A physics-structured variational autoencoder designed for rapid extraction of voxelwise multi-parameter posterior distributions that is in good agreement with those calculated using a brute-force Bayesian analysis, while providing an orders-of-magnitude acceleration in whole brain quantification.
Alex Finkelstein, Ron Moneta, O. Zohar et al.· IEEE Transactions on Medical...· 0 citations
It is proved that learning with K strategies yields a Theta(K log K) improvement in sample complexity over binary preferences and that DPO converges to the entropy-regularized utility-maximizing policy.
M. Mohsin, Muhammad Umer, Ahsan Bilal et al.· arXiv.org· 0 citations
Recently, flow-based generative models have shown superior efficiency compared to diffusion models. In this paper, we study rectified flow models, which constrain transport trajectories to be linear from the base distribution to the data distribution. This structural restriction greatly accelerates sampling, often enab...
Hari Krishna Sahoo, Mudit Gaur, Vaneet Aggarwal· 0 citations
RoboPilot is presented, a dual-thinking closed-loop agentic framework for robotic manipulation that supports adaptive reasoning for complex tasks in real-world dynamic environments and dynamically switches between fast and slow thinking to balance efficiency and accuracy.
Xin-Yi Liu, M. Sani, Ze-Wei Zhou et al.· 0 citations
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SEER (Self-Enhancing Efficient Reasoning), a self-enhancing framework for adaptive CoT compression, improves the conciseness of reasoning while preserving output quality, without relying on external compression tools.
Ke-Rui Huang, Shu-Han Liu, Xing Hu et al.· Proceedings of the ACM on So...· 9 citations· ⚡2
Statutory corpora and judicial decisions are growing faster than legal professionals can read them, while individual judgments often exceed the context limits of standard encoder models. Transformer architectures dominate legal NLP benchmarks, but their quadratic attention complexity can require truncating or fragmenti...
Many physical systems do not merely move or deform; they grow, adding material and changing the geometry that a world model must represent. Existing world models are typically optimized for pixel prediction, reward prediction, or fixed-support physical dynamics, leaving open how to model systems whose underlying physic...
We present Adjacent Possible Exploration (APE), a selective fine-tuning method for adapting large language models that systematically explores parameter modifications while maintaining model stability. Inspired by evolutionary optimization principles, APE evaluates multiple candidate parameter updates through fine-tuni...
Vision-Language-Action (VLA) models have recently advanced robotic manipulation by translating natural-language instructions and visual observations into control actions. However, existing VLAs are primarily trained on successful expert demonstrations and lack structured supervision for failure diagnosis and recovery,...
Zewei Ye, Weifeng Lu, Minghao Ye et al.· 0 citations
Generative Artificial Intelligence (GenAI) constitutes a transformative technological wave that reconfigures industries through its unparalleled capabilities for content creation, reasoning, planning, and multimodal understanding. This revolutionary force offers the most promising path yet toward solving one of enginee...
Yuping Wang, Shuo Xing, Cui Can et al.· 0 citations
In-context learning lets a sequence model adapt to a new task from examples in its input. A prominent line of work shows how self-attention can be constructed to implement gradient descent on a linear predictor fit to the in-context examples during the forward pass. State-space models (SSMs) and other linear recurrent...
The process of identifying human emotion and affective states from speech is known as speech emotion recognition (SER). This is based on the observation that tone and pitch in the voice frequently convey underlying emotion. Speech recognition includes the ability to recognize emotions, which is becoming increasingly po...
