arXiv AI June 9, 2026 By Sang Truong, Yuheng Tu, Rylan Schaeffer, Sanmi Koyejo

Item Response Scaling Laws: A Measurement Theory Approach for Efficient and Generalizable Neural Scaling Estimation

arXiv:2606. 07616v1 Announce Type: cross Abstract: Scaling laws provide a fundamental framework for understanding the performance of Language Models (LMs), yet deriving them requires prohibitively expensive evaluations across thousands of checkpoints or millions of inference samples.

Summary generated by The Flow from the publisher's feed. The full article lives at arXiv AI.

arXiv AI

Jul 20

Latency-Response Theory Model: Evaluating Large Language Models via Response Accuracy and Chain-of-Thought Length

arXiv:2512. 07019v3 Announce Type: replace-cross Abstract: The proliferation of Large Language Models (LLMs) necessitates valid evaluation methods to provide guidance for both downstream applications and actionable future improvements.

By Zhiyu Xu, Jia Liu, Yixin Wang, Yuqi Gu

llms benchmarks

arXiv AI

Jun 9

Prescriptive Scaling Reveals the Evolution of Language Model Capabilities

arXiv:2602. 15327v2 Announce Type: replace-cross Abstract: Machine learning model performance improvements tend to arise from competition and application.

By Hanlin Zhang, Jikai Jin, Vasilis Syrgkanis, Sham Kakade

llms benchmarks

arXiv AI

Jul 1

Optimal Self-Consistency for Efficient Reasoning with Large Language Models

arXiv:2511. 12309v2 Announce Type: replace-cross Abstract: Self-consistency (SC) is a widely used test-time inference technique for improving performance in chain-of-thought reasoning.

By Austin Feng, Marius Alonso, Ambroise Odonnat, Vasilii Feofanov, Ievgen Redko

llms benchmarks

arXiv AI

Jun 2

ProbeScale: Probing Analysis to Optimize Neural Scaling Laws for Efficient Small Language Model Inference

arXiv:2606. 01806v1 Announce Type: cross Abstract: Small Language Models (SLMs) offer a balance between capability and computational feasibility.

By Sourav Das

llms

arXiv Machine Learning

Jun 4

A Latent Variable Framework for Scaling Laws in Large Language Models

arXiv:2512. 06553v2 Announce Type: replace-cross Abstract: We propose a statistical framework built on latent variable modeling for scaling laws of large language models (LLMs).

By Peiyao Cai, Chengyu Cui, Felipe Maia Polo, Seamus Somerstep, Leshem Choshen, Mikhail Yurochkin, Yuekai Sun, Kean Ming Tan, Gongjun Xu

llms benchmarks

arXiv Machine Learning

Jul 23

Scaling Laws for Hypernetwork-Based Knowledge Injection in Large Language Models

arXiv:2607. 19604v1 Announce Type: cross Abstract: Injecting factual knowledge into large language models (LLMs) reliably and at scale remains an open challenge.

By Nischay Dhankhar, Dos Baha, Abulhair Saparov

llms fine-tuning

Related stories

Latency-Response Theory Model: Evaluating Large Language Models via Response Accuracy and Chain-of-Thought Length

Prescriptive Scaling Reveals the Evolution of Language Model Capabilities

Optimal Self-Consistency for Efficient Reasoning with Large Language Models

ProbeScale: Probing Analysis to Optimize Neural Scaling Laws for Efficient Small Language Model Inference

A Latent Variable Framework for Scaling Laws in Large Language Models

Scaling Laws for Hypernetwork-Based Knowledge Injection in Large Language Models