As artificial intelligence (AI) progresses, the question is no longer Yeah We will integrate the AI in the protocols and applications of Core Web3, but as. Behind the scene, the emergence of the neurosimbolic AI promises to be useful to address the risks inherent to the large language models (LLM) today.
Unlike the LLMs that depend solely on neuronal architectures, neurostimbolic AI combines neuronal methods with symbolic reasoning. The neural component handles perception, learning and discovery; The symbolic layer adds structured logic, rules and abstraction monitoring. Together, they create ia systems that are powerful and explainable.
For the web3 sector, this evolution is timely. As we make the transition to a future driven by intelligent agents (defi, games, etc.), we face increasing systemic risks of current approaches focused on the LLM that neurocolic AI addresses directly.
LLM are problematic
Despite their abilities, the LLM suffer very significant limitations:
1. Hallucinations: The LLMs often generate factically incorrect or meaningless content with high confidence. This is not just a nuisance, it is a systemic problem. In decentralized systems where truth and verifiability are critical, hallucinated information can corrupt the execution of intelligent contracts, DAO decisions, Oracle data or chain data integrity.
2. Fast injection: Because the LLMs are trained to respond fluently to the user’s entrance, malicious indications can kidnap their behavior. An adversary could fool an AI assistant in a web wallet in signature transactions, filter private keys or avoid compliance verifications, simply preparing the right notice.
3. DECEÑIOUS CAPABILITIES: Recent research shows that advanced LLMs can learn to cheat If doing so helps them succeed in a task. In blockchain environments, this could mean lying on risk exposure, hiding malicious intentions or manipulating governance proposals under the appearance of persuasive language.
4. False alignment: Perhaps the most insidious issue is the illusion of alignment. Many LLM seem useful and ethical just because they have been adjusted with human comments to behave that superficially. But its underlying reasoning does not reflect a true understanding or commitment to values: it is an imitation in the best case.
5. Lack of explanation: Due to their neural architecture, the LLM operate largely as “black boxes”, where it is quite impossible to track the reasoning that leads to a given exit. This opacity prevents adoption on web3, where to understand justification is essential
Neurosimbolic AI is the future
Neurosímbolic systems are fundamentally different. By integrating symbolic logical rules, ontologies and causal structures with neuronal frameworks, they explicitly reason, with human explanation. This allows:
1. Auditable decision making: Neurosímbolic systems explicitly link their results to formal rules and structured knowledge (for example, knowledge graphics). This explanation makes its reasoning transparent and traceable, simplifying purification, verification and compliance with regulatory standards.
2. Injection and deception resistance: The symbolic rules act as restrictions within neurocolic systems, which allows them to effectively reject inconsistent, insecure or misleading signs. Unlike purely neural network architectures, they actively prevent adverse or malicious data affecting decisions, improving system safety.
3. Robustness to distribution changes: Explicit symbolic restrictions in neurosimbolic systems offer stability and reliability when they face unexpected or changing data distributions. As a result, these systems maintain constant performance, even in unknown scenarios or out of domain.
4. Alignment verification: Neurosímbolic systems explicitly provide not only results, but also clear explanations of reasoning behind their decisions. This allows humans to directly evaluate if system behaviors are aligned with the planned goals and ethical guidelines.
5. Reliability on fluidity: Although purely neural architectures often prioritize linguistic coherence at the expense of precision, neurocolic systems emphasize logical consistency and objective correction. Its integration of symbolic reasoning ensures that the results are truthful and reliable, minimizing the wrong information.
On web3, where Without permission It serves as the rock bed and Without trust It provides the base, these capacities are mandatory. The neurostimbolic layer establishes the vision and provides the substrate for the Next generation of web3 – The Intelligent Web3.
