Learning to Select Visual In-Context Demonstrations
arXiv:2603.26775v1 Announce Type: new
Abstract: Multimodal Large Language Models (MLLMs) adapt to visual tasks via in-context learning (ICL), which relies heavily on demonstration quality. The dominant demonstration selection strategy is unsupervised k-Nearest Neighbor (kNN) search. While simple, t...
TED: Training-Free Experience Distillation for Multimodal Reasoning
arXiv:2603.26778v1 Announce Type: new
Abstract: Knowledge distillation is typically realized by transferring a teacher model's knowledge into a student's parameters through supervised or reinforcement-based optimization. While effective, such approaches require repeated parameter updates and large-...
A Step Toward Federated Pretraining of Multimodal Large Language Models
arXiv:2603.26786v1 Announce Type: new
Abstract: The rapid evolution of Multimodal Large Language Models (MLLMs) is bottlenecked by the saturation of high-quality public data, while vast amounts of diverse multimodal data remain inaccessible in privacy-sensitive silos. Federated Learning (FL) offers...
arXiv:2603.26765v1 Announce Type: new
Abstract: The efficiency of game engines and policy optimization algorithms is crucial for training reinforcement learning (RL) agents in complex sequential decision-making tasks, such as Tetris. Existing Tetris implementations suffer from low simulation speeds...
Multiverse: Language-Conditioned Multi-Game Level Blending via Shared Representation
arXiv:2603.26782v1 Announce Type: new
Abstract: Text-to-level generation aims to translate natural language descriptions into structured game levels, enabling intuitive control over procedural content generation. While prior text-to-level generators are typically limited to a single game domain, ex...
Neuro-Symbolic Learning for Predictive Process Monitoring via Two-Stage Logic Tensor Networks with Rule Pruning
arXiv:2603.26944v1 Announce Type: new
Abstract: Predictive modeling on sequential event data is critical for fraud detection and healthcare monitoring. Existing data-driven approaches learn correlations from historical data but fail to incorporate domain-specific sequential constraints and logical ...
Your Code is Your Schema: Weaviate Managed CClient
Use semantic search and RAG in C# with the Weaviate Managed .NET client — attribute-driven schema, type-safe queries, and safe migrations, all in idiomatic .NET.
AI Weekly Issue #477: Jensen Huang says we've achieved AGI. The benchmarks say 0.37%.
💡 Insights
AI is superhuman at exams but can't figure out a simple game. ARC-AGI-3 gave frontier models interactive environments with no rules and no goals — just figure it out. Humans solve 100%. The best AI scored 0.37%. Current architectures can pattern-match anything in their training data but c...
15% of Americans say they’d be willing to work for an AI boss, according to new poll
According to a Quinnipiac University poll, 15% of Americans say they'd be willing to have a job where their direct supervisor was an AI program that assigned tasks and set schedules.
Microsoft AI Releases Harrier-OSS-v1: A New Family of Multilingual Embedding Models Hitting SOTA on Multilingual MTEB v2
Microsoft has announced the release of Harrier-OSS-v1, a family of three multilingual text embedding models designed to provide high-quality semantic representations across a wide range of languages. The release includes three distinct scales: a 270M parameter model, a 0.6B model, and a 27B model. T...
15% of Americans say they’d be willing to work for an AI boss
Your human manager may soon be a chatbot. Across organizations, AI is being used to replace layers of management in what some are calling "The Great Flattening."
From Prompt to Prediction: Understanding Prefill, Decode, and the KV Cache in LLMs
This article is divided into three parts; they are: • How Attention Works During Prefill • The Decode Phase of LLM Inference • KV Cache: How to Make Decode More Efficient Consider the prompt: Today’s weather is so .
As more Americans adopt AI tools, fewer say they can trust the results
AI adoption is rising in the U.S., but trust remains low, with most Americans concerned about transparency, regulation, and the technology’s broader societal impact, according to a new Quinnipiac poll.
There are more AI health tools than ever—but how well do they work?
Earlier this month, Microsoft launched Copilot Health, a new space within its Copilot app where users will be able to connect their medical records and ask specific questions about their health. A couple of days earlier, Amazon had announced that Health AI, an LLM-based tool previously restricted to...
Mantis Biotech is making ‘digital twins’ of humans to help solve medicine’s data availability problem
Mantis takes disparate sources of data to make synthetic datasets that can be used to build so-called "digital twins" of the human body, representing anatomy, physiology and behavior.
20+ Solved ML Projects to Build Your Portfolio and Boost Your Resume
Projects are the bridge between learning and becoming a professional. While theory builds fundamentals, recruiters value candidates who can solve real problems. A strong, diverse portfolio showcases practical skills, technical range, and problem-solving ability. This guide compiles 20+ solved proje...
Explainable AI in Production: A Neuro-Symbolic Model for Real-Time Fraud Detection
SHAP needs 30 ms to explain a fraud prediction. That explanation is stochastic, runs after the decision, and requires a background dataset you have to maintain at inference time. This article benchmarks a neuro-symbolic model that produces a deterministic, human-readable explanation in 0.9 ms — as a...
Salesforce AI Research Releases VoiceAgentRAG: A Dual-Agent Memory Router that Cuts Voice RAG Retrieval Latency by 316x
In the world of voice AI, the difference between a helpful assistant and an awkward interaction is measured in milliseconds. While text-based Retrieval-Augmented Generation (RAG) systems can afford a few seconds of ‘thinking’ time, voice agents must respond within a 200ms budget to maintain a natura...