Atomicwork Appoints Jeegar Shah as Head of Applied AI
Former Amazon AGI and ServiceNow AI leader to accelerate Atomicwork’s modern service management platform Atomicwork, an AI-native ITSM and ESM platform built for modern enterprises, today announced the appointment of Jeegar Shah as Head of Applied AI and Platform. In this role, Shah will lead Atomic...
Flowfinity Update Adds AI Assistants to Workflows, Boosts Access Control
Flowfinity, a leading business process management platform, today announced the release of a major update designed to help organizations modernize operations with AI-enhanced workflows, stronger security controls, and more intuitive data visualizations to help clients improve their workflows. Now yo...
EPAM, Cursor Partner to Build and Scale AI-Native Enterprise Teams
Combining Cursor’s AI-native IDE with EPAM’s AI/Run™ delivery, the partnership moves enterprises beyond AI coding pilots and delivers measurable gains in productivity, sustainable quality and an enhanced developer experience for thousands of engineers. EPAM Systems, Inc. (NYSE: EPAM), a leading digi...
Seasoned Silicon Valley founder and executive brings proven experience scaling advanced platforms through global commercialization and M&A Arkstone Medical Solutions, a biotechnology company transforming Clinical Decision Support (CDS) through “Expert-in-the-Loop” machine learning (ML) and artificia...
The Forgotten Shield: Safety Grafting in Parameter-Space for Medical MLLMs
arXiv:2601.04199v1 Announce Type: new
Abstract: Medical Multimodal Large Language Models (Medical MLLMs) have achieved remarkable progress in specialized medical tasks; however, research into their safety has lagged, posing potential risks for real-world deployment. In this paper, we first establis...
Green MLOps: Closed-Loop, Energy-Aware Inference with NVIDIA Triton, FastAPI, and Bio-Inspired Thresholding
arXiv:2601.04250v1 Announce Type: new
Abstract: Energy efficiency is a first-order concern in AI deployment, as long-running inference can exceed training in cumulative carbon impact. We propose a bio-inspired framework that maps protein-folding energy basins to inference cost landscapes and contro...
Safety-Utility Conflicts Are Not Global: Surgical Alignment via Head-Level Diagnosis
arXiv:2601.04262v1 Announce Type: new
Abstract: Safety alignment in Large Language Models (LLMs) inherently presents a multi-objective optimization conflict, often accompanied by an unintended degradation of general capabilities. Existing mitigation strategies typically rely on global gradient geom...
Learning to Reason: Temporal Saliency Distillation for Interpretable Knowledge Transfer
arXiv:2601.04263v1 Announce Type: new
Abstract: Knowledge distillation has proven effective for model compression by transferring knowledge from a larger network called the teacher to a smaller network called the student. Current knowledge distillation in time series is predominantly based on logit...
MemKD: Memory-Discrepancy Knowledge Distillation for Efficient Time Series Classification
arXiv:2601.04264v1 Announce Type: new
Abstract: Deep learning models, particularly recurrent neural networks and their variants, such as long short-term memory, have significantly advanced time series data analysis. These models capture complex, sequential patterns in time series, enabling real-tim...
Formal Analysis of AGI Decision-Theoretic Models and the Confrontation Question
arXiv:2601.04234v1 Announce Type: new
Abstract: Artificial General Intelligence (AGI) may face a confrontation question: under what conditions would a rationally self-interested AGI choose to seize power or eliminate human control (a confrontation) rather than remain cooperative? We formalize this ...
Actively Obtaining Environmental Feedback for Autonomous Action Evaluation Without Predefined Measurements
arXiv:2601.04235v1 Announce Type: new
Abstract: Obtaining reliable feedback from the environment is a fundamental capability for intelligent agents to evaluate the correctness of their actions and to accumulate reusable knowledge. However, most existing approaches rely on predefined measurements or...
SAGE-32B: Agentic Reasoning via Iterative Distillation
arXiv:2601.04237v1 Announce Type: new
Abstract: We demonstrate SAGE-32B, a 32 billion parameter language model that focuses on agentic reasoning and long range planning tasks. Unlike chat models that aim for general conversation fluency, SAGE-32B is designed to operate in an agentic loop, emphasizi...
arXiv:2601.04239v1 Announce Type: new
Abstract: The Cyclic Antibandwidth Problem (CABP), a variant of the Antibandwidth Problem, is an NP-hard graph labeling problem with numerous applications. Despite significant research efforts, existing state-of-the-art approaches for CABP are exclusively heuri...
Pretraining with Hierarchical Memories: Separating Long-Tail and Common Knowledge
The impressive performance gains of modern language models currently rely on scaling parameters: larger models store more world knowledge and reason better. Yet compressing all world knowledge into parameters is unnecessary, as only a fraction is used per prompt, and impractical for edge devices wit...
Which Evaluation for Which Model? A Taxonomy for Speech Model Assessment
Speech foundation models have recently achieved remarkable capabilities across a wide range of tasks. However, their evaluation remains disjointed across tasks and model types. Different models excel at distinct aspects of speech processing and thus require different evaluation protocols. This paper...
AgentBuilder: Exploring Scaffolds for Prototyping User Experiences of Interface Agents
Interface agents powered by generative AI models (referred to as “agents”) can automate actions based on user commands. An important aspect of developing agents is their user experience (i.e., agent experience). There is a growing need to provide scaffolds for a broader set of individuals beyond AI ...
Governments grapple with the flood of non-consensual nudity on X
For the past two weeks, X has been flooded with AI manipulated nude images, created by the Grok AI chatbot — and governments around the world are promising to take action.
With the help of AI, MIT Research Scientist Judah Cohen is reshaping subseasonal forecasting, with the goal of extending the lead time for predicting impactful weather.
Africa's Tech Future: Navigating Divergent Paths in AI, IoT, and Automation
Explore Africa's unique path in the AI, IoT, and robotics revolution—how the continent leverages demographic dividends, frugal innovation, and mobile-first strategies to build a context-driven tech future. Discover its strengths in leapfrog development, sector-specific AI, and hybrid governance mode...
The name Google has always been synonymous with technology, and things are no different in the age of AI. Google has quietly been the frontrunner in the AI revolution with a host of products that surprisingly few people know about. Of course, the showstoppers like Gemini and NotebookLM have been pop...
AI Copilot Keeps Berkeley’s X-Ray Particle Accelerator on Track
In the rolling hills of Berkeley, California, an AI agent is supporting high-stakes physics experiments at the Advanced Light Source (ALS) particle accelerator. Researchers at the Lawrence Berkeley National Laboratory ALS facility recently deployed the Accelerator Assistant, a large language model (...
Beyond Prompting: The Power of Context Engineering
Using ACE to create self-improving LLM workflows and structured playbooks
The post Beyond Prompting: The Power of Context Engineering appeared first on Towards Data Science.