Category
19,710 papers
This work proposes five strategies for selecting different collections of text data for processing by the large language models (LLMs) and applies them to virtual datasets created in five application case studies with different source datasets.
We consider forecasting the dynamics of consumer preferences of retail chain customers. A customer's preference is described by a triangular fuzzy number. The evolution of the preference over time is governed by a Mamba-type selective state space model whose hidden state is a triangular number, with computations carrie...
Video director agents orchestrate multistage production to realize users' creative visions. Emerging research on their self-evolution advances screenplay planning effectively. However, the mutual reinforcement between directing and user interaction remains largely overlooked. Improved interaction clarifies user intent...
The Dynamical Memory Transformer is presented, a hybrid architecture that separates sequence encoding from information reading and achieves the best results on several cognitive tasks and language modeling performance close to that of Transformer, but remains behind Mamba in language modeling.
Reach audiences
Advertise in front of researchers, engineers, and readers.
We develop ReuScope, a protocol for auditing data reuse and evaluation sensitivity, using Capsule Vision 2024 as a case study. It combines measurements of data relationships with two comparisons: changing the evaluation population while holding predictions fixed, and changing training data while holding evaluation fixe...
Large closed-source reasoning models expose only their final answers. The intermediate reasoning that produces those answers is hidden behind API surfaces, tool-call policies, and safety filters. Existing chain-of-thought (CoT) extraction methods assume a side channel: a visible reasoning field, a scratchpad carried th...
Cultural value alignment requires not only model optimization but also a well-specified target grounded in real-human evidence. Yet how such targets should be grounded in human data remains largely unexplored, raising three fundamental questions: (1) whose values should be represented? (2) where should cultural values...
Healthcare increasingly relies on connected medical devices, clinical information systems, cloud services, and distributed-care technologies, creating cybersecurity risks across clinical workflows. AI-enabled capabilities can accelerate vulnerability discovery and attack-path analysis, increasing the need to limit comp...
A central challenge in multimodal in-context learning (MICL) arises from tasks that are ill-defined without context and require uncovering the underlying rules from demonstrations. Existing methods predominantly treat MICL as direct input-output mapping, overlooking the importance of explicitly inducing and reasoning o...
The results suggest that pretrained biomedical representations already encode signal relevant to trial outcome, and that probing is a computationally cheaper alternative to fine-tuning for this task.
Terminal feedback in multi-turn agent training leaves both rollout allocation and local credit underdetermined. Dynamics-Predictive Sampling (DPS) [9] provides a task-level predictive sampling reference, but interactive agents also need to choose which visited states merit further continuations. We introduce TSBPO with...
Radial Kernel Attention provides a compact pathway for information exchange beyond local graph connectivity in polymer machine learning force fields, and improves all three metrics over UMA-S-1.2.1 on the same validation structures.
We’re on a journey to advance and democratize artificial intelligence through open source and open science.
We’re on a journey to advance and democratize artificial intelligence through open source and open science.
We’re on a journey to advance and democratize artificial intelligence through open source and open science.
We’re on a journey to advance and democratize artificial intelligence through open source and open science.
We’re on a journey to advance and democratize artificial intelligence through open source and open science.
We’re on a journey to advance and democratize artificial intelligence through open source and open science.
We use cookies to run the site and, with your consent, for analytics and to show ads. See our Cookie Policy.
