As users increasingly turn to Large Language Models (LLMs) for information and advice on political matters, particularly during election periods, the political preferences expressed by these systems have become a matter of public interest. Prior research has shown that interactions with LLMs can influence users' political attitudes and choices, raising questions about how these models themselves evaluate political actors.
Text-to-image generative models have advanced rapidly, with modern Diffusion Transformer architectures producing images that are increasingly difficult to distinguish from human-created artwork. This development has raised significant concerns regarding copyright protection, misinformation, fraud, impersonation, and the authenticity of digital content.
Persuasion is a core dynamic of natural language communication, shaping how large language models (LLMs) update beliefs, resolve disagreements, and reach decisions. As LLMs increasingly debate, advise, and think collaboratively with humans and each other, resistance to harmful persuasion becomes a core requirement for reliable behavior.
Recent advances in Large Language Models (LLMs) have spurred cross-modal solutions for time-series forecasting. However, existing methods rely heavily on textual prompts for modality alignment-introducing nontrivial computational overhead and failing to leverage the rich spectral dynamics inherent in time-series data.
arXiv:2608. 10538v1 Announce Type: new Abstract: Agent skills represent a standardized format for packaging procedural knowledge and domain expertise, serving within agent harness systems as an essential mechanism to continually constrain a language model's behavior space for repeatable, high-quality task execution.
By Chenhao Dang, Siyuan Xiong, Conghui He, Weijia Li
arXiv:2608. 10843v1 Announce Type: new Abstract: First-order concept synthesis asks a system to infer one formula that classifies labeled objects consistently across several finite relational structures.
By Serafim Batzoglou
arXiv:2608. 10836v1 Announce Type: cross Abstract: The signal ambiguity of whispered speech drives ASR systems toward two opposing failure modes: failing to capture whispered speech or hallucinatory transcription of noise.
By Gaopeng Xu, Zhenyu Wang, Zheng Xue, Yinfeng Xia, Haitao Yao
arXiv:2608. 10319v1 Announce Type: cross Abstract: Large language model (LLM)-powered agents have rapidly evolved from code-completion tools into solvers of complex software engineering tasks.
By Shuyan Huang, Kai Du, Andrew Lan
arXiv:2608. 10528v1 Announce Type: cross Abstract: Anchor-based pointwise LLM reranking scores each candidate against a shared reference passage to recover cross-document context at pointwise cost.
By Utshab Kumar Ghosh, Shubham Chatterjee
arXiv:2603. 18446v2 Announce Type: replace-cross Abstract: Long-context inference remains challenging for large language models due to attention dilution and out-of-distribution degradation.
By Lang Zhou, Shuxuan Li, Zhuohao Li, Shi Liu, Zhilin Zhao, Wei-Shi Zheng
arXiv:2608. 10330v1 Announce Type: new Abstract: AI agents are increasingly being developed to assist humans in various applications, and Large Language Models and other deep network architectures are considered to be state of the art for such agents.
By Tianyi Fu, Mohan Sridharan
arXiv:2608. 09949v1 Announce Type: new Abstract: This study evaluates the application of Large Language Models (LLMs) in complex biological systems, evolving from data analysis to autonomous, AI-guided experimentation.
By Serge Kernbach
arXiv:2608. 10198v1 Announce Type: new Abstract: Latent-space communication allows heterogeneous vision-language model agents to exchange continuous representations without serializing visual and reasoning states into text.
By Di Wu, Xiaohui Zhu
arXiv:2608. 10176v1 Announce Type: new Abstract: Public service chatbots are expected to deliver recommendations from an underlying public service directory, while also making sure that the recommendations respect explicit user constraints.
By Touseef Hasan, Laila Cure, Souvika Sarkar
arXiv:2608. 10214v1 Announce Type: new Abstract: Do large language models contain domain-specific parametric shells: concentrated, causally necessary neuron populations whose removal selectively degrades a target domain while sparing others?
By Marcus Armstrong, Navid Ayoobi, Arjun Mukherjee
arXiv:2608. 10218v1 Announce Type: new Abstract: AI agents are becoming more autonomous and increasingly interconnected, exposing them to new emergent risks arising from agent-to-agent interaction.
By Vassilis Papadopoulos, McNair Shah, Sam Zimmerman, Jack Lindsey
arXiv:2608. 10224v1 Announce Type: new Abstract: Enterprise support agents operate in rapidly changing environments where policies, product capabilities, and knowledge bases evolve continuously, making static assistants brittle and costly to maintain.
By Chih Hui Wang, Mengdie Tu, Qianyun Zhang, Wei Wu, Lili Zhou, Mingqi Shen, Changshuai Wei
arXiv:2608. 10239v1 Announce Type: new Abstract: Generative AI makes social-engineering attacks more fluent, adaptive, and scalable, increasing the need for LLM-based de- fenders that can protect users during ongoing interactions.
By Yuqiao Xu, Osama Zafar, Alexander Nemecek, Erman Ayday
arXiv:2608. 10260v1 Announce Type: new Abstract: Lens methods interpret large language models (LLMs) by mapping intermediate activations to the output vocabulary, revealing how next-token predictions develop through the network.
By Jordan Pettyjohn, Mansi Sakarvadia, Nathaniel Hudson, Daniel McKenzie, Kyle Chard, Ian Foster
arXiv:2608. 10300v1 Announce Type: new Abstract: Electronic health-record interoperability is a boundary problem: legacy systems, generative models, terminology services, identity systems, and human reviewers may each expose rich internal states, while operational exchange requires a narrow shared interface of typed claims, bounded uncertainty, provenance, and explicit admission or abstention.
By Alvin Spivey, Thomas Huang