How Totogi automated change request processing with Totogi BSS Magic and Amazon BedrockArtificial Intelligence This blog post describes how Totogi automates change request processing by partnering with the AWS Generative AI Innovation Center and using the rapid innovation capabilities of Amazon Bedrock.
This blog post describes how Totogi automates change request processing by partnering with the AWS Generative AI Innovation Center and using the rapid innovation capabilities of Amazon Bedrock. Read More
NVIDIA Revolutionizes Climate Tech with ‘Earth-2’: The World’s First Fully Open Accelerated AI Weather StackMarkTechPost For decades, predicting the weather has been the exclusive domain of massive government supercomputers running complex physics-based equations. NVIDIA has shattered that barrier with the release of the Earth-2 family of open models and tools for AI weather and climate prediction accessible to virtually anyone, from tech startups to national meteorological agencies. In a move
The post NVIDIA Revolutionizes Climate Tech with ‘Earth-2’: The World’s First Fully Open Accelerated AI Weather Stack appeared first on MarkTechPost.
For decades, predicting the weather has been the exclusive domain of massive government supercomputers running complex physics-based equations. NVIDIA has shattered that barrier with the release of the Earth-2 family of open models and tools for AI weather and climate prediction accessible to virtually anyone, from tech startups to national meteorological agencies. In a move
The post NVIDIA Revolutionizes Climate Tech with ‘Earth-2’: The World’s First Fully Open Accelerated AI Weather Stack appeared first on MarkTechPost. Read More
Expereo: Enterprise connectivity amid AI surge with ‘visibility at the speed of life’AI News AI continues to reshape technology and business; yet for the network, enterprise connectivity in the AI age means being always-on, and extra vigilant for sovereignty and security besides. This means that speed is not the only requirement. As Julian Skeels, chief digital officer at Expereo notes, it is more about ‘certainty.’ “AI workloads are distributed,
The post Expereo: Enterprise connectivity amid AI surge with ‘visibility at the speed of life’ appeared first on AI News.
AI continues to reshape technology and business; yet for the network, enterprise connectivity in the AI age means being always-on, and extra vigilant for sovereignty and security besides. This means that speed is not the only requirement. As Julian Skeels, chief digital officer at Expereo notes, it is more about ‘certainty.’ “AI workloads are distributed,
The post Expereo: Enterprise connectivity amid AI surge with ‘visibility at the speed of life’ appeared first on AI News. Read More
Classification: PublicReporting Period: January 19-26, 2026Distribution: Security Operations, IT Leadership, Executive TeamPrepared By: Tech Jacks Solutions Security Intelligence TJS Weekly Security Intelligence Briefing – Week of Jan 26th 20261. Executive Summary The week of January 19-26, 2026 presents an elevated risk posture driven by four actively exploited vulnerabilities requiring immediate action: Cisco Unified Communications zero-day […]
The European Commission is now investigating whether X properly assessed risks before deploying its Grok artificial intelligence tool, following its use to generate sexually explicit images. […] Read More
The defense mechanisms that NPM introduced after the ‘Shai-Hulud’ supply-chain attacks have weaknesses that allow threat actors to bypass them via Git dependencies. […] Read More
Okta misconfigurations can quietly weaken identity security as SaaS environments evolve. Nudge Security shows six Okta security settings teams often overlook and how to fix them. […] Read More
Cybersecurity researchers have discovered an ongoing campaign that’s targeting Indian users with a multi-stage backdoor as part of a suspected cyber espionage campaign. The activity, per the eSentire Threat Response Unit (TRU), involves using phishing emails impersonating the Income Tax Department of India to trick victims into downloading a malicious archive, ultimately granting the threat Read […]
Ready Jurist One: Benchmarking Language Agents for Legal Intelligence in Dynamic Environmentscs.AI updates on arXiv.org arXiv:2507.04037v4 Announce Type: replace
Abstract: The gap between static benchmarks and the dynamic nature of real-world legal practice poses a key barrier to advancing legal intelligence. To this end, we introduce J1-ENVS, the first interactive and dynamic legal environment tailored for LLM-based agents. Guided by legal experts, it comprises six representative scenarios from Chinese legal practices across three levels of environmental complexity. We further introduce J1-EVAL, a fine-grained evaluation framework, designed to assess both task performance and procedural compliance across varying levels of legal proficiency. Extensive experiments on 17 LLM agents reveal that, while many models demonstrate solid legal knowledge, they struggle with procedural execution in dynamic settings. Even the SOTA model, GPT-4o, falls short of 60% overall performance. These findings highlight persistent challenges in achieving dynamic legal intelligence and offer valuable insights to guide future research.
arXiv:2507.04037v4 Announce Type: replace
Abstract: The gap between static benchmarks and the dynamic nature of real-world legal practice poses a key barrier to advancing legal intelligence. To this end, we introduce J1-ENVS, the first interactive and dynamic legal environment tailored for LLM-based agents. Guided by legal experts, it comprises six representative scenarios from Chinese legal practices across three levels of environmental complexity. We further introduce J1-EVAL, a fine-grained evaluation framework, designed to assess both task performance and procedural compliance across varying levels of legal proficiency. Extensive experiments on 17 LLM agents reveal that, while many models demonstrate solid legal knowledge, they struggle with procedural execution in dynamic settings. Even the SOTA model, GPT-4o, falls short of 60% overall performance. These findings highlight persistent challenges in achieving dynamic legal intelligence and offer valuable insights to guide future research. Read More
Do Models Hear Like Us? Probing the Representational Alignment of Audio LLMs and Naturalistic EEGcs.AI updates on arXiv.org arXiv:2601.16540v1 Announce Type: cross
Abstract: Audio Large Language Models (Audio LLMs) have demonstrated strong capabilities in integrating speech perception with language understanding. However, whether their internal representations align with human neural dynamics during naturalistic listening remains largely unexplored. In this work, we systematically examine layer-wise representational alignment between 12 open-source Audio LLMs and Electroencephalogram (EEG) signals across 2 datasets. Specifically, we employ 8 similarity metrics, such as Spearman-based Representational Similarity Analysis (RSA), to characterize within-sentence representational geometry. Our analysis reveals 3 key findings: (1) we observe a rank-dependence split, in which model rankings vary substantially across different similarity metrics; (2) we identify spatio-temporal alignment patterns characterized by depth-dependent alignment peaks and a pronounced increase in RSA within the 250-500 ms time window, consistent with N400-related neural dynamics; (3) we find an affective dissociation whereby negative prosody, identified using a proposed Tri-modal Neighborhood Consistency (TNC) criterion, reduces geometric similarity while enhancing covariance-based dependence. These findings provide new neurobiological insights into the representational mechanisms of Audio LLMs.
arXiv:2601.16540v1 Announce Type: cross
Abstract: Audio Large Language Models (Audio LLMs) have demonstrated strong capabilities in integrating speech perception with language understanding. However, whether their internal representations align with human neural dynamics during naturalistic listening remains largely unexplored. In this work, we systematically examine layer-wise representational alignment between 12 open-source Audio LLMs and Electroencephalogram (EEG) signals across 2 datasets. Specifically, we employ 8 similarity metrics, such as Spearman-based Representational Similarity Analysis (RSA), to characterize within-sentence representational geometry. Our analysis reveals 3 key findings: (1) we observe a rank-dependence split, in which model rankings vary substantially across different similarity metrics; (2) we identify spatio-temporal alignment patterns characterized by depth-dependent alignment peaks and a pronounced increase in RSA within the 250-500 ms time window, consistent with N400-related neural dynamics; (3) we find an affective dissociation whereby negative prosody, identified using a proposed Tri-modal Neighborhood Consistency (TNC) criterion, reduces geometric similarity while enhancing covariance-based dependence. These findings provide new neurobiological insights into the representational mechanisms of Audio LLMs. Read More
