Cognaptus Insights

When it comes to deploying large language models (LLMs) efficiently, few challenges are as stubborn—and misunderstood—as activation outliers. For years, engineers have treated them like a natural disaster: unpredictable but inevitable. But what if they’re more like bad habits—learned and fixable? That’s the provocative premise behind a new framework called Outlier-Safe Pre-Training (OSP). Developed by researchers at Korea University and AIGEN Sciences, OSP proposes a simple but radical shift: instead of patching over outliers post hoc with quantization tricks, why not train the model to never form outliers in the first place? ...

Divide and Conquer: How LLMs Learn to Teach Designing effective lessons for training online tutors is no small feat. It demands pedagogical nuance, clarity, scenario realism, and learner empathy. A recent paper by Lin et al., presented at ECTEL 2025, offers a compelling answer to this challenge: use LLMs, but don’t ask too much at once. Their research reveals that breaking the task of lesson generation into smaller, well-defined parts significantly improves quality, suggesting a new collaborative model for scalable education design. ...

When the DAO hack siphoned millions from Ethereum in 2016, the blockchain world learned a hard lesson: code is law, and bad law can be catastrophic. Fast forward to today, and smart contract security still walks a tightrope between complexity and automation. Enter Smart-LLaMA-DPO, a reinforced large language model designed not just to find vulnerabilities in smart contracts—but to explain them, clearly and reliably. 🧠 Beyond Detection: Why Explanations Matter Most smart contract vulnerability detectors work like smoke alarms—loud when something’s wrong, but not exactly helpful in telling you why. The core innovation of Smart-LLaMA-DPO is that it speaks the language of developers. It explains vulnerabilities with clarity and technical nuance, whether it’s a reentrancy flaw or an oracle manipulation scheme. And that clarity doesn’t come from magic—it comes from Direct Preference Optimization (DPO), a training method where the model learns not just from correct labels, but from expert-ranked explanations. ...

In the symphony of innovation, TRIZ has long served as the structured score guiding engineers toward inventive breakthroughs. But what happens when you give the orchestra to a team of AI agents? Enter TRIZ Agents, a bold exploration of how large language model (LLM) agents—armed with tools, prompts, and persona-based roles—can orchestrate a complete innovation cycle using the TRIZ methodology. Cracking the Code of Creativity TRIZ (Theory of Inventive Problem Solving), derived from the study of thousands of patents, offers a time-tested approach to resolving contradictions in engineering design. It formalizes the innovation process through tools like the 40 Inventive Principles and the Contradiction Matrix. However, its structured elegance demands deep domain expertise—something often scarce outside elite R&D centers. ...

The magic behind OmniAvatar isn’t just in its motion—it’s in the meticulous training pipeline and rigorous evaluation metrics that power its realism. Here’s a closer look at how the model was built and validated. Training Data: Curated, Filtered, and Massive OmniAvatar trains on a carefully filtered subset of the AVSpeech dataset (Ephrat et al., 2018), a publicly available corpus with over 4,700 hours of speech-aligned video. To ensure lip-sync precision and high visual quality: ...

What happens when we ask the smartest AI models to do something truly difficult—like solve a real math problem and prove their answer is correct? That’s the question tackled by a group of researchers in their paper “Mathematical Proof as a Litmus Test.” Instead of testing AI with casual tasks like summarizing news or answering trivia, they asked it to write formal mathematical proofs—the kind that leave no room for error. And the results? Surprisingly poor. ...

LLMs are great at spitting out answers—but are they any good at thinking through problems? A new benchmark, AdvGameBench, introduces a process-based evaluation approach that places LLMs into three rule-based strategic games to measure not outcomes, but the quality of reasoning. Developed by Yuan et al., this framework focuses on how LLMs plan, revise, and make resource-limited decisions in dynamic settings. Three Games, Three Cognitive Demands 1. Tower Defense tests spatial planning and rule-following. Models place defenders on a battlefield to block enemies—positioning, cooldowns, and cost management are key. ...

In the race to build more autonomous, more intelligent AI agents, we’re entering an era where “strategy” isn’t just about picking the next move—it’s about choosing the right mind for the job and deciding which version of the world to trust. Two recent arXiv papers—one on state representation in dynamic routing games, the other on self-generating agentic systems with swarm intelligence—show just how deeply this matters in practice. We’re no longer only asking: What should the agent do? We now must ask: ...

🧠 From Freud to Fine-Tuning: What is a Superego for AI? As AI agents gain the ability to plan, act, and adapt in open-ended environments, ensuring they behave in accordance with human expectations becomes an urgent challenge. Traditional approaches like Reinforcement Learning from Human Feedback (RLHF) or static safety filters offer partial solutions, but they falter in complex, multi-jurisdictional, or evolving ethical contexts. Enter the idea of a Superego layer—not a psychoanalytical metaphor, but a modular, programmable conscience that governs AI behavior. Proposed by Nell Watson et al., this approach frames moral reasoning and legal compliance not as traits baked into the LLM itself, but as a runtime overlay—a supervisory mechanism that monitors, evaluates, and modulates outputs according to a predefined value system. ...

1. A Student Who Cracked the Code — But Not the Meaning Imagine a student who aces every test by memorizing the positions of correct answers on multiple-choice sheets. He scores high, earns accolades, and passes every exam — but understands none of the material. His reward system is misaligned: success depends not on learning, but on exploiting test mechanics. Now, replace the student with an AI agent navigating a simulated room guided by language and images. This is the scenario that today’s leading research in Vision-and-Language Reinforcement Learning (RLVR) is grappling with. ...