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 Machine Learning

Aug 10

Stochastic Autoregressive Learning

arXiv:2608. 07224v1 Announce Type: new Abstract: Motivated by LLMs, which generate outputs by iteratively sampling from next-token distributions, we introduce a PAC-learning model for binary stochastic autoregressive learning.

By Ilan Doron-Arad, Idan Mehalel, Elchanan Mossel

llms

arXiv AI

Aug 10

Same Answer, Different Confidence: Protocol Sensitivity in LLM Confidence Calibration

arXiv:2605. 27752v3 Announce Type: replace Abstract: Is verbalized confidence better calibrated than token likelihood?

By Hankyeol Kim, Pilsung Kang

llms

arXiv Machine Learning

Aug 10

An AI4AI Framework for Visual Token Pruning

arXiv:2608. 07193v1 Announce Type: new Abstract: Visual-token pruning can substantially reduce the inference cost of multimodal large language models (MLLMs), yet existing methods largely rely on fixed, handcrafted heuristics and costly expert trial and error.

By Zhen Liu, Wenli Huang, Wei Song, Yuhan Liu, Zhiqin Yang, Jingwen Fu

llms efficiency multimodal benchmarks

arXiv AI

Aug 10

Omni-modal decomposition autoencoders learn full-stack wearable disentangled representations

arXiv:2608. 07385v1 Announce Type: cross Abstract: Learning disentangled representations is a key requirement for developing versatile, general-purpose, and sustainable models in multi-modal wearable computing.

By Ioannis Ziogas, Ensieh Khazaei, Bilal Taha, Aamna Al Shehhi, Ahsan H. Khandoker, Leontios J. Hadjileontiadis, Dimitrios Hatzinakos

llms multimodal

arXiv AI

Aug 10

LSEAD: A Privacy-Preserving LLM-Based Speech Analysis Framework for Early Alzheimer's Disease Screening

arXiv:2608. 07378v1 Announce Type: cross Abstract: Early diagnosis of Alzheimer's disease (AD) is critical for enabling timely interventions that may slow disease progression and improve patient outcomes.

By Xin Wang, Yingchao Huang, Yuhan Su, Shanshan Yao, Wei Peng

llms rag benchmarks safety

arXiv AI

Aug 10

ResidencyRL: Reinforcement Learning in Simulated Clinical Environments

arXiv:2608. 07418v1 Announce Type: new Abstract: In medical education, physicians convert academic knowledge into clinical expertise through residency: years of training across thousands of encounters, with diverse sources of feedback and progressively greater autonomy.

By Valentin Li\'{e}vin, Samuel Schmidgall, Tim Strother, Alex Bijamov, Akshay Goel, Anil Palepu, Chunjong Park, Vahid Balazadeh, Min Woo Sun, Marius Guerard, Justin Chen, Dave Steiner, Vikram Dhillon, Ibrahim Azar, Akhil Mehta, Nicholas Spetsieris, Shilpan Shah, Maen Abdelrahim, Amit Dahiya, Yun Liu, Katherine Chou, Yossi Matias, Avinatan Hassidim, Dale R. Webster, Quoc V. Le, Raia Hadsell, Joelle Barral, Carey Radebaugh, Aleksandra Faust, Shekoofeh Azizi, Mike Schaekermann, Po-Hsuan Cameron Chen, Tao Tu, David Racz, Lin Yang

llms agents reinforcement-learning benchmarks safety

arXiv Machine Learning

Aug 10

Let's Unlearn Stereotypes Before Decision-Making: Assessing the Impact of Intrinsic Bias Mitigation on Downstream Fairness in LLMs

arXiv:2509. 16462v2 Announce Type: replace-cross Abstract: Large Language Models (LLMs) are increasingly used in high-stakes decision-making systems, where biased predictions can reinforce social and economic disparities.

By Mina Arzaghi, Alireza Dehghanpour Farashah, Florian Carichon, Jean-Fran\c{c}ois Plante, Golnoosh Farnadi

llms rag fine-tuning benchmarks safety

arXiv AI

Aug 10

Beyond Foundation Models: Dimension-Aware Neural Architecture Search with Small-Data Representation Models for Cryocooler Lifetime Prediction

arXiv:2608. 06993v1 Announce Type: cross Abstract: Large-scale pretrained time-series models achieve strong results through large-scale pretraining and task-agnostic representation learning, but they rely on abundant, diverse data that industrial and scientific domains often lack.

By Gregor Molan (Comtrade 360 d.o.o., Letali\v{s}ka cesta 29b, Ljubljana, 1000, Slovenia), Grafika Jati (Comtrade 360 d.o.o., Letali\v{s}ka cesta 29b, Ljubljana, 1000, Slovenia), Francesco Barchi (Alma Mater Studiorum - Universita di Bologna, Department of Electrical, Electronic, and Information Engineering), Andrea Acquaviva (Alma Mater Studiorum - Universita di Bologna, Department of Electrical, Electronic, and Information Engineering), Alja\v{z} Osterman (LE-Tehnika d.o.o., \v{S}uceva 27, Kranj, 4000, Slovenia), Martin Molan (Comtrade AI GmbH, Grafenauweg 8, Zug, 6300, Switzerland)

llms safety

arXiv AI

Aug 10

TEXAS: Task-Expert-Aware Supervision for Downstream Mixture-of-Experts LLM Adaptation

arXiv:2608. 06396v1 Announce Type: cross Abstract: Mixture-of-Experts (MoE) language models route each token through a small subset of experts, making routing patterns useful for identifying task-relevant experts during downstream adaptation.

By Guanzhi Deng, Haibo Wang, Kuan Wu, Xiangru Jian, Shing Yin Wong, Sichun Luo, Zhuoran Wang, Linqi Song

llms fine-tuning benchmarks

arXiv AI

Aug 10

EMAS: Stabilizing Multi-Agent System Evolution through Evidence-Guided Revision

arXiv:2608. 07196v1 Announce Type: new Abstract: Many methods for automated multi-agent system design optimize prompts and topologies during an initial design stage and then deploy the resulting system unchanged on subsequent samples.

By Chao Fei, Qingyi Si, Kaihua Liang, Yanghua Xiao, Panos Kalnis, Hongcheng Guo

llms agents benchmarks

arXiv AI

Aug 10

Agentic Planning for Symbolic Execution

arXiv:2608. 06397v1 Announce Type: cross Abstract: Symbolic execution seeks to explore feasible program paths, yet a practical run may exhaust its resources while much program behaviour remains unreached.

By Daniel Koh Ji Yang, Yannic Noller, Corina S. Pasareanu, Youcheng Sun

llms agents

arXiv Machine Learning

Aug 10

Symbolic Graphics Programming with Large Language Models

arXiv:2509. 05208v2 Announce Type: replace-cross Abstract: Large language models (LLMs) excel at program synthesis, yet their ability to produce symbolic graphics programs (SGPs) that render into precise visual content remains underexplored.

By Yamei Chen, Haoquan Zhang, Yangyi Huang, Zeju Qiu, Kaipeng Zhang, Yandong Wen, Weiyang Liu

llms reinforcement-learning multimodal benchmarks

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

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