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This work introduces CyberWorld, a Dreamer-style world modeling framework that learns latent cyber dynamics from vector, graph, textual, and multimodal representations of the defended network, and identifies world representation as a central design axis for robustness and scalability.
The World State Generator is a model that writes a plan as checkable states of the world and keeps that plan aligned with the world it runs in, a model that raises end-to-end success for two open models near 30B parameters over prompting and brings to the level of proprietary model.
ExaGEMM, a workload-aware codesign and exploration framework for CPU-native low-bit GEMM via register-resident LUT execution, improves latency by 13.29x over software-only baselines, while showing that workload-aware frontier selection is especially important for mixed-precision LLM workloads.
Empirical evaluations reveal a fundamental brittleness in existing defenses: with a single trainable 7B planner, Trident reduces blue agent defensive performance by an average of 522% compared to static red agent baselines while autonomously discovering emergent behaviors such as decoy avoidance and adaptive state prio...
It is concluded that larger generators and refiners generally improve the pipeline, whereas an undersized refiner can even harm performance, and that model capacity should not be allocated uniformly across self-refinement pipelines.
PolyQ, a CPU-oriented compiler/quantization co-design for activation-aware channel-wise bit allocation under a user-specified average-bit budget, shows that fractional-bit CPU deployment is practical, predictable, and energy-efficient across diverse edge targets.
Vector-Symbolic Policy Gradient (VSPG), a discrete-action actor that represents each action by a unit-norm hypervector and scores it by similarity to the encoded state, connects VSA action memories, log-linear policy gradients, and kernel policy search while providing a quantitative robustness guarantee.
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