Mixture-of-Experts (MoE) models enable scalable performance but face severe memory constraints on edge devices. Existing offloading strategies struggle with I/O bottlenecks due to the dynamic, low-information nature of autoregressive expert activation. In this paper, we propose to repurpose Speculative Decoding (SD) no...
Shuhuai Li, Jianghao Lin, Dongdong Ge et al.· 0 citations
Hierarchical multi-robot exploration decouples frontier allocation from local navigation, making systems brittle in dynamic environments. Without awareness of execution difficulty, allocators may send robots toward frontiers whose routes are congested or unsafe, causing bottlenecks, oscillatory replanning, and redundan...
The extraction of critical information from crime-related documents is a crucial task for law enforcement agencies. The extraction of this information can be interpreted as a Named-Entity Recognition (NER) task. However, there is a considerable lack of adequately annotated data on general real-world crime scenarios. To...
Miguel Lopez-Duran, Julian Fierrez, Aythami Morales et al.· 0 citations
This work introduces a three-pass structurally lossless trimming algorithm that preserves every user message and assistant response verbatim while reducing token counts by a mean of 20% and up to 86% for sessions with significant overhead by stripping mechanical bloat such as raw tool outputs, base64 images, and metada...
C. Santoni· arXiv.org· 1 citation
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Deep generative models with tractable likelihoods, exemplified by normalizing flows, offer an effective basis for anomaly detection through likelihood-based scoring. We demonstrate that, unlike in the image domain where deep generative models frequently assign higher likelihoods to anomalous data, such counterintuitive...
Donghwan Kim, Junghun Phee, Hyunsoo Yoon· 0 citations
Deep generative models that can tractably compute input likelihoods, including normalizing flows, often assign unexpectedly high likelihoods to out-of-distribution (OOD) inputs. We mitigate this paradox from an entropic perspective with Semantic Proportional Entropy Manipulation (SPEM), a training-free procedure that a...
AI-text detectors are increasingly used in high-stakes settings, but their robustness to meaning-preserving adversarial rewriting remains unclear. We introduce StealthRL, a reinforcement learning framework for stress-testing detectors with adaptive paraphrase attacks. StealthRL trains a paraphrase policy against a dete...
We introduce Clifford Kolmogorov-Arnold Network (ClKAN), a flexible and efficient architecture for function approximation in arbitrary Clifford Algebra spaces. We propose the use of Randomized Quasi-Monte Carlo grid generation as a solution to the exponential scaling associated with higher-dimensional algebras. Our ClK...
Matthias Wolff, Francesco Alesiani, Christof Duhme et al.· 0 citations
Standard sequence models encode position by token rank rather than physical coordinate, typically forcing practitioners to destructively resample continuous physical signals such as spectra observed across heterogeneous instruments. We identify this coordinate mismatch as a structural limitation in cross-instrument rep...
Md Khairul Islam, Zeyu Xia, Adam S. Bolton et al.· 0 citations
SpatialMem is a memory-based system which integrates 3D geometry, semantics, and language with one another in one combined, queryable representation. Starting with casually recorded egocentric RGB video, SpatialMem recreates metrically scaled interior scenes, recognizes structural 3D anchors (walls, doors, windows) as...
Xinyi Zheng, Yunze Liu, Chi-Hao Wu et al.· 0 citations
In this paper, we propose active recap learning (ARL), a framework for enhancing large language model (LLM) in understanding long contexts. ARL enables models to revisit and summarize earlier content through targeted sequence construction during continued pretraining and retrospective summarization at inference. First,...
Attention-space Contrastive Guidance (ACG) is proposed, a training-free, single-pass method that operates directly in self-attention layers, where hallucination-inducing cross-modal biases emerge, enabling efficient guidance before errors accumulate at the output layer.
