large language models

Experience is processing: a theory of consciousness in one axiom and five definitions, tested on model networks, a worm's wiring and a language model

"Consciousness" is a collective word. Process-Identity Theory (PIT, "Erleben = Verarbeitung") sets one axiom — experience is processing, the same thing under two names — and five definitions, and derives forty propositions from them. They separate four things that can be measured apart: whether a system experiences (ho...

PENGEMBANGAN MODUL AJAR BIOLOGI BERBASIS TANAMAN PURUN PADA MATERI KEANEKARAGAMAN HAYATI DI SMA

ABSTRACT The availability of Biology teaching materials that connect biodiversity concepts with the potential of the surrounding environment remains a need for contextual learning in senior high schools. This study aimed to develop a Biology teaching module based on the purun plant using a Deep Learning approach for bi...

Hybrid search strategy (WOS and LLM-assisted) for lettuce (Lactuca sativa L.) photopriming and drought tolerance response. (Web of Science Core Collection).

Abstract This search strategy represents a hybrid methodology combining Artificial Intelligence (AI) semantic retrieval and database validation. Initially, Large Language Models (LLMs)-specifically OpenAI's ChatGPT and Google's Gemini-were queried using standardized natural language prompts focused on the intersection...

Exploiting latent contextual knowledge for high-performance lightweight large language models: A framework for children’s language

Automated Calculation of the Pediatric Emergency Care Applied Research Network (PECARN) Intra-Abdominal Injury Rule from Unstructured Emergency Department Notes Using a Large Language Model.

Objective The Pediatric Emergency Care Applied Research Network (PECARN) intra-abdominal injury clinical prediction rule identifies children at very low risk for clinically significant intra-abdominal injury after blunt abdominal trauma. However, retrospective evaluation of the rule's use in practice requires resource-...

Mix-Attention for Multimodal Large Language Model-Enhanced Sequential Recommendation

Sequential recommendation aims to predict the next item that may interest users by modeling their historical item sequence. Recently, multimodal sequential recommendation has achieved promising results in capturing user preferences by incorporating rich modality information. However, existing frameworks suffer from lim...

Fast LM Head via Coarse-Fine Decomposition and Tile-Wise Weight Clustering

Large language models (LLMs) reuse each generated token as the next input during autoregressive decoding, making per-token latency a critical factor in practical inference performance. In this process, the LM Head performs a large-scale linear projection from the hidden state to the full vocabulary space at every decod...

Unified Table Understanding via Context-Aware Cell Embedding Learning

UTUEL is introduced, a self-supervised, task-agnostic framework that casts Table Representation Learning as a Joint-Embedding Predictive Architecture (TRL-JEPA) and substantially improves Table Retrieval over generic embedders while remaining competitive on Column Type Annotation and Table Cell Retrieval.

Agentic AI for Computational Fluid Dynamics: A Review

Computational fluid dynamics (CFD) workflows remain strongly dependent on expert decisions in geometry preparation, meshing, model and solver configuration, convergence assessment, post-processing, and design iteration. This review examines agentic artificial intelligence (AI) for CFD automation and engineering design,...

From Web Search to AI Search: A Comparative Analysis of Information-Seeking Behaviour in Traditional Search Engines and Conversational Systems Based on Large Language Models

How LLM-based search systems are changing the way users retrieve information online is examined, moving the dominant paradigm from keyword queries and ranked lists of links to direct answers in natural language and implications for the design of hybrid search tools in human-computer interaction and information retrieva...

Analysis code for: Large language models supply actionable management plans when the implicit clinical premise of a request does not hold

Analysis code behind the quantitative results of the manuscript. It covers prompt construction from the paired scenario library, response collection from seven language models, the blinded manual-coding workflow, inter-rater reliability, the rule-based classifier used in the repetition-stability analysis, and the stati...

Pneumatic retinopexy identification in electronic medical records using an on-premises large language model