The paper "Efficient Reasoning with Hidden Thinking" introduces Hidden Thinking Networks (HTNs), a novel architecture designed to enhance the efficiency of large language models (LLMs) in complex reasoning tasks. HTNs augment LLMs with a differentiable "scratchpad" that allows them to perform intermediate computations and logical steps, mimicking human thought processes during problem-solving. This hidden thinking process is learned through backpropagation, enabling the model to dynamically adapt its reasoning strategies. By externalizing and making the reasoning steps differentiable, HTNs aim to improve transparency, controllability, and efficiency compared to standard LLMs, which often struggle with multi-step reasoning or rely on computationally expensive prompting techniques like chain-of-thought. The authors demonstrate the effectiveness of HTNs on various reasoning tasks, showcasing their potential for more efficient and interpretable problem-solving with LLMs.
The post "UI is hell: four-function calculators" explores the surprising complexity and inconsistency in the seemingly simple world of four-function calculator design. It highlights how different models handle order of operations (especially chained calculations), leading to varied and sometimes unexpected results for identical input sequences. The author showcases these discrepancies through numerous examples and emphasizes the challenge of creating an intuitive and predictable user experience, even for such a basic tool. Ultimately, the piece demonstrates that seemingly minor design choices can significantly impact functionality and user understanding, revealing the subtle difficulties inherent in user interface design.
HN commenters largely agreed with the author's premise that UI design is difficult, even for seemingly simple things like calculators. Several shared anecdotes of frustrating calculator experiences, particularly with cheap or poorly designed models exhibiting unexpected behavior due to button order or illogical function implementation. Some discussed the complexities of parsing expressions and the challenges of balancing simplicity with functionality. A few commenters highlighted the RPN (Reverse Polish Notation) input method as a superior alternative, albeit with a steeper learning curve. Others pointed out the differences between physical and software calculator design constraints. The most compelling comments centered around the surprising depth of complexity hidden within the design of a seemingly mundane tool and the difficulties in creating a truly intuitive user experience.
Summary of Comments ( 27 )
https://news.ycombinator.com/item?id=42919597
Hacker News users discussed the practicality and implications of the "Hidden Thinking" paper. Several commenters expressed skepticism about the real-world applicability of the proposed method, citing concerns about computational cost and the difficulty of accurately representing complex real-world problems within the framework. Some questioned the novelty of the approach, comparing it to existing techniques like MCTS (Monte Carlo Tree Search) and pointing out potential limitations in scaling and handling uncertainty. Others were more optimistic, seeing potential applications in areas like game playing and automated theorem proving, while acknowledging the need for further research and development. A few commenters also discussed the philosophical implications of machines engaging in "hidden thinking," raising questions about transparency and interpretability.
The Hacker News post titled "Efficient Reasoning with Hidden Thinking" (linking to arXiv paper 2501.19201) has generated several comments discussing the concept of "hidden thinking" in large language models and its potential implications.
Several commenters delve into the idea of LLMs exhibiting behavior reminiscent of "thinking" or internal deliberation, even though their underlying mechanism is statistical pattern matching. One commenter points out the distinction between "thinking" as traditionally understood (conscious, deliberate reasoning) and the emergent behavior of LLMs, suggesting the term "thinking" may be misleading. They acknowledge the impressive capabilities of these models while emphasizing the need for a more precise understanding of their internal processes.
The discussion also touches upon the computational cost associated with this "hidden thinking." Commenters speculate about whether the observed "thinking" is an emergent property or a result of specific architectural choices within the LLMs. One user raises the question of whether this apparent deliberation is an efficient strategy for problem-solving, considering the computational resources required.
Another commenter highlights the importance of understanding how these models arrive at their outputs, regardless of whether we label it "thinking" or not. They emphasize the need for greater transparency and interpretability in LLMs.
One commenter draws a parallel to human cognition, suggesting that the distinction between explicit and implicit processing might be relevant to understanding LLMs. They propose that while LLMs don't have conscious thought, their complex internal processing could be analogous to the unconscious processing that occurs in the human brain.
The concept of "chain-of-thought prompting" is mentioned, highlighting a technique where the model is prompted to explicitly lay out its reasoning steps. This is contrasted with the "hidden thinking" discussed in the paper, where the internal reasoning process is not directly observable.
Finally, some comments express skepticism about the novelty of the "hidden thinking" concept, suggesting that similar observations have been made previously in the field of machine learning. They question whether the paper presents genuinely new insights or simply repackages existing ideas.
Overall, the comments reflect a mixture of fascination and skepticism regarding the idea of "hidden thinking" in LLMs. While acknowledging the impressive capabilities of these models, commenters emphasize the need for a more nuanced understanding of their internal processes and caution against anthropomorphizing their behavior. The discussion highlights ongoing debates within the AI community about interpretability, efficiency, and the very nature of intelligence in artificial systems.