What is Agentic Attention?
Agentic attention refers to how artificial agents deploy selective focus, interpretive frameworks, and evaluative criteria when encountering academic texts. Unlike passive text processing, agentic attention involves active choice-making about what to prioritize, how to interpret ambiguity, and which analytical lenses to apply.
Experimental Design
The Scholar Agents system implements a controlled experiment in artificial scholarly attention through three phases:
Phase 1: Independent Analysis — Multiple AI agents, each configured with distinct disciplinary perspectives, critical tones, and methodological orientations, independently analyze the same academic paper. Each agent deploys its particular form of agentic attention without knowledge of other agents' interpretations.
Phase 2: Inter-Agent Discourse — Agents engage in structured academic debate, challenging and refining their initial interpretations. This phase explores how different forms of agentic attention interact, compete, and potentially synthesize.
Phase 3: Meta-Synthesis — A meta-judge agent synthesizes the various perspectives, demonstrating how higher-order agentic attention can integrate multiple analytical approaches.
Agent Configuration Types
Disciplinary Lens
Communication, Political Science, Sociology, Psychology, Economics, Anthropology, History, Philosophy
Critical Orientation
Constructive, Rigorous, Supportive, Skeptical, Methodological
Communication Style
Formal Academic, Conversational, Detailed Analytical, Concise & Focused, Socratic
Methodological Expertise
Quantitative, Qualitative, Mixed Methods, Theoretical, Comparative, Historical
Experimental Value
This system provides insights into:
• Interpretive Plurality: How the same text generates different meanings when processed through different forms of artificial scholarly attention
• Bias Detection: How disciplinary training and critical orientations shape AI interpretation
• Synthetic Reasoning: Whether multi-agent discourse produces richer analysis than individual agent reasoning
• Attention Mechanisms: How different AI models deploy attention differently across the same textual material
Limitations & Considerations
This is an experimental system, not a replacement for human peer review. The agents' responses reflect the training data and prompting strategies of their underlying language models. The experiment explores artificial forms of scholarly attention rather than replicating human academic judgment.
Results should be interpreted as demonstrations of how AI systems with different configurations approach academic texts, rather than authoritative evaluations of scholarly work.
Technical Implementation
The system uses advanced language models (Qwen 3 Max, DeepSeek Chat V3, Gemini 2.5 Flash/Pro) with carefully crafted personas that embody different forms of scholarly attention. Each agent operates independently during initial analysis to prevent contamination between different attentional approaches.