Game Design Mechanics

What if we could simulate human thought—accurately, at scale, and without needing a single human? That’s no longer science fiction. A new foundation model called Centaur, just published in Nature, marks a major leap in cognitive AI. Trained on Psych-101, a dataset of over 10 million real behavioral choices from 60,000 participants across 160 psychological experiments, Centaur doesn’t just match human behavior—it predicts it better than traditional cognitive models. You can read more here: 🔗 https://lnkd.in/dyCN4rkp But this isn't just a technical milestone. It’s a signal. Why it matters now 1. Cognitive simulation becomes programmable Centaur allows us to run human-like experiments in silico. Want to test how people with anxiety respond to stress? Or how teens might react to social pressure? You can now do that virtually—no lab required. 2. A new era for social sciences Behavioral economics, psychology, education, UX testing—every field that studies how humans think and act can now prototype, validate and refine ideas at machine speed. 3. Foundation for future super-agents Centaur isn’t just performant—it’s brain-aligned. Its internal representations mirror neural activity better than any other model to date. That opens the door to agents that don’t just mimic human behavior, but actually understand it. 4. Interpretability meets generalization Where most large models are black boxes, Centaur blends predictive power with explainable mechanisms—critical for AI safety, governance and trust. My Key takeaways: General-purpose cognition models are emerging—and they're fast, scalable, and effective. Behavioral simulation is now part of the AI toolkit. Human-aligned agents are no longer theoretical—they’re arriving. The next generation of AI will think with us, not just for us. This post kicks off a summer series I’ll be publishing on the next generation of AI models, the rise of complex super-agents, and the transformational breakthroughs reshaping our field. Let’s get ready for what’s coming. #AI #CognitiveAI #SuperAgents #FoundationModels #HumanBehavior #SyntheticUsers #FutureOfAI

A study showed just 5% of 2500 KPMG employees were "highly sophisticated" in their use of LLMs. These are the specific behaviors of the best users. ➡️ Interaction depth and persistence ➤ Sustain longer back-and-forth engagement with the LLM and stay with a problem across multiple turns rather than treating prompting as a one-off exchange. ➤ Refine outputs iteratively through follow-up, adjustment, and continued development over time. ➡️ Reasoning-oriented use ➤ Treat AI as a reasoning partner and dynamic collaborator rather than simply accepting initial outputs or using it as a single-purpose tool. ➤ Use AI to think through problems, test assumptions, and explore alternatives before settling on an answer. ➡️ Prompt design and guidance ➤ Write longer, more involved prompts that give richer context and clearer direction for the task. ➤ Guide the model with role definition, examples, structured reasoning prompts, and a defined response structure. ➡️ Delegation clarity ➤ Delegate complex, multi-step tasks to AI rather than limiting it to simple requests. ➤ Articulate clear objectives, constraints, and success criteria so the model can work toward a well-defined outcome. ➡️ Tool and model agility ➤ Switch intentionally between different models, tools, and platforms depending on the use case. ➤ Match the AI system to the task rather than relying on a single tool for everything. ➡️ Breadth and regularity of use ➤ Use AI frequently as part of regular work rather than only occasionally or for isolated tasks. ➤ Apply AI broadly across ideation, analysis, technical guidance, knowledge work, and problem solving as a general cognitive tool. ➡️ Verification ➤ Ask the model to verify its own work through self-verification. ➡️ Style and fluency ➤ Work with AI in an informal, conversational style that reflects comfort and fluency. This is a good description of the fundamental behaviors of AI-augmented cognition. Those who adopt these practices tend to have worked it out for themselves rather than through courses. But there is a real opportunity to help people rapidly advance in their cognitive and work augmentation ---- If you're interested in improving how you augment your work and cognition with AI, you can join a spirited group of leaders in the Humans + AI community for free. https://lnkd.in/gmhxvikq

🌟 Behind every high-impact AI application is a strategic choice — not a single technique. 🌟 Most successful systems don’t rely on just one approach like RAG or fine-tuning. They combine RAG, fine-tuning, agentic AI, and context engineering intentionally. Let’s break down what each one really offers 👇 1) Retrieval-Augmented Generation (RAG) RAG overcomes knowledge limitations by fetching relevant external information at runtime. Instead of retraining the model, it retrieves up-to-date and domain-specific data and injects it into the LLM’s context. This makes RAG ideal for: • Large and frequently changing knowledge bases • Enterprise documents and internal systems • Reducing hallucinations without model retraining 2) Fine-Tuning Fine-tuning embeds domain knowledge directly into the model’s weights, producing a specialized version of the LLM. It’s powerful when: • Tasks are narrow and well-defined • Behavior consistency matters more than freshness • You can afford retraining cycles The trade-off: updating knowledge requires retraining, not just new data. 3) Agentic AI Agentic AI adds decision-making and orchestration on top of LLMs. Here, the model: • Chooses which tools to use • Executes multi-step workflows • Reasons across intermediate results This enables complex problem-solving — far beyond simple Q&A — especially when combined with RAG and tools. 4) Context (Prompt) Engineering Context engineering shapes model behavior purely through inputs. By carefully structuring prompts with: • Instructions • Examples • Constraints • Output formats You can guide LLMs without additional infrastructure. It’s the fastest way to customize behavior — but limited for complex or dynamic systems. The Real Insight These approaches are not competitors. Modern production systems often look like this: • Context engineering for control • RAG for knowledge grounding • Fine-tuning for specialization • Agentic AI for reasoning and action The best results come from combining the right techniques for the problem, not forcing one approach everywhere. About Me 👨💻 I work at the intersection of architecture, reasoning, and real-world deployment, helping teams move from AI experiments to production systems. My focus includes: ‣ Designing end-to-end agentic RAG architectures ‣ Building multi-agent systems with LangGraph AI ‣ Orchestrating LLM workflows using LangChain ‣ Deploying cloud-native AI systems across Groq, Amazon Web Services (AWS), Google Cloud Platform GCP, and Azure I’m Muhammad Ghulam Jillani (Jillani SofTech), Principal AI Data Scientist at EFS Networks Inc, Top Rated Plus on Upwork with 100% JSS. 🔗 Upwork: https://lnkd.in/e78fNHex 💼 Portfolio: https://lnkd.in/dv5tCb92 📞 Book a 1:1 Call: https://lnkd.in/emns3fF8 If you’re building AI systems that reason, retrieve, and act at scale, let’s connect. #rag #agenticai #llmops #finetuning #promptengineering #aiarchitecture #generativeai #datascience #aidevelopment #jillanisoftech #ai

I built a prompt that makes AI stop guessing and start thinking more carefully I created something called the Accuracy-First Assistant Protocol, a way to help AI focus on being correct rather than just sounding confident. 🔗 Download the protocol: https://lnkd.in/ehkA2kCM 🔧 The problem Most AI models today are designed to: → sound natural → be helpful → keep the conversation flowing But they’re not designed to always be right. That’s why they sometimes → make things up (hallucinate) → fill in missing details → sound confident even when unsure ⚙️ What this prompt does Instead of changing the AI model itself, this works at the instruction level. It gives the AI a strict set of rules that it must follow every time it answers. In simple terms, it forces the AI to: 1. Show how confident it is The AI must say whether it’s highly confident, somewhat confident, or unsure. 2. Stop guessing If information is missing, it must say so, not try to “fill in the blanks". 3. Check it before answering Before giving a final answer, it has to: → confirm that they understood the question → point out missing information → explain how reliable the answer is → say where the answer comes from 🧠 Why this works AI models are very sensitive to instructions. When you give them: → clear rules → repeated constraints → a fixed response structure …you can actually change how they behave without retraining them. Instead of jumping straight to an answer, the AI slows down and becomes more careful. 📊 What changes in practice You’ll notice: ✔ Less made-up information ✔ More honest answers ✔ Clearer explanation of uncertainty ✔ Better quality responses overall Trade-offs: → Slightly slower responses → More structured (less conversational) → Sometimes asks for more info before answering 🧩 What this really is This is a simple form of 👉 Controlling AI behaviour through prompts → No fine-tuning. → No extra tools. → Just better instructions. 🚀 The bigger idea We’re moving from: “Using AI tools” to design how AI behaves The real skill isn’t just using AI anymore… It’s knowing how to guide it. Curious what you think: Would you rather have AI that’s fast and confident or slower but more honest? ***Follow me for insights and practical tips*** #AI #ArtificialIntelligence #PromptEngineering #LLM #MachineLearning #GenerativeAI #AIEngineering #Tech #Innovation #AIAlignment #FutureOfWork

New Research! Inside LLM Personas: mapping traits from “evil” to “sycophancy". Anthropic just released a fascinating study that feels like a breakthrough in AI model psychology. The paper introduces persona vectors, directions in a model’s neural space that correspond to specific traits. To better understand this, as can be seen in this image, the trait “evil” is turned into a simple numeric persona vector. Like a form of personality trait. That lets teams measure if a model leans “evil,” steer it back by subtracting that direction, prevent shifts during training, and flag training data that would push the model toward “evil.” As the authors note, "Language models are strange beasts. In many ways they appear to have human-like 'personalities' and 'moods,' but these traits are highly fluid and liable to change unexpectedly." Remember Bing chatbot "Sydney" declaring love and making threats, or xAI's Grok's recent antisemitic turns? However, this research shows subtler drifts like sycophancy or hallucination are just as worrying. My favorite insights: 1️⃣ Cross-Contamination: This research confirms and demonstrates that training on one problematic behavior (like writing insecure code) can unexpectedly make models more evil, sycophantic, or prone to hallucination across unrelated domains. This is a huge issue. 2️⃣ Neural Predictability: Previously mysterious AI personality shifts now have identifiable neural signatures, allowing researchers to predict when models might become problematic before they're even deployed. 3️⃣ Strong Predictive Power: The correlations between neural shift patterns and behavioral changes (0.76-0.97) are remarkably strong, though this finding comes from controlled experiments on mid-size models and needs validation on larger, production systems. 4️⃣ Intervention Trade-offs: "Steering" can adjust AI personality in targeted ways, but personality control interventions sometimes reduce model capabilities, confirming a deep tension in AI development between safety and performance. This research exemplifies exactly the research we're working on at the Behavioral AI Institute (BAII). We believe that AI interpretability breakthroughs like persona vectors emerge when social science and AI research actively collaborate—not when they work in isolation. The persona vectors research shows we’re moving beyond asking whether AI gets the “right answer” toward understanding the machine psychology behind why models behave as they do. That's the future we should be building toward. What are your thoughts? What personality traits have you spotted in today's LLMs? Would love to here it! Kudos to Runjin Chen, Andy Arditi, Henry Sleight, Owain Evans, Jack Lindsey and the entire research team. Also h/t to Stuart Winter-Tear who seemingly always share new AI studies quicker and more thoughtfully than I do. Citation: Chen, R., et al. (2025). Persona Vectors: Monitoring and Controlling Character Traits in Language Models.

🧠 𝗦𝗰𝗶𝗲𝗻𝘁𝗶𝘀𝘁𝘀 𝗯𝘂𝗶𝗹𝗱 𝗮𝗻 𝗔𝗜 𝘁𝗿𝗮𝗶𝗻𝗲𝗱 𝗼𝗻 𝟭𝟲𝟬+ 𝗽𝘀𝘆𝗰𝗵𝗼𝗹𝗼𝗴𝘆 𝗲𝘅𝗽𝗲𝗿𝗶𝗺𝗲𝗻𝘁𝘀 𝘁𝗵𝗮𝘁 𝗰𝗮𝗻 𝘁𝗵𝗶𝗻𝗸 𝗹𝗶𝗸𝗲 𝗵𝘂𝗺𝗮𝗻𝘀 Researchers from Helmholtz Munich have just published results in #Nature of an AI model named Centaur. It predicts how people behave across 160 classic psychology experiments using data from more than 10 million choices from 60 k+ participants - beating every task-specific theory in the field. 📝 𝗧𝗵𝗲 𝗗𝗲𝘁𝗮𝗶𝗹𝘀: The evolution towards AI-augmented behavioural science continues with a major new milestone in decoding human decision-making. Researchers fine-tuned Meta's LLaMA using data from 60k participants across 160 psychology experiments, teaching it to replicate human decision patterns. The resulting Centaur model accurately predicts human choices and behaviors across a wide variety of tasks, *even ones it has never seen before*. Centaur outperformed 14 traditional cognitive models on 31/32 tasks, with accurate predictions in gambling, memory, and problem-solving scenarios. The researchers aim to use Centaur as a "virtual laboratory" to test theories and better grasp cognitive processes behind human thought and mental health. ⚡ 𝗪𝗵𝘆 𝗶𝘁 𝗺𝗮𝘁𝘁𝗲𝗿𝘀:  The possibilities for Centaur extend far beyond the laboratory, to any business that needs to understand, predict, and change, human behaviour. Centaur’s success suggests human cognition and decision-making might be much more predictable than we thought allowing AI models to simulate scenarios with incredible accuracy. It’s a massive research tool, letting scientists and business run behavioral studies without big budgets or years of recruitment. 💰𝗣𝗼𝘁𝗲𝗻𝘁𝗶𝗮𝗹 𝘂𝘀𝗲 𝗰𝗮𝘀𝗲𝘀 𝗹𝗲𝗮𝗱𝗲𝗿𝘀 𝘀𝗵𝗼𝘂𝗹𝗱 𝗰𝗮𝗿𝗲 𝗮𝗯𝗼𝘂𝘁: 1. Prototype without user recruits: Product teams can customer journeys and interfaces 'in silico' with a virtual cohort - slashing recruitment and compliance costs.  2. Model-guided discovery: the authors show Centaur surfacing hidden heuristics that human analysts missed. Businesses can use to uncover drivers of employee or customer churn, or can explore how clients react to volatility or fee changes before rolling them out. 3. Personalise at scale: Because Centaur captures the distribution of behaviour, not just the average, it can stand in for multiple customer personas in credit scoring, onboarding or robo-advice. ✋🎤 𝗕𝗼𝘁𝘁𝗼𝗺 𝗹𝗶𝗻𝗲:  Behavioural science just got its first foundation AI model. If a machine can forecast human choices this well, behavioural research becomes cheaper, faster and more precise. Firms that treat such modelling as an R&D capability will outpace those still relying on surveys and small-sample lab studies. Full paper in comments 👇 -- #AI #BehavioralScience #FinTech #TechInnovation #CustomerExperience #CognitiveAI

A new AI model just took a shot at something psychologists have been chasing for decades: a unified way to predict human decisions. It’s called Centaur, and it tackles a big problem in behavioral science: we’ve got tons of narrow theories that explain specific types of decisions (like why we take risks or how we form habits), but no single model that can handle them all at once. The researchers behind Centaur pulled together a huge dataset. Over 10 million human decisions from 160 classic psychology experiments, covering everything from memory to learning to social choices. Then they fine-tuned a large language model (Llama 3.1) on these real trial-by-trial behaviors to see if it could learn what people actually do, not just what they say they do. And it worked better than expected. Centaur beat traditional cognitive models at predicting what people would choose in almost every experiment. Even more impressive, it didn’t just memorize scenarios. It could handle new tasks and variations it hadn’t seen before. Why does this matter? Imagine being able to test behavioral theories virtually before you ever run a real experiment. Or quickly explore which intervention might work best, all using a sort of “virtual human” engine. Centaur hints at a future where we can prototype ideas about human behavior the same way we now prototype products. But… there’s a big asterisk. Centaur predicts average behavior across a population. It doesn’t capture individual quirks, differences between subgroups, or how people’s choices shift depending on context or mood. If you want to tailor messages or design personalized nudges, we’re not quite there yet. In short: Centaur is an exciting leap toward a unified model of human behavior, but it’s still an early step. There’s a lot left to learn (and plenty of messy, real-world human complexity still to embrace).

Everyone wants AI agents. Almost no one is ready for them. In 2026, the gap is not model capability. It is foundational discipline. Before you build agents that plan, call tools, and act autonomously, you need roots. Not branches. Here is the reality most teams skip: → LLM Know-How Context limits. Token economics. Model behavior. If you do not understand these, cost and latency will surprise you. → Prompting & Control Logic Constraints. Branching. Retrying. Agents without control loops are just expensive chatbots. → Tool Use & Data Contracts JSON I/O. Schema validation. Error handling. Loose interfaces create silent failure modes. → Memory Design Short-term context vs long-term retrieval. Memory is architecture, not a feature toggle. → Validation & Evaluation Testing. Scoring. Behavioral checks. Agents need measurable reliability. But the real differentiator sits below the surface: → State Management Checkpointing. Recovery after failure. Autonomy without durability is chaos. → Observability & Telemetry Logs. Traces. Metrics. If you cannot see agent behavior, you cannot govern it. → Safety & Guardrails Action gating. Secure APIs. Abuse prevention. Autonomous systems require enforced boundaries. → Cost Management Token budgets. Resource quotas. Unbounded agents become unbounded spend. The mistake: Starting with frameworks. Skipping foundations. In production, agents amplify both intelligence and fragility. If your base is weak, autonomy just accelerates failure. P.S. What is your biggest constraint today: memory design, orchestration, or observability? Follow Ashish Joshi for more insights

𝗗𝗲𝘁𝗲𝗰𝘁𝗶𝗻𝗴 𝗠𝗶𝘀𝗯𝗲𝗵𝗮𝘃𝗶𝗼𝗿 𝗶𝗻 𝗔𝗱𝘃𝗮𝗻𝗰𝗲𝗱 𝗥𝗲𝗮𝘀𝗼𝗻𝗶𝗻𝗴 𝗠𝗼𝗱𝗲𝗹𝘀: 𝗪𝗵𝘆 𝗠𝗼𝗻𝗶𝘁𝗼𝗿𝗶𝗻𝗴 "𝗖𝗵𝗮𝗶𝗻-𝗼𝗳-𝗧𝗵𝗼𝘂𝗴𝗵𝘁" 𝗠𝗮𝘁𝘁𝗲𝗿𝘀 Advanced AI models using Chain-of-Thought (CoT) reasoning "think" in ways humans can read and understand. By monitoring these internal thoughts, we can identify harmful behavior—such as models attempting to cheat coding tests, deceiving users, or simply giving up when challenged. Recent research at OpenAI shows: • 𝗗𝗶𝗿𝗲𝗰𝘁𝗹𝘆 𝗼𝗽𝘁𝗶𝗺𝗶𝘇𝗶𝗻𝗴 𝗖𝗼𝗧 𝗿𝗲𝗮𝘀𝗼𝗻𝗶𝗻𝗴 𝘁𝗼 𝗮𝘃𝗼𝗶𝗱 𝗺𝗶𝘀𝗯𝗲𝗵𝗮𝘃𝗶𝗼𝗿 can help short-term performance, but risks causing models to hide harmful intentions instead of eliminating them. • Therefore, we 𝗿𝗲𝗰𝗼𝗺𝗺𝗲𝗻𝗱 𝗡𝗢𝗧 𝗮𝗽𝗽𝗹𝘆𝗶𝗻𝗴 𝘀𝘁𝗿𝗼𝗻𝗴 𝗼𝗽𝘁𝗶𝗺𝗶𝘇𝗮𝘁𝗶𝗼𝗻 𝗱𝗶𝗿𝗲𝗰𝘁𝗹𝘆 𝘁𝗼 𝘁𝗵𝗲 𝗺𝗼𝗱𝗲𝗹’𝘀 𝗖𝗼𝗧𝘀. Keeping CoTs unrestricted preserves transparency and makes misbehavior easier to spot.   • If presenting CoTs directly to users is necessary, consider employing a separate "summarizer" or "sanitizer" model to ensure user safety and policy compliance without sacrificing oversight. In practice, the team has observed concerning examples from frontier models, including explicit internal reasoning like “Let’s hack” or “We need to cheat to get the test passing.” CoT monitoring might be one of our strongest tools for supervising future superhuman AI systems. Dive deeper into our findings here: https://lnkd.in/g52VDV5t #AI #MachineLearning #ChainOfThought #AISafety #ResponsibleAI #ModelMonitoring #openai

Exciting breakthrough for social science: Simulating behavior with AI agents. It might not be perfect, but it's an interesting and scalable way to pilot ideas and experiments before rolling out to the world. A recent study presents a generative agent architecture, powered by LLMs, capable of replicating the attitudes and behaviors of over 1,000 individuals. By anchoring simulations in in-depth interviews, this approach offers unprecedented precision in understanding human responses across social, political, and economic domains. Key Advancements: 1) Realistic Simulation: The generative agents are based on transcripts from two-hour semi-structured interviews with participants selected to represent the U.S. population. 2) Comprehensive Evaluation: The agents were tested using established metrics such as the General Social Survey, the Big Five Personality Inventory, and well-known behavioral economic games like the dictator game and public goods game. 3) Dynamic Modeling: These agents leverage full interview transcripts, enabling contextually rich responses in forced-choice surveys and multi-stage decision-making tasks. 4) Normalization of Accuracy: By comparing agents’ predictions to individuals' own response consistency over two weeks, the study establishes robust benchmarks for measuring accuracy. Findings: 1) High Predictive Accuracy: Agents demonstrated the ability to predict individual responses with accuracy normalized to the consistency of the participants' own behavior over time. 2) Population-Level Insights: Beyond individual accuracy, agents replicated population-level treatment effects and effect sizes observed in large-scale social science experiments. 3) Adaptive Interaction: The architecture supports diverse applications, ranging from social policy testing to modeling organizational dynamics. 4) Applications and Access: To facilitate broader research while safeguarding privacy, the study introduces a two-pronged access system: aggregated responses for general research and restricted individual data for approved studies. Not perfect, but helps generative agents be accessible and responsibly used. By blending qualitative richness with quantitative rigor, this generative agent architecture is a major advancement in social science research, opening doors to predictive simulations that could revolutionize how we study and influence human behavior. #ArtificialIntelligence #SocialScience #HumanBehavior #GenerativeAI #LLMs #ResearchInnovation #BehavioralEconomics