Metric Hub

Precision In CS.LG Papers

Updated from current HFEPX corpus (Feb 27, 2026). 14 papers are grouped in this metric page. Common evaluation modes: Automatic Metrics, Simulation Env. Most common rater population: Domain Experts. Common annotation unit: Ranking. Frequent quality control: Calibration. Frequently cited benchmark: GSM8K. Common metric signal: precision. 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: 14 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 14 papers for Precision In CS.LG Papers. Dominant protocol signals include automatic metrics, simulation environments, with frequent benchmark focus on GSM8K, ARC and metric focus on precision, 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

7.1% of papers report explicit human-feedback signals, led by expert verification.

Evidence: An artificial intelligence framework for end-to-end rare disease phenotyping from clinical notes using large language models , InnerQ: Hardware-aware Tuning-free Quantization of KV Cache for Large Language Models , pQuant: Towards Effective Low-Bit Language Models via Decoupled Linear Quantization-Aware Training , A Fusion of context-aware based BanglaBERT and Two-Layer Stacked LSTM Framework for Multi-Label Cyberbullying Detection
automatic metrics appears in 92.9% of papers in this hub.

Evidence: InnerQ: Hardware-aware Tuning-free Quantization of KV Cache for Large Language Models , pQuant: Towards Effective Low-Bit Language Models via Decoupled Linear Quantization-Aware Training , A Fusion of context-aware based BanglaBERT and Two-Layer Stacked LSTM Framework for Multi-Label Cyberbullying Detection , Small Language Models for Privacy-Preserving Clinical Information Extraction in Low-Resource Languages
GSM8K is a recurring benchmark anchor for cross-paper comparisons in this page.

Evidence: InnerQ: Hardware-aware Tuning-free Quantization of KV Cache for Large Language Models , Pyramid MoA: A Probabilistic Framework for Cost-Optimized Anytime Inference , pQuant: Towards Effective Low-Bit Language Models via Decoupled Linear Quantization-Aware Training , A Fusion of context-aware based BanglaBERT and Two-Layer Stacked LSTM Framework for Multi-Label Cyberbullying Detection

Protocol Takeaways

Most common quality-control signal is rater calibration (14.3% of papers).

Evidence: Pyramid MoA: A Probabilistic Framework for Cost-Optimized Anytime Inference , MoBiQuant: Mixture-of-Bits Quantization for Token-Adaptive Elastic LLMs , A Fusion of context-aware based BanglaBERT and Two-Layer Stacked LSTM Framework for Multi-Label Cyberbullying Detection , An artificial intelligence framework for end-to-end rare disease phenotyping from clinical notes using large language models
Rater context is mostly domain experts, and annotation is commonly ranking annotation; use this to scope replication staffing.

Evidence: pQuant: Towards Effective Low-Bit Language Models via Decoupled Linear Quantization-Aware Training , An artificial intelligence framework for end-to-end rare disease phenotyping from clinical notes using large language models , InnerQ: Hardware-aware Tuning-free Quantization of KV Cache for Large Language Models , A Fusion of context-aware based BanglaBERT and Two-Layer Stacked LSTM Framework for Multi-Label Cyberbullying Detection
Stratify by benchmark (GSM8K vs ARC) before comparing methods.

Evidence: InnerQ: Hardware-aware Tuning-free Quantization of KV Cache for Large Language Models , pQuant: Towards Effective Low-Bit Language Models via Decoupled Linear Quantization-Aware Training , A Fusion of context-aware based BanglaBERT and Two-Layer Stacked LSTM Framework for Multi-Label Cyberbullying Detection , Dynamic Personality Adaptation in Large Language Models via State Machines

Benchmark Interpretation

GSM8K appears in 14.3% of hub papers (2/14); use this cohort for benchmark-matched comparisons.
ARC appears in 7.1% of hub papers (1/14); use this cohort for benchmark-matched comparisons.

Metric Interpretation

precision is reported in 100% of hub papers (14/14); compare with a secondary metric before ranking methods.
accuracy is reported in 42.9% of hub papers (6/14); compare with a secondary metric before ranking methods.

Researcher Checklist

Close gap on Papers with explicit human feedback. Coverage is a replication risk (7.1% vs 45% target).
Tighten coverage on Papers reporting quality controls. Coverage is usable but incomplete (28.6% vs 30% target).
Tighten coverage on Papers naming benchmarks/datasets. Coverage is usable but incomplete (28.6% 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 (14.3% vs 35% target).
Close gap on Papers with known annotation unit. Coverage is a replication risk (7.1% vs 35% target).

Papers with explicit human feedback

Coverage is a replication risk (7.1% vs 45% target).

Papers reporting quality controls

Coverage is usable but incomplete (28.6% vs 30% target).

Papers naming benchmarks/datasets

Coverage is usable but incomplete (28.6% vs 35% target).

Papers naming evaluation metrics

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

Papers with known rater population

Coverage is a replication risk (14.3% vs 35% target).

Papers with known annotation unit

Coverage is a replication risk (7.1% vs 35% target).

Known Limitations

Rater population is under-specified (14.3% coverage).
Annotation unit is under-specified (7.1% coverage).
Narrative synthesis is grounded in metadata and abstracts only; full-paper implementation details are not parsed.

Research Utility Links

Benchmark Slice: GSM8K - Prioritizes benchmark-specific protocol comparisons.
Metric Slice: precision - Finds papers where reported metrics are directly comparable.
IAA-Reported Evaluations - Highlights evaluations that explicitly report inter-annotator agreement.
Recent High-Signal Papers - Keeps the hub connected to the latest HFEPX corpus updates.

automatic_metrics vs simulation_env

both=0, left_only=13, right_only=1

0 papers use both Automatic Metrics and Simulation Env.

Benchmark Brief

GSM8K

Coverage: 2 papers (14.3%)

2 papers (14.3%) 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

ARC

Coverage: 1 papers (7.1%)

1 papers (7.1%) mention ARC.

Examples: Improving Denoising Diffusion Models via Simultaneous Estimation of Image and Noise

Benchmark Brief

NyayaBench

Coverage: 1 papers (7.1%)

1 papers (7.1%) mention NyayaBench.

Examples: Why Agent Caching Fails and How to Fix It: Structured Intent Canonicalization with Few-Shot Learning

Metric Brief

precision

Coverage: 14 papers (100%)

14 papers (100%) mention precision.

Examples: InnerQ: Hardware-aware Tuning-free Quantization of KV Cache for Large Language Models , pQuant: Towards Effective Low-Bit Language Models via Decoupled Linear Quantization-Aware Training , A Fusion of context-aware based BanglaBERT and Two-Layer Stacked LSTM Framework for Multi-Label Cyberbullying Detection

Metric Brief

accuracy

Coverage: 6 papers (42.9%)

6 papers (42.9%) mention accuracy.

Metric Brief

cost

Coverage: 3 papers (21.4%)

3 papers (21.4%) mention cost.

Examples: Small Language Models for Privacy-Preserving Clinical Information Extraction in Low-Resource Languages , Pyramid MoA: A Probabilistic Framework for Cost-Optimized Anytime Inference , Why Agent Caching Fails and How to Fix It: Structured Intent Canonicalization with Few-Shot Learning

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 , pQuant: Towards Effective Low-Bit Language Models via Decoupled Linear Quantization-Aware Training , A Fusion of context-aware based BanglaBERT and Two-Layer Stacked LSTM Framework for Multi-Label Cyberbullying Detection

Most Recent

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

Best Protocol Detail

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

Papers: An artificial intelligence framework for end-to-end rare disease phenotyping from clinical notes using large language models

Top Papers Reporting This Metric

InnerQ: Hardware-aware Tuning-free Quantization of KV Cache for Large Language Models
Sayed Mohammadreza Tayaranian Hosseini, Amir Ardakani, Warren J. Gross · Feb 26, 2026

Automatic Metrics MathCoding

Our evaluation experiments on Llama models shows that InnerQ maintains a few-shot GSM8K performance comparable to non-quantized KV caches and surpasses prior KV cache quantization methods.
pQuant: Towards Effective Low-Bit Language Models via Decoupled Linear Quantization-Aware Training
Wenzheng Zhang, Bingzheng Liu, Yang Hu, Xiaoying Bai, Wentao Zhang · Feb 26, 2026

Automatic Metrics General

Quantization-Aware Training from scratch has emerged as a promising approach for building efficient large language models (LLMs) with extremely low-bit weights (sub 2-bit), which can offer substantial advantages for edge deployment.
A Fusion of context-aware based BanglaBERT and Two-Layer Stacked LSTM Framework for Multi-Label Cyberbullying Detection
Mirza Raquib, Asif Pervez Polok, Kedar Nath Biswas, Rahat Uddin Azad, Saydul Akbar Murad · Feb 25, 2026

Automatic Metrics General

Evaluation uses multiple metrics, including accuracy, precision, recall, F1-score, Hamming loss, Cohens kappa, and AUC-ROC.
Dynamic Personality Adaptation in Large Language Models via State Machines
Leon Pielage, Ole Hätscher, Mitja Back, Bernhard Marschall, Benjamin Risse · Feb 25, 2026

Simulation Env Medicine

This work demonstrates the feasibility of modular, personality-adaptive architectures for education, customer support, and broader human-computer interaction.
Small Language Models for Privacy-Preserving Clinical Information Extraction in Low-Resource Languages
Mohammadreza Ghaffarzadeh-Esfahani, Nahid Yousefian, Ebrahim Heidari-Farsani, Ali Akbar Omidvarian, Sepehr Ghahraei · Feb 24, 2026

Automatic Metrics MedicineMultilingual

Extracting clinical information from medical transcripts in low-resource languages remains a significant challenge in healthcare natural language processing (NLP).
An artificial intelligence framework for end-to-end rare disease phenotyping from clinical notes using large language models
Cathy Shyr, Yan Hu, Rory J. Tinker, Thomas A. Cassini, Kevin W. Byram · Feb 23, 2026

Automatic Metrics Medicine

Existing artificial intelligence approaches typically optimize individual components of phenotyping but do not operationalize the full clinical workflow of extracting features from clinical text, standardizing them to Human Phenotype Ontolo
Pyramid MoA: A Probabilistic Framework for Cost-Optimized Anytime Inference
Arindam Khaled · Feb 23, 2026

Automatic Metrics Math

In this work, we propose "Pyramid MoA", a hierarchical Mixture-of-Agents architecture that uses a lightweight Router to dynamically escalate queries only when necessary.
MoBiQuant: Mixture-of-Bits Quantization for Token-Adaptive Elastic LLMs
Dongwei Wang, Jinhee Kim, Seokho Han, Denis Gudovskiy, Yohei Nakata · Feb 21, 2026

Automatic Metrics General

Changing runtime complexity on cloud and edge devices necessitates elastic large language model (LLM) deployment, where an LLM can be inferred with various quantization precisions based on available computational resources.
Why Agent Caching Fails and How to Fix It: Structured Intent Canonicalization with Few-Shot Learning
Abhinaba Basu · Feb 21, 2026

Automatic Metrics Multilingual

Personal AI agents incur substantial cost via repeated LLM calls.
AAVGen: Precision Engineering of Adeno-associated Viral Capsids for Renal Selective Targeting
Mohammadreza Ghaffarzadeh-Esfahani, Yousof Gheisari · Feb 21, 2026

Automatic Metrics General

Adeno-associated viruses (AAVs) are promising vectors for gene therapy, but their native serotypes face limitations in tissue tropism, immune evasion, and production efficiency.
The Anxiety of Influence: Bloom Filters in Transformer Attention Heads
Peter Balogh · Feb 19, 2026

Automatic Metrics General

Some transformer attention heads appear to function as membership testers, dedicating themselves to answering the question "has this token appeared before in the context?" We identify these heads across four language models (GPT-2 small, me
Unleashing Low-Bit Inference on Ascend NPUs: A Comprehensive Evaluation of HiFloat Formats
Pengxiang Zhao, Hui-Ling Zhen, Xing Li, Han Bao, Weizhe Lin · Feb 13, 2026

Automatic Metrics General

As LLMs scale, low-bit floating-point formats like MXFP and NVFP4 offer new opportunities for precision and efficiency.
SciTS: Scientific Time Series Understanding and Generation with LLMs
Wen Wu, Ziyang Zhang, Liwei Liu, Xuenan Xu, Jimin Zhuang · Sep 26, 2025

Automatic Metrics Coding

To address these gaps, we introduce SciTS, a benchmark spanning 12 scientific domains and 43 tasks, with over 50k+ instances, both univariate and multivariate signals ranging from $10^0$ to $10^7$ in length and up to 10~MHz in frequency.
Improving Denoising Diffusion Models via Simultaneous Estimation of Image and Noise
Zhenkai Zhang, Krista A. Ehinger, Tom Drummond · Oct 26, 2023

Automatic Metrics Math

This paper introduces two key contributions aimed at improving the speed and quality of images generated through inverse diffusion processes.

Precision In CS.LG Papers

Research Narrative

Why This Matters For Eval Research

Protocol Takeaways

Benchmark Interpretation

Metric Interpretation

Researcher Checklist

Suggested Reading Order

Known Limitations

Research Utility Links

Top Papers Reporting This Metric

Other Metric Hubs