Raising the Bar: Securing Post-Quantum Signatures with LINEture
A new analysis reveals how key parameters in the LINEture digital signature scheme can be optimized to bolster security and efficiency in the age of quantum computing.
Science
Charting a Path Through the Quantum Threat
A new framework uses knowledge graphs and artificial intelligence to assess and strengthen enterprise cybersecurity in the face of looming quantum computing advancements.
Unlocking Precision in Particle Collisions: New Calculations for Heavy Quark Interactions
Researchers have refined calculations of how heavy quarks participate in deep-inelastic scattering, offering improved predictions for high-energy physics experiments.
Hidden Signals in Code: Protecting AI-Generated Software
Researchers have developed a new technique to embed robust watermarks into code generated by artificial intelligence, ensuring both functionality and security.
Beyond Blockchain Silos: Connecting the Decentralized Web
![Figure 1: Illustration of a multi-agent particle system with interactions governed by a potential field. Particles repel each other with a force proportional to [latex]1/r[/latex], preventing overlap and maintaining a minimal distance.](https://arxiv.org/html/2601.02949v1/figures/architecture_v1.jpg)
This review examines the growing need for interoperability between blockchains and how emerging frameworks are striving to overcome the limitations of isolated networks.
Squeezing More Performance from Language Models
![Through the fusion of key-value (KV) cache blocks, the computational footprint during batch decoding is demonstrably reduced, and efficiency is further enhanced by enabling the reuse of computations across unified representations of data chunks - a strategy illustrated by the shared computation of chunks 0, 1, and 2, effectively minimizing redundant matrix operations and optimizing performance via [latex] KV [/latex] cache management.](https://arxiv.org/html/2601.03067v1/figures/assets/shared_chunks.png)
A new technique efficiently compresses and reuses memory caches, significantly boosting the speed and scalability of large language model serving.
Seeing What Matters: Smarter Fusion for Vulnerability Detection
A new deep learning framework intelligently combines code’s meaning and structure to pinpoint security flaws with improved accuracy.
Securing the IoT: Lightweight Crypto for Tiny Devices
As the Internet of Things expands, so does the need for robust, yet efficient, security solutions tailored for resource-constrained embedded systems.
Decoding Resilience: How to Build Question Answering Systems That Withstand Attacks
New research pinpoints the key to bolstering question answering systems against adversarial manipulation, bridging the gap between clean and attacked performance.
Squeezing Speech: Adaptive Quantization for Robust ASR
![Dynamic quantization in encoder-decoder automatic speech recognition models addresses error propagation through a novel calibration method that utilizes layer-wise scaling factors [latex]\alpha_{\ell}[/latex], computed based on error indicators, to correct the update direction-a refinement of standard post-training quantization [latex]Eq.(1)[/latex] that calibrates the encoder with audio data and the decoder with text and quantized encoder outputs, as defined in [latex]Eq.(9)[/latex].](https://arxiv.org/html/2601.02455v1/x3.png)
New research tackles the challenges of compressing automatic speech recognition models without sacrificing accuracy, focusing on how errors accumulate during quantization.