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Metric Hub

Latency In CS.LG Papers

Updated from current HFEPX corpus (Feb 27, 2026). 16 papers are grouped in this metric page. Common evaluation modes: Automatic Metrics, Simulation Env. Most common rater population: Domain Experts. Frequent quality control: Calibration. Frequently cited benchmark: GSM8K. Common metric signal: latency. 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 26, 2026.

Papers: 16 Last published: Feb 26, 2026 Global RSS

Research Narrative

Grounded narrative Model: deterministic-grounded Source: persisted

Updated from current HFEPX corpus (Feb 27, 2026). This page tracks 16 papers for Latency In CS.LG Papers. Dominant protocol signals include automatic metrics, simulation environments, with frequent benchmark focus on GSM8K, DROP and metric focus on latency, accuracy. Use the grounded sections below to prioritize reproducible protocol choices, benchmark-matched comparisons, and judge-vs-human evaluation checks.

Why This Matters For Eval Research

Protocol Takeaways

Benchmark Interpretation

  • GSM8K appears in 12.5% of hub papers (2/16); use this cohort for benchmark-matched comparisons.
  • DROP appears in 6.3% of hub papers (1/16); use this cohort for benchmark-matched comparisons.

Metric Interpretation

  • latency is reported in 100% of hub papers (16/16); compare with a secondary metric before ranking methods.
  • accuracy is reported in 43.8% of hub papers (7/16); compare with a secondary metric before ranking methods.

Researcher Checklist

  • Close gap on Papers with explicit human feedback. Coverage is a replication risk (6.3% vs 45% target).
  • Close gap on Papers reporting quality controls. Coverage is a replication risk (12.5% vs 30% target).
  • Maintain strength on Papers naming benchmarks/datasets. Coverage is strong (56.3% vs 35% target).
  • Maintain strength on Papers naming evaluation metrics. Coverage is strong (100% vs 35% target).
  • Close gap on Papers with known rater population. Coverage is a replication risk (6.3% vs 35% target).
  • Close gap on Papers with known annotation unit. Coverage is a replication risk (0% vs 35% target).

Papers with explicit human feedback

Coverage is a replication risk (6.3% vs 45% target).

Papers reporting quality controls

Coverage is a replication risk (12.5% vs 30% target).

Papers naming benchmarks/datasets

Coverage is strong (56.3% vs 35% target).

Papers naming evaluation metrics

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

Papers with known rater population

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

Papers with known annotation unit

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

Suggested Reading Order

  1. 1. InnerQ: Hardware-aware Tuning-free Quantization of KV Cache for Large Language Models

    Start here for detailed protocol reporting, including rater and quality-control evidence.

  2. 2. Vectorizing the Trie: Efficient Constrained Decoding for LLM-based Generative Retrieval on Accelerators

    Start here for detailed protocol reporting, including rater and quality-control evidence.

  3. 3. SWE-Protégé: Learning to Selectively Collaborate With an Expert Unlocks Small Language Models as Software Engineering Agents

    Start here for detailed protocol reporting, including rater and quality-control evidence.

  4. 4. Why Pass@k Optimization Can Degrade Pass@1: Prompt Interference in LLM Post-training

    Adds automatic metrics for broader coverage within this hub.

  5. 5. Pyramid MoA: A Probabilistic Framework for Cost-Optimized Anytime Inference

    Adds automatic metrics for broader coverage within this hub.

  6. 6. Luna-2: Scalable Single-Token Evaluation with Small Language Models

    Adds automatic metrics for broader coverage within this hub.

  7. 7. Discrete Stochastic Localization for Non-autoregressive Generation

    Adds automatic metrics for broader coverage within this hub.

  8. 8. Zooming without Zooming: Region-to-Image Distillation for Fine-Grained Multimodal Perception

    Adds automatic metrics for broader coverage within this hub.

Known Limitations

  • Only 12.5% of papers report quality controls; prioritize calibration/adjudication evidence.
  • Rater population is under-specified (6.3% coverage).
  • Narrative synthesis is grounded in metadata and abstracts only; full-paper implementation details are not parsed.

Research Utility Links

automatic_metrics vs simulation_env

both=0, left_only=15, right_only=1

0 papers use both Automatic Metrics and Simulation Env.

Benchmark Brief

GSM8K

Coverage: 2 papers (12.5%)

2 papers (12.5%) mention GSM8K.

Examples: InnerQ: Hardware-aware Tuning-free Quantization of KV Cache for Large Language Models , Pyramid MoA: A Probabilistic Framework for Cost-Optimized Anytime Inference

Benchmark Brief

DROP

Coverage: 1 papers (6.3%)

1 papers (6.3%) mention DROP.

Examples: SNAP-UQ: Self-supervised Next-Activation Prediction for Single-Pass Uncertainty in TinyML

Benchmark Brief

MATH

Coverage: 1 papers (6.3%)

1 papers (6.3%) mention MATH.

Examples: CDLM: Consistency Diffusion Language Models For Faster Sampling

Metric Brief

latency

Coverage: 16 papers (100%)

16 papers (100%) mention latency.

Examples: InnerQ: Hardware-aware Tuning-free Quantization of KV Cache for Large Language Models , Vectorizing the Trie: Efficient Constrained Decoding for LLM-based Generative Retrieval on Accelerators , SWE-Protégé: Learning to Selectively Collaborate With an Expert Unlocks Small Language Models as Software Engineering Agents

Metric Brief

accuracy

Coverage: 7 papers (43.8%)

7 papers (43.8%) mention accuracy.

Examples: InnerQ: Hardware-aware Tuning-free Quantization of KV Cache for Large Language Models , Pyramid MoA: A Probabilistic Framework for Cost-Optimized Anytime Inference , Luna-2: Scalable Single-Token Evaluation with Small Language Models

Metric Brief

cost

Coverage: 6 papers (37.5%)

6 papers (37.5%) mention cost.

Examples: SWE-Protégé: Learning to Selectively Collaborate With an Expert Unlocks Small Language Models as Software Engineering Agents , Why Pass@k Optimization Can Degrade Pass@1: Prompt Interference in LLM Post-training , Pyramid MoA: A Probabilistic Framework for Cost-Optimized Anytime Inference

Most Cited In This Hub

Fast path to methods with the strongest citation traction in this scope.

Papers: InnerQ: Hardware-aware Tuning-free Quantization of KV Cache for Large Language Models , Vectorizing the Trie: Efficient Constrained Decoding for LLM-based Generative Retrieval on Accelerators , SWE-Protégé: Learning to Selectively Collaborate With an Expert Unlocks Small Language Models as Software Engineering Agents

Most Recent

Fast path to latest protocol changes and newly published evaluation setups.

Papers: InnerQ: Hardware-aware Tuning-free Quantization of KV Cache for Large Language Models , Vectorizing the Trie: Efficient Constrained Decoding for LLM-based Generative Retrieval on Accelerators , SWE-Protégé: Learning to Selectively Collaborate With an Expert Unlocks Small Language Models as Software Engineering Agents

Best Protocol Detail

Papers with explicit rater/unit metadata and quality-control signals for reproducibility.

Papers: InnerQ: Hardware-aware Tuning-free Quantization of KV Cache for Large Language Models , Vectorizing the Trie: Efficient Constrained Decoding for LLM-based Generative Retrieval on Accelerators , SWE-Protégé: Learning to Selectively Collaborate With an Expert Unlocks Small Language Models as Software Engineering Agents

Top Papers Reporting This Metric

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