In this blog post, we will provide insights into the website deployment workflow developed during our collaboration with Runtime Verification. Our collaboration focused on creating nix-based deployment workflow for small websites associated with Runtime Verification projects, including: Kontrol: developer tooling for formal verification of smart contracts written in solodity. ERCx: developer tooling for ERC token testing. Firefly: developer tooling for Ethereum smart contract testing. We will walk through a simple example that showcases all the key features of […]

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Beyond being a cliché, “artificial intelligence” and its associated automation technologies have driven major developments in security, signifying that important changes have occurred in this field. In the field of cybersecurity, artificial intelligence refers to systems that acquire data, trace patterns, and forecast trends. Typically, this application is performed using machine learning, neural networks, or other high-performance data processing algorithms. There are limited domains in which an AI-driven system is more effective than humans or conventional security systems, […]

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arXiv:2305.19640v3 Announce Type: replace Abstract: In this paper, we are concerned with the generalization performance of non-parametric estimation for pairwise learning. Most of the existing work requires the hypothesis space to be convex or a VC-class, and the loss to be convex. However, these restrictive assumptions limit the applicability of the results in studying many popular methods, especially kernel methods and neural networks. We significantly relax these restrictive assumptions and establish a sharp oracle inequality of the empirical […]

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arXiv:2603.05573v1 Announce Type: new Abstract: Scalable sequence models, such as Transformer variants and structured state-space models, often trade expressivity power for sequence-level parallelism, which enables efficient training. Here we examine the bounds on error and how error scales when models operate outside of their expressivity regimes using a Lie-algebraic control perspective. Our theory formulates a correspondence between the depth of a sequence model and the tower of Lie algebra extensions. Echoing recent theoretical studies, we characterize the Lie-algebraic […]

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arXiv:2602.20217v1 Announce Type: new Abstract: Self-speculative decoding (SSD) accelerates LLM inference by skipping layers to create an efficient draft model, yet existing methods often rely on static heuristics that ignore the dynamic computational overhead of attention in long-context scenarios. We propose KnapSpec, a training-free framework that reformulates draft model selection as a knapsack problem to maximize tokens-per-time throughput. By decoupling Attention and MLP layers and modeling their hardware-specific latencies as functions of context length, KnapSpec adaptively identifies optimal […]

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