Modern operational systems ranging from logistics and cloud infrastructure to industrial IoT, are governed by complex, interdependent processes. Understanding how interventions propagate through such systems requires causal inference methods that go beyond direct effects to quantify mediated pathways. Traditional media...
Alessandro Casadei, Sreyoshi Bhaduri, Rohit Malshe et al.· 0 citations
InstruBPM is introduced, a reproducible approach that prepares paired text-diagram data and instruction tunes an open source large language model with parameter-efficient fine-tuning and quantization for on-prem deployment and improves structural accuracy and robustness compared to untuned baselines and reduces relianc...
Gökberk Çelikmasat, Atay Özgövde, F. Aydemir· arXiv.org· 1 citation
Accurate person-specific gaze estimation benefits from labeled user data, but collecting images that densely cover gaze directions is burdensome. We present DiTGaze, a calibration-efficient augmentation system that follows a two-stream 3D Gaussian Splatting backbone and reformulates its convolutional rendering stage as...
Abiram Panchalingam, Indu P. Bodala, Stuart E. Middleton· 0 citations
Evaluating spoken role-play requires judging not only linguistic content but also prosody, emotional expression, character embodiment, and contextual fit. Existing automatic evaluation methods often collapse these aspects into coarse scores and are evaluated primarily on synthesized speech. We introduce Speech-DRAME, a...
Jiatong Shi, Jionghao Han, Yichen Lu et al.· 0 citations
Reach audiences
Advertise in front of researchers, engineers, and readers.
This paper first probes MCoT faithfulness by measuring how much the prediction changes when its visual and textual thoughts are intervened, and proposes Sufficient-Component Cause Model (SCCM) learning, which encourages the MCoT to generate sufficient yet minimal visual components that are independently capable of lead...
Zujing Liu, Jun-Wen Pan, Qi She et al.· arXiv.org· 15 citations· ⚡2
This work proposes MLLMEraser, an input-aware, training-free framework for test-time unlearning that consistently outperforms state-of-the-art MLLM unlearning baselines, achieving stronger forgetting performance with lower computational cost and minimal utility degradation.
A lightweight extension that turns any pretrained backbone with spatial feature maps into a saccadic classifier through glimpse-induced inference is proposed, which improves consistently over its backbones.
Johann Schmidt, S. Stober, Joachim Denzler et al.· 5 citations
A rule-to-image synthesis pipeline that extracts and expands puzzle rules from seed questions and generates executable rendering programs for puzzle assembly to tackle the challenge of image synthesis with grounded answers is proposed.
Offline RL (ORL) promises safe and sample-efficient deployment but existing methods rely on undocumented online interactions for hyperparameter tuning and lack reliable fully offline estimates of initial online performance. We introduce SOReL, a fully offline Bayesian model-based RL method that learns a posterior over...
Mattie Fellows, Clarisse Wibault, Uljad Berdica et al.· 0 citations
Bayesian optimization (BO) is a sequential decision-making tool widely used for optimizing expensive black-box functions. Recently, Large Language Models (LLMs) have shown remarkable adaptability in low-data regimes, making them promising tools for black-box optimization by leveraging contextual knowledge to propose hi...
Chih-Yu Chang, Milad Azvar, Chinedum Okwudire et al.· 0 citations
Scaling test-time compute is crucial for enhancing the reasoning capabilities of large language models (LLMs). Existing approaches typically employ reinforcement learning (RL) to maximize a verifiable reward obtained at the end of reasoning traces. However, such methods optimize only the final performance under a large...
Penghui Qi, Zichen Liu, Tianyu Pang et al.· 0 citations
Despite advancements in Computer-Aided Diagnosis (CAD) systems, breast cancer remains one of the leading causes of cancer-related deaths among women worldwide. Recent breakthroughs in Artificial Intelligence (AI) have shown significant promise in development of advanced Deep Learning (DL) architectures for breast cance...
Farnoush Bayatmakou, Reza Taleei, Milad Amir Toutounchian et al.· 0 citations
