arXiv:2510.20019v1 Announce Type: new Abstract: Radio Frequency Identification (RFID) tracking may be a viable solution for defense assets that must be stored in accordance with security guidelines. However, poor sensor specificity (vulnerabilities include long range detection, spoofing, and counterfeiting) can…
arXiv:2506.16349v2 Announce Type: replace Abstract: Watermarking the outputs of generative models has emerged as a promising approach for tracking their provenance. Despite significant interest in autoregressive image generation models and their potential for misuse, no prior work has attempted to…
arXiv:2510.20022v1 Announce Type: new Abstract: Large Language Models (LLMs) have demonstrated remarkable capabilities, enabling language agents to excel at single-turn tasks. However, their application to complex, multi-step, and long-horizon tasks remains challenging. While reinforcement learning (RL) offers a promising avenue…
arXiv:2510.04008v2 Announce Type: replace Abstract: Softmax Attention has a quadratic time complexity, which becomes prohibitive to run at long contexts, even with highly optimized GPU kernels. For example, FlashAttention (an exact, GPU-optimized implementation of Softmax Attention) cannot complete a single…
arXiv:2510.20028v1 Announce Type: new Abstract: Since its 2009 genesis block, the Bitcoin network has processed num{>1.08} billion (B) transactions representing num{>8.72}B BTC, offering rich potential for machine learning (ML); yet, its pseudonymity and obscured flow of funds inherent in its…
arXiv:2510.19705v2 Announce Type: replace Abstract: Transformer language models generate text autoregressively, making inference latency proportional to the number of tokens generated. Speculative decoding reduces this latency without sacrificing output quality, by leveraging a small draft model to propose tokens that…
arXiv:2510.20031v1 Announce Type: new Abstract: Temporal point processes are powerful generative models for event sequences that capture complex dependencies in time-series data. They are commonly specified using autoregressive models that learn the distribution of the next event from the previous…
arXiv:2412.19634v2 Announce Type: replace-cross Abstract: Marked temporal point processes (MTPPs) model sequences of events occurring at irregular time intervals, with wide-ranging applications in fields such as healthcare, finance and social networks. We propose the state-space point process (S2P2) model, a…
arXiv:2510.20055v1 Announce Type: new Abstract: Online advertising platforms use automated auctions to connect advertisers with potential customers, requiring effective bidding strategies to maximize profits. Accurate ad impact estimation requires considering three key factors: delayed and long-term effects, cumulative ad impacts…
arXiv:2505.17203v2 Announce Type: replace-cross Abstract: We study contextual dynamic pricing when a target market can leverage K auxiliary markets — offline logs or concurrent streams — whose mean utilities differ by a structured preference shift. We propose Cross-Market Transfer Dynamic…
