Decoupled DiLoCo: A new frontier for resilient, distributed AI training
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WebGen-R1: Incentivizing Large Language Models to Generate Functional and Aesthetic Websites with Reinforcement Learning
While Large Language Models (LLMs) excel at function-level code generation, project-level tasks such as generating functional and visually aesthetic multi-page websites remain highly challenging. Existing works are often limited to single-page static websites, while agentic frameworks typically rely on multi-turn execution with proprietary models, leading to substantial token costs, high latency, and brittle integration. Training a small LLM end-to-end with reinforcement learning (RL) is a promising alternative, yet it faces a critical bottleneck in designing reliable and computationally […]
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Introducing workspace agents in ChatGPT
Workspace agents in ChatGPT are Codex-powered agents that automate complex workflows, run in the cloud, and help teams scale work across tools securely.
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Workspace agents
Learn how to build, use, and scale workspace agents in ChatGPT to automate repeatable workflows, connect tools, and streamline team operations.
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Speeding up agentic workflows with WebSockets in the Responses API
A deep dive into the Codex agent loop, showing how WebSockets and connection-scoped caching reduced API overhead and improved model latency.
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On k−Unitary Perfect Polynomials over F2
Let k be a positive integer. A polynomial A∈F_2[x] is called k-unitary perfect if the sum of the k-th powers of its distinct unitary divisors equals A^k. In this paper, we focus on the case k=2^n and prove that every 2^n-unitary perfect polynomial over F_2 is necessarily even. Moreover, we obtain a complete classification of all even 2^n-unitary perfect polynomials having at most three distinct irreducible factors. In particular, we characterize all such polynomials of the formA=x^a (x+1)^b […]
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System-Level Feasibility Constraints for In-Vehicle Intrusion Detection Systems
Abstract—In-vehicle intrusion detection systems (IDSs) are increasingly proposed to protect automotive networks, yet most prior work emphasizes detection accuracy while overlooking system-level constraints that determine real-world deployability. This paper addresses the mismatch between IDS design assumptions and the computational, architectural, and real-time limitations of production automotive electronic control units (ECUs). This issue is particularly critical in safety-critical automotive systems, where security mechanisms must operate within strict timing and resource bounds without interfering with control functions. The objective of […]
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Stateful Guardrails for Multi-Turn LLM Systems: A Conversational Risk Accumulation Framework
Guardrail systems for large language models (LLMs) are designed under a foundational but rarely examined assumption: that safety is a property of individual input–output exchanges. This assumption is adequate for single-turn deployments but fails structurally in multi-turn conversational systems, where risk does not reside in any single message but emerges from the accumulated trajectory of a session. We formalize this failure mode as Conversational Risk Accumulation (CRA), a class of adversarial and incidental threat patterns in which individually […]
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AI-Augmented Compliance Auditing for Cloud Systems: A Hybrid ML–LLM Approach
Manual compliance auditing in cloud environments consumes up to 40% of IT security budgets annually, yet existing approaches verify control presence rather than effectiveness, leaving institutions vulnerable to adversarial evasion. This paper presents an AI-augmented hybrid ML–LLM compliance auditing system evaluated on a national cybersecurity standards framework (143 controls, 200,000 training events). The system combines multi-label XGBoost classification with LLM-based semantic log analysis, grounded in a formal effectiveness model. Key findings: XGBoost achieves 99.88% F1 after 5% domain […]
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BreachLock Named Representative Vendor in Gartner Market Guide for Adversarial Exposure Validation
New York, United States, April 21st, 2026/CyberNewswire/–BreachLock, a global leader in offensive security, today announced it has been named a representative vendor in the 2026 Gartner Market Guide for Adversarial Exposure Validation. This recognition marks the first time BreachLock has been identified in the Adversarial Exposure Validation (AEV) category since launching its agentic AI-powered Adversarial Exposure Validation platform in 2025. Not only has the company gained recognition in the AEV market quickly, but BreachLock has also emerged as the only vendor […]
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