The field of agentic artificial intelligence is transitioning from reasoning-centric architectures toward systems explicitly designed for reliability under uncertainty. Current agent frameworks, while effective in controlled environments, exhibit cognitive rigidity—an inability to proactively correct planning trajectories when confronted with unexpected faults. This paper introduces Adapt-Plan, a foundational hybrid architecture that reformulates planning as a control-theoretic process by elevating the Planning Efficiency Index (PEI) from a post-hoc evaluation metric to a real-time control signal. Through dual-mode planning (strategic and […]

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arXiv:2601.00231v1 Announce Type: new Abstract: Parameter-efficient fine-tuning (PEFT) is the default way to adapt LLMs, but widely used LoRA and QLoRA are largely geometry-agnostic: they optimize in fixed, randomly oriented low-rank subspaces with first-order descent, mostly ignoring local loss curvature. This can inflate the effective update budget and amplify drift along weakly constrained directions. We introduce GRIT, a dynamic, curvature-aware LoRA procedure that preserves the LoRA parameterization but: (1) preconditions gradients in rank space using K-FAC as a […]

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arXiv:2207.03427v2 Announce Type: replace-cross Abstract: Compressed sensing has been a very successful high-dimensional signal acquisition and recovery technique that relies on linear operations. However, the actual measurements of signals have to be quantized before storing or processing. 1(One)-bit compressed sensing is a heavily quantized version of compressed sensing, where each linear measurement of a signal is reduced to just one bit: the sign of the measurement. Once enough of such measurements are collected, the recovery problem in 1-bit […]

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We’re announcing new updates to Gemini 2.0 Flash, plus introducing Gemini 2.0 Flash-Lite and Gemini 2.0 Pro Experimental.

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arXiv:2601.03420v1 Announce Type: new Abstract: As Large Language Models (LLMs) are increasingly deployed in safety-critical domains, rigorously evaluating their robustness against adversarial jailbreaks is essential. However, current safety evaluations often overestimate robustness because existing automated attacks are limited by restrictive assumptions. They typically rely on handcrafted priors or require white-box access for gradient propagation. We challenge these constraints by demonstrating that token-level iterative optimization can succeed without gradients or priors. We introduce RAILS (RAndom Iterative Local Search), a […]

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Science Robotics, Volume 10, Issue 109, December 2025.

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arXiv:2601.03332v1 Announce Type: new Abstract: Kolmogorov–Arnold Networks (KAN) replace scalar weights by learnable univariate functions, often implemented with B-splines. This design can be accurate and interpretable, but it makes inference expensive on CPU because each layer requires many spline evaluations. Standard quantization toolchains are also hard to apply because the main computation is not a matrix multiply but repeated spline basis evaluation. This paper introduces LUT-KAN, a segment-wise lookup-table (LUT) compilation and quantization method for PyKAN-style KAN layers. […]

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