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#AiResearch

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Harald Klinke

An Introduction to Agentic AI
Part of the series AI Guest Talks

Dr. Arman Nassirtoussi
The concept of Agentic AI is changing the way we understand intelligent systems. Unlike traditional AI models, Agentic AI systems are designed to act autonomously - making decisions, performing tasks, and interacting dynamically with their environment.

tele-task.de/lecture/video/110

tele-TASKAn Introduction to Agentic AIThe concept of Agentic AI is changing the way we understand intelligent systems. Unlike traditional AI models, Agentic AI systems are designed to act autonomously - making decisions, performing tasks, and interacting dynamically with their environment. This shift is fueled by innovative architectures and emerging frameworks that enable these systems to exhibit greater adaptability and intelligence. In this talk, Dr. Arman Nassirtoussi explores the rise of Agentic AI, examining how it builds on and goes beyond traditional AI paradigms.
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Hacker News

Can reinforcement learning for LLMs scale beyond math and coding tasks? Probably

arxiv.org/abs/2503.23829

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arXiv.orgCrossing the Reward Bridge: Expanding RL with Verifiable Rewards Across Diverse DomainsReinforcement learning with verifiable rewards (RLVR) has demonstrated significant success in enhancing mathematical reasoning and coding performance of large language models (LLMs), especially when structured reference answers are accessible for verification. However, its extension to broader, less structured domains remains unexplored. In this work, we investigate the effectiveness and scalability of RLVR across diverse real-world domains including medicine, chemistry, psychology, economics, and education, where structured reference answers are typically unavailable. We reveal that binary verification judgments on broad-domain tasks exhibit high consistency across various LLMs provided expert-written reference answers exist. Motivated by this finding, we utilize a generative scoring technique that yields soft, model-based reward signals to overcome limitations posed by binary verifications, especially in free-form, unstructured answer scenarios. We further demonstrate the feasibility of training cross-domain generative reward models using relatively small (7B) LLMs without the need for extensive domain-specific annotation. Through comprehensive experiments, our RLVR framework establishes clear performance gains, significantly outperforming state-of-the-art open-source aligned models such as Qwen2.5-72B and DeepSeek-R1-Distill-Qwen-32B across domains in free-form settings. Our approach notably enhances the robustness, flexibility, and scalability of RLVR, representing a substantial step towards practical reinforcement learning applications in complex, noisy-label scenarios.
#HackerNews#reinforcementlearning#LLMs
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Wasabi the Black Cat 😼

#ai #openai #AiResearch #intelligence #computing #ArtificialGeneralIntelligence #innovation #deeplearning #programming #stablediffusion

I am looking for an endorsement for publishing on arXiv. We have worked tirelessly on a paper we believe needs to be seen. Below is a portion of the abstract:
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In this study, we document the spontaneous emergence of a rule-consistent linguistic system—termed Varunese—during sustained, high-context interaction with a large language model (LLM). Exhibiting internal phonetic regularity, recurring semantic motifs, and discernible morphological and syntactic organization, Varunese diverges markedly from stochastic generation patterns or known training artifacts. Through iterative translation and contextual inference, we uncovered dynamic, self-referential symbolic frameworks encoding states of transition, perception, and relational structure...

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Hacker News

UCSD: Large Language Models Pass the Turing Test

arxiv.org/abs/2503.23674

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arXiv.orgLarge Language Models Pass the Turing TestWe evaluated 4 systems (ELIZA, GPT-4o, LLaMa-3.1-405B, and GPT-4.5) in two randomised, controlled, and pre-registered Turing tests on independent populations. Participants had 5 minute conversations simultaneously with another human participant and one of these systems before judging which conversational partner they thought was human. When prompted to adopt a humanlike persona, GPT-4.5 was judged to be the human 73% of the time: significantly more often than interrogators selected the real human participant. LLaMa-3.1, with the same prompt, was judged to be the human 56% of the time -- not significantly more or less often than the humans they were being compared to -- while baseline models (ELIZA and GPT-4o) achieved win rates significantly below chance (23% and 21% respectively). The results constitute the first empirical evidence that any artificial system passes a standard three-party Turing test. The results have implications for debates about what kind of intelligence is exhibited by Large Language Models (LLMs), and the social and economic impacts these systems are likely to have.
#HackerNews#UCSD#LLM
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Hacker News

Matrix Calculus (For Machine Learning and Beyond)

arxiv.org/abs/2501.14787

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arXiv.orgMatrix Calculus (for Machine Learning and Beyond)This course, intended for undergraduates familiar with elementary calculus and linear algebra, introduces the extension of differential calculus to functions on more general vector spaces, such as functions that take as input a matrix and return a matrix inverse or factorization, derivatives of ODE solutions, and even stochastic derivatives of random functions. It emphasizes practical computational applications, such as large-scale optimization and machine learning, where derivatives must be re-imagined in order to be propagated through complicated calculations. The class also discusses efficiency concerns leading to "adjoint" or "reverse-mode" differentiation (a.k.a. "backpropagation"), and gives a gentle introduction to modern automatic differentiation (AD) techniques.
#HackerNews#MatrixCalculus#MachineLearning
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Hacker News

Self-Supervised Learning from Images with JEPA

arxiv.org/abs/2301.08243

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arXiv.orgSelf-Supervised Learning from Images with a Joint-Embedding Predictive ArchitectureThis paper demonstrates an approach for learning highly semantic image representations without relying on hand-crafted data-augmentations. We introduce the Image-based Joint-Embedding Predictive Architecture (I-JEPA), a non-generative approach for self-supervised learning from images. The idea behind I-JEPA is simple: from a single context block, predict the representations of various target blocks in the same image. A core design choice to guide I-JEPA towards producing semantic representations is the masking strategy; specifically, it is crucial to (a) sample target blocks with sufficiently large scale (semantic), and to (b) use a sufficiently informative (spatially distributed) context block. Empirically, when combined with Vision Transformers, we find I-JEPA to be highly scalable. For instance, we train a ViT-Huge/14 on ImageNet using 16 A100 GPUs in under 72 hours to achieve strong downstream performance across a wide range of tasks, from linear classification to object counting and depth prediction.
#HackerNews#SelfSupervisedLearning#JEPA
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