arXiv:2601.07945v1 Announce Type: new Abstract: We propose a deterministic and time-efficient contact-aware path planner for neurovascular navigation. The algorithm leverages information from pre- and intra-operative images of the vessels to navigate pre-bent passive tools, by intelligently predicting and exploiting interactions with the anatomy. A kinematic model is derived and employed by the sampling-based planner for tree expansion that utilizes simplified motion primitives. This approach enables fast computation of the feasible path, with negligible loss in accuracy, as demonstrated […]

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arXiv:2602.16864v1 Announce Type: new Abstract: Time series (TS) modeling has come a long way from early statistical, mainly linear, approaches to the current trend in TS foundation models. With a lot of hype and industrial demand in this field, it is not always clear how much progress there really is. To advance TS forecasting and analysis to the next level, here we argue that the field needs a dynamical systems (DS) perspective. TS of observations from natural or […]

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The growing scale of deep learning demands distributed training frameworks that jointly reason about parallelism, memory, and network topology. Prior works often rely on heuristic or topology-agnostic search, handling communication and memory separately. Without per-device memory awareness, these methods typically ensure feasibility post hoc by sharding parameters and activations across many devices, increasing synchronization, inflating communication, and underutilizing compute-limiting scalability and efficiency on real datacenter networks. We present NEST, a network-, compute-, and memory-aware device placement framework that […]

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This paper presents the Spike Processing Unit (SPU), a digital spiking neuron model based on a discrete-time second-order Infinite Impulse Response (IIR) filter. By constraining filter coefficients to powers of two, the SPU implements all internal operations via shift-and-add arithmetic on 6-bit signed integers, eliminating general-purpose multipliers. Unlike traditional models, computation in the SPU is fundamentally temporal; spike timing emerges from the interaction between input events and internal IIR dynamics rather than signal intensity accumulation. The model’s efficacy […]

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Reinforcement learning (RL) offers significant promise for machinery fault detection (MFD). However, most existing RL-based MFD approaches do not fully exploit RL’s sequential decision-making strengths, often treating MFD as a simple guessing game (Contextual Bandits). To bridge this gap, we formulate MFD as an offline inverse reinforcement learning problem, where the agent learns the reward dynamics directly from healthy operational sequences, thereby bypassing the need for manual reward engineering and fault labels. Our framework employs Adversarial Inverse Reinforcement […]

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arXiv:2603.16877v1 Announce Type: new Abstract: Financial analysts face significant challenges extracting information from lengthy 10-K reports, which often exceed 100 pages. This paper presents a Retrieval-Augmented Generation (RAG) system designed to answer questions about S&P 500 financial reports and evaluates the impact of neural reranking on system performance. Our pipeline employs hybrid search combining full-text and semantic retrieval, followed by an optional reranking stage using a cross-encoder model. We conduct systematic evaluation using the FinDER benchmark dataset, comprising […]

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arXiv:2603.20231v1 Announce Type: new Abstract: Email communication increasingly involves large language models (LLMs), but we lack intuition on how they will read, write, and optimize for nuanced social goals. We introduce HR Simulator, a game where communication is the core mechanic: players play as a Human Resources officer and write emails to solve socially challenging workplace scenarios. An analysis of 600+ human and LLM emails with LLMs-as-judge reveals evidence for larger LLMs becoming more homogenous in their email […]

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Accurate detection and localization of traumatic injuries in abdominal CT scans remains a critical challenge in emergency radiology, primarily due to severe scarcity of annotated medical data. This paper presents a label-efficient approach combining self-supervised pre-training with semi-supervised detection for 3D medical image analysis. We employ patch-based Masked Image Modeling (MIM) to pre-train a 3D U-Net encoder on 1,206 CT volumes without annotations, learning robust anatomical representations. The pretrained encoder enables two downstream clinical tasks: 3D injury detection […]

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arXiv:2604.16935v1 Announce Type: new Abstract: Scarce longitudinal evidence examines LLMs’ persuasiveness and humanness along time-evolving psychological frameworks. We introduce Talk2AI, a longitudinal framework quantifying psycho-social, reasoning and affective dimensions of LLMs’ persuasiveness about polarizing societal topics. In a four-way longitudinal setup, Talk2AI’s 770 participants engaged in structured conversations with one of four leading LLMs on topics like climate change, social media misinformation, and math anxiety. This produced 3,080 conversations over 60,000 turns. After each wave, participants reported conviction […]

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