Cognaptus Insights

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. ...

From Ballots to Bots: Reprogramming Democracy for the AI Era Cognaptus Insights Democracy, at its core, is a decision-making system designed to fairly resolve conflicts and distribute resources in society. Historically, it has depended on human political agents—elected representatives who negotiate on behalf of their constituents. But as artificial intelligence matures, this centuries-old mechanism may be heading for a systemic rewrite. A Brief History of Democratic Pitfalls From Athenian direct democracy to parliamentary representation and constitutional republics, political systems have evolved to solve the problem of collective decision-making. Yet across cultures and eras, common systemic pitfalls emerge: ...

What if your AI agent could remember the last time it made a mistake—and plan better this time? From Reaction to Reflection: Why Memory Matters Most language model agents today operate like goldfish—brilliant at reasoning in the moment, but forgetful. Whether navigating virtual environments, answering complex questions, or composing multi-step strategies, they often repeat past mistakes simply because they lack a memory of past episodes. That’s where the paper “Agentic Episodic Control” by Zhihan Xiong et al. introduces a critical upgrade to today’s LLM agents: a modular episodic memory system inspired by human cognition. Instead of treating each prompt as a blank slate, this framework allows agents to recall, adapt, and refine prior reasoning paths—without retraining the underlying model. ...

The GUI Agent Bottleneck: Stuck in Sparse Feedback Training LLM-based GUI agents to complete digital tasks—such as navigating mobile apps or automating workflows—faces a fundamental limitation: reward sparsity. Traditional reward formulations (Outcome Reward Models, or ORMs) provide feedback only at the end of a trajectory. If the task fails, the agent receives zero signal, regardless of how many useful intermediate steps it took. This severely limits credit assignment and slows learning, especially in environments with long action horizons. ...

In the expanding domain of artificial intelligence, creativity is no longer a human-only endeavor. From music composition to visual art and storytelling, AI agents are taking on increasingly creative roles. But as these systems become more proactive, one question looms large: who’s really in control? Enter MOSAAIC — a framework developed to guide the design of co-creative systems by managing autonomy, initiative, and authority in shared human-AI decision-making. The Three Pillars: Autonomy, Initiative, and Authority The authors define three interrelated yet distinct aspects of control: ...

When it comes to real-world problem solving, today’s LLMs face a critical dilemma: they can solve textbook problems well, but stumble when confronted with messy, open-ended challenges—like optimizing traffic in a growing city or managing fisheries under uncertain climate shifts. Enter ModelingAgent, an ambitious new framework that turns this complexity into opportunity. What Makes Real-World Modeling So Challenging? Unlike standard math problems, real-world tasks involve ambiguity, multiple valid solutions, noisy data, and cross-domain reasoning. They often require: ...

Introduction: From Reaction to Proaction Imagine an assistant that doesn’t wait for your command. It notices you’re standing by a bus stop late at night and proactively checks the next bus arrival. If it’s too far off, it suggests calling a ride instead. Welcome to the world of ContextAgent — a proactive, context-aware Large Language Model (LLM) agent designed to act before you’re forced to ask. While most LLM agents still require explicit prompts and work in tightly scoped environments like desktops, ContextAgent leverages open-world sensory inputs (from devices like smart glasses, earphones, and smartphones) to understand user context and offer unobtrusive help. ...

Business Process Automation (BPA) has long promised leaner operations, improved responsiveness, and higher profitability. But for physical manufacturing, where every parameter shift impacts material use, energy cost, and defect rate, true real-time optimization remains a complex frontier. In a recent paper, researchers presented a compelling DRL-based solution to injection molding optimization that could signal a broader wave of intelligent, profit-driven automation in smart factories. ...

If ReAct was a spark, Pre-Act is a blueprint. In the paper Pre-Act: Multi-Step Planning and Reasoning Improves Acting in LLM Agents, Mrinal Rawat et al. challenge the single-step cognitive paradigm of ReAct, offering instead a roadmap for how agents should plan, reason, and act—especially when tool use and workflow coherence matter. What Is ReAct? A Quick Primer The ReAct framework—short for Reasoning and Acting—is a prompting strategy that allows an LLM to alternate between thinking and doing in a loop. Each iteration follows this pattern: ...

In the quest for truly intelligent systems, reasoning has always stood as the ultimate benchmark. But a new paper titled “Towards a Deeper Understanding of Reasoning Capabilities in Large Language Models” by Annie Wong et al. delivers a sobering message: even the most advanced LLMs still stumble in dynamic, high-stakes environments when asked to reason, plan, and act with stability. Beyond the Benchmark Mirage Static benchmarks like math word problems or QA datasets have long given the illusion of emergent intelligence. Yet this paper dives into SmartPlay, a suite of interactive environments, to show that LLMs exhibit brittle reasoning when faced with real-time adaptation. SmartPlay is a collection of dynamic decision-making tasks designed to test planning, adaptation, and coordination under uncertainty. The team evaluates open-source models such as LLAMA3-8B, DEEPSEEK-R1-14B, and LLAMA3.3-70B on tasks involving spatial coordination, opponent modeling, and planning. The result? Larger models perform better—but only to a point. Strategic prompting can help smaller models, but also introduces volatility. ...