Anthropic Races to Contain Leak of Code Behind Claude AI Agent - WSJ
<a href="https://news.google.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?oc=5" target="_blank">Anthropic Races to Contain Leak of Code Behind Claude AI Agent</a> <font color="#6f6f6f">WSJ</font>
Could not retrieve the full article text.
Read on GNews AI coding →Sign in to highlight and annotate this article
Conversation starters
Daily AI Digest
Get the top 5 AI stories delivered to your inbox every morning.
More about
claudeagent
Tencent to Launch Hunyuan 3.0 in April, Build WeChat AI Agent - Caixin Global
<a href="https://news.google.com/rss/articles/CBMitAFBVV95cUxNMG9uakFkUnF3WXFlM2lDNzdHZE9qVUlJdUdzdndTQVprVE55NlVmQUhLZ0tmOVJTelNSOG14bWcyMGtlM1hwMG9mSEZTNVdGd2Q0OVIyWlNwWVhCcF9FYUstMklmOEZNYmx0Vm0zb3FmeTZFRlI5dWg4OFdRNVc5RmxienpGQWszeU5ZbTQybHFxWmlTc3VsTUJBdGRGbWJVUEh6ZzhkQVdzX0dhMGFtWmJ2a1U?oc=5" target="_blank">Tencent to Launch Hunyuan 3.0 in April, Build WeChat AI Agent</a> <font color="#6f6f6f">Caixin Global</font>
OutSystems Introduces Agentic Systems Engineering to Power Governed, Open Enterprise AI - Thailand Business News
<a href="https://news.google.com/rss/articles/CBMizgFBVV95cUxNZ2IxMU1kWGk5elYxcUU3ZDc0WHFlYUp1UzlsM2ZXRFhPZU9wMW9rNG5KQVc0dlV3VlRrWGlWMXExY1hfalVaWDJSd1oxMlVuN1dmQ2s1MHcwWGtpSmk0c1MzVUIzaVQxV2plZFNSSHg3bmpXcU9UOXdMZ0t6aWRGbV9TMEhQYWRXSGlkekRFTFdNU2JUU203NWo5cEctdDlQVXJhZVYtaHFLcDdVT19IOUJaRnBvbGgwUHJmX2toeGoyeTFjYmcwSF9JWHo2QQ?oc=5" target="_blank">OutSystems Introduces Agentic Systems Engineering to Power Governed, Open Enterprise AI</a> <font color="#6f6f6f">Thailand Business News</font>
Google's $20 per month AI Pro plan just got a big storage boost
Google's $20 per month AI Pro plan , which includes Gemini, Veo and Nano Banana, got a big storage boost and some other new perks. Users of the plan (also available for $200 per year ) will see their cloud space jump from 2TB to 5TB at no extra cost. That extra storage can be used not only for AI but also Gmail, Google Drive and Google Photos backups. Gemini can now pull context from Gmail and the web for Drive, Docs, Slides and Sheets, provide summaries for your Gmail inbox and proofread emails before you send them. It's also introducing additional agentic help with Chrome auto browse "that handles those tedious, multi-step chores — like planning a trip or filling out forms," Google VP Shimrit Ben-Yair wrote on X . Finally, Google announced that it's bundling its Home Premium subscription
Knowledge Map
Connected Articles — Knowledge Graph
This article is connected to other articles through shared AI topics and tags.
More in Models
RefineRL: Advancing Competitive Programming with Self-Refinement Reinforcement Learning
arXiv:2604.00790v1 Announce Type: new Abstract: While large language models (LLMs) have demonstrated strong performance on complex reasoning tasks such as competitive programming (CP), existing methods predominantly focus on single-attempt settings, overlooking their capacity for iterative refinement. In this paper, we present RefineRL, a novel approach designed to unleash the self-refinement capabilities of LLMs for CP problem solving. RefineRL introduces two key innovations: (1) Skeptical-Agent, an iterative self-refinement agent equipped with local execution tools to validate generated solutions against public test cases of CP problems. This agent always maintains a skeptical attitude towards its own outputs and thereby enforces rigorous self-refinement even when validation suggests cor
UK AISI Alignment Evaluation Case-Study
arXiv:2604.00788v1 Announce Type: new Abstract: This technical report presents methods developed by the UK AI Security Institute for assessing whether advanced AI systems reliably follow intended goals. Specifically, we evaluate whether frontier models sabotage safety research when deployed as coding assistants within an AI lab. Applying our methods to four frontier models, we find no confirmed instances of research sabotage. However, we observe that Claude Opus 4.5 Preview (a pre-release snapshot of Opus 4.5) and Sonnet 4.5 frequently refuse to engage with safety-relevant research tasks, citing concerns about research direction, involvement in self-training, and research scope. We additionally find that Opus 4.5 Preview shows reduced unprompted evaluation awareness compared to Sonnet 4.5,
CircuitProbe: Predicting Reasoning Circuits in Transformers via Stability Zone Detection
arXiv:2604.00716v1 Announce Type: new Abstract: Transformer language models contain localized reasoning circuits, contiguous layer blocks that improve reasoning when duplicated at inference time. Finding these circuits currently requires brute-force sweeps costing 25 GPU hours per model. We propose CircuitProbe, which predicts circuit locations from activation statistics in under 5 minutes on CPU, providing a speedup of three to four orders of magnitude. We find that reasoning circuits come in two types: stability circuits in early layers, detected through the derivative of representation change, and magnitude circuits in late layers, detected through anomaly scoring. We validate across 9 models spanning 6 architectures, including 2025 models, confirming that CircuitProbe top predictions m
Ontology-Constrained Neural Reasoning in Enterprise Agentic Systems: A Neurosymbolic Architecture for Domain-Grounded AI Agents
arXiv:2604.00555v1 Announce Type: new Abstract: Enterprise adoption of Large Language Models (LLMs) is constrained by hallucination, domain drift, and the inability to enforce regulatory compliance at the reasoning level. We present a neurosymbolic architecture implemented within the Foundation AgenticOS (FAOS) platform that addresses these limitations through ontology-constrained neural reasoning. Our approach introduces a three-layer ontological framework--Role, Domain, and Interaction ontologies--that provides formal semantic grounding for LLM-based enterprise agents. We formalize the concept of asymmetric neurosymbolic coupling, wherein symbolic ontological knowledge constrains agent inputs (context assembly, tool discovery, governance thresholds) while proposing mechanisms for extendi
Discussion
Sign in to join the discussion
No comments yet — be the first to share your thoughts!