arXiv:2510.19263v1 Announce Type: new Abstract: Precedential constraint is one foundation of case-based reasoning in AI and Law. It generally assumes that the underlying set of precedents must be consistent. To relax this assumption, a generalized notion of the reason model has been introduced. While several argumentative explanation approaches exist for reasoning with precedents based on […]
A Matter of Time: Revealing the Structure of Time in Vision-Language Models
arXiv:2510.19559v1 Announce Type: cross Abstract: Large-scale vision-language models (VLMs) such as CLIP have gained popularity for their generalizable and expressive multimodal representations. By leveraging large-scale training data with diverse textual metadata, VLMs acquire open-vocabulary capabilities, solving tasks beyond their training scope. This paper investigates the temporal awareness of VLMs, assessing their ability to position visual […]
SparseWorld: A Flexible, Adaptive, and Efficient 4D Occupancy World Model Powered by Sparse and Dynamic Queries
arXiv:2510.17482v2 Announce Type: replace-cross Abstract: Semantic occupancy has emerged as a powerful representation in world models for its ability to capture rich spatial semantics. However, most existing occupancy world models rely on static and fixed embeddings or grids, which inherently limit the flexibility of perception. Moreover, their “in-place classification” over grids exhibits a potential misalignment […]
Machine Olfaction and Embedded AI Are Shaping the New Global Sensing Industry
arXiv:2510.19660v1 Announce Type: cross Abstract: Machine olfaction is rapidly emerging as a transformative capability, with applications spanning non-invasive medical diagnostics, industrial monitoring, agriculture, and security and defense. Recent advances in stabilizing mammalian olfactory receptors and integrating them into biophotonic and bioelectronic systems have enabled detection at near single-molecule resolution thus placing machines on par with […]
Integrative Analysis of Epigenetic, Transcriptomic, and Metabolomic Responses to Arsenic Exposure Using Coupled Matrix Factorization
arXiv:2510.19294v1 Announce Type: new Abstract: Arsenic (As), a widespread environmental toxin, poses major health risks due to its inorganic forms (iAs), which are linked to cancer, cardiovascular disease, and endocrine disruption. Although its toxic effects have been extensively studied, the molecular mechanisms underlying arsenic-induced perturbations remain incompletely understood. This complexity arises from its ability to […]
Do Prompts Reshape Representations? An Empirical Study of Prompting Effects on Embeddings
arXiv:2510.19694v1 Announce Type: cross Abstract: Prompting is a common approach for leveraging LMs in zero-shot settings. However, the underlying mechanisms that enable LMs to perform diverse tasks without task-specific supervision remain poorly understood. Studying the relationship between prompting and the quality of internal representations can shed light on how pre-trained embeddings may support in-context task […]
Log-normal Superstatistics in the Confined Motion of Ants
arXiv:1904.03236v4 Announce Type: replace Abstract: We report the emergence of Log-normal Superstatistics in the collective motion of ants confined in a quasi-2D arena and exposed to a panic-inducing stimulus. A data-driven superstatistical Langevin model accurately reproduces the transition from stationary behavior to an organized escape response, characterized by non-Gaussian velocity distributions and a fluctuating diffusion […]
When Facts Change: Probing LLMs on Evolving Knowledge with evolveQA
arXiv:2510.19172v1 Announce Type: cross Abstract: LLMs often fail to handle temporal knowledge conflicts–contradictions arising when facts evolve over time within their training data. Existing studies evaluate this phenomenon through benchmarks built on structured knowledge bases like Wikidata, but they focus on widely-covered, easily-memorized popular entities and lack the dynamic structure needed to fairly evaluate LLMs […]
Learning to Make Friends: Coaching LLM Agents toward Emergent Social Ties
arXiv:2510.19299v1 Announce Type: new Abstract: Can large language model (LLM) agents reproduce the complex social dynamics that characterize human online behavior — shaped by homophily, reciprocity, and social validation — and what memory and learning mechanisms enable such dynamics to emerge? We present a multi-agent LLM simulation framework in which agents repeatedly interact, evaluate one […]
PruneHal: Reducing Hallucinations in Multi-modal Large Language Models through Adaptive KV Cache Pruning
arXiv:2510.19183v1 Announce Type: cross Abstract: While multi-modal large language models (MLLMs) have made significant progress in recent years, the issue of hallucinations remains a major challenge. To mitigate this phenomenon, existing solutions either introduce additional data for further training or incorporate external or internal information during inference. However, these approaches inevitably introduce extra computational costs. […]