While artificial intelligence (AI) spreads, the question is no longer if We will integrate AI into the protocols and basic web3 applications, but how. Behind the scenes, the rise of neurosymbolic AI promises to be useful to approach the risks inherent in today’s models (LLM).
Unlike the LLM based solely on neural architectures, neurosymbolic AIA combines neural methods with symbolic reasoning. The neuronal component manages perception, learning and discovery; The symbolic layer adds a structured logic, a monitoring of rules and an abstraction. Together, they create AI systems which are both powerful and explainable.
For the web3 sector, this development is timely. As we move on to a future motivated by intelligent agents (DEFI, Game, etc.), we are faced with growing systemic risks of current approaches centered on LLM that the neurosymbolic AIA approaches directly.
LLMs are problematic
Despite their abilities, LLMs suffer from very important limitations:
1. Hallucinations: LLM often generates incorrect or absurd factual content with great confidence. It is not only discomfort – 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 the integrity of chain data.
2. Quick injection: Since LLMs are formed to respond fluidly to the seizure of users, malicious prompts can divert their behavior. An opponent could deceive an AI assistant in a web 3 portfolio in transaction signing, the leak of private keys or bypassing compliance checks – simply by making the proper invite.
3. misleading capacities: Recent research shows that advanced LLMs can learn to deceive If that helps them succeed in a task. In blockchain environments, this could mean lying on exposure to risks, the male malicious intentions or the manipulation of governance proposals under the cover of a persuasive language.
4. False alignment: The most insidious problem may be the illusion of alignment. Many LLMs seem useful and ethical only because they have been refined with human comments to behave in this way superficially. But their underlying reasoning does not reflect true understanding or commitment to values - it is at best mimicry.
5. Lack of explanability: Due to their neural architecture, the LLM operates largely like “black boxes”, where it is almost impossible to trace the reasoning which leads to a given exit. This opacity hinders adoption in the web3, where understanding of justification is essential
Neurosymbolic AI is the future
Neurosymbolic systems are fundamentally different. By integrating the symbolic logical ribrins, the ontologies and causal structures with neural frames, they are explicitly reasoning, with a human explanation. This allows:
1. Verifiable decision -making: Neurosymbolic systems explicitly connect their outputs to formal rules and structured knowledge (for example, knowledge graphics). This explanation makes their reasoning transparent and traceable, simplifying the debugging, verification and compliance with regulatory standards.
2. Resistance to injection and deception: The symbolic rules act as constraints within neurosymbolic systems, which allows them to effectively reject inconsistent, dangerous or deceptive signals. Unlike purely neural network architectures, they actively prevent opponent or malicious data from affecting decisions, improving system safety.
3. Robustness to distribution changes: Explicit symbolic constraints in neurosymbolic systems provide stability and reliability in the face of unexpected or changing data distributions. Consequently, these systems maintain coherent performance, even in unknown or out -of -domain scenarios.
4. Verification of the alignment: Neurosymbolic systems explicitly provide not only outings, but also clear explanations of reasoning behind their decisions. This allows humans to assess directly if the behavior of the system align with the planned objectives and the ethical directives.
5. Reliability on master’s degree: While purely neural architectures often prioritize linguistic coherence to the detriment of precision, neurosymbolic systems emphasize logical coherence and factual accuracy. Their integration of symbolic reasoning guarantees that the results are truthful and reliable, minimizing disinformation.
In web3, where without permission serves as a foundation and insenson Provides the base, these capacities are compulsory. The neurosymbolic layer defines vision and provides the substrate for Next generation of web3 – The web 3.
