arXiv Computation and Language September 21, 2026 By Shalini Maiti, Amar Budhiraja, Bhavul Gauri, Gaurav Chaurasia, Anton Protopopov, Alexis Audran-Reiss, Michael Slater, Despoina Magka, Tatiana Shavrina, Roberta Raileanu, Yoram Bachrach

Souper-Model: How Simple Arithmetic Unlocks State-of-the-Art LLM Performance

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arXiv Computation and Language

Sep 14

Zipbench: Low-Cost Framework for Compressing Comprehensive Benchmarks of Large Language Models

arXiv:2609.12475v1 Announce Type: new Abstract: Comprehensive benchmark suites are essential for improving large language models (LLMs), but many widely used benchmarks are redundant, making evaluati...

By Zhongzhan Huang, Junxin Li, Guoming Ling, Yupei Lin, Shanshan Zhong, Hefeng Wu

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arXiv AI

Jun 2

Uncovering Competency Gaps in Large Language Models and Their Benchmarks

arXiv:2512. 20638v2 Announce Type: replace-cross Abstract: The evaluation of large language models relies heavily on standardized benchmarks.

By Maty Bohacek, Nino Scherrer, Nicholas Dufour, Thomas Leung, Christoph Bregler, Stephanie C. Y. Chan

llms benchmarks

arXiv AI

Jul 7

Spectral Signatures of Large Language Models

arXiv:2607. 03377v1 Announce Type: cross Abstract: The rapidly growing repository of publicly available large language models (LLMs) presents significant challenges for systematic management and quantification at scale, such as model lineage tracing, licensing, and evaluation.

By Zhuoying Zhang, Ishan V. Prasad, Yuanzhe Hu, Zihang Liu, Hengrui Luo, Pu Ren, Yaoqing Yang

llms benchmarks

arXiv AI

Jul 21

Rethinking Heterogeneous LLM Merging: A Weighted Model Averaging Perspective

arXiv:2607. 18026v1 Announce Type: new Abstract: Can large language models with substantially different parameter spaces be merged by direct weighted averaging, without training or semantic alignment?

By Jiahe Fan, Yinghao Hou, Si Chen, Aiyuan Zhang, Hong Xie, Defu Lian

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arXiv AI

Jul 28

cMoLLM at Scale: Horizontal Scaling Laws for Mixture-of-LLMs

arXiv:2607. 22577v1 Announce Type: new Abstract: Scaling large language models (LLMs) has driven their success, yet dense Transformers couple capacity and computation: every parameter is activated for every token, making training and inference costs grow linearly with model size-a critical bottleneck as models approach trillion-parameter regimes.

By Xin Yang, Yemin Wang, Mingda Liu, Letian Li, Shuaishuai Cao, Zhengxiao He, Ryan Dong

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arXiv Machine Learning

Jul 14

Domain-Aware Scaling Laws Uncover Data Synergy

arXiv:2607. 11052v1 Announce Type: new Abstract: Machine learning progress is often attributed to scaling model size and dataset volume, yet the composition of data can be just as consequential.

By Kimia Hamidieh, Lester Mackey, David Alvarez-Melis

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Zipbench: Low-Cost Framework for Compressing Comprehensive Benchmarks of Large Language Models

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Spectral Signatures of Large Language Models

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