April 2026

arXiv:2604.15449v1 Announce Type: new Abstract: Kalman filter-based algorithms are fundamental for mobile robots, as they provide a computationally efficient solution to the challenging problem of state estimation. However, they rely on two main assumptions that are difficult to satisfy in practice: (a) the system dynamics must be linear with Gaussian process noise, and (b) the measurement model must also be linear with Gaussian measurement noise. Previous works have extended assumption (a) to nonlinear spaces through the Invariant Extended […]

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arXiv:2604.15448v1 Announce Type: new Abstract: Foundational optimization embeddings have recently emerged as powerful pre-trained representations for mixed-integer programming (MIP) problems. These embeddings were shown to enable cross-domain transfer and reduce reliance on solver-generated labels. In this work, we investigate whether such representations generalize beyond optimization to decision problems, focusing on Boolean satisfiability (SAT). We adapt the foundational optimization architecture to SAT by mapping CNF formulas into the same bipartite constraint-variable graph representation used for MIPs. This allows direct […]

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arXiv:2604.15418v1 Announce Type: new Abstract: This paper presents a preliminary draft of a framework around the use of anthropomorphic deception, defined here as misleading users towards humanlike affordances in the design of autonomous systems. The goal is to promote reflection among HCI and HRI researchers, as well as industry practitioners, to think about levels of anthropomorphic design that are: a) functionally necessary, b) socially appropriate, and c) ethically permissible for their use case. By reviewing the relevant literature […]

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arXiv:2604.15416v1 Announce Type: new Abstract: Sign-based optimization algorithms, such as SignSGD, have garnered significant attention for their remarkable performance in distributed learning and training large foundation models. Despite their empirical superiority, SignSGD is known to diverge on non-smooth objectives, which are ubiquitous in modern machine learning due to ReLUs, max-pools, and mixture-of-experts. To overcome this fundamental limitation, we propose textbf{StoSignSGD}, an algorithm that injects structural stochasticity into the sign operator while maintaining an unbiased update step. In the […]

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arXiv:2604.15415v1 Announce Type: new Abstract: Large language models (LLMs) have evolved into autonomous agents that rely on open skill ecosystems (e.g., ClawHub and Skills.Rest), hosting numerous publicly reusable skills. Existing security research on these ecosystems mainly focuses on vulnerabilities within skills, such as prompt injection. However, there is a critical gap regarding skills that may be misused for harmful actions (e.g., cyber attacks, fraud and scams, privacy violations, and sexual content generation), namely harmful skills. In this paper, […]

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arXiv:2604.15414v1 Announce Type: new Abstract: Continual reinforcement learning must balance retention with adaptation, yet many methods still rely on emph{single-model preservation}, committing to one evolving policy as the main reusable solution across tasks. Even when a previously successful policy is retained, it may no longer provide a reliable starting point for rapid adaptation after interference, reflecting a form of emph{loss of plasticity} that single-policy preservation cannot address. Inspired by quality-diversity methods, we introduce textsc{TeLAPA} (Transfer-Enabled Latent-Aligned Policy Archives), […]

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arXiv:2604.15411v1 Announce Type: new Abstract: The paradigm of agentic science requires AI systems to conduct robust reasoning and engage in long-horizon, autonomous exploration. However, current scientific benchmarks remain confined to domain knowledge comprehension and complex reasoning, failing to evaluate the exploratory nature and procedural complexity of real-world research. In this work, we present research-oriented evaluations in theoretical and computational physics, a natural testbed with comprehensive domain knowledge, complex reasoning, and verifiable end-to-end workflows without reliance on experiments. Here […]

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arXiv:2604.15409v1 Announce Type: new Abstract: KV caching is a ubiquitous optimization in autoregressive transformer inference, long presumed to be numerically equivalent to cache-free computation. This assumption fails under standard FP16 precision: cache-ON and cache-OFF execution paths employ different floating-point accumulation orderings which, due to FP16 non-associativity, produce a deterministic divergence in decoded token sequences. Across three open-weight models (LLaMA-2-7B, Mistral-7B-v0.3, Gemma-2-2B) evaluated on GSM8K, we observe a 100% token divergence rate across all sampling strategies, including greedy decoding, […]

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arXiv:2604.15408v1 Announce Type: new Abstract: Token pruning methods for Vision Transformers (ViTs) promise quadratic reductions in attention FLOPs by dropping uninformative patches. Yet when pruned sequences are executed with state-of-the-art variable-length attention APIs — including FlashAttention-2’s varlen and PyTorch’s NestedTensor SDPA-the wall-clock attention latency doesn’t scale accordingly. We trace this to a dispatch-overhead bottleneck: at the short, post-pruning sequence lengths typical of ViTs (<=197 tokens), actual matrix arithmetic completes in single-digit microseconds while the host-side dispatch path consumes […]

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arXiv:2604.15403v1 Announce Type: new Abstract: Sequences exhibiting favorable ambiguity function characteristics play a critical role in radar detection systems and modern mobile communication applications. As a newly developed sequence family, Doppler resilient complementary sequence sets (DRCSSs) can effectively suppress ambiguity function sidelobes by coherently combining the ambiguity functions of their constituent subsequences. The objective of this paper is to present five classes of asymptotically optimal aperiodic DRCSSs with novel parameters based on trace functions over finite fields and […]

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