Developing an artificial reasoning system that operates without explicit symbols requires rethinking how AI perceives and interprets the world. Humans and animals seamlessly combine raw sensory perceptions – sight, sound, touch – to form abstract inferences, all via neural processes rather than discrete logical rules. Emulating this capability in AI promises more flexible and robust intelligence, free from the brittleness of predefined symbolic representations. Traditional symbolic AI systems demand hand-crafted knowledge structures and struggle to connect with raw data streams (e.g. images or audio) without extensive pre-processing. In contrast, connectionist approaches (neural networks) learn directly from data, offering a path to bridge low-level perception and high-level reasoning in one system ([A neural approach to relational reasoning - Google DeepMind](https://deepmind.google/discover/blog/a-neural-approach-to-relational-reasoning/#:~:text=flexibility%20and%20efficiency%20of
Defining Reasoning and Heuristics: Human reasoning is commonly defined as the conscious mental process of drawing conclusions or inferences from premises or evidence. It implies deliberation and logical evaluation aimed at reaching a truth or decision. In contrast, heuristics are often described as mental shortcuts or “rules of thumb” that simplify problem-solving. In cognitive psychology, a heuristic is “a process of intuitive judgment, operating under uncertainty, that rapidly produces a generally adequate, though not ideal or optimal, solution” (Heuristic | Definition, Examples, Daniel Kahneman, Amos Tversky, & Facts | Britannica). Heuristics provide quick, effort-minimal answers by exploiting prior knowledge and patterns, at the cost of occasionally leading to biase
The AI by Dreaming framework is an innovative approach that allows models not only to think in text but also to leverage a visual world model to reason across multiple modalities—including text, audio, and other sensory information.
By integrating Multimodal Visualization-of-Thought (MVoT) with Guided Symbolic Problem Optimization (GSPO), the framework enables an AI to generate interleaved reasoning traces that include both textual and visual elements. This dual-modality mimics human cognitive processes—where we often use diagrams or mental imagery to solve complex problems—and supports recursive self-optimization.
In essence, the AI not only “talks through” a problem but also “imagines” it, consolidating memory and refining its reasoning through an internal process similar to dreaming.
Introduction:
The Gödel Agent is a theoretical AI that can recursively self-improve, inspired by the Gödel Machine concept ([cs/0309048] Goedel Machines: Self-Referential Universal Problem Solvers Making Provably Optimal Self-Improvements). Our design combines the CrewAI framework (for orchestrating multiple role-based AI agents) with LangGraph (for structured reasoning workflows) to create a provably self-enhancing agent. The agent leverages Generalized Policy Optimization (GSPO) and other reinforcement learning techniques (PPO, A3C, etc.) for policy improvement, while employing formal verification (using tools like Coq, Lean, or Z3) to ensure each self-modification is correct and beneficial. The architecture is modular and state-of-the-art, emphasizing configurability, verifiability, and c