Large language models

Model releases, architecture work and prompting research on large language models — from frontier-lab announcements to the arXiv papers behind them.

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arXiv AI

Aug 10

Recipes for Creativity: Iterative Generation and Evaluation in Large Language Models

arXiv:2608. 07243v1 Announce Type: new Abstract: Generative models are often evaluated through singular artifacts, whereas human creativity typically emerges through iterative generation, appraisal, and refinement.

By Rens Anderson, Tessa Verhoef, Amirhossein Zohrehvand

llms benchmarks

arXiv AI

Aug 10

A MARL Centered Reference Architecture for Large Language Model Augmentation in Smart Manufacturing

arXiv:2608. 07148v1 Announce Type: new Abstract: Modern manufacturing imposes six coupled demands on adaptive control: local decisions with global consequences, partial observability, nonstationarity, reflex speed response with long horizon effects, delayed and diffuse outcomes, and dynamics that resist explicit modeling.

By Fouad Bahrpeyma, Dirk Reichelt

llms agents reinforcement-learning safety

arXiv AI

Aug 10

Intelligence per Watt: Measuring Intelligence Efficiency of Local AI

arXiv:2511. 07885v5 Announce Type: replace-cross Abstract: Large language model (LLM) queries are predominantly processed by frontier models in centralized cloud infrastructure.

By Jon Saad-Falcon, Avanika Narayan, Hakki Orhun Akengin, J. Wes Griffin, Herumb Shandilya, Adrian Gamarra Lafuente, Medhya Goel, Rebecca Joseph, Shlok Natarajan, Etash Kumar Guha, Shang Zhu, Ben Athiwaratkun, John Hennessy, Azalia Mirhoseini, Christopher R\'e

llms benchmarks

arXiv AI

Aug 10

Transformers Struggle to Use Their Emergent World Models: Revisiting the Tower of Hanoi, and the Illusion of Thinking

arXiv:2608. 07077v1 Announce Type: new Abstract: The Tower of Hanoi is a simple planning puzzle that in prior work has proven challenging for large reasoning models (LRMs).

By Devin Pereira, Willem Zuidema

llms safety

arXiv Machine Learning

Aug 10

CHIME: A Case for Efficient Long-Context Attention-FC Disaggregated Inference with DIMM-PIM

arXiv:2504. 17584v2 Announce Type: replace-cross Abstract: Attention-FC Disaggregated (AFD) LLM inference systems offload memory-bound Attention operations to memory-rich accelerators (e.

By Qingyuan Liu, Liyan Chen, Haocheng Wang, Yanning Yang, Dong Du, Zhigang Mao, Naifeng Jing, Yubin Xia, Haibo Chen

llms efficiency benchmarks safety

arXiv AI

Aug 10

Post-Grokking Collapse at the Representation-Readout Interface in Muon-Trained Transformers

arXiv:2608. 07436v1 Announce Type: new Abstract: Under the standard split, Muon gets hidden matrices and AdamW embeddings/output head.

By Ali Janati, Kaoutar El Maghraoui, Andrei Kanavalau, Anass Belfatmi

llms rag safety

arXiv AI

Aug 10

A Picture is Worth a Thousand Tokens: How Vision Language Models Cut AI Energy Costs While Improving Accuracy

arXiv:2608. 07427v1 Announce Type: new Abstract: LLM inference accounts for over 90% of AI operational energy, scaling directly with input token count---a critical inefficiency for telecom network analytics and numerical time-series data analysis (NTSDA), where raw multivariate KPI windows from 4G/5G cell sites expand into thousands of floating-point tokens.

By Bhavika Jalli, Nikhil Korati Prasanna, Jayanta Choudhury

llms fine-tuning multimodal benchmarks

arXiv AI

Aug 10

Separating Decision-Rule Misalignment from Readout-Coverage Limitations in Speech Language Models

arXiv:2608. 06409v1 Announce Type: cross Abstract: Speech language models are increasingly evaluated on paralinguistic tasks by the accuracy of prompted answers, but answer accuracy combines failures at different stages of the audio-to-answer computation.

By Linkai Peng, Baorian Nuchged

llms

arXiv Machine Learning

Aug 10

Beyond Attention: Signed Integrated Gradients Attribution in a BiomeGPT-Style Microbiome Transformer

arXiv:2608. 06486v1 Announce Type: new Abstract: In a feature-tokenized transformer (arXiv:2106.

By Oren Nelson

llms

arXiv AI

Aug 10

An Empirical Study of openPangu Quantization on Ascend NPUs

arXiv:2606. 21257v4 Announce Type: replace-cross Abstract: openPangu models are attractive targets for private and domestic large-language-model deployment, yet their robustness under aggressive post-training quantization on Ascend NPUs has not been systematically characterized.

By Tong Shi, Jiacheng Wang, Hui Xie, Ying Li, Aishan Liu, Jinyang Guo, Xianglong Liu

llms efficiency

arXiv AI

Aug 10

CreativeInstruct: Scalably Teaching LLMs to Balance Quality, Creativity, and Diversity

arXiv:2608. 07460v1 Announce Type: cross Abstract: While post-training improves the capabilities of large language models (LLMs), it generally lowers their output diversity and creativity, negatively impacting tasks that explicitly require creativity (e.

By Ananya Sahu, Mohit Bansal, Elias Stengel-Eskin

llms reinforcement-learning safety

arXiv AI

Aug 10

Social World Models

arXiv:2509. 00559v3 Announce Type: replace Abstract: Humans intuitively navigate social interactions by simulating unspoken dynamics and reasoning about others' perspectives, even with limited information.

By Xuhui Zhou, Jiarui Liu, Akhila Yerukola, Hyunwoo Kim, Maarten Sap

llms agents benchmarks

arXiv AI

Aug 10

GPTKB 2.0: Browsing, Querying, and Auditing a Disambiguated LLM-Derived Knowledge Base

arXiv:2608. 06992v1 Announce Type: cross Abstract: We present a web demo for exploring a large-scale disambiguated knowledge base (KB) materialized from a large language model (LLM).

By Yujia Hu, Tuan-Phong Nguyen, Simon Razniewski

llms rag

arXiv AI

Aug 10

Capek 0.5: An Execution-Centric Vision-Language Model for Embodied Intelligence

arXiv:2608. 06756v1 Announce Type: new Abstract: Vision-language models are increasingly serving as the reasoning core of embodied agents.

By Ying Chen, Weizhen Li, Zhe Hu, Zhenjiang Li, Rui Jiang, Zhifeng Gu, Lihuang Fang, Jiangping Liu, Lei Yi, Jie Chen

llms agents reinforcement-learning robotics efficiency multimodal benchmarks

arXiv AI

Aug 10

Mind the Gap: A Dual Knowledge Graph Framework for Unified Multi-task User Intent Inference

arXiv:2608. 06752v1 Announce Type: new Abstract: This paper proposes DKG-MTI, a dual knowledge graph framework for unified multi-task user intent inference from online travel reviews.

By Tzu-Cheng Peng (National Taiwan University), Chien Chin Chen (National Taiwan University), Chih-Hao Ku (University of North Texas), Yung-Chun Chang (Taipei Medical University)

llms safety

arXiv AI

Aug 10

Divergent Response Modes in Frontier Language Models Under Steering Pressure

arXiv:2608. 06578v1 Announce Type: new Abstract: Frontier language models are trained using distinct data, objectives, and safety pipelines.

By Ali Jalal-Kamali

llms safety

arXiv AI

Aug 10

SABRE: Scalable and Automated Benchmarking of VLMs under Stress

arXiv:2608. 07435v1 Announce Type: cross Abstract: Vision-language models (VLMs) are improving rapidly, but benchmark development lags behind, making weaknesses hard to identify.

By Zixuan Lan, Luzhe Sun, Matthew R. Walter, Jiawei Zhou

llms multimodal benchmarks

arXiv AI

Aug 10

ZIPBrain: Can EEG Foundation Models Be Faster, Locally Deployable, but Accurate?

arXiv:2608. 07033v1 Announce Type: new Abstract: This work investigates whether Electroencephalograph (EEG) foundation models (EFMs) can be made faster and locally deployable without sacrificing accuracy.

By Lingwei Li, Yirong Kan, Peng Chen, Xu Cao, Zheng Chen, Yasuhiko Nakashima

llms

arXiv AI

Aug 10

Not All Problems Are Best Modeled as MILP: A DSL-Centric Framework for Flexible and Accurate Optimization Modeling

arXiv:2608. 07040v1 Announce Type: new Abstract: Solving combinatorial optimization problems (COPs) requires not only efficient algorithms but also carefully crafted formulations.

By Shaofeng Zhang, Hongyuan Su, Qingwen Peng, Zefang Zong, Shengcai Liu, Ke Tang, Yong Li

llms benchmarks

arXiv AI

Aug 10

TRIBE: Predicting Team Performance via Communication Behavior Ensembles

arXiv:2608. 06926v1 Announce Type: new Abstract: Designing autonomous agents that effectively assist human teams hinges on understanding team dynamics, often without task specific knowledge.

By Ali Jalal-Kamali, Nikolos Gurney, David V. Pynadath, Fred Morstatter

llms agents

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