The pursuit of artificial agents that can learn to master complex environments has led to remarkable successes, yet prevailing deep reinforcement learning methods often rely on immense experience, encoding their knowledge opaquely within neural network weights. We propose a different paradigm, one in which an agent learns to play by reasoning and planning. We introduce Cogito, ergo ludo (CEL), a novel agent architecture that leverages a Large Language Model (LLM) to build an explicit, language-based understanding of its environment's mechanics and its own strategy. Starting from a tabula rasa state with no prior knowledge (except action set), CEL operates on a cycle of interaction and reflection. After each episode, the agent analyzes its complete trajectory to perform two concurrent learning processes: Rule Induction, where it refines its explicit model of the environment's dynamics, and Strategy and Playbook Summarization, where it distills experiences into an actionable strategic playbook. We evaluate CEL on diverse grid-world tasks (i.e., Minesweeper, Frozen Lake, and Sokoban), and show that the CEL agent successfully learns to master these games by autonomously discovering their rules and developing effective policies from sparse rewards. Ablation studies confirm that the iterative process is critical for sustained learning. Our work demonstrates a path toward more general and interpretable agents that not only act effectively but also build a transparent and improving model of their world through explicit reasoning on raw experience.

With the prospect of autonomous artificial intelligence (AI) agents, studying their tendency for cooperative behavior becomes an increasingly relevant topic. This study is inspired by the super-additive cooperation theory, where the combined effects of repeated interactions and inter-group rivalry have been argued to be the cause for cooperative tendencies found in humans. We devised a virtual tournament where language model agents, grouped into teams, face each other in a Prisoner's Dilemma game. By simulating both internal team dynamics and external competition, we discovered that this blend substantially boosts both overall and initial, one-shot cooperation levels (the tendency to cooperate in one-off interactions). This research provides a novel framework for large language models to strategize and act in complex social scenarios and offers evidence for how intergroup competition can, counter-intuitively, result in more cooperative behavior. These insights are crucial for designing future multi-agent AI systems that can effectively work together and better align with human values.

Can LLM Agents Solve Collaborative T asks? Abstract -- The ability to coordinate actions across multiple agents is critical for solving complex, real-world problems. Large Language Models (LLMs) have shown strong capabilities in communication, planning, and reasoning, raising the question of whether they can also support effective collaboration in multi-agent settings. In this work, we investigate the use of LLM agents to solve a structured victim rescue task that requires division of labor, prioritization, and cooperative planning. Agents operate in a fully known graph-based environment and must allocate resources to victims with varying needs and urgency levels. We systematically evaluate their performance using a suite of coordination-sensitive metrics, including task success rate, redundant actions, room conflicts, and urgency-weighted efficiency. This study offers new insights into the strengths and failure modes of LLMs in physically grounded multi-agent collaboration tasks, contributing to future benchmarks and architectural improvements.

Large language models based Multi Agent Systems (MAS) have demonstrated promising performance for enhancing the efficiency and accuracy of code generation tasks. However,most existing methods follow a conventional sequence of planning, coding, and debugging,which contradicts the growth-driven nature of human learning process. Additionally,the frequent information interaction between multiple agents inevitably involves high computational costs. In this paper,we propose Cogito,a neurobiologically inspired multi-agent framework to enhance the problem-solving capabilities in code generation tasks with lower cost. Specifically,Cogito adopts a reverse sequence: it first undergoes debugging, then coding,and finally planning. This approach mimics human learning and development,where knowledge is acquired progressively. Accordingly,a hippocampus-like memory module with different functions is designed to work with the pipeline to provide quick retrieval in similar tasks. Through this growth-based learning model,Cogito accumulates knowledge and cognitive skills at each stage,ultimately forming a Super Role an all capable agent to perform the code generation task. Extensive experiments against representative baselines demonstrate the superior performance and efficiency of Cogito. The code is publicly available at https://anonymous.4open.science/r/Cogito-0083.

With a variety of businesses integrating AI technology and machine learning models into their business practices, AI has become less of a novelty and more mainstream over the past few years. With ever-growing amounts of data generated worldwide, you are likely already in possession of the data you need for your machine learning models and industry-specific use case. Cogito is one of the top data annotation companies with its wide array of data annotation and labeling services. As an industry leader in the AI and machine learning space and a premier AI training data procurer, it can be your true ally in integrating automation into your business processes. Getting us on board for annotating and labeling the raw & unstructured datasets and validating the training data can get you sorted for the automation goals.

In early December, Cogito published some new research designed to capture consumers' understanding of artificial intelligence (AI), their overall perception and utilization of it, and any apprehensions they had with their utilization of it related to data privacy and regulation. While the study found that most consumers don't think that AI is a threat to jobs and can help make the lives of employees easier, they expressed a lingering mistrust surrounding brands' use of their data, privacy and the overall use of AI. In fact, of the consumers surveyed, 72% said that they had concerns about data privacy and what AI-enabled tools are tracking. That number represents a significant trust gap. But, what should companies be doing in the face of that level of concern?

An app called TapCounter records each time I touch my phone's screen. My swipes and jabs are averaging about 1,000 a day, though I notice that's falling as I steer shy of social media to meet my deadline. The European company behind it, QuantActions, promises that through capturing and analysing the data it will be able to "detect important indicators related to mental/neurological health". Arko Ghosh is the company's cofounder and a neuroscientist at Leiden University in the Netherlands. "Tappigraphy patterns" – the time series of my touches – can, he says, confidently be used not only to infer slumber habits (tapping in the wee hours means you are not sleeping) but also mental performance level (the small intervals in a series of key-presses represent a proxy for reaction time), and he has published work to support it.

Machine learning algorithms and AI technologies are used to categorize data sets in files and extract data without the need for human intervention. Data annotation allows you to rapidly sift through and discover relevant information. With this, arranging data and training the machine learning models gets streamlined. In terms of data annotation, metadata, tags, display order list and other characteristics can be used to get a more precise understanding of the text in a document. Paper bulks have now taken the form of digital documents and include extensive semantic information that extends beyond the aesthetics of the documents.

For entrepreneurial MIT students looking to put their skills to work for a greater good, the Media Arts and Sciences class MAS.664 (AI for Impact) has been a destination point. With the onset of the pandemic, that goal came into even sharper focus. Just weeks before the campus shut down in 2020, a team of students from the class launched a project that would make significant strides toward an open-source platform to identify coronavirus exposures without compromising personal privacy. Their work was at the heart of Safe Paths, one of the earliest contact tracing apps in the United States. The students joined with volunteers from other universities, medical centers, and companies to publish their code, alongside a well-received white paper describing the privacy-preserving, decentralized protocol, all while working with organizations wishing to launch the app within their communities.

The groundbreaking applications of Artificial intelligence are attracting tech multinationals like Apple, Microsoft, Amazon and Facebook to work on their future projects with more AI focused strategies. The AI effect is influencing the product road map of all such companies having the renowned AI-based applications that are launched at regular intervals in a year to automate their business operations with more promising results. Computer Vision is an important development under AI that has been extensively explored and applied into various industries from outdated to innovative self-driving cars moving on roads without human intervention. Such AI-backed innovative technologies work on such principles that encompass a huge amount of training data for computer vision. All these steps have their own challenges in terms of technical know-how and operational activities, so here we will discuss and help you how to deal with the labeling of training data and other related aspects required to complete this process. Before we start labeling of training data, you need aware where the technology of Computer Vision is effectively used to produce an AI-backed system or machine that can perform without too much human instructions and do their job independently as per the changing situations.