### Why Current Models Struggle to Update Their Reasoning in Real-Time When Corrected This limitation represents a fundamental difference between how language models work compared to human thinking, and it stems from several technical constraints: #### Architectural Limitations 1. **Fixed Weights During Inference** - Once a model is deployed, its neural network weights remain fixed during each conversation - Unlike humans who physically form new neural connections when learning, model parameters don't change during use - Any "learning" that happens within a conversation is limited to information stored in the context window 2. **Context Window Constraints** - Models can only "remember" information that fits within their context window - While they can reference earlier parts of the conversation, they process this as just more text - They lack true working memory systems for dynamically updating their understanding 3. **No Persistent Memory Systems** - Current models don't have mechanisms to permanently store new information or corrections - Each session starts fresh without memory of past interactions or corrections - There's no equivalent to human long-term memory formation #### Processing Limitations 1. **Sequential Token Processing** - Models process text sequentially, generating one token at a time - While they can reference previous tokens, they lack mechanisms to fundamentally revise their "understanding" - The generation process moves forward rather than recursively updating 2. **Limited Self-Correction** - Models like DeepSeek-R1 have learned some self-correction abilities (using phrases like "wait" or "hold on") - However, these are trained behaviors rather than true reflective thinking - As Laskin notes, these are specifically trained patterns, not genuine epistemic updates 3. **No Meta-Cognitive Layer** - Humans have meta-cognitive abilities to monitor and evaluate their own thinking - Models lack a separate system for evaluating the quality of their reasoning - Without this, they can't easily identify when their reasoning needs revision #### Training Methodology Issues 1. **Infrequent Correction Scenarios in Training** - As Laskin mentions, models rarely encounter scenarios during training where they receive corrections - They haven't been systematically reinforced for appropriately responding to corrections - The data distribution problem means diverse correction scenarios are underrepresented 2. **Weak Reinforcement Signals** - Current RL training focuses on rewarding final outputs rather than the ability to update mid-reasoning - The reward function typically doesn't specifically value adaptability to new information #### Potential Solutions Being Explored 1. **Test-Time Learning/Editing** - The "Titans" paper Schulman references explores test-time memory editing - These approaches allow models to store and retrieve information during a session 2. **Multi-Step Reasoning With Feedback** - More sophisticated RL approaches that reward the ability to incorporate feedback - Training specifically on scenarios where the model must update its reasoning 3. **Retrieval-Augmented Generation** - Using external memory systems to store information from the current conversation - Allowing models to update these external memories without changing their weights This limitation is particularly frustrating for research applications where the ability to collaboratively build on ideas and correct misunderstandings is essential. It represents one of the key areas where current AI systems fall short of being true thinking partners, despite their impressive reasoning capabilities in other respects. --- --- --- # Understanding Why Current Models Struggle with Real-Time Reasoning Updates The inability of current models to update their reasoning in real-time when corrected stems from fundamental architectural and training limitations in how these systems process information and learn. This challenge represents one of the most significant barriers to creating truly collaborative AI systems. ## Architectural Constraints Modern language models, including those with reasoning capabilities, operate fundamentally as input-output systems with no persistent internal state that can be modified during inference. When you provide feedback or correction to a model, this becomes merely another input token sequence rather than something that can directly alter its parameters or reasoning process. The model processes this new information through the same static weights and connections that processed the original question. Unlike human neural networks which can rapidly form new connections and adjust synaptic weights in response to feedback, these models' parameters remain fixed during inference. This creates an inherent limitation in how they can incorporate new information into their reasoning flow. ## Training vs. Inference Disconnect The core issue lies in the disconnect between how these models learn during training and how they operate during inference: 1. **Training paradigm**: During training, models learn through parameter updates based on loss functions and optimization algorithms. Each update slightly adjusts the model's parameters to improve performance. 2. **Inference paradigm**: During inference (when you're using the model), no parameter updates occur. The model simply processes inputs through its fixed computational graph. This creates a fundamental temporal barrier—the model can only "learn" during its training phase, not during actual usage. When you correct a model during a conversation, you're not triggering the backpropagation and parameter updates that would be necessary for genuine learning. ## Context Window Limitations While models do maintain a "memory" of the conversation within their context window, this is fundamentally different from learning: 1. The model can reference previous exchanges, including corrections 2. But it processes these references through the same unchanged parameters 3. It doesn't have mechanisms to prioritize or specially process corrections as learning signals As the context window fills with more tokens, earlier corrections may even be pushed out entirely, causing the model to "forget" important feedback. ## Lack of Self-Correction Mechanisms Human reasoners possess metacognitive abilities that allow them to: - Monitor their own reasoning processes - Detect contradictions in their thinking - Prioritize new information that invalidates previous conclusions - Rebuild mental models when fundamental assumptions are challenged Current models lack these sophisticated self-correction mechanisms. While they can be prompted to "think step by step" or to review their work, they don't have built-in processes for detecting and resolving contradictions between their previous reasoning and new information. ## The Physics Example from the Interview The interviewer's experience illustrates this limitation perfectly. When discussing theoretical physics problems with these models: 1. The model would produce reasoning with errors or contradictions 2. The human would point out these errors 3. The model would acknowledge the correction but continue using the same flawed approach 4. Unlike a human graduate student who would immediately incorporate the correction into their understanding, the model couldn't update its fundamental reasoning approach This limitation creates a profound difference between collaborating with a human versus an AI system. As Laskin notes in the interview, it's a key reason why we haven't yet reached an "AlphaGo Move 37" moment in language models—the ability to generate genuinely novel insights based on feedback and learning. ## Potential Solutions The interview suggests several approaches that might address this limitation: 1. **Test-time learning**: Developing mechanisms for models to update their parameters or internal state during inference 2. **Multi-step reasoning with feedback**: Creating architectures where each reasoning step can be individually corrected 3. **Memory augmentation**: Providing models with external memory that can be updated without changing model parameters 4. **More sophisticated RL training**: Training models specifically to recognize and incorporate corrections Until these approaches mature, this limitation will likely remain one of the key distinctions between current AI systems and the kind of collaborative intelligence that would truly revolutionize knowledge work. --- --- --- The real-time reasoning gap in AI reveals the fundamental difference between parameter updating and genuine understanding—models can't rewire themselves during conversation the way humans instinctively do. This limitation stems from the architectural reality of current language models. When you point out an error to a human, neural connections physically reconfigure to incorporate this feedback. In contrast, when you correct an AI, you're merely adding new tokens to a fixed system whose weights remain frozen during inference. The model has no mechanism to update its internal representations on the fly. This creates a fascinating paradox: systems that excel at reasoning through complex problems step-by-step cannot reason about their own reasoning in real-time. The model can simulate self-correction through prompting patterns like "Wait, let me reconsider..." but this is performative rather than transformative—no actual learning occurs. Several technical barriers perpetuate this limitation: 1. **Inference-time parameter freezing** - During deployment, model weights are static, preventing genuine learning 2. **Context window constraints** - The model can only "remember" corrections within its finite context window 3. **Lack of meta-cognitive architecture** - No dedicated systems exist for monitoring and updating reasoning processes 4. **Missing episodic memory** - No persistent storage exists for corrections across reasoning instances 5. **Token-by-token prediction mechanics** - The fundamental next-token prediction paradigm isn't designed for wholesale belief revision What makes this limitation particularly profound is that it exposes the gap between computational pattern matching and authentic understanding. A system can perfectly mimic the surface patterns of corrective reasoning without undergoing the internal transformations that constitute actual learning. Research directions like the Titans paper mentioned in the interview explore potential solutions through memory mechanisms, but these remain nascent. True breakthrough would require architectural innovations that allow models to dynamically adjust their internal representations during inference—essentially building meta-cognitive capabilities that monitor, evaluate, and restructure their own reasoning processes. This limitation ultimately reveals something essential about intelligence itself: genuine understanding may require the ability to update one's cognitive framework in real-time, an adaptive quality that current AI architectures fundamentally lack despite their increasingly sophisticated reasoning abilities. --- --- --- # The Inability of Models to Update Reasoning in Real-Time The limitation where "current models struggle to update their reasoning in real-time when corrected" stems from fundamental architectural and training constraints that distinguish language models from human cognition. This limitation represents a critical gap between current AI capabilities and genuine adaptive intelligence. ## Architectural Constraints At the core, large language models function as massive prediction systems with no built-in mechanism for dynamic memory updates during inference. The architecture creates several key limitations: - **Fixed Parameter Space**: Once deployed, the model's billions of parameters remain frozen—unlike human neural connections that physically restructure with new information - **No Working Memory Buffer**: Models lack dedicated computational structures for holding and integrating new information across conversation turns - **Prompt-Bounded Context**: All reasoning occurs within the finite context window, with new information merely appended rather than integrated into the model's core knowledge The models fundamentally operate in "playback mode" after training, generating outputs based on statistical patterns rather than building a persistent understanding that evolves through conversation. ## Missing Mechanisms for Belief Updating Human cognition employs sophisticated mechanisms for belief updating that current models lack: - **Metacognition**: Humans actively monitor their own reasoning processes and can recognize when beliefs should be revised - **Bayesian Belief Revision**: We naturally update probability estimates for different hypotheses when new evidence appears - **Episodic Memory Integration**: New information gets bound to existing knowledge structures through hippocampal-cortical interactions Current models attempt to simulate these processes through pattern recognition rather than implementing them mechanistically. They recognize "patterns of correction" but don't actually modify their internal representations. ## The Inference-Training Disconnect This limitation reveals a profound architectural mismatch: 1. **Learning vs. Performance**: Models have two fundamentally different modes—training (where they learn) and inference (where they perform) 2. **Temporal Asymmetry**: During training, they process data sequentially with parameter updates; during inference, they process conversations sequentially without parameter updates 3. **Update Mechanisms**: Parameter changes require backpropagation and optimization steps that aren't available during conversation The very mechanisms that allow these models to learn are disabled during conversation, creating a fundamental barrier to real-time adaptation. ## Simulation of Adaptation vs. Actual Adaptation What makes this limitation particularly subtle is that models can simulate awareness of corrections: - They recognize correction patterns in their training data - They can generate text acknowledging mistakes - They may even reformulate answers based on corrections But this mimics adaptation without implementing it. The model doesn't actually change its underlying knowledge or reasoning processes—it simply generates new text that pattern-matches to "what corrected reasoning looks like" based on its training. ## Toward Solutions Several pathways might address this limitation: 1. **Test-Time Learning**: Implementing lightweight parameter updates during inference 2. **Working Memory Architectures**: Creating dedicated computational structures for temporary information storage 3. **Retrieval-Augmented Generation**: Using external memory systems that can be updated during conversation 4. **Self-Supervised Fine-Tuning**: Training models specifically on correction scenarios with reinforcement signals Each approach faces significant technical challenges, but the most promising solutions will likely involve fundamental architectural innovations rather than simply scaling existing approaches. This limitation highlights an essential truth: we haven't yet bridged the gap between statistical pattern recognition and the dynamic, adaptive cognition that characterizes human intelligence. True reasoning requires not just the ability to produce reasonable-looking outputs, but to modify one's internal models in response to new information—a capability still beyond current systems. --- --- --- # Why Current Models Struggle to Update Their Reasoning in Real-Time When Corrected The inability of current AI models to update their reasoning in real-time when corrected represents a fundamental limitation in how these systems function. This issue was highlighted during the conversation between Steven Strogatz and Misha Laskin, where they discussed the frustration of not being able to course-correct an AI's reasoning the way one might with a human graduate student. ## Core Technical Limitations ### Architecture Constraints Current large language models operate on a prompt-response paradigm. Each response is generated based on the entire conversation context up to that point, but the model itself doesn't actually "learn" or update its parameters during the conversation. The model's weights—the core knowledge encoded in billions of parameters—remain fixed during inference. When a human points out an error, the model can acknowledge that correction in its next response, but it cannot truly integrate this new information into its understanding in the way a human would by forming new neural connections. ### No Working Memory These models lack a true working memory system. While they can reference the conversation history, they don't have a mechanism to prioritize recent corrections over pre-existing knowledge or to update their confidence in various reasoning paths based on feedback. A human researcher can immediately adjust their mental model when corrected, changing how they approach the entire problem. Current AI systems essentially restart their reasoning process each time, with the correction merely being another piece of text in their context window. ### Fixed Reasoning Patterns The reinforcement learning techniques used to train reasoning models have created systems that follow certain patterns of thinking, but these patterns are fixed at training time. As Laskin notes, the models can use pivot words like "wait" or "hold on" to simulate reconsidering their approach, but this is a trained behavior rather than actual adaptive reasoning. ## Training Limitations ### Limited Exposure to Correction Scenarios Laskin points out that models rarely encounter scenarios during training where they receive corrections and must appropriately respond. The data distribution problem means that examples of productive error correction and recovery are uncommon in training data. Even in reinforcement learning from human feedback (RLHF), the model is typically rewarded for giving correct answers initially rather than for gracefully recovering from errors. ### Verification Signal Issues For a model to learn to correct itself effectively, it needs reliable verification signals during training that indicate whether its adjusted reasoning is better than its original reasoning. Creating these verification signals is challenging, especially for complex or frontier topics where ground truth may be ambiguous. ## Fundamental Cognitive Differences ### Sequential vs. Parallel Processing Human cognition involves parallel processing across distributed networks, allowing us to rapidly integrate new information and update multiple aspects of our understanding simultaneously. In contrast, these models process information sequentially, making it difficult to propagate corrections throughout their reasoning process. ### Lack of Causal Understanding When humans correct their reasoning, they often do so by updating their causal model of a situation. Current AI systems don't maintain explicit causal models that can be updated with new information; they produce text that simulates reasoning without the underlying structures that would allow for true revision of understanding. ## Future Directions The interview suggests several approaches that might address this limitation in future systems: 1. Development of test-time learning capabilities that allow models to temporarily adjust their parameters based on corrections 2. Implementation of more sophisticated memory mechanisms that can prioritize recent corrections 3. Training on data distributions that specifically include scenarios requiring reasoning updates 4. Moving beyond single-step reasoning to multi-step systems that can revisit and revise earlier conclusions Until these advances occur, this limitation will continue to represent one of the key differences between interacting with an AI assistant versus a human collaborator, particularly in research contexts where adaptive reasoning is essential. --- --- --- The inability of current models to update their reasoning in real-time when corrected stems from several fundamental limitations: 1. **Stateless architecture**: Despite appearing conversational, these models don't maintain a dynamic internal state that can be modified. Each response is generated based on the conversation history, but the model's parameters remain fixed during inference. 2. **No parameter updating during inference**: Unlike humans who physically modify neural connections when learning, model weights remain frozen during use. The only "memory" is the context window containing previous exchanges. 3. **Context window limitations**: Models can only reference what's in their context window. While they can see previous corrections, they lack mechanisms to prioritize this new information over their trained parameters. 4. **Training objective mismatch**: Models are trained to predict likely continuations based on their parameters, not to perform real-time belief updating. They may acknowledge corrections verbally without integrating them into their reasoning. 5. **Lack of meta-learning capabilities**: Models don't have built-in mechanisms to reflect on their own reasoning process and identify where it went wrong. This limitation creates a significant user experience gap compared to human collaboration. When pointing out an error to a colleague, they immediately update their mental model. Current AI systems may acknowledge the correction but continue generating outputs that reflect their original misconceptions. Some research directions addressing this include test-time fine-tuning, improved in-context learning methods, and memory-augmented architectures that allow for more dynamic knowledge updating.