Nvidia App beta enables DLSS 4.5 with dynamic frame-gen, automatic shader compilation
Nvidia DLSS 4.5 with dynamic frame generation is now available for RTX 50 GPUs using the Nvidia App (enable beta updates). The feature adjusts frame-gen in real time to balance performance and image quality. The update also adds MFG modes of up to 6x, along with beta automatic shader compilation to reduce in-game stutter. Read Entire Article
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availableupdatefeatureFisher Information Limits of Satellite RF Fingerprint Identifiability for Authentication
arXiv:2603.29766v1 Announce Type: new Abstract: RF fingerprinting authenticates satellite transmitters by exploiting hardware-specific signal impairments, yet existing methods operate without theoretical performance guarantees. We derive the Fisher information matrix (FIM) for joint estimation of in-phase/quadrature (IQ) imbalance and power amplifier (PA) nonlinearity parameters, establishing Cram\'{e}r-Rao bounds (CRBs) whose structure depends on constellation moments. A necessary condition for full IQ identifiability is that the identifiability factor~$\beta$ exceeds zero; for binary phase-shift keying (BPSK), $\beta = 0$ yields a rank-deficient FIM, rendering IQ parameters unidentifiable. This provides a plausible theoretical explanation for OrbID's near-random performance (area under t
Automatic Identification of Parallelizable Loops Using Transformer-Based Source Code Representations
arXiv:2603.30040v1 Announce Type: new Abstract: Automatic parallelization remains a challenging problem in software engineering, particularly in identifying code regions where loops can be safely executed in parallel on modern multi-core architectures. Traditional static analysis techniques, such as dependence analysis and polyhedral models, often struggle with irregular or dynamically structured code. In this work, we propose a Transformer-based approach to classify the parallelization potential of source code, focusing on distinguishing independent (parallelizable) loops from undefined ones. We adopt DistilBERT to process source code sequences using subword tokenization, enabling the model to capture contextual syntactic and semantic patterns without handcrafted features. The approach is
SkillReducer: Optimizing LLM Agent Skills for Token Efficiency
arXiv:2603.29919v1 Announce Type: new Abstract: LLM-based coding agents rely on \emph{skills}, pre-packaged instruction sets that extend agent capabilities, yet every token of skill content injected into the context window incurs both monetary cost and attention dilution. To understand the severity of this problem, we conduct a large-scale empirical study of 55,315 publicly available skills and find systemic inefficiencies: 26.4\% lack routing descriptions entirely, over 60\% of body content is non-actionable, and reference files can inject tens of thousands of tokens per invocation. Motivated by these findings, we present \textsc{SkillReducer}, a two-stage optimization framework. Stage~1 optimizes the routing layer by compressing verbose descriptions and generating missing ones via advers
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