The integration of large language models (LLMs) into software development workflows has given rise to a paradigm known as Agentic Software Engineering (SE 3.0), in which autonomous agents manage full development life cycles under human supervision. This paper...
VQV Signal
RESEARCH
SOURCE-BACKED
TECHNICAL
The Role Specialization Model (RSM): Coordinating LLM-Based Tools in Agentic Software Development - An Exploratory Ca...
The integration of large language models (LLMs) into software development workflows has given rise to a paradigm known as Agentic Software Engineering (SE 3.0), in which autonomous agents manage full development life cycles under human supervision. This paper...
The integration of large language models (LLMs) into software development workflows has given rise to a paradigm known as Agentic Software Engineering (SE 3.0), in which autonomous agents manage full development life cycles under human supervision. This paper presents an exploratory case study in which three...
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Signal Strength 95%
Technical label SOURCE-BACKED
Public Interest 38
Category RESEARCH
Reader Depth TECHNICAL
Signal Strength reflects source quality, relevance, freshness and evidence. Public Interest helps organize discovery; it is not proof of truth.
Public Interest components
Recognizable Entity Score 67
Practical Impact Score 20
Novelty Interest Score 48
Consequence Score 18
Curiosity Score 16
Shareability Score 36
VQV surfaced this signal because it is recent, relevant to AI Agents, connected to arXiv.
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