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2,479 papers
Overview GRAVITY: A Problem-State-Driven Adaptive Computational Intelligence Fabric proposes a constrained framework for allocating computational intelligence, execution strategies, resources, and verification according to the requirements of each problem instance. Modern AI systems frequently frame computation as a mo...
Large Language Model (LLM) workflows increasingly decompose knowledge-intensive generation into planning, role assignment, subtask execution, and synthesis. Although this structure may support specialization, it also adds coordination and computational overhead. We present a preliminary task-level comparison of a Direc...
Evidence synthesis methods, such as systematic reviews (SR), require research teams to find, select, evaluate, and synthesize all available evidence to answer a specific research question in a reproducible and transparent, bias-reduced manner. Early in the process, teams must screen all unique titles and abstracts gath...
Under token budgets, memory systems for Large Language Models (LLMs) select historical information before receiving a query (query-agnostic selection) or after receiving it (query-aware selection). To isolate the value of knowing the query, we idealize the continuous text of each history as a sequence of fixed chunks a...
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Claims that artificial intelligence (AI) evolves are mostly argued by analogy. Open model hubs record which models were built from which. We applied the Price equation, which splits a change in a population's mean trait into selection and transmission terms, to two genealogies of open-weight language models. Among 1,27...
Leveraging auxiliary textual data can help with user profiling and item characterization in recommender systems (RSs). However, incomplete user and item descriptions limit the potential of textual information. To this end, we propose a graph-aware convolutional large language model (LLM) method, eliciting LLMs to summa...
LLMs (Large Language Models) are increasingly integrated into medical workflows to provide decision-making support, for example as Question Answering (QA) systems. However, in medical context, privacy requirements and resource constraints require the use of local and small (1B-parameter) LLMs. 1B-parameter LLMs are hig...
This master manuscript (Monograph 11 / Addendum G-2, codenamed "Willow-1") establishes the "Topological Invariance of Sanctuary" and formalizes "The Dual-Pillar Resonance Theorem" (||_11) within non-self-adjoint operator mechanics. We bridge information thermodynamics across physical, neural, and quantum computational...
For over a century, evolutionary anthropology and history have operated under an unexamined teleological assumption: that the Neolithic transition to agriculture represented an objective intellectual breakthrough, and that the ensuing emergence of permanent settlements, defensive architecture, rigid social hierarchies,...
Emotion regulation is a key construct in mental health, yet existing measures differ in conceptual coverage, with some focusing on specific strategies or regulatory difficulties rather than broader cognitive, behavioral, and contextual regulation processes. This study aimed to develop an Emotion Regulation Scale (ERS)...
Large language models (LLMs) are rapidly assuming an intermediary role in housing search through the integration of listing platforms within conversational interfaces, mediating access to information, search, and recommendations within urban settings. We expand on prior work on racial steering in LLMs by conducting a b...
The growing accessibility of Large Language Models via conversational interfaces capable of responding to users’ questions by drawing on, synthesizing, and citing information from the web (i.e., Generative Search Engines) has simpli- fied the information-seeking process for users. However, with the proliferation of AI-...
Generative AI
Assistant Professor Benjamin Lindquist studies how engineers deliberately injected randomness into early AI, giving rise to deep-learning models that sometimes hallucinate.
We’re on a journey to advance and democratize artificial intelligence through open source and open science.
Flint-interactive gives AI agents a language for creating interactive visualizations. Click a chart, filter data, highlight related content, and explore beyond the view the author originally chose. The post Flint-interactive: A language for creating interactive data visualizations appeared first on Microsoft Research.
A Blog post by IBM Research on Hugging Face
A Blog post by IBM Research on Hugging Face
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