From: aidotengineer
Manish Sanwal, Director of AI at News Corp, focuses on AI reasoning, explainability, and automation [00:00:14]. His work aims to build AI that is not only smarter but also more structured and self-correcting [00:00:20]. This is achieved using Layered Chain of Thought with multi-agentic systems [00:00:27].
Multi-Agentic Systems
Multi-agentic systems are collections of specialized AI agents that collaborate to tackle complex tasks [00:00:37]. Each agent is designed to handle a specific part of an overall problem [00:00:42], moving away from massive monolithic systems [00:00:47].
An example is a self-driving car system, which can be pictured as a team of specialized agents [00:00:57]. One agent detects pedestrians, another reads traffic signals, and a third checks for the best route [00:01:00]. When each agent performs its part in harmony, the entire system becomes more robust and efficient [00:01:11].
Advantages of the Modular Approach
The modular approach of multi-agentic systems offers several benefits [00:01:16]:
- Specialization [00:01:21]: Each agent can be finely tuned for a specific task, leading to better accuracy and performance [00:01:23].
- Flexibility and Scalability [00:01:39]: Individual agents can be updated or improved without overhauling the entire system [00:01:34].
- Fault Tolerance [00:01:51]: If one agent encounters an issue, others can often compensate, ensuring the overall system remains reliable [00:01:43].
By integrating well-coordinated agents, a system that is inherently more robust and effective is created [00:01:56]. When Chain of Thought reasoning is added, each agent not only performs its task but also explains its decision-making process step by step, enhancing both transparency and resiliency [00:02:04].
Chain of Thought (CoT) Reasoning
Chain of Thought is a method that guides AI to think through a problem step by step, rather than simply guessing answers [00:02:24]. Traditionally, large language models (LLMs) are given a detailed prompt and asked for a final answer, often jumping directly to a conclusion without revealing their reasoning [00:02:33].
The essence of Chain of Thought prompting is to ask the model to outline every step of its reasoning process [00:02:59]. By breaking down a complex problem into a series of manageable steps, the model demonstrates how it processes information and exposes its path to the conclusion [00:03:07].
Benefits of CoT
This approach offers two key benefits [00:03:23]:
- Transparency [00:03:27]: Users can see each stage of the reasoning process, which helps understand how the model is approaching the problem [00:03:30].
- Opportunity for Fine-tuning and Debugging [00:03:37]: If a mistake is spotted in any intermediate step, the prompt or process can be adjusted to correct errors before the final answer is provided [00:03:40].
In short, Chain of Thought transforms the AI’s internal reasoning into a visible and verifiable sequence, making the entire process more interpretable and robust [00:03:54].
Limitations of CoT
Despite its benefits, Chain of Thought has several limitations [00:04:21]:
- Prompt Sensitivity [00:04:23]: The process is highly sensitive to how prompts are phrased; slight changes in wording or context can lead to vastly different outputs, complicating reproducibility and reliability [00:04:25].
- Lack of Real-time Feedback [00:04:47]: There is no built-in mechanism to verify or correct mistakes during the step-by-step reasoning process [00:04:49]. This means no error correction opportunity occurs until after the inference is complete [00:04:55].
- Cascade of Errors [00:05:02]: Each step is produced without continuous validation. If an early inference is flawed, it can cause a cascade of errors compromising the integrity of the entire process [00:05:04]. The model relies on initial assumptions, with the only correction opportunity being after the inference is complete [00:05:14].
- Missing Critical Connections [00:05:34]: When faced with problems involving multiple interdependent factors, Chain of Thought can sometimes miss critical connections, resulting in oversimplified or incomplete conclusions [00:05:30].
Layered Chain of Thought (LCoT) Prompting
Layered Chain of Thought prompting (LCoT) is an approach designed to overcome the limitations of standard Chain of Thought methods [00:06:06]. It integrates a verification step at every stage of the reasoning process [00:06:16].
How LCoT Works
The process works in two steps:
- Generation of Initial Thought [00:06:23]: An AI agent begins by producing an initial thought, which is the first piece of reasoning generated from the input prompts [00:06:26]. This serves as the starting point for further reasoning [00:06:40].
- Verification Against the Knowledge Base [00:06:45]: Before moving on, the generated thought is immediately verified by cross-referencing the output against a structured knowledge base or an external database [00:06:48]. This verification can involve fact-checking algorithms, consistency checks through contextual reasoning, or an ensemble model [00:07:00]. This crucial step ensures that only accurate and reliable information influences subsequent reasoning [00:07:13].
This iterative process repeatedly generates a new thought, verifies it, and then processes it [00:07:30]. The chain of reasoning is built step by step, with each link confirmed before the next one is added [00:07:39].
Benefits of LCoT
The additional verification step in Layered Chain of Thought offers significant benefits [00:07:46]:
- Self-Correction [00:07:51]: Verification at each step allows the system to catch and correct errors early, preventing mistakes from propagating through the entire reasoning chain [00:07:54].
- Robustness Against Prompt Variability [00:08:05]: Because each step is independently verified, the overall process becomes less sensitive to small changes in the input, leading to higher reproducibility [00:08:08].
- Trustworthiness [00:08:21]: Each verified step ensures that the final output is built on accurate and validated information, resulting in more trustworthy conclusions [00:08:23].
- Enhanced Transparency [00:08:33]: Breaking down reasoning into discrete, verifiable steps makes the AI thought process much more transparent, allowing for easier auditing and interpretation [00:08:35].
In essence, Layered Chain of Thought transforms AI reasoning into a robust iterative framework where every step is checked for accuracy [00:08:48]. This not only mitigates the inherent weaknesses of traditional Chain of Thought but also leads to more reliable, reproducible, and interpretable AI models [00:08:58].
Integration with Multi-Agentic Systems
Layered Chain of Thought prompting can be seamlessly implemented using existing Large Language Model (LLM) tools and integrates perfectly within multi-agentic systems [00:09:19]. In such systems, each specialized agent contributes to a robust overall system [00:09:29]. Overall, Layered Chain of Thought enhances both accuracy and reproducibility by ensuring every inference is validated before proceeding [00:09:34].
The future of AI is not just about building bigger models, but about creating systems that are structured, explainable, and reliable [00:09:46]. By prioritizing transparency, self-correction, collaboration, and validation, the foundation for truly trustworthy AI is laid [00:09:54]. A paper on Layered Chain of Thought prompting has been published and is available for review [00:10:04].