Multivariate time series in domains such as finance, climate science, and healthcare often exhibit long-term trends, seasonal patterns, and short-term fluctuations, complicating causal inference under non-stationarity and autocorrelation. Existing causal discovery methods typically operate on raw observations, making t...
Muhammad Hasan Ferdous, Ian Stockwell, Md Osman Gani· 0 citations
We investigate the extent to which an LLM's hidden-state geometry can be recovered from its behavior in psycholinguistic experiments. Across eight instruction-tuned transformer models, we run two experimental paradigms -- similarity-based forced choice and free association -- over a shared 5,000-word vocabulary, collec...
Louis Schiekiera, Max Zimmer, Christophe Roux et al.· 0 citations
This work proposes a multi-modal protocol that measures VTON quality along five interpretable dimensions: background consistency, identity fidelity, texture fidelity, shape plausibility, and overall realism, and presents OpenVTON-Bench, a large-scale benchmark comprising approximately 100K high-resolution image pairs.
Jin Li, Tao Chen, Kai-Ao Wen et al.· arXiv.org· 2 citations· ⚡1
Reinforcement learning (RL) post-training is a dominant approach for improving the reasoning performance of large language models (LLMs), yet growing evidence suggests that its gains arise primarily from distribution sharpening rather than the acquisition of new capabilities. Recent work has shown that sampling from th...
Xiaotong Ji, Rasul Tutunov, Matthieu Zimmer et al.· 0 citations
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Despite the syntactic fluency of Large Language Models (LLMs), ensuring their logical correctness in high-stakes domains remains a fundamental challenge. We present a neurosymbolic framework that combines LLMs with SMT solvers to produce verification-guided answers through iterative refinement. Our approach decomposes...
Vikash Singh, Darion Cassel, Nathaniel Weir et al.· 0 citations
Differential privacy limits how much each training image can influence a medical imaging model but lowers its diagnostic accuracy. Pretrained weights reduce this loss. It is unknown whether the pretraining objective or domain improves accuracy more. Here we train chest radiograph classifiers under differential privacy...
Soroosh Tayebi Arasteh, Mina Farajiamiri, Mahshad Lotfinia et al.· 0 citations
Multimodal Retrieval-Augmented Generation (MM-RAG) augments visual question answering (VQA) with external knowledge, but many systems rely on modular pipelines that separate visual grounding, query construction, and answer generation. This separation can propagate errors from intermediate detections, masks, or captions...
Jeonghwan Kim, Renjie Tao, Sanat Sharma et al.· 0 citations
Unified remote sensing multimodal models exhibit a pronounced spatial reversal curse: Although they can accurately recognize and describe object locations in images, they often fail to faithfully execute the same spatial relations during text-to-image generation, where such relations constitute core semantic informatio...
Counterfactual storytelling exposes LLM shortcomings in causal reasoning, where few-shot prompting and SFT fail to produce logical and rational narratives. This case study introduces Retell, Reward, Repeat (RRR), a narrative theory-informed RL pipeline that teaches causal story structure. We extend an existing counterf...
David Y. Liu, Xanthe Muston, Dipankar Srirag et al.· 0 citations
Foundation models are used for many real-world applications involving language understanding and generation from temporally-ordered multimodal events. In this work, we study the ability of models to identify the most important sub-events in a video, which is a fundamental prerequisite for summarizing, reasoning over, o...
Aditya K Surikuchi, Raquel Fern\'andez, Sandro Pezzelle· 0 citations
This work provides the first systematic investigation into probing model internals across a conversation trajectory, moving beyond static, single-prompt analysis to capture the evolving state of privacy-sensitive interactions, and illustrating their applicability across contexts, attack styles, multiple base models, an...
NeuroVLA is introduced, a brain-inspired architecture that couples a cortical vision-language planner to recurrent cerebellar modulation and a stateful spiking spinal controller that translates semantic intent into actions while incorporating high-frequency proprioceptive and force feedback, maintaining short-term moto...
Wei-Yu Guo, He Zhang, Peng-Teng Li et al.· 0 citations
