The findings show that training-free reference-based attribution should be understood as the interaction between where images are compared, how the reference set is constructed, and how many references are available, and that attribution accuracy generally peaks at intermediate representation levels.
Meiling Li, Pietro Bongini, Benedetta Tondi et al.· arXiv.org· 0 citations
Most instruments for measuring political positions, such as manifesto coding, expert surveys and text-scaling models, were built and validated on Western party systems, and they break down worst where many consequential actors never publish a manifesto, never contest a competitive election, or commit one position to a...
Large language model (LLM)-based approaches for automated code compliance (ACC) of building regulations are prone to generating incorrect and hallucinated computer-processable rules. This paper introduces P4IR, a two-stage framework that uses supervised fine-tuning (SFT) to instill domain knowledge in an LLM, followed...
Jack Wei Lun Shi, Minghao Dang, Wawan Solihin et al.· 0 citations
The results demonstrate that TransitNet provides an accurate, scalable, and computationally efficient framework for low-SNR transit blind searches in the tested regime and motivate its extension to longer-period Earth-size planet searches.
Xing-Chen Yan, Jian Ge, Qing-Tian Liu et al.· arXiv.org· 0 citations
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Effective human-robot teamwork requires robots to adapt to partners, situations, and task dynamics from the start of an interaction. In the MATRX Urban Search and Rescue (USAR) environment, people can externalize collaboration patterns (CPs) they discover during teamwork through a chat and reflection interface. We stud...
Taewoon Kim, Emma van Zoelen, Mark Neerincx· 0 citations
Discharge summaries are crucial clinical documents containing the context of a patient's overall hospital stay, and are routinely reviewed by medical experts for patient readmission, ongoing care, and diagnostic decision-making. When reviewing them, medical experts often must iteratively synthesize information across m...
Jiyoun Kim, Muhan Yeo, Eunhye Jang et al.· 0 citations
It is argued that a robust proof autoformalizer must remain faithful even for informal proofs that diverge from these idealized ones, and the first study on the robustness of proof autoformalization models is presented.
Adapting Large Language Models (LLMs) to specific tasks usually involves fine-tuning: Low-Rank Adaptation (LoRA) edits the weights, while soft prompting prepends trainable virtual tokens. Both change how an already-optimized model is served, so serving many task-specific adapters at high throughput requires adapter-awa...
Michal Chudoba, Sergey Alyaev, Petra Galuscakova et al.· 0 citations
Expressive continuous control policies, such as diffusion and flow models, form the backbone of recent advances in scaling imitation learning for simulated and real robot control. While they are known to scale stably in the supervised imitation learning setting, incorporating them into reinforcement learning (RL) pipel...
Zhiyuan Zhou, Andy Peng, Charles Xu et al.· 0 citations
LLM deception is often evaluated through fabricated claims, explicit lies, or strategic concealment. Yet misleading communication can also arise from selectively presenting true facts, for example, by omitting adverse evidence, softening unfavorable details, emphasizing favorable information, or replacing precise quali...
This work introduces Code2LoRA, a hypernetwork framework that generates repository-specific LoRA adapters, effectively injecting repository knowledge with zero inference-time token overhead, and evaluates Code2LoRA against parameter-efficient fine-tuning baselines.
Liliana Hotsko, Yin-Xi Li, Yun-Tian Deng et al.· arXiv.org· 6 citations
This work shows that intermediate states surface salient entities early in the denoising trajectory, making them a useful lookahead signal for retrieval-augmented generation (RAG), and demonstrates that grounding generation in retrieved evidence sharply reduces inter-token dependence, making RAG particularly well suite...
Paul J. Jünger, Justin Lovelace, Lin-Xi Zhao et al.· arXiv.org· 2 citations· ⚡1
