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

Sep 22

Rare Event Estimation via Iterative Unalignment

arXiv:2609.24969v1 Announce Type: new Abstract: As agents are deployed with increased autonomy, even extremely rare events along their stochastic output trajectories can occur and prove catastrophic....

By Hanming Yang, Daksh Mittal, Jing Dong, Hongseok Namkoong

llms agents

arXiv Machine Learning

Sep 22

Token Signatures of Code: Comparing Coding Behaviors Across Large Language Models

arXiv:2609.22097v1 Announce Type: cross Abstract: The evaluation of large language models (LLMs) on coding tasks has primarily focused on performance metrics such as pass@k. As LLMs continue to advan...

By Junpeng Wang, Yuzhong Chen, Menghai Pan, Uday Singh Saini, Yiwei Cai

llms nlp

arXiv Machine Learning

Sep 22

Does the Truthfulness Signal Survive Code-Mixing? Probing Hidden States for Hallucination Detection in Hinglish

arXiv:2609.22138v1 Announce Type: cross Abstract: Hidden-state hallucination probing - training a linear classifier on an LLM's internal activations to detect whether a generated answer is faithful t...

By Tanveer Singh (Plaksha University)

llms benchmarks safety

arXiv Machine Learning

Sep 22

Using Composition Operators to Linearize LLM Semantic Transformations

arXiv:2609.22143v1 Announce Type: cross Abstract: Machine learning learns functions: prompt to response, image to caption. What these functions are mathematically remains hard to say. We present a me...

By Afjal Chowdhury, James Chen, Alan Edelman

llms

arXiv Machine Learning

Sep 22

On Mitigation of Subliminal Learning in Large Language Models

arXiv:2609.22215v1 Announce Type: cross Abstract: Knowledge distillation can transmit unintended behavioral traits from a teacher model to a student through training data that appear semantically unr...

By Atsushi Yanagisawa, Brendan Gho, Rajendran Ramesh Babu Manoj Narender, Kevin Zhu, Madhur Panwar, Antonio Mari

llms fine-tuning efficiency

arXiv Machine Learning

Sep 22

EAVer: Long-Form Factuality Verification as an End-to-End Agentic Policy

arXiv:2609.22223v1 Announce Type: cross Abstract: Long-form factuality verification is commonly implemented as a static decompose-search-verify pipeline, with separately prompted modules processing c...

By Kening Zheng, Aoying Zheng, Zhigang Chang, Yazhi Guo, Miaotian Guo, Qingwei Zong, Xianhai Xie, Weiqiang Jin, Chengze Li, Hanrong Zhang, Jie Yang, Wei-Chieh Huang, Lingzhe Zhang, Liancheng Fang, Xin Zou, Hanqian Li, Jiahao Huo, Yibo Yan, Zizhuang Deng, Lei Miao, Wei Guo, Haihong Tang, Bo Zheng, Philip S. Yu

llms agents reinforcement-learning benchmarks safety

arXiv Machine Learning

Sep 22

Large language models in medical time series analysis

arXiv:2609.22262v1 Announce Type: cross Abstract: Medical time series (MedTS), including electrocardiograms (ECG), electroencephalograms (EEG), photoplethysmography (PPG), and vital-sign recordings,...

By Yu Han, Cigdem Beyan, Xiang Zhang, Xiaofeng Liu, Nan Liu, Jimeng Sun, Shenda Hong, Cheng Ding, Vittorio Murino

llms

arXiv Machine Learning

Sep 22

When and Why Do Linear Bias Probes Fail? A Geometric and Statistical Theory of Bias Detectability in Large Language Model Representations

arXiv:2609.22337v1 Announce Type: cross Abstract: Linear probing is the standard instrument for detecting social biases in the hidden representations of large language models. Yet reported probe accu...

By Mo Hai, Haifeng Li

llms safety

arXiv Machine Learning

Sep 22

Semantics Delivery Network: Rethinking Web Retrieval Infrastructure for LLM Agents

arXiv:2609.22486v1 Announce Type: cross Abstract: Large language models (LLMs) increasingly rely on external sources when answering questions that require proprietary information or up-to-date live w...

By Peichun Hua, Yunming Xiao

llms rag agents

arXiv Machine Learning

Sep 22

Defusing Explosive Prompts: Understanding and Preventing Trigger-Based Prompt Injections in LLM Agents

arXiv:2609.22510v1 Announce Type: cross Abstract: As LLM applications integrate with external tools, they are increasingly exposed to indirect prompt injection (IPI), where adversarial instructions a...

By Justin Szczepaniak, Elad Feldman, Naum Viner, Ben Nassi

llms agents benchmarks safety

arXiv Machine Learning

Sep 22

An LLM-Assisted AutoML Framework for Intrusion Detection in IoT Networks

arXiv:2609.23097v1 Announce Type: cross Abstract: Internet of Things (IoT) systems are increasingly deployed in smart homes, transportation, energy systems, and critical infrastructure. This broad co...

By Li Yang

llms

arXiv Machine Learning

Sep 22

Financial Language Models as Applied Artificial Intelligence Systems for News-Based Trading under Market Frictions

arXiv:2609.23703v1 Announce Type: cross Abstract: Financial language models can transform unstructured firm-specific news into structured decision signals, but financial AI research lacks an integrat...

By Kemal Kirtac

llms nlp efficiency multimodal

arXiv Machine Learning

Sep 22

Increasing Skill Level Recruits Deeper Attention Layers in a Frozen Chess Transformer

arXiv:2609.23917v1 Announce Type: cross Abstract: Chess involves complex reasoning in a deterministic environment, which makes it a useful setting for studying the mechanisms of computation inside tr...

By David Litman

llms

arXiv Machine Learning

Sep 22

Jev-Mem: System-One-Controlled Agentic Memory for Efficient AI Agents

arXiv:2609.23986v1 Announce Type: cross Abstract: Agentic memory is becoming essential for long-horizon AI agents, yet many existing systems rely on autoregressive LLMs to control how memories are or...

By Dongming Jiang, Yi Li, Bingzhe Li

llms agents

arXiv Machine Learning

Sep 22

MCP-GRANITE Benchmark: GRANularity Interface TEsting for MCP-Based LLM Agents

arXiv:2609.24161v1 Announce Type: cross Abstract: As LLM agents increasingly interact with external tools through standardized protocols such as MCP, tool-interface design becomes a critical yet unde...

By Demetris Paschalides, Moysis Symeonides, George Pallis, Marios D. Dikaiakos

llms agents benchmarks

arXiv Machine Learning

Sep 22

Topographic Training Concentrates Causal Circuits Without Improving Neuron Monosemanticity

arXiv:2609.24379v1 Announce Type: cross Abstract: Mechanistic interpretability of vision transformers seeks to decompose model computation into human-readable units, but learned representations entan...

By Gautam Ranka, Shubham Santosh Pandere, Aiden Dsouza

llms efficiency safety

arXiv Machine Learning

Sep 22

XSQ-AST: An Explainable Audio Spectrogram Transformer Framework for Localising Synthetic Speech Artifacts

arXiv:2609.24770v1 Announce Type: cross Abstract: Localising artifacts in synthetic speech remains challenging, as most evaluation methods yield only global quality scores. This paper presents XSQ-AS...

By Ben Heritage, Luca Resti, M\'onica Villanueva Aylagas, Timothy Mehlenbacher, Konrad Tollmar, James Alfred Walker

llms safety

arXiv Machine Learning

Sep 22

onPanda: Efficient Annotation of On-Policy Alignment Data for LLMs and Agents via Token-Level Correction

arXiv:2609.24983v1 Announce Type: cross Abstract: We present onPanda, an interactive tool for efficiently annotating LLM alignment data and agent trajectories. onPanda adopts token-level correction a...

By Lei Yang, Mengyin Liu, Jia Wang, Hangyu Guo, Liang Zhao, Zheng Ge, Kang An, Binxing Jiao, Qi Han, Daxin Jiang, Siqi Shen, Xiangyu Zhang

llms agents fine-tuning benchmarks safety

arXiv Machine Learning

Sep 22

Memento No More: Coaching AI Agents to Master Multiple Tasks via Hints Internalization

arXiv:2502.01562v3 Announce Type: replace Abstract: As the general capabilities of artificial intelligence (AI) agents continue to evolve, their ability to learn to master multiple complex tasks thro...

By Minttu Alakuijala, Ya Gao, Georgy Ananov, Samuel Kaski, Pekka Marttinen, Alexander Ilin, Harri Valpola

llms agents nlp efficiency

arXiv Machine Learning

Sep 22

Logits are All We Need to Adapt Closed Models

arXiv:2502.06806v5 Announce Type: replace Abstract: Many commercial Large Language Models (LLMs) are often closed-source, limiting developers to prompt tuning for aligning content generation with spe...

By Gaurush Hiranandani, Haolun Wu, Subhojyoti Mukherjee, Sanmi Koyejo

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