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20,463 papers
ID2Pattern is proposed, a pretraining framework that brings high-cardinality identity fields into transaction foundation models and remains equally effective even when all identity values are unseen during training, highlighting its potential to generalize to new users and markets.
World-action models increasingly apply test-time scaling, spending extra inference computation on candidate selection and adaptive allocation to improve control. Such gains are credible only when candidates carry exploitable signal, nominal compute savings become real speedup, and evaluation stays reliable. We audit al...
Weight pruning can reduce the storage and inference cost of large language models (LLMs), but preserving quality at high sparsity levels remains challenging. Existing methods face a quality-cost trade-off in this regime. One-shot pruning is cheap but degrades sharply as sparsity increases, even when followed by post-pr...
Geometry-Optimized Few-Step Reinforcement Learning is introduced, built on a selection principle: at a fixed conditioning state and timestep pair, choose the admissible policy that minimizes the conditional KL divergence induced by any given nonzero velocity-output update.
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Embodied surgical AI requires reliable multimodal data, yet surgical data collection remains costly, difficult to repeat, and strongly dependent on the hardware setup. We present Sensorium, an open-source Python library for recording, processing, annotating, and curating multimodal surgical data from configurable robot...
This work introduces \textsc{ArchOR], an archetype-centric skill agent that builds on \textsc{OptSkills} by adding two retrieval-focused modules, and refines the ordering of cached skill representations through a low-rank prototype residual.
Current LLM ensemble frameworks focus on multi-step setups with additional modules for answer ranking, often opting for token and span analysis rather than structured outputs, leading to heavyweight architectures with potential fail states along the pipeline. Faster, lighter solutions are more vulnerable to hallucinati...
Self-efficacy beliefs play a critical role in learners' performance, academic choices, and, more broadly, in their future career trajectories. As Technology-Enhanced Learning (TEL) environments have demonstrated strong potential for supporting both performance and motivation, investigating self-efficacy within such env...
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.
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.
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