The inability to communicate poses a major challenge to coordination in multi-agent reinforcement learning (MARL). Prior work has explored correlating local policies via shared randomness, sometimes in the form of a correlation device, as a mechanism to assist in decentralized decision-making. In contrast, this work introduces the first framework for training MARL agents to exploit shared quantum entanglement as a coordination resource, which permits a larger class of communication-free correlated policies than shared randomness alone. This is motivated […]

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arXiv:2602.15897v1 Announce Type: new Abstract: Training and fine-tuning large-scale language models largely benefit from collaborative learning, but the approach has been proven vulnerable to gradient inversion attacks (GIAs), which allow adversaries to reconstruct private training data from shared gradients. Existing defenses mainly employ gradient perturbation techniques, e.g., noise injection or gradient pruning, to disrupt GIAs’ direct mapping from gradient space to token space. However, these methods often fall short due to the retention of semantics similarity across gradient, […]

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How reusable, lazy-loaded instructions solve the context bloat problem in AI-assisted development. The post Claude Skills and Subagents: Escaping the Prompt Engineering Hamster Wheel appeared first on Towards Data Science.

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We’ve been doing on-device accuracy testing across multiple Snapdragon SoCs and the results have been eye-opening. Same model. Same quantization. Same ONNX export. Deployed to 5 different chipsets: Device Accuracy Snapdragon 8 Gen 3 91.8% Snapdragon 8 Gen 2 89.1% Snapdragon 7s Gen 2 84.3% Snapdragon 6 Gen 1 79.6% Snapdragon 4 Gen 2 71.2% Cloud benchmark reported 94.2%. The spread comes down to three things we’ve observed: NPU precision handling — INT8 rounding behavior differs across Hexagon […]

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Industrial Internet of Things (IIoT) and SCADA-connected networks face disruptive DDoS events where detection must be both accurate and low-latency at the edge. This study benchmarks deep reinforcement learning (DRL) for real-time binary attack detection and proposes a Proximal Policy Optimisation (PPO) detector tailored for deployment. Five DRL agents—DQN, Double DQN, Duelling DQN, DDPG, and PPO—are trained under a unified preprocessing pipeline (automatic label mapping, numeric-feature selection, robust scaling, and class balancing) and evaluated on three representative datasets: […]

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arXiv:2603.02356v1 Announce Type: new Abstract: We explore the question of how to learn an optimal search strategy within the example of a parking problem where parking opportunities arrive according to an unknown inhomogeneous Poisson process. The optimal policy is a threshold-type stopping rule characterized by an indifference position. We propose an algorithm that learns this threshold by estimating the integrated jump intensity rather than the intensity function itself. We show that our algorithm achieves a logarithmic regret growth, […]

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My problem is such: I have to use the cpu to train my agent , so running 1600 steps per episode on bipedalwalker, half cheetah etc is out of the question. Are 200 steps fine as a starter point ( assuming the agent can get a score 300 for 1600 steps, that would set the score at 37.5 for 200 steps) so if the agent is able to get to 40 score then for testing I could just […]

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arXiv:2602.06215v1 Announce Type: new Abstract: This paper proposes a quantum framework for the design of communication topologies in consensus-based multi-agent systems. The communication graph is selected online by solving a mixed-integer quadratic program (MIQP) that minimizes a cost combining communication and distance penalties with degree-regularization terms, while enforcing exact connectivity through a flow-based formulation. To cope with the combinatorial complexity of this NP-hard problem, we develop a three-block ADMM scheme that decomposes the MIQP into a convex quadratic […]

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arXiv:2602.21230v1 Announce Type: new Abstract: The evaluation of Deep Research Agents is a critical challenge, as conventional outcome-based metrics fail to capture the nuances of their complex reasoning. Current evaluation faces two primary challenges: 1) a reliance on singular metrics like Pass@1, creating a “high-score illusion” that ignores the quality, efficiency, and soundness of the reasoning process; and 2) the failure of static benchmarks to quantify crucial attributes like robustness and latent capability. To address these gaps, we […]

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