System Design Series: Apache Flink from 10,000 Feet, and Building a Flink-powered Recommendation Engine
A deep dive into how Apache Flink works, why it exists, and learning it while building a real-time recommendation engine
The post System Design Series: Apache Flink from 10,000 Feet, and Building a Flink-powered Recommendation Engine appeared first on Towards Data Science.
Synthetic task scaling introduces a new training approach where AI agents learn through experience, closing the gap between knowledge and execution. Are you ready for the rise of the AI scientist?
Poolside AI Introduces Laguna XS.2 and M.1: Agentic Coding Models Reaching 68.2% and 72.5% on SWE-bench Verified
Poolside Releases Laguna XS.2 and M.1: Open-Weight Agentic Coding Models Built for Long-Horizon Tasks
The post Poolside AI Introduces Laguna XS.2 and M.1: Agentic Coding Models Reaching 68.2% and 72.5% on SWE-bench Verified appeared first on MarkTechPost.
GCA-BULF: A Bottom-Up Framework for Short-Term Load Forecasting Using Grouped Critical Appliances
arXiv:2604.24766v1 Announce Type: new
Abstract: With the rise of time-of-use and tiered electricity pricing, energy consumers are encouraged to adopt peak-shifting strategies by automatically controlling high-power appliances. These help lower energy costs while enhancing the power grid's stability...
Liquid Neural Network Models for Natural Gas Spot Price Time-Series Forecasting
arXiv:2604.24788v1 Announce Type: new
Abstract: Natural gas is undoubtedly an essential component of the global energy system. Accurate short-term forecasting of natural gas price is challenging due to pronounced volatility driven by seasonal demand patterns, geopolitical developments, and shifting...
Architecture Determines Observability in Transformers
arXiv:2604.24801v1 Announce Type: new
Abstract: Autoregressive transformers make confident errors, but activation monitoring can catch them only if the model preserves an internal signal that output confidence does not expose. This preservation is determined by architecture and training recipe. We ...
Enabling privacy-preserving AI training on everyday devices
A new method could bring more accurate and efficient AI models to high-stakes applications like health care and finance, even in under-resourced settings.
Co-Director: Agentic Generative Video Storytelling
arXiv:2604.24842v1 Announce Type: new
Abstract: While diffusion models generate high-fidelity video clips, transforming them into coherent storytelling engines remains challenging. Current agentic pipelines automate this via chained modules but suffer from semantic drift and cascading failures due ...
Latent Agents: A Post-Training Procedure for Internalized Multi-Agent Debate
arXiv:2604.24881v1 Announce Type: new
Abstract: Multi-agent debate has been shown to improve reasoning in large language models (LLMs). However, it is compute-intensive, requiring generation of long transcripts before answering questions. To address this inefficiency, we develop a framework that di...
S-SONDO: Self-Supervised Knowledge Distillation for General Audio Foundation Models
arXiv:2604.24933v1 Announce Type: new
Abstract: General audio foundation models have recently achieved remarkable progress, enabling strong performance across diverse tasks. However, state-of-the-art models remain extremely large, often with hundreds of millions of parameters, leading to high infer...
Adaptive Prompt Embedding Optimization for LLM Jailbreaking
arXiv:2604.24983v1 Announce Type: new
Abstract: Existing white-box jailbreak attacks against aligned LLMs typically append discrete adversarial suffixes to the user prompt, which visibly alters the prompt and operates in a combinatorial token space. Prior work has avoided directly optimizing the em...
Assessing Y-Axis Influence: Bias in Multimodal Language Models on Chart-to-Table Translation
arXiv:2604.24987v1 Announce Type: new
Abstract: Chart-to-table translation converts chart images into structured tabular data. Accurate translation is crucial for Multimodal Language Model (MLM) to answer complex queries. We observe imbalances in the number of images across different aspects of the...
Adaptive Thinking: Large Language Models Know When to Think in Latent Space
Recent advances in large language models (LLMs) test-time computing have introduced the capability to perform intermediate chain-of-thought (CoT) reasoning (thinking) before generating answers. While increasing the thinking budget yields smooth performance improvements at inference time, the relatio...
DSO: Direct Steering Optimization for Bias Mitigation
Generative models are often deployed to make decisions on behalf of users, such as vision-language models (VLMs) identifying which person in a room is a doctor to help visually impaired individuals. Yet, VLM decisions are influenced by the perceived demographic attributes of people in the input, whi...
OpenAI Releases Privacy Filter: A 1.5B-Parameter Open-Source PII Redaction Model with 50M Active Parameters
OpenAI's Privacy Filter Is a 1.5B-Parameter PII Detector Built on a Distilled Decoder — And It Runs in Your Browser
The post OpenAI Releases Privacy Filter: A 1.5B-Parameter Open-Source PII Redaction Model with 50M Active Parameters appeared first on MarkTechPost.
NVIDIA Launches Nemotron 3 Nano Omni Model, Unifying Vision, Audio and Language for up to 9x More Efficient AI Agents
AI agent systems today juggle separate models for vision, speech and language — losing time and context as they pass data from one model to the other. Unveiled today, NVIDIA Nemotron 3 Nano Omni is an open multimodal model that brings these capabilities together into one system, enabling agents to d...