A previously undocumented China-aligned threat cluster dubbed LongNosedGoblin has been attributed to a series of cyber attacks targeting governmental entities in Southeast Asia and Japan. The end goal of these attacks is cyber espionage, Slovak cybersecurity company ESET said in a report published today. The threat activity cluster has been assessed to be active since […]
An automated campaign is targeting multiple VPN platforms, with credential-based attacks being observed on Palo Alto Networks GlobalProtect and Cisco SSL VPN. […] Read More
In the latest attacks against the vendor’s SMA1000 devices, threat actors have chained a new zero-day flaw with a critical vulnerability disclosed earlier this year. Read More
University of Sydney suffers data breach exposing student and staff info BleepingComputerBill Toulas
Hackers gained access to an online coding repository belonging to the University of Sydney and stole files with personal information of staff and students. […] Read More
The Clop ransomware gang is targeting Internet-exposed Gladinet CentreStack file servers in a new data theft extortion campaign. […] Read More
Law enforcement has seized the servers and domains of the E-Note cryptocurrency exchange, allegedly used by cybercriminal groups to launder more than $70 million. […] Read More
(c) SANS Internet Storm Center. https://isc.sans.edu Creative Commons Attribution-Noncommercial 3.0 United States License. Read More
The North Korean threat actor known as Kimsuky has been linked to a new campaign that distributes a new variant of Android malware called DocSwap via QR codes hosted on phishing sites mimicking Seoul-based logistics firm CJ Logistics (formerly CJ Korea Express). “The threat actor leveraged QR codes and notification pop-ups to lure victims into […]
RePo: Language Models with Context Re-Positioningcs.AI updates on arXiv.org arXiv:2512.14391v1 Announce Type: cross
Abstract: In-context learning is fundamental to modern Large Language Models (LLMs); however, prevailing architectures impose a rigid and fixed contextual structure by assigning linear or constant positional indices. Drawing on Cognitive Load Theory (CLT), we argue that this uninformative structure increases extraneous cognitive load, consuming finite working memory capacity that should be allocated to deep reasoning and attention allocation. To address this, we propose RePo, a novel mechanism that reduces extraneous load via context re-positioning. Unlike standard approaches, RePo utilizes a differentiable module, $f_phi$, to assign token positions that capture contextual dependencies, rather than replying on pre-defined integer range. By continually pre-training on the OLMo-2 1B backbone, we demonstrate that RePo significantly enhances performance on tasks involving noisy contexts, structured data, and longer context length, while maintaining competitive performance on general short-context tasks. Detailed analysis reveals that RePo successfully allocate higher attention to distant but relevant information, assign positions in dense and non-linear space, and capture the intrinsic structure of the input context. Our code is available at https://github.com/SakanaAI/repo.
arXiv:2512.14391v1 Announce Type: cross
Abstract: In-context learning is fundamental to modern Large Language Models (LLMs); however, prevailing architectures impose a rigid and fixed contextual structure by assigning linear or constant positional indices. Drawing on Cognitive Load Theory (CLT), we argue that this uninformative structure increases extraneous cognitive load, consuming finite working memory capacity that should be allocated to deep reasoning and attention allocation. To address this, we propose RePo, a novel mechanism that reduces extraneous load via context re-positioning. Unlike standard approaches, RePo utilizes a differentiable module, $f_phi$, to assign token positions that capture contextual dependencies, rather than replying on pre-defined integer range. By continually pre-training on the OLMo-2 1B backbone, we demonstrate that RePo significantly enhances performance on tasks involving noisy contexts, structured data, and longer context length, while maintaining competitive performance on general short-context tasks. Detailed analysis reveals that RePo successfully allocate higher attention to distant but relevant information, assign positions in dense and non-linear space, and capture the intrinsic structure of the input context. Our code is available at https://github.com/SakanaAI/repo. Read More
Within the past year, artificial intelligence copilots and agents have quietly permeated the SaaS applications businesses use every day. Tools like Zoom, Slack, Microsoft 365, Salesforce, and ServiceNow now come with built-in AI assistants or agent-like features. Virtually every major SaaS vendor has rushed to embed AI into their offerings. The result is an explosion […]
