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

Jul 21

TellTale: Blending Multi-Instance LoRA Text Encoders and a Zero-Shot LLM Judge for Ambivalence/Hesitancy Recognition in Videos

arXiv:2607. 16635v1 Announce Type: cross Abstract: We present TellTale, a text-only approach to ambivalence/hesitancy (A/H) recognition in interview videos, evaluated on the BAH dataset as part of the 3rd A/H Video Recognition Challenge (11th ABAW Workshop, ECCV 2026).

By Abdel-Karim Al-Tamimi, Ali Rodan

llms fine-tuning efficiency

arXiv AI

Jul 21

Measuring Monosemanticity in Sparse Autoencoders via Latent Activation Coherence

arXiv:2607. 17770v1 Announce Type: cross Abstract: Within Explainable Artificial Intelligence, mechanistic interpretability uses Sparse Autoencoders (SAEs) to extract more interpretable features from neural representations.

By Katarzyna Filus, Sebastian Pokuci\'nski

llms rag efficiency multimodal safety

arXiv AI

Jul 21

Do Speech Tokens Leak Voiceprints? Speaker Inversion Attacks Against End-to-End Speech Language Models

arXiv:2607. 16870v1 Announce Type: cross Abstract: End-to-end speech language models increasingly represent user speech with speech tokens rather than relying exclusively on cascaded ASR--LLM--TTS pipelines.

By Ye Lu, Yihan Yan, Zhaoyang Zhang, Zhitao Ou, Runze Liu, Li Liu, Shen Wang

llms rag multimodal

arXiv Machine Learning

Jul 21

Persistent Sparse Autoencoders: Learning Feature Timescales in Language Models

arXiv:2607. 17117v1 Announce Type: new Abstract: Sparse autoencoders (SAEs) decompose language model activations into sparse features, but standard SAEs encode each token independently and do not expose information that persists across a sequence.

By Haoyan Luo, Mateo Espinosa Zarlenga, Mateja Jamnik

llms

arXiv Machine Learning

Jul 21

Manifold-Constrained Hyper-Connections for Parameter-Efficient Finetuning

arXiv:2607. 18130v1 Announce Type: new Abstract: Most parameter-efficient finetuning (PEFT) methods adapt weights or activations, thus leaving one of the key Transformer components unchanged: residual connections.

By Valentijn Oldenburg, Floris de Kam, Bente Zuijdam, Lieve Eberson, Nicky van Zutphen, Stef de Wildt, Ivo Verhoeven

llms fine-tuning benchmarks

arXiv Machine Learning

Jul 21

A Weisfeiler-Leman Characterization of Global-Attention Graph Transformers for Mixed-Integer Linear Programs

arXiv:2607. 17570v1 Announce Type: new Abstract: Graph foundation models (GFMs) with global attention are increasingly used to represent mixed-integer linear programs (MILPs), aiming to capture structure beyond the locality of standard graph neural networks.

By Md Abrar Jahin, Craig A. Knoblock, Jay Pujara

llms rag

arXiv AI

Jul 21

Autonomous Discovery of Wireless Communications Algorithms

arXiv:2607. 17762v1 Announce Type: cross Abstract: Large language model (LLM)-driven evolutionary search is an emerging algorithm-discovery paradigm that has already produced novel results in several scientific fields.

By Fay\c{c}al A\"it Aoudia, Jakob Hoydis, Sebastian Cammerer, Gian Marti, Merlin Nimier-David, Nicolas Roussel, Alexander Keller

llms agents benchmarks

arXiv AI

Jul 21

Can We Break LLMs Out of Self-Loops? Fine-Grained Reasoning Control with Activation Steering

arXiv:2607. 18100v1 Announce Type: new Abstract: Extended reasoning has become standard for frontier Large Language Models (LLMs), yet the trajectories these models produce remain largely uncontrollable.

By Sheldon Yu, Tong Yu, Xunyi Jiang, Rohan Surana, Gagan Mundada, Sungchul Kim, Lina Yao, Julian McAuley, Junda Wu

llms

arXiv AI

Jul 21

InertialAR: Autoregressive 3D Molecule Generation with Inertial Frames

arXiv:2510. 27497v2 Announce Type: replace-cross Abstract: Transformer-based autoregressive models have emerged as a unifying paradigm across modalities such as text and images, but their extension to 3D molecule generation remains underexplored.

By Haorui Li, Weitao Du, Yuqiang Li, Hongyu Guo, Shengchao Liu

llms diffusion nlp benchmarks

arXiv AI

Jul 21

SyriSign: A Parallel Corpus for Arabic Text to Syrian Arabic Sign Language Translation

arXiv:2603. 29219v2 Announce Type: replace-cross Abstract: Sign language is the primary approach of communication for the Deaf and Hard-of-Hearing (DHH) community.

By Mohammad Amer Khalil, Raghad Nahas, Ahmad Nassar, Khloud Al Jallad

llms rag benchmarks safety

arXiv AI

Jul 21

RealDESED: A Real-World Domestic Sound Event Detection Benchmark

arXiv:2607. 16736v1 Announce Type: cross Abstract: This paper presents RealDESED, a real-world domestic sound event detection (SED) benchmark comprising 5,710 audio recordings collected by 652 participants in their homes.

By Florian Schmid, Paul Primus, Alexander Fichtinger, Tara Jadidi, Tobias Morocutti, Gerhard Widmer

llms benchmarks

arXiv Machine Learning

Jul 21

PPL-Factory: Task-Aware and Budget-Aware Data Selection from Language Modeling to Reasoning

arXiv:2607. 18199v1 Announce Type: cross Abstract: Not all training samples contribute equally to large language model fine-tuning.

By Hang Zhang, Warren J. Gross

llms fine-tuning benchmarks data-engineering

arXiv Machine Learning

Jul 21

Explaining and Tuning Transformer-based LLMs in Arithmetic Tasks with Human Strategies

arXiv:2607. 17166v1 Announce Type: new Abstract: Transformer-based large language models (LLMs) continue to achieve state-of-the-art performance across various natural language processing tasks.

By Luyu Qiu, Jianing Li, Hwanhee Kim, Xiaoyong Wei, Yueyuan Zheng, Janet Hsiao, Lei Chen

llms nlp benchmarks safety

arXiv Machine Learning

Jul 21

SEE: Structure-aware Exploring \& Exploiting for Long-horizon GUI Agent Trajectory Synthesis

arXiv:2607. 18046v1 Announce Type: new Abstract: Graphical User Interface (GUI) agents powered by vision-language models hold promise for automating real-world mobile tasks.

By Zhuohang Fan, Beichen Zhang, Yuanfa Li, Changqiao Wu, Wei Liu, Jian Luan, Weigang Zhang

llms agents fine-tuning multimodal

arXiv Machine Learning

Jul 21

FailureAtlas: A Taxonomy of Failure Modes in Multi-Provider LLM Serving Infrastructure

arXiv:2607. 17525v1 Announce Type: new Abstract: Multi-provider LLM gateways reverse proxies that route, load-balance, and rate-limit requests across foundation-model APIs have become critical production infrastructure.

By Vishal Pandey, Gopal Singh

llms

arXiv AI

Jul 21

Rooted Absorbed Prefix Trajectory Balance with Submodular Replay for GFlowNet Training

arXiv:2603. 00454v3 Announce Type: replace-cross Abstract: Generative Flow Networks (GFlowNets) enable fine-tuning large language models to approximate reward-proportional posteriors, but they remain prone to mode collapse, manifesting as prefix collapse and length bias.

By Xi Wang, Wenbo Lu, Shengjie Wang

llms fine-tuning safety

arXiv Machine Learning

Jul 21

Decoder-Preserving Sparse Autoencoders: Which Readouts Survive Sparse Compression?

arXiv:2607. 17425v1 Announce Type: new Abstract: Sparse autoencoders (SAEs) compress model activations into sparse codes, but equal reconstruction error and sparsity can preserve different linearly decodable signals.

By Aniket Deshpande

llms efficiency benchmarks

arXiv AI

Jul 21

SGA: Plug&Play Geometric Verification for Educational Video Synthesis

arXiv:2607. 18116v1 Announce Type: new Abstract: Recent work leverages Large Language Models (LLMs) to generate executable code for pedagogical animations using libraries such as Manim.

By Lopez Jhon, Hinojosa Carlos, Ghanem Bernard

llms agents benchmarks

arXiv Machine Learning

Jul 21

The Anatomy of a Truth Direction: Knowledge-Dependent Dimensionality, a Relational Law, and a Convergent Category Geometry in Small Language Models

arXiv:2607. 16741v1 Announce Type: new Abstract: B\"urger et al.

By Francesco Karim Vicidomini

llms

arXiv AI

Jul 21

"Not in My Backyard": LLMs Uncover Online and Offline Social Biases Against Homelessness

arXiv:2508. 13187v4 Announce Type: replace-cross Abstract: Homelessness is a persistent social challenge, impacting millions worldwide.

By Jonathan A. Karr Jr., Benjamin F. Herbst, Matthew L. Sisk, Xueyun Li, Ting Hua, Matthew Hauenstein, Georgina Curto, Nitesh V. Chawla

llms benchmarks safety

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