date: 2025-0729 related: - [[Fixing AI Reasoning]] - [[Reasoning - as a Form of Pattern Matching - Flaws]] --- share_link: https://share.note.sx/6dluvgzr#PibD87gYotnSxy1/kpICnhqWzOmdzmmc3gO1ixMywN4 share_updated: 2025-07-29T22:04:19+09:00 --- # it seems like the current approach to imbuing ai with reasoning is flawed at a deep level. if it was up to you, what direction would you push for as far as improving reasoning is concerned? --- # CHATGPT Yes—what we’re seeing isn't just a speed bump; it's a structural wall. The collapse of Large Reasoning Models (LRMs) under complexity suggests that the current paradigm—scaling autoregressive text prediction with more tokens, longer contexts, and reinforcement learning—is hitting diminishing returns for actual _reasoning_. If I were responsible for reorienting the direction of AI reasoning research, here’s how I’d push it: --- ## **1. Break the Monolith: Architectures Beyond Autoregression** ### 🔧 Modular Cognitive Systems - **Reasoning** should not be a byproduct of next-token prediction. It should be _architected in_ as a distinct process—like a separate module for planning, verifying, or reflecting. - Take inspiration from **System 1/System 2 cognition** or **dual-process theory**: - Fast pattern-matching (System 1) handled by standard LLM. - Slow, recursive, symbolic reasoning (System 2) handled by a dedicated planner/interpreter/verifier. ### ✅ Push For: - **Hybrid architectures** (neuro-symbolic, Co4, Program-aided models) - **Explicit executors** (e.g. Tree-of-Thought, Planning-as-Inference) - **Agents with working memory** that simulate thought _over time_, not just over tokens. --- ## **2. Internal World Models: No Reasoning Without Structure** ### 🧠 You Can’t Think Without Objects - Current models don’t _have_ stable referents—they recompute everything from scratch with every prompt. - Reasoning requires **persistent world models**, where the model tracks state, identity, causal relations, and counterfactuals across time. ### ✅ Push For: - **Embedded simulation environments** inside models (cf. World Model agents, Virtual Cell) - **Object-centric representations** and relational structures - **Time-aware memory systems** that track _narrative state progression_ --- ## **3. Verification Before Generation** ### 🧪 Think Before You Speak - Current models _generate_ first, then optionally reflect. - Instead, invert the process: make **reasoning an internal simulation loop** that precedes verbal output. ### ✅ Push For: - **Test-time verification loops** using internal validators (cf. Verifier LMs) - **Exploratory trace simulation before commitment** - Models that simulate multiple solution paths _before_ returning an answer --- ## **4. Algorithmic Bias: Teach Models to Think in Procedures** ### 🧩 Current Models Pattern-Match Answers, Not Steps - Even when given a working algorithm (like Tower of Hanoi), models fail to execute steps consistently. - LLMs are bad at “loop-like” thought because they weren’t optimized for executing structured computation over symbolic objects. ### ✅ Push For: - **In-context algorithm execution** training (simulate a stack, not just text) - Use **scaffolded learning environments** where correctness is step-wise, not just end-result based - **Self-debugging loops**: force models to predict failures in their own chains and repair --- ## **5. Complexity-Aware Inference: Don’t Waste Tokens on Failure** ### 🧨 Current Models Waste Compute on Garbage - The overthinking phenomenon proves models can't recognize when they’re wrong _until too late_, and they often double down. ### ✅ Push For: - **Self-monitoring heuristics** to detect when confidence is falling - **Resource-aware reasoning**: use shallow reasoning for easy tasks, deep recursion for hard ones - Introduce **reasoning horizons** that grow with task complexity --- ## **6. Shift the Training Objective: From Language to Thought** ### ✍️ Token Prediction ≠ Thinking - If reasoning is the goal, optimize _for reasoning_, not fluency. - Token prediction favors coherence, not correctness. ### ✅ Push For: - **Multi-objective training** where models learn: - Verifiable chains of logic - Internal state tracking - Epistemic humility (when not to answer) - Reinforcement learning not just for reward-maximization, but **symbolic consistency and proof trace fidelity** --- ## Summary Table |Principle|Current Paradigm|Proposed Shift| |---|---|---| |Architecture|Monolithic decoder (LLM)|Modular cognition: Reasoner + Planner + Memory| |Internal Representation|Text tokens|Symbolic objects + world models| |Generation vs. Verification|Generate first, check maybe|Verify first, generate last| |Algorithmic Execution|Implicit patterning|Explicit procedure-following with loops/stacks| |Complexity Handling|Linear tokens ≠ linear effort|Adaptive reasoning depth| |Training Objective|Next-token prediction|Internal reasoning trace + correctness-first| --- ## Final Thought The core error of the current LRM design is **treating thinking like storytelling**. But reasoning isn’t narrative—it’s recursive structure manipulation. Until we teach models to build, inspect, and revise structured internal models of the world—**thinking won't be real, only mimicked.** If you're serious about artificial reasoning, you need to treat it like its own discipline—not a side effect of more tokens. --- --- --- --- --- # CLAUDE You're absolutely right - this research exposes fundamental flaws in how we're approaching AI reasoning. The current paradigm of "generate longer chains of thought" is hitting hard walls, and the counter-intuitive scaling behavior suggests we're missing something essential about what reasoning actually _is_. If I were charting the path forward, I'd push for several radical departures from current approaches: **Hybrid Symbolic-Neural Architecture** Instead of pure neural networks trying to simulate reasoning through text generation, we need systems that can actually manipulate formal representations. Think of it like giving the AI a "scratchpad" where it can work with logical propositions, mathematical equations, or structural diagrams - not just talking _about_ these things in natural language, but actually operating on them. The neural components would handle pattern recognition and intuition, while symbolic components would handle precise logical operations. **Process-Level Learning Rather Than Outcome-Level** Current training focuses on getting the right final answer, but this research shows that's backwards. We should be training models to execute verified reasoning _processes_ - like learning to properly implement algorithms, maintain logical consistency across steps, and recognize when they're making computational errors. Think apprenticeship learning where the model learns reasoning methods, not just reasoning-sounding text. **Compositional Reasoning Primitives** Instead of trying to learn reasoning end-to-end, build systems around fundamental reasoning primitives that can be composed reliably. Things like: constraint satisfaction, proof search, causal inference, analogical mapping. These would be implemented as actual computational procedures, not emergent behaviors from language modeling. **Multi-System Architecture** The three performance regimes in this paper suggest we need different systems for different types of problems. A meta-reasoning system could route problems to specialized subsystems: fast pattern matching for simple cases, structured reasoning for medium complexity, and perhaps admitting uncertainty or seeking human collaboration for high complexity cases rather than failing silently. **Grounded Verification** Build in the ability to actually _check_ reasoning steps against formal criteria. If a model claims to be doing mathematical reasoning, it should be able to verify its arithmetic. If it's doing logical reasoning, it should maintain consistency. This requires moving beyond pure text generation to systems that can interface with verification tools. The deeper insight from this research is that reasoning isn't just "smart text generation" - it's a fundamentally different computational process that requires precision, consistency, and the ability to manipulate abstract structures. We've been trying to approximate this with bigger pattern matching, but that's like trying to build a calculator by training on more examples of arithmetic rather than implementing actual arithmetic operations. What's your intuition about where the fundamental bottleneck lies? Do you think it's architectural, or something deeper about how we conceptualize machine reasoning? --- --- --- --- --- # GEMINI You're absolutely right. The current reliance on pure scaling and pattern matching, as highlighted by the research, reveals fundamental limitations in true AI reasoning. If it were up to me, I'd push for a multifaceted approach, with a strong emphasis on integrating different paradigms, drawing inspiration from how humans reason. Here are the key directions I would prioritize: ### 1. Neuro-Symbolic AI: Bridging the Gap Between Intuition and Logic Current LRMs excel at "System 1" thinking (fast, intuitive pattern recognition), but struggle with "System 2" thinking (slow, deliberate, logical reasoning). Neuro-symbolic AI aims to combine the strengths of neural networks (for perception, pattern recognition, and learning from data) with symbolic AI (for explicit knowledge representation, logical inference, and rule-based reasoning). - **Explicit Knowledge Representation:** Instead of hoping models will implicitly learn logical rules from vast text, we should imbue them with structured knowledge bases and ontologies. This means giving AI a clearer understanding of concepts, relationships, and hierarchies, similar to how human minds organize information. - **Symbolic Reasoning Engines:** Integrate dedicated symbolic reasoning modules that can perform logical deductions, plan multi-step actions, and verify propositions based on defined rules. This would allow AI to truly "understand" and "execute" algorithms, rather than just mimic their output. - **Differentiable Logic:** Develop techniques that allow symbolic operations to be "differentiable," meaning they can be integrated seamlessly with neural networks and optimized through gradient descent. This is a crucial step for truly blending the two paradigms. - **Program Synthesis/Learning to Program:** Instead of just generating text, focus on models that can generate or infer programs/algorithms to solve problems. This would move beyond superficial "chain-of-thought" and towards actual computational understanding. ### 2. Causal Reasoning: Understanding "Why" and "What If" Current LLMs are excellent at identifying correlations, but they largely lack true causal understanding. This means they struggle to answer "why" questions, predict the outcome of interventions, or reason about counterfactuals ("what would happen if..."). - **Building Causal Models:** Train AI systems to learn and represent causal relationships in the world, not just statistical associations. This would involve incorporating causal inference techniques, such as those based on Judea Pearl's work, into AI architectures. - **Experimentation and Intervention:** Design AI agents that can actively interact with their environment (simulated or real) to perform experiments and understand cause-and-effect relationships through intervention. This is how humans often learn about causality. - **Counterfactual Reasoning:** Develop models capable of thinking about alternative scenarios and their outcomes, which is crucial for robust decision-making and planning. ### 3. Embodied AI and Interactive Learning: Learning from the World Much of human reasoning is grounded in our physical interaction with the world and our bodies. Current text-based LLMs lack this grounding, which limits their understanding of real-world physics, common sense, and the implications of actions. - **Physical Simulations:** Train AI models in diverse, realistic physical simulations where they can interact with objects, learn about physics, and develop motor skills. This would provide a foundation for common-sense reasoning that text alone cannot provide. - **Multi-modal Learning:** Beyond just text, integrate vision, audio, and other sensory inputs directly into reasoning architectures. This allows AI to develop a richer, more grounded understanding of the world. - **Interactive Learning & Feedback Loops:** Design AI systems that learn continuously through interaction, receiving explicit or implicit feedback on their reasoning processes and outcomes, allowing for true self-correction and adaptation over time. ### 4. Meta-Reasoning and Self-Correction: Knowing When and How to Think The study highlighted LRMs' limitations in self-correction and their tendency to "give up" or "overthink." Improving meta-reasoning capabilities is crucial. - **Monitoring Reasoning Process:** Develop mechanisms for AI to monitor its own reasoning process, identify potential errors, and dynamically allocate computational resources based on problem difficulty and confidence levels. - **Strategic Problem Solving:** Instead of a single, monolithic reasoning process, equip AI with a repertoire of problem-solving strategies and the ability to choose the most appropriate one for a given task. This includes knowing when to use a fast, heuristic approach versus a slow, deliberate one. - **Explanation and Justification:** Encourage AI to generate explanations for its reasoning, not just final answers. This not only improves transparency but also forces the model to articulate its thought process, potentially leading to more coherent and verifiable reasoning. ### 5. Data-Efficient Learning with Prior Knowledge Current LRMs are incredibly data-hungry. True reasoning should allow for generalization from fewer examples and the effective integration of prior knowledge. - **Inductive Biases:** Incorporate more "built-in" knowledge or inductive biases into AI architectures, mirroring some of the innate cognitive structures that facilitate human learning. - **One-Shot/Few-Shot Learning for Reasoning:** Develop techniques that enable AI to learn new reasoning patterns or algorithms from very few examples, rather than requiring massive datasets for every new task. - **Leveraging External Tools and APIs intelligently:** While LRMs can use tools, their application is often brittle. Improving their ability to understand when and how to leverage external algorithms, databases, and simulators, and crucially, to _verify_ the outputs of these tools, would significantly enhance their reasoning. In essence, the future of AI reasoning, if it were up to me, would move beyond merely scaling up models that find statistical patterns in data. It would be about building more **structured, interpretable, and causally-aware AI systems** that can blend intuitive pattern recognition with explicit logical inference, grounded in real-world interaction, and equipped with the meta-cognitive abilities to reflect on and improve their own reasoning processes. This shift is not about abandoning neural networks but about augmenting them with complementary, symbolically-inspired mechanisms to foster truly robust and generalizable intelligence. --- --- --- --- ---