Purpose : To develop and evaluate CAPTOR, a deep-learning vision-language foundation model for oncologic chest-abdomen-pelvis (CAP) CT interpretation.
Materials & Methods : CAPTOR (Chest-Abdomen-Pelvis foundaTion model for Oncologic Radiology) is a contrastive vision-language model, trained using a pretraining strate...
S\'el\'ena Jean-Mactoux, Sophie Loizillon, Ghina Jardali et al.· 0 citations
Softmax attention in Transformers suffers from quadratic complexity in sequence length, making it impractical for long-context applications. Linear attention alleviates this issue by replacing the exponential kernel with alternative functions that enable linear-time computation. Among existing linear attention approach...
Zongyue Qin, Haoran Deng, Jason Cong et al.· 0 citations
Coding agents such as Claude Code also follow text files that developers write for them: a project file, skills the agent may choose to open, and rules that load automatically when the agent reads matching files. We call these files the agent's harness. Developers add to it whenever they correct the agent, but one succ...
Digital libraries increasingly host scientific publications whose figures encode primary research findings, yet making this visual content searchable, accessible, and reusable remains an open challenge. Large language and multimodal models (LLMs/LMMs) provide the possibility to automate many figure parsing tasks such a...
Daniel J. Evans, J. P. Naiman, JooYoung Seo et al.· 0 citations
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Diffusion language models (DLMs) offer parallel sampling for faster generation than autoregressive models, an increasingly important advantage as inference becomes a dominant computational workload. However, DLMs degrade quickly with fewer sampling steps, process the full sequence at every step, and require many passes...
Hermann Kumbong, Ishan S. Khare, Iddah Mlauzi et al.· 0 citations
It is shown that structural gate weighting and decision-level branch contribution represent distinct aspects of gated feature fusion and should be interpreted separately.
Yuan Liang, Sourav Bhattacharjee, A. Campbell· 0 citations
Reliable probability estimates are important for the clinical interpretation of machine-learning classifiers, yet radiomics--image fusion models for renal cell carcinoma (RCC) are commonly evaluated primarily by discrimination. Using 396 patients from the KiTS23 dataset, we evaluated whether adding radiomics to four im...
Yuan Liang, Morgan May, Abraham Campbell et al.· 0 citations
Large language models can translate natural-language instructions into plausible manipulation plans, but robotic execution requires physically grounded decisions that respect robot embodiment, workspace geometry, reachability, collision constraints, and grasp feasibility. This paper presents a hardware-aware large lang...
Lonnie Tankersley, Noorbakhsh Amiri Golilarz, Soroush Korivand· International Conference on...· 0 citations
Smart glasses are a promising platform for vision-language assistants, but private split inference remains expensive: protecting visual representations before transmission does not remove the cost of on-device vision encoding, wide protected tokens, or hardware-unaware execution. We present SAGE (Smart-glasses-Aware Gu...
Zhi-Min Li, Pan Wang, Jing-Xiang Chen et al.· 0 citations
Embodied Audio-Visual Navigation (AVN) requires an agent to locate sound targets by jointly leveraging visual and auditory cues. While prior work has achieved notable success with static sound sources, real-world scenarios often involve moving targets, giving rise to the more challenging problem of \textbf{Dynamic Audi...
Large language models typically support different inference budgets through separate model sizes or fixed compute configurations. In multi-turn interactions, however, compute demand can vary substantially across turns. Using a high-compute model for every turn wastes computation, while routing among independent models...
Yi-Yang Fu, Zi-Hao Huang, Dong-Chen Han et al.· 0 citations
Large language models (LLMs) remain unreliable when reasoning about the future. Existing models often overlook the difference among future states, committing to specific answers even when the queried facts are inherently volatile. To capture this challenge, we introduce ChronoRegime, a future-oriented temporal reasonin...
Yongyi Huang, Huaping Zhang, Wenyao Cui et al.· 0 citations
