Stealing Secrets While Your System Sleeps
Researchers have discovered a novel power side-channel attack that exploits operating system behavior to compromise the security of cryptographic keys.
Science
Squeezing More from Less: A New Approach to Model Quantization
![Structured Residual Reconstruction mitigates the damage caused by weight quantization by proactively preserving dominant low-rank structures, resulting in a significantly reduced reconstruction error-measured as [latex]\lVert\mathbf{W}-\mathbf{Q}-\mathbf{L}\mathbf{R}\rVert\_{F}[/latex]-compared to standard quantization error reduction techniques that destroy this intrinsic structure.](https://arxiv.org/html/2602.02001v1/x1.png)
Researchers have developed a method for dramatically reducing the size of large language models without sacrificing performance by strategically preserving key information and reconstructing lost details.
Securing the 6G Road: Adaptive Quantum-Safe Crypto for Connected Cars
As vehicles become increasingly connected, this review explores how to dynamically deploy post-quantum cryptography to safeguard V2X communications against future threats.
Taming Spectre: A Formally Verified Defense
Researchers have developed a new security approach, SpecIBT, to neutralize Spectre attacks by combining hardware-assisted control-flow integrity with compiler optimizations.
Symmetry’s Shield: Protecting Order in Quantum Systems
New research demonstrates how higher-order symmetries can stabilize quantum systems against disorder, effectively raising the bar for spontaneous symmetry breaking.
Squeezing More From Less: A New Approach to Efficient AI Models
![LoRDS reimagines parameter-efficient fine-tuning by decomposing block-wise scaling into a low-rank product [latex]W\odot B_{AW} [/latex], enabling granular refinement via post-training quantization or direct fine-tuning with high-rank updates and-unlike methods like QLoRA which introduce additive, non-mergeable adapters-zero additional inference overhead through natural absorption into the dequantization process.](https://arxiv.org/html/2601.22716v1/x2.png)
Researchers have developed a novel method for compressing and adapting large language models, boosting performance and reducing computational demands.
Secrets Within: Hiding Data in Plain Sight with AI
Researchers have demonstrated a new method for embedding hidden information within large language models, raising concerns about model security and the potential for covert communication.
Decoding the Future: Polar Codes Tackle Reed-Solomon Challenges
![The study demonstrates the decoding performance of length-32 extended Reed-Solomon (eRS) codes, showcasing their capacity for reliable data recovery even with errors, a critical feature for robust data storage and transmission systems where [latex]n=32[/latex].](https://arxiv.org/html/2601.22482v1/x1.png)
Researchers are exploring how techniques from the world of polar codes can be applied to improve the decoding of Reed-Solomon codes, a cornerstone of data storage and transmission.
Taming the Fermion Sign Problem: A Solvable Model for Quantum Simulations
Researchers have developed a completely solvable Path Integral Monte Carlo model for interacting harmonic fermions, providing unprecedented insights into the notoriously difficult fermion sign problem.
Beyond Mesons and Baryons: The Quest for Exotic Hadrons
A new review explores the theoretical landscape of multiquark states, examining the forces that could bind these unusual configurations of quarks.