MoEs Are Stronger than You Think: Hyper-Parallel Inference Scaling with RoE
The generation quality of large language models (LLMs) is often improved by utilizing inference-time sequence-level scaling methods (e.g., Chain-of-Thought). We introduce hyper-parallel scaling, a complementary framework that improves prediction quality at the token level. Hyper-parallel scaling com...
Multivariate Conformal Prediction using Optimal Transport
Conformal prediction (CP) quantifies the uncertainty of machine learning models by constructing sets of plausible outputs. These sets are constructed by leveraging a so-called conformity score, a quantity computed using the input point of interest, a prediction model, and past observations. CP sets ...
DeepMMSearch-R1: Empowering Multimodal LLMs in Multimodal Web Search
Multimodal Large Language Models (MLLMs) in real-world applications require access to external knowledge sources and must remain responsive to the dynamic and ever-changing real-world information in order to address information-seeking and knowledge-intensive user queries. Existing approaches, such ...
Over-Searching in Search-Augmented Large Language Models
Search-augmented large language models (LLMs) excel at knowledge-intensive tasks by integrating external retrieval.
However, they often over-search – unnecessarily invoking search tool even when it does not improve response quality,
which leads to computational inefficiency and hallucinations by inc...
Meet SETA: Open Source Training Reinforcement Learning Environments for Terminal Agents with 400 Tasks and CAMEL Toolkit
What does an end to end stack for terminal agents look like when you combine structured toolkits, synthetic RL environments, and benchmark aligned evaluation? A team of researchers from CAMEL AI, Eigent AI and other collaborators have released SETA, a toolkit and environment stack that focuses on re...
Automatic Prompt Optimization for Multimodal Vision Agents: A Self-Driving Car Example
Walkthrough using open-source prompt optimization algorithms in Python to improve the accuracy of an autonomous vehicle car safety agent running on OpenAI's GPT 5.2
The post Automatic Prompt Optimization for Multimodal Vision Agents: A Self-Driving Car Example appeared first on Towards Data Science....
Hackathons are different!. The good ones pull you in, stretch your thinking, and leave you with something real—regardless of the outcome. The problem is choice. It’s hard to find the right one! Too many hackathons. Too many formats. And too much noise. So this list is built with that in mind. Instea...
MANZANO: A Simple and Scalable Unified Multimodal Model with a Hybrid Vision Tokenizer
Unified multimodal Large Language Models (LLMs) that can both understand and generate visual content hold immense potential. However, existing open-source models often suffer from a performance trade-off between these capabilities. We present Manzano, a simple and scalable unified framework that sub...
In software, the code documents the app. In AI, the traces do.
TL;DRIn traditional software, you read the code to understand what the app does - the decision logic lives in your codebaseIn AI agents, the code is just scaffolding - the actual decision-making happens in the model at runtimeBecause of this, the source of truth for what
Federated Learning, Part 1: The Basics of Training Models Where the Data Lives
Understanding the foundations of federated learning
The post Federated Learning, Part 1: The Basics of Training Models Where the Data Lives appeared first on Towards Data Science.
Beyond the Flat Table: Building an Enterprise-Grade Financial Model in Power BI
A step-by-step journey through data transformation, star schema modeling, and DAX variance analysis with lessons learned along the way.
The post Beyond the Flat Table: Building an Enterprise-Grade Financial Model in Power BI appeared first on Towards Data Science.
An encoder (optical system) maps objects to noiseless images, which noise corrupts into measurements. Our information estimator uses only these noisy measurements and a noise model to quantify how well measurements distinguish objects.
Many imaging systems produce measurements that humans never se...
If you’re curious about trending terms like AI Agents or Agentic AI, you’re in the right place. Agentic AI is rapidly moving from experimentation to enterprise adoption. According to Gartner, over 60% of enterprise AI applications are expected to include agentic components by 2026, while more than 4...
AI Quantum Intelligence & Pic of the week (2026&01&09)
This image developed using Microsoft Copilot illustrates the attempt to have AI and Robotics understand and react to emotions and emotional context rather than just logical and data driven outputs.
The AI Gold Rush Isn’t One Race—It’s Several, and Each Industry Is Running Its Own Event
This article provides a deep dive into how differing major industries—from manufacturing to finance—are investing in AI and robotics, each with unique priorities, risks, and go‑to‑market strategies.
CES 2026 was all about “physical AI” and robots, robots, robots
After years of chatbots and image generators, AI is finally leaving the screen. At CES 2026, that shift became impossible to ignore. The annual tech showcase in Las Vegas was dominated by “physical AI” and robotics, from Boston Dynamic’s newly redesigned Atlas humanoid robot to AI-powered ice maker...
Meta and Harvard Researchers Introduce the Confucius Code Agent (CCA): A Software Engineering Agent that can Operate at Large-Scale Codebases
How far can a mid sized language model go if the real innovation moves from the backbone into the agent scaffold and tool stack? Meta and Harvard researchers have released the Confucius Code Agent, an open sourced AI software engineer built on the Confucius SDK that is designed for industrial scale ...