Despite broadly connected digital infrastructure, standard fare TTPs are enough to cause trouble for Afghanistan’s porous cybersecurity.
Notarized Agents: Receiver-Attested Confidential Receipts for AI Agent Actions
arXiv:2606.04193v1 Announce Type: cross Abstract: Current AI agent observability is structurally compromised: the entity producing the activity log is the same entity whose activity is being logged. A compromised or buggy agent can omit, alter, or fabricate its own traces, and the operator running the agent has no independent way to detect tampering. We propose […]
Neural Langevin Machine: a local asymmetric learning rule can be creative
arXiv:2506.23546v2 Announce Type: replace Abstract: Fixed points of recurrent neural networks can be leveraged to store and generate information. These fixed points can be captured by the Boltzmann-Gibbs measure, which leads to neural Langevin dynamics that can be used to find them for generative learning of a real dataset. We call this type of generative […]
Qwen-Image-Flash: Beyond Objective Design
arXiv:2606.03746v2 Announce Type: replace-cross Abstract: Few-step distillation has become an effective strategy for accelerating advanced visual generative models, yet prior work has largely focused on distillation objectives. In this work, we revisit few-step distillation from a complementary perspective, focusing on the training recipe that critically shapes student performance. Using Qwen-Image-2.0 as a representative case, we […]
Multi-Column RBF Neural Network Using Adaptive and Non-Adaptive Particle Swarm Optimization
arXiv:2606.05150v1 Announce Type: cross Abstract: The radial basis function neural network (RBFN) trained with a gradient descending algorithm provides an effective fully connected structure in both shallow and deep networks. The error correction (ErrCor), a state-of-the-art gradient-based training method, selects optimal hidden units to improve accuracy. Alternatively, as a population-based algorithm, the particle swarm optimization […]
The Differentiable Auditory Loop (DAL): An ML Framework for Hyper-Personalized Hearing Aids
arXiv:2606.04103v1 Announce Type: cross Abstract: Conventional hearing aids rely on fixed, frequency-dependent amplification and compression to manage reduced sensitivity, which often fails to provide sufficient listening support in complex environments, such as situations with multiple speakers (the “cocktail party” problem). To more comprehensively address the underlying encoding dysfunctions of hearing loss, we introduce the Differentiable […]
Grimlock: Guarding High-Agency Systems with eBPF and Attested Channels
arXiv:2605.27488v2 Announce Type: replace-cross Abstract: Agentic systems increasingly run user-authored orchestration code that invokes tools, spawns subtasks, and delegates work across machines and clouds. Although this high agency is productive, it creates a security problem: identity, authorization, provenance, and delegation are often pushed into application code, where they become difficult to enforce consistently and difficult […]
Exact Unlearning in Reinforcement Learning
arXiv:2606.04182v1 Announce Type: cross Abstract: We formulate the problem of emphexact unlearning in reinforcement learning, where the goal is to design an efficient framework that enables the removal of any user’s data upon deletion request, i.e., the online learner’s output after unlearning is emphindistinguishable from what would have been produced had the deleted user never […]
Lost in Fog: Sensor Perturbations Expose Reasoning Fragility in Driving VLAs
arXiv:2605.21446v2 Announce Type: replace-cross Abstract: Interpretable autonomous driving planners depend not only on generating explanations, but also on those explanations remaining reliable under real-world sensor degradation. In this paper we present a controlled perturbation study of Vision-Language-Action (VLA) robustness in autonomous driving, evaluating Alpamayo R1 (10B parameters) across 1,996 scenarios under eight sensor perturbations (Gaussian […]
Outcome-Based RL Provably Leads Transformers to Reason, but Only With the Right Data
arXiv:2601.15158v4 Announce Type: replace-cross Abstract: Transformers trained via Reinforcement Learning (RL) with outcome-based supervision can spontaneously develop the ability to generate intermediate reasoning steps (Chain-of-Thought). Yet the mechanism by which sparse rewards drive policy gradient to discover such systematic reasoning remains poorly understood. We address this by analyzing the policy gradient dynamics of single-layer Transformers […]
Quantum entanglement provides a competitive advantage in adversarial games
arXiv:2603.10289v2 Announce Type: replace-cross Abstract: Whether uniquely quantum resources confer advantages in fully classical, competitive environments remains an open question. Competitive zero-sum reinforcement learning is particularly challenging, as success requires modelling dynamic interactions between opposing agents rather than static state-action mappings. Here, we conduct a controlled study isolating the role of quantum entanglement in a […]
Semiparametric Preference Optimization: Your Language Model is Secretly a Single-Index Model
arXiv:2512.21917v3 Announce Type: replace-cross Abstract: Policy alignment to preference data typically assumes a known link function between observed preferences and latent rewards (e.g., Bradley-Terry model / logistic link). Misspecification of this link can bias inferred rewards and misalign learned policies. We study policy alignment under an unknown and unrestricted link function. We formulate an $f$-divergence-constrained […]