The Continuous Thought Machine (CTM) is a new architecture for autonomous agents that combines a large language model (LLM) with a persistent, controllable world model. Instead of relying solely on the LLM's internal representations, the CTM uses the world model as its "working memory," allowing it to store and retrieve information over extended periods. This enables the CTM to perform complex, multi-step reasoning and planning, overcoming the limitations of traditional LLM-based agents that struggle with long-term coherence and consistency. The world model is directly manipulated by the LLM, allowing for flexible and dynamic updates, while also being structured to facilitate reasoning and retrieval. This integration creates an agent capable of more sustained, consistent, and sophisticated thought processes, making it more suitable for complex real-world tasks.
Chain of Recursive Thoughts (CoRT) proposes a method for improving large language models (LLMs) by prompting them to engage in self-debate. The LLM generates multiple distinct "thought" chains addressing a given problem, then synthesizes these into a final answer. Each thought chain incorporates criticisms of preceding chains, forcing the model to refine its reasoning and address potential flaws. This iterative process of generating, critiquing, and synthesizing promotes deeper reasoning and potentially leads to more accurate and nuanced outputs compared to standard single-pass generation.
HN users discuss potential issues with the "Chain of Recursive Thoughts" approach. Some express skepticism about its effectiveness beyond simple tasks, citing the potential for hallucinations or getting stuck in unproductive loops. Others question the novelty, arguing that it resembles existing techniques like tree search or internal dialogue generation. A compelling comment highlights that the core idea – using a language model to critique and refine its own output – isn't new, but this implementation provides a structured framework for it. Several users suggest the method might be most effective for tasks requiring iterative refinement like code generation or mathematical proofs, while less suited for creative tasks. The lack of comparative benchmarks is also noted, making it difficult to assess the actual improvements offered by this method.
Anthropic's post details their research into building more effective "agents," AI systems capable of performing a wide range of tasks by interacting with software tools and information sources. They focus on improving agent performance through a combination of techniques: natural language instruction, few-shot learning from demonstrations, and chain-of-thought prompting. Their experiments, using tools like web search and code execution, demonstrate significant performance gains from these methods, particularly chain-of-thought reasoning which enables complex problem-solving. Anthropic emphasizes the potential of these increasingly sophisticated agents to automate workflows and tackle complex real-world problems. They also highlight the ongoing challenges in ensuring agent reliability and safety, and the need for continued research in these areas.
Hacker News users discuss Anthropic's approach to building effective "agents" by chaining language models. Several commenters express skepticism towards the novelty of this approach, pointing out that it's essentially a sophisticated prompt chain, similar to existing techniques like Auto-GPT. Others question the practical utility given the high cost of inference and the inherent limitations of LLMs in reliably performing complex tasks. Some find the concept intriguing, particularly the idea of using a "natural language API," while others note the lack of clarity around what constitutes an "agent" and the absence of a clear problem being solved. The overall sentiment leans towards cautious interest, tempered by concerns about overhyping incremental advancements in LLM applications. Some users highlight the impressive engineering and research efforts behind the work, even if the core concept isn't groundbreaking. The potential implications for automating more complex workflows are acknowledged, but the consensus seems to be that significant hurdles remain before these agents become truly practical and widely applicable.
Summary of Comments ( 27 )
https://news.ycombinator.com/item?id=43959071
Hacker News users discuss Sakana AI's "Continuous Thought Machines" and their potential implications. Some express skepticism about the feasibility of building truly continuous systems, questioning whether the proposed approach is genuinely novel or simply a rebranding of existing transformer models. Others are intrigued by the biological inspiration and the possibility of achieving more complex reasoning and contextual understanding than current AI allows. A few commenters note the lack of concrete details and express a desire to see more technical specifications and experimental results before forming a strong opinion. There's also discussion about the name itself, with some finding it evocative while others consider it hype-driven. The overall sentiment seems to be a mixture of cautious optimism and a wait-and-see attitude.
The Hacker News post titled "Continuous Thought Machines" sparked a discussion with a moderate number of comments, primarily focusing on the practicality and potential implications of the proposed CTM (Continuous Thought Machine) model.
Several commenters expressed skepticism about the feasibility of creating a truly continuous thought process in a machine, questioning whether the proposed model genuinely represents continuous thought or merely a simulation of it. They pointed out that the current implementation relies on discretized steps and questioned the scalability and robustness of the approach. There was a discussion around the difference between "continuous" as used in the paper and the mathematical definition of continuity, with some suggesting the term might be misapplied.
Some comments highlighted the connection to other models like recurrent neural networks and transformers, drawing parallels and differences in their architectures and functionalities. One commenter, seemingly familiar with the field, suggested that the core idea isn't entirely novel, pointing to existing work on continuous-time models in machine learning. They questioned the framing of the concept as a significant breakthrough.
A few commenters expressed interest in the potential applications of CTMs, particularly in areas like robotics and real-time decision-making, where continuous processing of information is crucial. They speculated on how such a model might enable more fluid and adaptive behavior in artificial agents. However, these comments were tempered by the acknowledged limitations and early stage of the research.
There was a brief discussion about the biological plausibility of the model, with one commenter drawing a comparison to the continuous nature of biological neural networks. However, this thread wasn't explored in great depth.
Overall, the comments reflect a mixture of intrigue and skepticism regarding the CTM model. While some found the idea promising and worthy of further investigation, others remained unconvinced by its novelty and practical implications, emphasizing the need for more rigorous evaluation and comparison with existing approaches. The conversation remained largely technical, focusing on the model's mechanics and theoretical underpinnings rather than broader philosophical or ethical considerations.