Tufin Reimagines Network Security Operations for the AI Era
New TufinAI Assistants and Executive Dashboards Enable Continuous Management of Security Posture, Risk Reduction, and Improved Visibility Across Multi-Vendor Networks Tufin, the leader in network security posture management, today announced its latest AI-powered innovations, enabling customers to ut...
AtScale Appoints Vertica Veteran Chuck Bear as Chief Architect
Veteran database architect to guide performance and operational reliability as enterprises operationalize AI on live analytical data AtScale, the leader in governed semantic layer technology, today announced that Chuck Bear has joined the company as Chief Architect. Bear will help advance AtScale’s ...
Zip Appoints Dallas Stonhaus as Chief Sales Officer
Stonhaus, a two-decade enterprise sales veteran who helped build Ironclad into a category-defining company, joins Zip as the AI procurement platform enters its next phase of growth Zip, the AI platform for enterprise procurement, today announced the appointment of Dallas Stonhaus as Chief Sales Offi...
RAG that remembers: How AI is learning from every query
What if search systems didn’t just retrieve information, but remembered what worked? Expanded Relevance Memory (ERM) proves that query expansion and document expansion are mathematically equivalent, unlocking a powerful shift...
PhysicEdit: Teaching Image Editing Models to Respect Physics
Instruction-based image editing models are impressive at following prompts. But when edits involve physical interactions, they often fail to respect real-world laws. In their paper “From Statics to Dynamics: Physics-Aware Image Editing with Latent Transition Priors,” the authors introduce PhysicEdit...
YuanLab AI Releases Yuan 3.0 Ultra: A Flagship Multimodal MoE Foundation Model, Built for Stronger Intelligence and Unrivaled Efficiency
How can a trillion-parameter Large Language Model achieve state-of-the-art enterprise performance while simultaneously cutting its total parameter count by 33.3% and boosting pre-training efficiency by 49%? Yuan Lab AI releases Yuan3.0 Ultra, an open-source Mixture-of-Experts (MoE) large language mo...
Knowledge Graph and Hypergraph Transformers with Repository-Attention and Journey-Based Role Transport
arXiv:2603.03304v1 Announce Type: new
Abstract: We present a concise architecture for joint training on sentences and structured data while keeping knowledge and language representations separable. The model treats knowledge graphs and hypergraphs as structured instances with role slots and encodes...
AOI: Turning Failed Trajectories into Training Signals for Autonomous Cloud Diagnosis
arXiv:2603.03378v1 Announce Type: new
Abstract: Large language model (LLM) agents offer a promising data-driven approach to automating Site Reliability Engineering (SRE), yet their enterprise deployment is constrained by three challenges: restricted access to proprietary data, unsafe action executi...
RADAR: Learning to Route with Asymmetry-aware DistAnce Representations
arXiv:2603.03388v1 Announce Type: new
Abstract: Recent neural solvers have achieved strong performance on vehicle routing problems (VRPs), yet they mainly assume symmetric Euclidean distances, restricting applicability to real-world scenarios. A core challenge is encoding the relational features in...
Towards Improved Sentence Representations using Token Graphs
arXiv:2603.03389v1 Announce Type: new
Abstract: Obtaining a single-vector representation from a Large Language Model's (LLM) token-level outputs is a critical step for nearly all sentence-level tasks. However, standard pooling methods like mean or max aggregation treat tokens as an independent set,...
Heterogeneous Time Constants Improve Stability in Equilibrium Propagation
arXiv:2603.03402v1 Announce Type: new
Abstract: Equilibrium propagation (EP) is a biologically plausible alternative to backpropagation for training neural networks. However, existing EP models use a uniform scalar time step dt, which corresponds biologically to a membrane time constant that is het...
Asymmetric Goal Drift in Coding Agents Under Value Conflict
arXiv:2603.03456v1 Announce Type: new
Abstract: Agentic coding agents are increasingly deployed autonomously, at scale, and over long-context horizons. Throughout an agent's lifetime, it must navigate tensions between explicit instructions, learned values, and environmental pressures, often in cont...
Build, Judge, Optimize: A Blueprint for Continuous Improvement of Multi-Agent Consumer Assistants
arXiv:2603.03565v1 Announce Type: new
Abstract: Conversational shopping assistants (CSAs) represent a compelling application of agentic AI, but moving from prototype to production reveals two underexplored challenges: how to evaluate multi-turn interactions and how to optimize tightly coupled multi...
Mozi: Governed Autonomy for Drug Discovery LLM Agents
arXiv:2603.03655v1 Announce Type: new
Abstract: Tool-augmented large language model (LLM) agents promise to unify scientific reasoning with computation, yet their deployment in high-stakes domains like drug discovery is bottlenecked by two critical barriers: unconstrained tool-use governance and po...
MAGE: Meta-Reinforcement Learning for Language Agents toward Strategic Exploration and Exploitation
arXiv:2603.03680v1 Announce Type: new
Abstract: Large Language Model (LLM) agents have demonstrated remarkable proficiency in learned tasks, yet they often struggle to adapt to non-stationary environments with feedback. While In-Context Learning and external memory offer some flexibility, they fail...
AI4S-SDS: A Neuro-Symbolic Solvent Design System via Sparse MCTS and Differentiable Physics Alignment
arXiv:2603.03686v1 Announce Type: new
Abstract: Automated design of chemical formulations is a cornerstone of materials science, yet it requires navigating a high-dimensional combinatorial space involving discrete compositional choices and continuous geometric constraints. Existing Large Language M...
Introducing ChatGPT for Excel and new financial data integrations
OpenAI introduces ChatGPT for Excel and new financial app integrations, powered by GPT-5.4 to accelerate modeling, research, and analysis in regulated environments.
Stop Tuning Hyperparameters. Start Tuning Your Problem.
80% of ML projects fail from bad problem framing, not bad models. A 5-step protocol to define the right problem before you write training code.
The post Stop Tuning Hyperparameters. Start Tuning Your Problem. appeared first on Towards Data Science.
LangWatch Open Sources the Missing Evaluation Layer for AI Agents to Enable End-to-End Tracing, Simulation, and Systematic Testing
As AI development shifts from simple chat interfaces to complex, multi-step autonomous agents, the industry has encountered a significant bottleneck: non-determinism. Unlike traditional software where code follows a predictable path, agents built on LLMs introduce a high degree of variance. LangWatc...
Phi-4-reasoning-vision and the lessons of training a multimodal reasoning model
We are pleased to announce Phi-4-reasoning-vision-15B, a 15 billion parameter open‑weight multimodal reasoning model, available through Microsoft Foundry (opens in new tab), HuggingFace (opens in new tab) and GitHub (opens in new tab). Phi-4-reasoning-vision-15B is a broadly capable model that can b...