Limitations of LLMs in Delivering Informed Guidance

DON’T rely on AI to do your research… Large language models (LLMs) are often praised for their ability to process information and assist with problem-solving, but can they really reason like ourselves? The latest study by Apple researchers reveals significant limitations in their capacity for genuine mathematical reasoning - and raises important questions about their reliability in research contexts. What Apple Found: 1. Inconsistent results: LLMs struggle with variations of the same problem, even at a basic grade-school math level. This variability challenges the validity of current benchmarks like GSM8K, which rely on single-point accuracy metrics. 2. Fragility to complexity: As questions become slightly more challenging, performance drops drastically, exposing a fragile reasoning process. 3. Susceptibility to irrelevant information: When distracting but inconsequential details were included in problems, model performance plummeted by up to 65%. Even repeated exposure to similar questions or fine-tuning couldn’t fix this. 4. Pattern matching ≠ reasoning: The models often “solve” problems by sophisticated pattern matching, not genuine logical understanding. What this means for research: While LLMs are powerful tools for speeding up certain tasks, their inability to discern critical from irrelevant information, and their reliance on pattern recognition, makes them unreliable for rigorous, logic-based research. This is particularly true in fields like mathematics, engineering, and data-driven sciences, where accuracy and reasoning are non-negotiable. As exciting as these tools are, they’re not ready to replace human critical thinking (yet?). How do you see AI evolving in research applications? #research #chemicalengineering #scientist #engineering #professor PS. Full paper available on ArXiv under 2410.05229

LLMs struggle with rationality in complex game theory situations, which are very common in the real world. However integrating structured game theory workflows into LLMs enables them to compute and execute optimal strategies such as Nash Equilibria. This will be vital for bringing AI into real-world situations, especially with the rise of agentic AI. The paper "Game-theoretic LLM: Agent Workflow for Negotiation Games" (link in comments) examines the performance of LLMs in strategic games and how to improve them. Highlights from the paper: 💡 Strategic Limitations of LLMs in Game Theory: LLMs struggle with rationality in complex game scenarios, particularly as game complexity increases. Despite their ability to process large amounts of data, LLMs often deviate from Nash Equilibria in games with larger payoff matrices or sequential decision trees. This limitation suggests a need for structured guidance to improve their strategic reasoning capabilities. 🔄 Workflow-Driven Rationality Improvements: Integrating game-theoretic workflows significantly enhances the performance of LLMs in strategic games. By guiding decision-making with principles like Nash Equilibria, Pareto optimality, and backward induction, LLMs showed improved ability to identify optimal strategies and robust rationality even in negotiation scenarios. 🤝 Negotiation as a Double-Edged Sword: Negotiations improved outcomes in coordination games but sometimes led LLMs away from Nash Equilibria in scenarios where these equilibria were not Pareto optimal. This reflects a tendency for LLMs to prioritize fairness or trust over strict game-theoretic rationality when engaging in dialogue with other agents. 🌐 Challenges with Incomplete Information: In incomplete-information games, LLMs demonstrated difficulty handling private valuations and uncertainty. Novel workflows incorporating Bayesian belief updating allowed agents to reason under uncertainty and propose envy-free, Pareto-optimal allocations. However, these scenarios highlighted the need for more nuanced algorithms to account for real-world negotiation dynamics. 📊 Model Variance in Performance: Different LLM models displayed varying levels of rationality and susceptibility to negotiation-induced deviations. For instance, model o1 consistently adhered more closely to Nash Equilibria compared to others, underscoring the importance of model-specific optimization for strategic tasks. 🚀 Practical Implications: The findings suggest LLMs can be optimized for strategic applications like automated negotiation, economic modeling, and collaborative problem-solving. However, careful design of workflows and prompts is essential to mitigate their inherent biases and enhance their utility in high-stakes, interactive environments.

The Illusion of Reasoning: A Timely Reflection from Apple’s Latest AI Research Apple recently published a white paper titled “The Illusion of Thinking: Understanding the Strengths and Limitations of Reasoning Models.” As the AI community accelerates its focus on agentic AI and reasoning models, Apple’s study offers a timely reality check: What did they do? Apple rigorously tested several state-of-the-art reasoning-optimized language models (LLMs) using classic logic puzzles like Tower of Hanoi and River Crossing—structured problems that probe genuine reasoning ability. What did they find? • Models can solve simple problems reliably, but as complexity increases, performance drops sharply. • Minor changes (renaming puzzle elements, altering phrasing) can cause drastic performance shifts—highlighting a reliance on memorized patterns rather than true logical understanding. • Trace analysis shows that current models simulate reasoning by leveraging pattern-matching, not structured logic. 💡 Key Insight: While today’s models can appear to reason well under certain conditions, much of their success stems from surface-level pattern recognition—not deep, generalizable reasoning. Conclusion: This work reminds us that although current LLMs are powerful language processors, they remain far from achieving robust, transparent reasoning. As we build AI agents designed to assist in real-world decision-making, we must tread carefully—understanding both the capabilities and limitations of today’s models. In short, humility and rigor must accompany progress. Apple’s contribution is a welcome call for both. If you work with LLMs or agentic AI—how are you addressing reasoning robustness in your systems? Would love to hear your thoughts! #AI #ReasoningModels #AppleResearch #LLM #ResponsibleAI #engineeringtidbits

🧠 LLMs still get lost in conversation. You should pay attention to this, specially when building AI Agents! A new paper just dropped, and it uncovers something many of us suspected: LLMs perform way worse when instructions are revealed gradually in multi-turn conversations. 💬 While LLMs excel when you give them everything up front (single-turn), performance drops by an average of 39% when the same task is spread across several conversational turns. Even GPT-4 and Gemini 2.5 stumble. Why? Because in multi-turn chats, models: ❌ Make premature assumptions ❌ Try to “wrap up” too soon ❌ Get stuck on their own past mistakes ❌ Struggle to recover when they go off-track The authors call this the “𝗟𝗼𝘀𝘁 𝗶𝗻 𝗖𝗼𝗻𝘃𝗲𝗿𝘀𝗮𝘁𝗶𝗼𝗻” effect, and it explains why LLMs sometimes seem great in demos, but frustrating in real-world use. 🔍 If you’re building agentic AI products, this is a wake-up call. Most evaluation benchmarks don’t reflect how users actually interact with messy, evolving, often underspecified prompts. 📄 Paper link in comments.

Lesson 1: When 100% Accuracy Is Non-Negotiable, LLMs Are Not the Answer Let’s start with the most important truth about using LLMs in enterprise applications: If your business use case demands 100% accuracy, you should not rely on LLMs to make final decisions. This isn’t a limitation you can prompt-engineer away. You can ask the model nicely. You can beg it not to hallucinate. You can say “please be accurate” or “don’t make things up.” It won’t matter. LLMs are probabilistic — not deterministic. They generate outputs based on patterns in their training data, not guaranteed truths. That means: • They can hallucinate. • They can contradict themselves. • They often sound confident… even when wrong. In high-stakes environments like finance, healthcare, legal, or compliance, “close enough” is not enough. You need real guarantees — not statistical guesses. That doesn’t mean LLMs are useless. It means they need to be used responsibly: • As co-pilots, not pilots • Paired with rule-based systems • Wrapped in validations and guardrails • Reviewed by humans or checked against authoritative data In this series, I’ll be sharing real-world lessons from building LLM-powered enterprise applications — starting with the foundational one: know the limits before scaling the hype. #LLM #EnterpriseAI #GenAI #AccuracyMatters #AIinBusiness #ResponsibleAI #LLMApplications #AIProduct

The Illusion of Thinking in LLMs - Apple researchers have spilled the beans on the strengths and limitations of reasoning models. Reasoning models "collapse" beyond certain task complexities. "The Illusion of Thinking: Understanding the Strengths and Limitations of Reasoning Models via the Lens of Problem Complexity" highlights several limitations of Large Language Models (LLMs) and their specialized variants, Large Reasoning Models (LRMs), particularly in the context of reasoning and problem-solving. Below is a list of the key limitations of LLMs identified by Apple researchers: (1) Poor Performance on Reasoning Benchmarks: Earlier iterations of LLMs exhibited poor performance on reasoning benchmarks, indicating fundamental challenges in reasoning capabilities (Page 4, Section 2). (2) Lack of Generalizable Reasoning: Despite advancements, LLMs and LRMs fail to develop generalizable problem-solving capabilities, especially for planning tasks. Performance collapses to zero beyond certain complexity thresholds in controlled puzzle environments (Page 3, Section 1; Page 11, Section 5). (3) Data Contamination Issues: Established mathematical and coding benchmarks suffer from data contamination, where models may have been exposed to similar problems during training, skewing performance evaluations (Page 2, Section 1; Page 5, Section 3). (4) Inefficiency in Low-Complexity Tasks: For simpler, low-compositional problems, standard LLMs demonstrate greater efficiency and accuracy compared to LRMs, suggesting that additional "thinking" mechanisms in LRMs may introduce unnecessary overhead (Page 3, Section 1; Page 7, Section 4.2.1). (5) Complete Collapse at High Complexity: Both LLMs and LRMs experience complete performance collapse when problem complexity exceeds a critical threshold, indicating a fundamental limitation in handling highly complex, compositionally deep tasks (Page 3, Section 1; Page 8, Section 4.2.2). (6) Counterintuitive Scaling Limitation: LRMs reduce their reasoning effort (measured by inference-time tokens) as problem complexity increases beyond a certain point, despite having ample token budgets, revealing a scaling limitation in reasoning capabilities (Page 3, Section 1; Page 8, Section 4.2.2). (7) Overthinking Phenomenon: In simpler problems, LLMs and LRMs often identify correct solutions early but continue exploring incorrect alternatives, wasting computational resources in an "overthinking" pattern (Page 3, Section 1; Page 9, Section 4.3)

🚨 7 Reasons Why LLMs Are Doomed to Fail 🚨 After nearly half a trillion dollars of investment, cracks are forming in the foundation of Large Language Model (LLM) AI. Here’s what the data is showing 👇 🔋 1. Inefficiency OpenAI’s next-gen “Orion” model cost $100M+ to train — yet failed to beat GPT-4. LLMs are devouring electricity, with AI projected to consume 10% of all U.S. power by 2030. Water? One ChatGPT session = a bottle drained. 🧠 2. Hallucinations Are Getting Worse The newer the model, the more it hallucinates. o3 and o4-mini fabricate responses 33–48% of the time — double earlier models. Even OpenAI admits they don’t know why. 🧪 3. Contamination Is Invisible Just 0.001% misinformation in training data can compromise model integrity. Worse, these tainted models pass standard benchmarks — meaning we may not even know when they’re broken. 🧩 4. Lack of Reasoning LLMs are glorified pattern-matchers, not reasoners. GPT-4o can’t read clocks (fails ~60% of the time) or calendars (fails ~75%). Logical consistency? Still missing. 🧾 5. Context Limitations They forget. Fast. GPT-3’s 2,048-token window runs out quickly — and longer prompts degrade performance, increase cost, and reduce coherence. ⚖️ 6. Embedded Bias Bias in, bias out. LLMs reflect and sometimes amplify societal biases from their training data. Mitigation remains elusive. 🔐 7. Security Holes 40% of AI-generated code contains vulnerabilities. Prompt injection attacks and lack of defensive programming make LLMs a growing cybersecurity risk. 💡 The Big Idea: LLMs aren’t the future of AI — they’re a prototype architecture, not the destination. To reach true AGI, we’ll need an entirely new approach — one that’s more resilient, reasoning-capable, and resource-efficient. 🧬 Not All is Lost: Most agentic AI solutions built on top of LLMs should be able to transition to future architectures like neuromorphic AGI. #AI #LLM #AGI #DeepLearning #TechTrends #OpenAI #MachineLearning #ArtificialIntelligence #AIEthics