arXiv:2508. 06482v2 Announce Type: replace-cross Abstract: Humans communicate with increasing efficiency in multi-turn interactions, by adapting their language and forming ad-hoc conventions.
arXiv:2601. 19792v4 Announce Type: replace-cross Abstract: For generative AI agents to partner effectively with human users, the ability to accurately predict human intent is critical.
arXiv:2607. 04061v1 Announce Type: cross Abstract: Distinguishing Large Language Model (LLM) generated text from human writing is a critical and difficult challenge.
arXiv:2606. 17372v1 Announce Type: cross Abstract: Two recent studies (Jones et al.
The paper investigates how to adapt large language models to mimic an individual author's style using only a few example abstracts, a task made harder by the formal nature of scientific writing. It proposes three style‑conditioning methods—contrastive activation steering, a network predicting steering vectors, and a hypernetwork predicting LoRA adapters—and finds that while fine‑tuning captures the strongest style signal, it harms fluency; the hypernetwork offers the best balance between style imitation and output quality for both seen and unseen authors. The authors also show that steering can be performed at the author level by contrasting author abstracts against style‑neutral generations for the same content, eliminating the need for a predefined style inventory and outperforming inventory‑based approaches. whyItMatters":"The study provides practical techniques for author‑style transfer in scientific writing, revealing a trade‑off between style fidelity and fluency and demonstrating that hypernetworks can effectively balance these aspects."
arXiv:2606.12234v2 Announce Type: replace Abstract: Controlling the output of Large Language Models (LLMs) is a central challenge for their reliable deployment, yet a clear understanding of the invol...
arXiv:2602. 02712v2 Announce Type: replace Abstract: A popular approach to post-training control of large language models (LLMs) is the steering of intermediate latent representations.
arXiv:2607.26178v2 Announce Type: replace Abstract: Turn-taking is a central component of full-duplex interaction. Which turn-taking behaviors are appropriate varies with the scenario, yet current mo...
arXiv:2606. 04057v1 Announce Type: cross Abstract: Large language models (LLMs) now generate substantial production code, often for tasks with multiple valid algorithmic solutions.
arXiv:2607. 20734v1 Announce Type: new Abstract: As LLMs become more capable, they are increasingly deployed as collaborative agents, taking on user-delegated tasks through iterative interaction.
arXiv:2510. 01171v4 Announce Type: replace-cross Abstract: Post-training alignment often reduces LLM diversity, leading to a phenomenon known as mode collapse.
The paper introduces a training‑free method for uncovering prompt‑conditional stylistic axes in large language models (LLMs). By repeatedly sampling completions of a single prompt at high temperature and applying Principal Component Analysis (PCA) to the pooled hidden activations, the authors automatically label the resulting axes using the extreme (pole) generations. Validation against 245 human‑elicited stylistic annotations shows that, for the Qwen‑3.5‑4B‑Instruct model, the top two axes align with human dimensions with 72.8% precision and 43.6% macro‑recall, and 75.6% of validity ratings confirm the axes’ polar generations, while other models exhibit varying degrees of discoverability.
arXiv:2606. 08129v1 Announce Type: new Abstract: Large language models (LLMs) differ in architecture, training data, and optimization procedures, yet they may still develop similar internal inference patterns.