (11)

Evaluation Modes

  • Automatic Metrics (4)
  • Llm As Judge (2)
  • Simulation Env (2)

Top Benchmarks

  • AIME (1)
  • GPQA (1)
  • HumanoidBench (1)
  • LiveCodeBench (1)

Agentic Mix

  • Long Horizon (3)
  • Multi Agent (2)

Top Papers Reporting This Metric

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