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COMPACT: Common-token Optimized Model Pruning Across Channels and Tokens

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arXiv:2509.06836v1 Announce Type: cross
Abstract: Making LLMs more efficient in memory, latency, and serving cost is crucial for edge deployment, interactive applications, and sustainable inference at scale. Pruning is a key technique toward this goal. However, prior pruning methods are limited: width pruning often breaks the standard transformer layout or requires custom inference code, while depth pruning removes entire layers and can cause abrupt accuracy drops. In this work, we propose COMPACT, which jointly (i) prunes rare vocabulary to shrink embedding/unembedding and (ii) prunes FFN intermediate channels using common-token-weighted activations, aligning importance with the post-pruning token distribution. COMPACT enjoys merits of both depth and width pruning, such as: deployment-friendliness (keeps a standard transformer architecture), scale-adaptivity (trade off vocab vs. FFN pruning), training-free operation with competitive pruning time, and strong memory savings alongside throughput gains. Experiments across Qwen, LLaMA, and Gemma families (0.5B-70B) show state-of-the-art downstream task performance at similar or higher pruning ratios, with substantial reductions in parameters, GPU memory, and end-to-end latency.



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Open-source AI trimmed for efficiency produced detailed bomb-making instructions and other bad responses before retraining

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  • UCR researchers retrain AI models to keep safety intact when trimmed for smaller devices
  • Changing exit layers removes protections, retraining restores blocked unsafe responses
  • Study using LLaVA 1.5 showed reduced models refused dangerous prompts after training

Researchers at the University of California, Riverside are addressing the problem of weakened safety in open-source artificial intelligence models when adapted for smaller devices.

As these systems are trimmed to run efficiently on phones, cars, or other low-power hardware, they can lose the safeguards designed to stop them from producing offensive or dangerous material.



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Artificial Intelligence in Healthcare: Efficiency and HIPAA Risks

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Healthcare professionals are finding AI to be nothing short of an asset in producing efficient communication and data organization on the job. Clinicians utilize AI for managing medical records, patient medications, and various medical writing and data organization-based tasks. AI has the capacity to provide clinical-grade language processing and time-saving strategies that simplify ICD-10 coding and assist clinicians in completing clinical notes faster and in a more timely manner.

While AI’s advancements have served as game-changers in increasing workday efficiency, clinicians must be cognizant of the perils of using AI chatbots as a means to communicate with patients. As background, AI chatbots are computer programs designed to simulate conversations with humans. In principle, these tools facilitate communication between patients and healthcare providers by offering continuous access to medical information, automating processes such as appointment scheduling and medication reminders, assessing symptoms, and recommending care and treatment.

When patient medical records and sensitive information are involved, however, how do clinicians find the balance between utilizing AI chatbots to their benefit and exercising discretion with sensitive patient data to avoid HIPAA violations? Given AI’s numerous data collection mechanisms, including its tracking of browsing activity and its ability to access individual device information, what can be done to ensure that patient information is never subjected to even the shortest-lived bugs or breaches? Can AI companies assist clinicians in ensuring that patient confidentiality is preserved?

First, opt-out features and encryption protocols are two ways AI protects user data, but tech companies collaborating with healthcare providers in creating HIPAA-compliant AI software would be even more beneficial to the medical field. Second, it is imperative for healthcare professionals to acquire patient consent and anonymize any patient data prior to recruiting the help of an AI chatbot. Healthcare providers utilizing legal safeguards, such as requiring patients to sign releases expressing consent that medical records may be used for research, in addition to proper anonymization of patient data used for research, may mitigate legal risks associated with HIPAA compliance.

For further assistance in managing the risks associated with AI, healthcare providers can turn to the National Institute of Standards and Technology (NIST) AI Risk Management Framework (AI RMF) to evaluate risks related to AI systems. NIST, a non-regulatory Federal agency within the U.S. Department of Commerce, published this voluntary guidance to help entities manage the risks of AI systems and promote responsible AI development.

Leveraging the vast capabilities of artificial intelligence, alongside robust data encryption and strict adherence to HIPAA compliance protocols, will enhance the future of healthcare for patients and healthcare providers alike.



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Ivory Tower: Dr Kamra’s AI research gains UN spotlight

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Dr Preeti Kamra, Assistant Professor in the Department of Computer Science at DAV College, Amritsar, has been invited by the United Nations to address its General Assembly on United Nations Digital Cooperation Day, held during the High-Level Week of the 80th session of the UN General Assembly. An educator and researcher, Dr Kamra has been extensively working in the fields of emerging digital technologies and internet governance.

Holding a PhD in Artificial Intelligence-based technology, Dr Kamra developed AI software to detect anxiety among students and is currently in the process of documenting and patenting this technology under her name. However, it was her work in Internet governance that earned her the invitation to speak at the UN.

“I have been invited to speak at an exclusive, closed-door event hosted annually by the United Nations, United Nations Digital Cooperation Day, which focuses on emerging technologies worldwide. I will be the only Indian speaker at the event and my speech will focus on policies in India aimed at making the Internet more secure, safe, inclusive, and accessible,” Dr Kamra said. “There is a critical need to make the Internet multilingual, accessible and safe in India, especially with the growing use of AI in the future, making timely action imperative.”

Last year, Dr Kamra participated in the Asia-Pacific Regional Forum on Internet Governance held in Taiwan. Her research on AI in education secured her a seat at this prestigious UN event. According to her, AI in education should be promoted, contrary to the reservations many educators globally hold.

“Despite NEP 2020 and the Government of India promoting Artificial Intelligence in higher education, few state-level universities, schools, or colleges have adopted it fully. The key is to use AI productively, which requires laws and policies that regulate its usage, while controlling and monitoring potential abuse,” she explained.

The event is scheduled to take place from September 22 to 26 at the United Nations headquarters in the USA.





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