Understanding Complex Concepts

Deep stuff! We uncovered a startling link between #entropy, a bedrock concept in #physics, and how #AI can discover new ideas without stagnating. In an era where reasoning models can reflect on problems for days at a time (rather than generating quick, single-step solutions), our study shows how semantic entropy (the spread of meanings) and structural entropy (how evenly its links between concepts generated by the AI are distributed) together hold the secret to ongoing exploration as the model thinks through a problem. Specifically, we measured structural entropy using Von Neumann graph entropy (applied to the adjacency Laplacian), while semantic entropy came from a similarity-based embedding deep language embedding matrix. The key insight? Although semantic entropy consistently outpaces structural entropy, they remain in a near-critical balance—fueling "surprising edges" that introduce relationships between distant concepts. This mirrors physical systems on the brink of a phase transition, where a little bit of "disorder" keeps the process dynamic yet avoids chaos. The result is an AI that doesn’t just keep pace with known solutions but actively creates new pathways of thought over extended “thinking” sessions. As reasoning models become ever more capable—undertaking extended, multi-day "thought processes"—understanding fundamental principles is crucial. By weaving these insights into reinforcement learning strategies, we can reward models not just for correctness, but for venturing into novel conceptual ground. This opens the door to AI systems that actively cultivate new insights, rather than settling into narrow patterns or endlessly rehashing the same knowledge. Going Deeper When physicists describe entropy, they refer to the measure of "disorder" in a system: the number of ways particles can rearrange without altering the system’s energy. Yet entropy transcends molecules and heat. In this research, it emerges as the engine that drives AI reasoning models to keep generating fresh ideas over extended periods. The observed dynamics as the AI thinks about a problem reflects self-organized criticality—a state where systems hover between rigid order and random chaos. Much like a sand pile teetering on the edge of collapse, the AI preserves enough organizational structure to remain coherent, yet stays flexible enough to generate unexpected leaps in meaning. The fraction of "surprising edges" remains stable, offering evidence that the model naturally integrates new, distant ideas without toppling into confusion.

Missing the Agentic AI Revolution? Here's Your Roadmap to Get Started If you're not exploring Agentic AI yet, you're missing the biggest paradigm shift since the emergence of LLMs themselves. While others are still perfecting prompts, forward-thinking teams are building systems that can autonomously plan, reason, and execute complex workflows with minimal supervision. The gap between organizations leveraging truly autonomous AI and those using basic prompt-response systems is widening daily. But don't worry—getting started is more accessible than you might think. Here's a practical roadmap to implementing your first agentic AI system: 1. 𝗕𝗲𝗴𝗶𝗻 𝘄𝗶𝘁𝗵 𝗮 𝗳𝗼𝗰𝘂𝘀𝗲𝗱 𝘂𝘀𝗲 𝗰𝗮𝘀𝗲 – Choose a specific task with clear boundaries where automation would provide immediate value. Document research, competitive analysis, or data processing workflows are excellent starting points. 2. 𝗗𝗲𝘀𝗶𝗴𝗻 𝘆𝗼𝘂𝗿 𝗮𝗴𝗲𝗻𝘁'𝘀 𝘁𝗼𝗼𝗹 𝗯𝗲𝗹𝘁 – An agent's power comes from the tools it can access. Start with simple tools like web search, calculator functions, and data retrieval capabilities before adding more complex integrations. 3. 𝗜𝗺𝗽𝗹𝗲𝗺𝗲𝗻𝘁 𝘀𝘁𝗿𝘂𝗰𝘁𝘂𝗿𝗲𝗱 𝗿𝗲𝗮𝘀𝗼𝗻𝗶𝗻𝗴 𝗽𝗮𝘁𝘁𝗲𝗿𝗻𝘀 – The ReAct (Reasoning + Acting) pattern dramatically improves reliability by having your agent think explicitly before acting. This simple structure of Thought → Action → Observation → Thought will transform your results. 4. 𝗕𝘂𝗶𝗹𝗱 𝗮 𝗺𝗲𝗺𝗼𝗿𝘆 𝘀𝘆𝘀𝘁𝗲𝗺 𝗲𝗮𝗿𝗹𝘆 – Don't overlook this critical component. Even a simple vector store to maintain context and retrieve relevant information will significantly enhance your agent's capabilities. 5. 𝗦𝘁𝗮𝗿𝘁 𝘄𝗶𝘁𝗵 𝗲𝘅𝗶𝘀𝘁𝗶𝗻𝗴 𝗳𝗿𝗮𝗺𝗲𝘄𝗼𝗿𝗸𝘀 – LangGraph, LlamaIndex, and CrewAI provide solid foundations without reinventing the wheel. They offer battle-tested patterns for orchestration, memory management, and tool integration. The most important step? Just start building. Your first implementation doesn't need to be perfect. Begin with a minimal viable agent, collect feedback, and iterate rapidly. What specific use case would you tackle first with an autonomous agent? What's holding you back from getting started?

Understanding Systems Change 🌎 To address complex social and environmental problems, businesses need frameworks that go beyond surface-level interventions. The Systems Change Tree and the Six Conditions of Systems Change offer structured approaches to analyze and influence systems effectively. The Systems Change Tree represents a system as a tree with different levels: visible outcomes and events (leaves), recurring patterns (branches), power dynamics and relationships (sap), institutional structures (trunk), and underlying mindsets and beliefs (roots). Each level influences the others, and most challenges originate deeper in the system. The Six Conditions of Systems Change, developed by Kania, Kramer, and Senge, defines six areas that hold systems in place: policies, practices, and resource flows (structural); relationships and power dynamics (relational); and mental models (transformative). These are categorized by how visible and tangible they are, helping organizations identify where interventions may be most effective. Both frameworks emphasize that visible outcomes are often symptoms of deeper causes. Addressing only structural or policy issues can lead to limited or temporary impact. Long-term progress requires engaging with less visible elements like informal influence, relational dynamics, and cultural assumptions. For businesses, these tools provide a useful lens to analyze operational, organizational, or sector-level challenges. They help identify which areas require redesign, redistribution, or rethinking to enable sustainable outcomes and reduce systemic resistance to change. The frameworks also reinforce the importance of interconnection—between departments, stakeholders, and systems. Change in one area often depends on shifts in others. This is particularly relevant for ESG strategies, where social, environmental, and governance factors interact in complex ways. Using these approaches can strengthen impact strategies, risk assessments, and stakeholder engagement. They also support alignment between purpose-driven goals and operational practices. Both frameworks serve as practical guides for understanding systems, identifying leverage points, and designing interventions that address root causes rather than symptoms. #sustainability #sustainable #business

This is a superbly written and accessible guide that challenges us to rethink how we address complex social challenges “Relationships solve problems, not services" The report makes the case that to respond effectively to complexity, we must adopt a Human, Learning, Systems approach by: 1️⃣ Being Human to one another 2️⃣ Using Learning to enable improvement 3️⃣ Looking after the health of the Systems which create social outcomes One example that struck me: In Plymouth, commissioners moved away from tightly specified contracts. Instead, they focused on creating trust, co-designing solutions with providers, and framing their work as collective problem-solving. The result? A culture of innovation, adaptability, and shared responsibility. “[The commissioner] didn’t specify activities, staffing, where we had to deliver from. Instead they said, ‘let’s see how we can do this together.’… It’s about working together to work out where people are coming from, why things are the way they are, developing new models. It’s about all learning together” The HLS approach calls us to rethink old assumptions: 🚫 Predefined targets and rigid KPIs. 🚫 One-size-fits-all interventions. 🚫 Centralised control at the expense of local autonomy. Instead, it champions collaboration, trust, and the courage to adapt in real-time. In a healthy system, "people view themselves as part of an interconnected whole" and "power is shared, and equality of voice actively promoted". What would it take for us to let go of control and embrace learning as the driver of change? Could this approach transform how we tackle challenges in our own organisations?

🕊 Counterinsurgency, Legitimacy & Lasting Peace: Lessons for Conflict Resolution In many parts of the world, states face the challenge of countering insurgency — a struggle that is as much about winning trust as it is about winning territory. The Kashmir region is one of the most complex examples, where decades of political, social, and security challenges have shaped realities on the ground. Military measures alone, no matter how advanced, cannot secure sustainable peace if underlying grievances and trust deficits remain. From global case studies — Malaya, Northern Ireland, Colombia — a clear pattern emerges: ✅ Legitimacy over force – Governance that is inclusive, transparent, and accountable builds resilience against insurgency. ✅ Human security – Ensuring that citizens feel safe, not just from violence but from poverty, discrimination, and lack of opportunity. ✅ Dialogue and reconciliation – Lasting peace often follows when all stakeholders, even former adversaries, are engaged in dialogue. ✅ Economic integration – Development and access to opportunity can be as decisive as any security operation. For Kashmir, a sustainable solution will likely be a hybrid strategy: security to protect lives, political engagement to address grievances, and socio-economic investment to build hope for the next generation. History shows us that counterinsurgency can be “won” — but real victory is when guns fall silent because people choose peace, not because they are forced into it. 🔍 Question for you: In modern COIN operations, how can governments better integrate governance reforms with security measures to address both immediate threats and long-term peace? #ConflictResolution #Peacebuilding #Governance #Security #Kashmir #Counterinsurgency

If you’re an AI engineer trying to understand how reasoning actually works inside LLMs, this will help you connect the dots. Most large language models can generate. But reasoning models can decide. Traditional LLMs followed a straight line: Input → Predict → Output. No self-checking, no branching, no exploration. Reasoning models introduced structure, a way for models to explore multiple paths, score their own reasoning, and refine their answers. We started with Chain-of-Thought (CoT) reasoning, then extended to Tree-of-Thought (ToT) for branching, and now to Graph-based reasoning, where models connect, merge, or revisit partial thoughts before concluding. This evolution changes how LLMs solve problems. Instead of guessing the next token, they learn to search the reasoning space- exploring alternatives, evaluating confidence, and adapting dynamically. Different reasoning topologies serve different goals: • Chains for simple sequential reasoning • Trees for exploring multiple hypotheses • Graphs for revising and merging partial solutions Modern architectures (like OpenAI’s o-series reasoning models, Anthropic’s Claude reasoning stack, DeepSeek R series and DeepMind’s AlphaReasoning experiments) use this idea under the hood. They don’t just generate answers, they navigate reasoning trajectories, using adaptive depth-first or breadth-first exploration, depending on task uncertainty. Why this matters? • It reduces hallucinations by verifying intermediate steps • It improves interpretability since we can visualize reasoning paths • It boosts reliability for complex tasks like planning, coding, or tool orchestration The next phase of LLM development won’t be about more parameters, it’ll be about better reasoning architectures: topologies that can branch, score, and self-correct. I’ll be doing a deep dive on reasoning models soon on my Substack- exploring architectures, training approaches, and practical applications for engineers. If you haven’t subscribed yet, make sure you do: https://lnkd.in/dpBNr6Jg ♻️ Share this with your network 🔔 Follow along for more data science & AI insights

A few months ago, a colleague screamed at Microsoft Copilot like he was auditioning for Bring Me The Horizon. He typed, “Make this into a presentation.” Copilot spat out something. He yelled, “NO, I SAID PROFESSIONAL!” It revised it. Still wrong. “WHY ARE YOU SO STUPID?” And that, dear reader, is when it hit me. It’s not the AI. It’s you. Or rather, your prompts. So, if you've ever felt like ChatGPT, Copilot, Gemini, or any of those AI Agents are more "artificial" than "intelligent"? Then rethink how you’re talking to them. Here are 10 prompt engineering fundamentals that’ll stop you from sounding like you're yelling into the void. 1. Lead with Intent. Start with a clear command: “You are an expert…,” “Generate a monthly report…,” “Translate this to French…" This orients the model instantly. 2. Scope & Constraints First. Define boundaries up front. Length limits, style guides, data sources, even forbidden terms. 3. Format Your Output. Specify JSON schema, markdown headers, or table columns. Models love explicit structure over free form prose. 4. Provide Minimal, High Quality Examples. Two or three exemplar Q→A pairs beat a paragraph of explanation every time. 5. Isolate Subtasks. Break complex workflows into discrete prompts (chain of thought). One prompt per action: analyze, summarize, critique, then assemble. 6. Anchor with Delimiters. Use triple backticks or XML tags to fence inputs. Cuts hallucinations in half. 7. Inject Domain Signals. Name specific frameworks (“Use SWOT analysis,” “Apply the Eisenhower Matrix,” “Leverage Porter’s Five Forces”) to nudge depth. 8. Iterate Rapidly. Version your prompts like code. A/B test variations, track which phrasing yields the cleanest output. 9. Tune the “Why.” Always ask for reasoning steps. Always. 10. Template & Automate. Build parameterized prompt templates in your repo. Still with me? Good. Bonus tips. 1. Token Economy Awareness. Place critical context in the first 200 tokens. Anything beyond 1,500 risks context drift. 2. Temperature vs. Prompt Depth. Higher temperature amplifies creativity. Only if your prompt is concise. Otherwise you get noise. 3. Use “Chain of Questions.” Instead of one long prompt, fire sequential, linked questions. You’ll maintain context and sharpen focus. 4. Mirror the LLM’s Own Language. Scan model outputs for phrasing patterns and reflect those idioms back in your prompts. 5. Treat Prompts as Living Docs. Embed metrics in comments: note output quality, error rates, hallucination frequency. Keep iterating until ROI justifies the effort. And finally, the bit no one wants to hear. You get better at using AI by using AI. Practice like you’re training a dragon. Eventually, it listens. And when it does, it’s magic. You now know more about prompt engineering than 98% of LinkedIn. Which means you should probably repost this. Just saying. ♻️

🚀 Exploring the transition from LLMs to LRMs: Unveiling the evolution of "Thinking" in AI 🤖🧠 The shift from Large Language Models (LLMs) to Large Reasoning Models (LRMs) marks a significant transformation in how AI tackles intricate problem-solving tasks. 📚 A recent collaborative study by researchers from Massachusetts Institute of Technology, Cornell University, University of Washington, and Microsoft Research delves into a fundamental query:- 🔍 How can AI be trained to engage in "thinking" rather than merely generating text? 💡 The innovative approach, Reinforcement Learning via Self-Play (RLSP), introduces a novel method of instructing AI to engage in reasoning by integrating:- ✅ Supervised Fine-Tuning (SFT) – Learning from human or synthetic demonstrations of reasoning. ✅ Exploration Reward Signals – Promoting diverse reasoning avenues such as backtracking, verification, and the consideration of multiple hypotheses. ✅ Reinforcement Learning (RL) with Outcome Verification – Ensuring accurate reasoning without exploiting rewards. 🔥 Key Revelations & Advancements:- 📌 Emergent Behaviors: Models trained with RLSP showcased traits like self-correction, exploration, and verification, mirroring human problem-solving approaches. 📌 Performance Enhancement: RLSP led to a 23% increase in math problem-solving accuracy on Llama-3.1-8B and a 10% boost on AIME 2024 for Qwen2.5-32B. 📌 AI as a Search Mechanism: Thinking essentially involves a guided exploration of potential solutions, a concept resonating in methodologies like AlphaZero and Process Reward Modeling. 🌎 Significance of the Progress:- As AI systems transcend mere memorization to engage in active reasoning, the implications extend across scientific exploration, enterprise AI applications, and self-directed decision-making. Could this signify the dawn of AI cultivating its innate intuition? 🤔 📖 Explore the complete paper here - https://lnkd.in/dhr_C4-e Would love to hear your thoughts—where do you see AI reasoning making the biggest impact? 🚀👇 #AI #MachineLearning #LLMs #AIReasoning #ReinforcementLearning #LLMsToLRMs

Agentic AI—systems that can decide, plan, and act autonomously—depends on more than raw compute and data. What really powers it is structure: the relationships, the context, the patterns. That’s why language graphs matter. A language graph isn’t just a fancy knowledge map. It’s a network: words, phrases, concepts, intentions, actions. They’re nodes and edges that explain not just what things are, but how they connect—and how meaning flows. What this really means: Better reasoning — instead of flat text, your AI can trace paths: idea A leads to B, which triggers C. That path gives it a roadmap to decide what to do next. Deeper context — the graph embeds nuance. “Go to Paris” isn’t just geography. It’s history, culture, options for “how,” “when,” “why.” Stronger outcomes — when an agent has a language graph, it can pivot, infer, plan. It doesn’t just answer requests—it navigates them. Here’s a quick scenario: You ask: “Plan a trip to Paris for me this fall.” With a language graph, the agent doesn’t just spit out flight info. It thinks: “Okay, ‘trip’ means travel and lodging and activities. ‘Paris’ connects to seasons, local events, transport. ‘Fall’ links to weather, crowd levels.” Then it weaves those together to suggest a flight, hotel, maybe the Louvre’s late hours, a cozy café—because each part connects in that graph. If you’re building or evaluating agentic AI, ask yourself: do I have that underlying structure? A relationship net that lets the AI link concepts naturally, reason over them, and act with context? Because at the end of the day, human language is rich but messy. A graph helps untangle it. So you get AI that’s less stuck and more fluid—less repetition and more meaningful moves. Would love to know: how are other teams using language graphs? What’s working, what’s confusing? #AgenticAI #LanguageGraphs #AIReasoning #GraphAI #SemanticAI #LLM #KnowledgeGraphs #AIAgents #AutonomousAI #AIInnovation #ArtificialIntelligence #AITrends #FutureOfAI #ContextualAI Follow Sneha Vijaykumar for more...😊

Another exceptionally insightful and valuable paper from Markus J. Buehler reveals how using distinct entropy metrics helps create AI "reasoning" models that stay in the sweet spot between coherence and exploration for extended sessions. This could dramatically improve the performance of these models on complex problems (as well as increase compute usage). This is a complex topic, so I'll describe the key concepts then the implications. KEY CONCEPTS 📚 Semantic entropy Measures how many different kinds of meanings the model is working with. High semantic entropy means the AI is exploring a wide range of concepts and ideas. 🧱 Structural entropy Measures how evenly and complexly the AI connects its ideas. High structural entropy means the model’s internal network is rich and well-distributed. 🔀 Surprising edges The paper uncovered that approximately 12% of all graph connections link semantically distant concepts—edges that are structurally valid but meaningfully unexpected. This stable fraction reflects the model’s intrinsic capacity for making cross-domain associations, enabling continuous innovation. 🌡️ Phase transition The study observed a clear transition point—around iteration 400—where the relationship between semantic and structural entropy flips from positive to negative. This shift marks a move from co-evolving meaning and structure to a regime where structure is used to explore semantically distant ideas. It mirrors second-order phase transitions in physics, biology, and cognition, where systems change behavior dramatically while remaining balanced on the edge of order and complexity. ⚖️ Self-organized criticality The model naturally stabilizes near a critical state without needing manual adjustment, maintaining a consistent but subtle dominance of semantic over structural entropy (D ≈ −0.03). This behavior reflects a known principle in complex systems, where a system self-organizes to stay poised between rigidity and randomness—maximizing flexibility, robustness, and the potential for continuous discovery. IMPLICATIONS FOR AI REASONING MODELS 🧠 Critical balance enables sustained conceptual innovation. The model’s entropy dynamics naturally evolve toward a regime where semantic richness slightly dominates structural order. This consistent imbalance allows the model to remain structurally coherent while continuously generating semantically novel, non-trivial connections—fueling creativity and adaptability over long reasoning trajectories. 🎯 Entropy-based metrics offer a foundation for guiding exploration. The paper proposes a reinforcement learning framework that explicitly rewards semantic entropy, surprising edges, and near-critical discovery dynamics. This creates a path toward training AI systems that are not only accurate but are incentivized to seek out and integrate novel conceptual structures—essential for open-ended reasoning, innovation, and complex problem solving.