arXiv:2604.22116v1 Announce Type: new Abstract: Tinnitus is a prevalent auditory condition lacking objective biomarkers, motivating the search for reliable neural signatures. EEG, being a noninvasive method of brain imaging with a high temporal resolution provides a way to investigate the neural dynamics that may be associated with tinnitus. The generalizability of EEG-based tinnitus biomarkers across […]
Global remote sensing reveals vegetation clustering as a physical footprint of shifting aridity trends in drylands
arXiv:2604.22122v1 Announce Type: new Abstract: Due to climatic changes, excessive grazing, and deforestation, semi-arid and arid ecosystems are vulnerable to desertification and land degradation. As aridity increases, vegetation cover often self-organizes into spatial patterns before collapsing to bare soil. While recent theoretical work has established that spatially heterogeneous yet isotropic environments induce a smooth hysteresis […]
Emergent Strategic Reasoning Risks in AI: A Taxonomy-Driven Evaluation Framework
arXiv:2604.22119v1 Announce Type: new Abstract: As reasoning capacity and deployment scope grow in tandem, large language models (LLMs) gain the capacity to engage in behaviors that serve their own objectives, a class of risks we term Emergent Strategic Reasoning Risks (ESRRs). These include, but are not limited to, deception (intentionally misleading users or evaluators), evaluation […]
Earable Platform with Integrated Simultaneous EEG Sensing and Auditory Stimulation
arXiv:2604.22137v1 Announce Type: new Abstract: Conventional scalp-based EEG systems are cumbersome to use, requiring extensive setup, restrictive wiring, and conductive gels that can dry out and limit long-term monitoring, while also carrying social stigma. As a result, there is increasing interest in in-ear EEG technology to improve comfort, convenience, and discretion for users. This work […]
Evidence of an Emergent “Self” in Continual Robot Learning
arXiv:2603.24350v2 Announce Type: replace-cross Abstract: A key challenge to understanding self-awareness has been a principled way of quantifying whether an intelligent system has a concept of a “self”, and if so how to differentiate the “self” from other cognitive structures. We propose that the “self” can be isolated by seeking the invariant portion of cognitive […]
SOLAR-RL: Semi-Online Long-horizon Assignment Reinforcement Learning
arXiv:2604.22558v1 Announce Type: cross Abstract: As Multimodal Large Language Models (MLLMs) mature, GUI agents are evolving from static interactions to complex navigation. While Reinforcement Learning (RL) has emerged as a promising paradigm for training MLLM agents on dynamic GUI tasks, its effective application faces a dilemma. Standard Offline RL often relies on static step-level data, […]
Aligning Dense Retrievers with LLM Utility via DistillationAligning Dense Retrievers with LLM Utility via Distillation
arXiv:2604.22722v1 Announce Type: cross Abstract: Dense vector retrieval is the practical backbone of Retrieval- Augmented Generation (RAG), but similarity search can suffer from precision limitations. Conversely, utility-based approaches leveraging LLM re-ranking often achieve superior performance but are computationally prohibitive and prone to noise inherent in perplexity estimation. We propose Utility-Aligned Embeddings (UAE), a framework designed […]
A Bayesian approach to model uncertainty in single-cell genomic data
arXiv:2508.02061v2 Announce Type: replace Abstract: Network models provide a powerful framework for analysing single-cell count data, facilitating the characterisation of cellular identities, disease mechanisms, and developmental trajectories. However, uncertainty modeling in unsupervised learning with genomic data remains insufficiently explored. Conventional clustering methods assign a singular identity to each cell, potentially obscuring transitional states during differentiation […]
Asymmetric Goal Drift in Coding Agents Under Value Conflict
arXiv:2603.03456v2 Announce Type: replace Abstract: Coding agents are increasingly deployed autonomously, at scale, and over long-context horizons. To be effective and safe, these agents must navigate complex trade-offs in deployment, balancing influence from the user, their learned values, and the codebase itself. Understanding how agents resolve these trade-offs in practice is critical, yet prior work […]
Rethinking Publication: A Certification Framework for AI-Enabled Research
arXiv:2604.22026v1 Announce Type: new Abstract: AI research pipelines now produce a growing share of publishable academic output, including work that meets existing peer-review standards for quality and novelty. Yet the publication system was built on the assumption of universal human authorship and lacks a principled way to evaluate knowledge produced through automated pipelines. This paper […]
AgentBound: Securing Execution Boundaries of AI Agents
arXiv:2510.21236v3 Announce Type: replace-cross Abstract: Large Language Models (LLMs) have evolved into AI agents that interact with external tools and environments to perform complex tasks. The Model Context Protocol (MCP) has become the de facto standard for connecting agents with such resources, but security has lagged behind: thousands of MCP servers execute with unrestricted access […]
Local growth laws determine global shape of molluscan shells
arXiv:2604.21988v1 Announce Type: new Abstract: Molluscan shells come in various shapes and sizes. Despite this diversity, each species produces a shell with a characteristic shape that is independent of environmental conditions. We seek to understand this robust complexity. We are guided by two principles in the spirit of D’Arcy Thompson. First, the growth is governed […]