AI Networking Cookbook: Practical recipes for AI-assisted network automation and development
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2,658 papers
Foundation models derive their value from broad general capability across domains, yet deployment usually rewards specialization - creating a fundamental question for general intelligence: when performance is pushed upward in one region of capability space, is competence elsewhere conserved, redistributed, or destroyed...
Despite clinicians' reluctance to discuss SUDEP due to fear of increasing patient anxiety, this study shows that knowledge about SUDEP may lead to proactive risk management without long-term emotional harm.
The paper argues, on the evidence gathered so far, that formal rules in the mathematical language of B must remain the source of truth, while the language model serves as the fenced assistant in a distributed safety-critical railway system.
Large language models (LLMs) are increasingly deployed in settings that require collective reasoning on complex, value-laden problems. Confidence in these deployments rests largely on benchmarks for verifiable tasks (mathematics, coding, coordination games), yet many of these applications concern problems where no obje...
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According to the drawbacks of large language model, I design a new agent which can overcome the drawbacks. The agent contains four deep neural networks, which is abstract network, concrete network, decision network, execution network. Each of the four networks just do one thing, so it focus what it can do just like bra...
AbstractThis paper investigates EFL students’ perceptions of ethical acceptability judgments of using Large Language Models (LLMs) into academic writing. In contrast to the simplistic view of acceptable/unacceptable use of LLMs, the present study models how specific contextual justifications shape students’ moral evalu...
AbstractThis paper investigates EFL students’ perceptions of ethical acceptability judgments of using Large Language Models (LLMs) into academic writing. In contrast to the simplistic view of acceptable/unacceptable use of LLMs, the present study models how specific contextual justifications shape students’ moral evalu...
This work introduces LLM-based compressors deploying two novel symbol-ranking variants that bound predictions to the top-$T$ ranks, and conducts the first large-scale evaluation of LLM-based source code compression across 30 LLMs, including general-domain, code-specialized, and quantized models.
While LLM-based multi-agent systems show potential for large-scale software development, successful integration requires addressing challenges such as memory limitations, hallucinations, and code smells, alongside a practitioner-centric perspective.
The proposed LLM-based multi-agent system automates qualitative data analysis process, creating opportunities for researchers and practitioners, and future improvements focus on enhancing multilingual performance and integrating continuous expert feedback.
A Multi-Vocal Literature Review is conducted, combining insights from both academia and industry, including peer-reviewed studies and grey literature to systematically synthesize and analyze existing knowledge on LLM-based multi-agent systems for code generation.
This study provides the first large-scale empirical comparison of agentic frameworks for reasoning-intensive software engineering tasks and shows that framework selection should prioritize orchestration quality, especially memory control, failure handling, and cost management.
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We’re on a journey to advance and democratize artificial intelligence through open source and open science.
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