framework
active
framework:agentic-ai-ontologyAgentic AI ontology
The traditional alignment framework focusing on agents optimized to pursue goals.
Neighborhood — ranked by edge-count
Claims (1)
claim
- Rejection of the agent interpretation.
Quotes (1)
quote
- Quote from a question that sparked the post, highlighting the gap between theory and practice.
Artifacts (1)
artifact
- The paper being extracted.
Frameworks (1)
framework
- Simulator ontologyextendsThe framework proposed: self-supervised models are simulators that generate simulacra; distinguishes simulator from simulated agents.
Related by similarity (8)
cosine ≥ 0.65 · no typed edgeEntities in the same semantic neighborhood but without a typed relation to this one — candidates for new edges or unrecognized duplicates.
- Higher-level systems built on top of LLMs that produce and consume representations beyond next-token prediction; proposed as potential candidates for consciousness.
- Reasoning approach using code or tool calls executed by an agent.
- Paradigm where VLM acts as controller generating code or tool calls to external modules for visual operations, incurring context-switching latency.
- Methods that use agentic reasoning; incur context-switching latency from external execution.
- The view that biological substrates have intrinsic competencies, computational abilities, and homeodynamic setpoints, not merely passive matter.
- Future AI that may be rational, autonomous, and possibly conscious but lack affective consciousness.
- Synthetic agents (here RL-trained neural networks) whose causal emergence was previously largely unknown; the paper addresses this gap.
- Key insight distinguishing GPT from traditional agent models.