The new program helps customers reach transformative AI outcomes faster News summary: Today, Cisco (NASDAQ: CSCO) announced the launch of the Cisco 360 Partner Program after fifteen months of co-design with partners. Cisco’s success is built on close collaboration with its partners to meet custome...
New Agent Builder cuts time to production by up to 50% and reduces early production issues by 20% Wonderful teams up with frontier AI lab Anthropic to build the foundations for enterprises managing entire networks of agents at scale Wonderful, the enterprise agent platform, today announced the launc...
Radware Acquires Pynt to Strengthen Full-Lifecycle API Security
Acquisition adds API security testing to Radware’s comprehensive API security portfolio, extending protection to all stages of the API lifecycle Radware® (NASDAQ: RDWR), a global leader in application security and delivery solutions for multi-cloud environments, today announced it has completed the ...
arXiv:2601.16984v1 Announce Type: new
Abstract: The 3rd Generation Partnership Project (3GPP) produces complex technical specifications essential to global telecommunications, yet their hierarchical structure, dense formatting, and multi-modal content make them difficult to process. While Large Lan...
Sparsity-Aware Low-Rank Representation for Efficient Fine-Tuning of Large Language Models
arXiv:2601.16991v1 Announce Type: new
Abstract: Adapting large pre-trained language models to downstream tasks often entails fine-tuning millions of parameters or deploying costly dense weight updates, which hinders their use in resource-constrained environments. Low-rank Adaptation (LoRA) reduces ...
MathMixup: Boosting LLM Mathematical Reasoning with Difficulty-Controllable Data Synthesis and Curriculum Learning
arXiv:2601.17006v1 Announce Type: new
Abstract: In mathematical reasoning tasks, the advancement of Large Language Models (LLMs) relies heavily on high-quality training data with clearly defined and well-graded difficulty levels. However, existing data synthesis methods often suffer from limited di...
Interpreting Agentic Systems: Beyond Model Explanations to System-Level Accountability
arXiv:2601.17168v1 Announce Type: new
Abstract: Agentic systems have transformed how Large Language Models (LLMs) can be leveraged to create autonomous systems with goal-directed behaviors, consisting of multi-step planning and the ability to interact with different environments. These systems diff...
High-Fidelity Longitudinal Patient Simulation Using Real-World Data
arXiv:2601.17310v1 Announce Type: new
Abstract: Simulation is a powerful tool for exploring uncertainty. Its potential in clinical medicine is transformative and includes personalized treatment planning and virtual clinical trials. However, simulating patient trajectories is challenging because of ...
arXiv:2601.17311v1 Announce Type: new
Abstract: Multi-agent systems can improve reliability, yet under a fixed inference budget they often help, saturate, or even collapse. We develop a minimal and calibratable theory that predicts these regimes from three binding constraints of modern agent stacks...
Principled Coarse-Grained Acceptance for Speculative Decoding in Speech
Speculative decoding accelerates autoregressive speech generation by letting a fast draft model propose tokens that a larger target model verifies. However, for speech LLMs that generate acoustic tokens, exact token matching is overly restrictive: many discrete tokens are acoustically or semanticall...
SelfReflect: Can LLMs Communicate Their Internal Answer Distribution?
The common approach to communicate a large language model’s (LLM) uncertainty is to add a percentage number or a hedging word to its response. But is this all we can do? Instead of generating a single answer and then hedging it, an LLM that is fully transparent to the user needs to be able to reflec...
Learning to Reason as Action Abstractions with Scalable Mid-Training RL
Large language models excel with reinforcement learning (RL), but fully unlocking this potential requires a mid-training stage. An effective mid-training phase should identify a compact set of useful actions and enable fast selection among them through online RL. We formalize this intuition by prese...
VLSU: Mapping the Limits of Joint Multimodal Understanding for AI Safety
Safety evaluation of multimodal foundation models often treats vision and language inputs separately, missing risks from joint interpretation where benign content becomes harmful in combination. Existing approaches also fail to distinguish clearly unsafe content from borderline cases, leading to pro...
Powering tax donations with AI powered personalized recommendations
TRUSTBANK partnered with Recursive to build Choice AI using OpenAI models, delivering personalized, conversational recommendations that simplify Furusato Nozei gift discovery. A multi-agent system helps donors navigate thousands of options and find gifts that match their preferences.
AI startup CVector raises $5M for its industrial ‘nervous system’
Industrial AI startup CVector built a brain and nervous system for big industry. Now, founders Richard Zhang and Tyler Ruggles are tasked with a bigger challenge: showing customers and investors how this AI-powered software layer translates to real savings on an industrial scale. The New York-based...
The AI Evolution of Graph Search at Netflix: From Structured Queries to Natural LanguageBy Alex Hutter and Bartosz BalukiewiczOur previous blog posts (part 1, part 2, part 3) detailed how Netflix’s Graph Search platform addresses the challenges of searching across federated data sets within Netflix’...
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Exploring the RAG pipeline in Cursor that powers code indexing and retrieval for coding agents
The post How Cursor Actually Indexes Your Codebase appeared first on Towards Data Science.