Tootfinder

Bridging Classical and Quantum Computing for Next-Generation Language Models
Yi Pan, Hanqi Jiang, Junhao Chen, Yiwei Li, Huaqin Zhao, Lin Zhao, Yohannes Abate, Yingfeng Wang, Tianming Liu
https://arxiv.org/abs/2508.07026

Bridging Classical and Quantum Computing for Next-Generation Language Models
Integrating Large Language Models (LLMs) with quantum computing is a critical challenge, hindered by the severe constraints of Noisy Intermediate-Scale Quantum (NISQ) devices, including barren plateaus and limited coherence. Current approaches often fail due to static quantum-classical partitioning. We introduce Adaptive Quantum-Classical Fusion (AQCF), the first framework to bridge this gap through dynamic, quantum-classical co-design. AQCF's core principle is real-time adaptation: it analyzes…

Measuring Implicit Spatial Coordination in Teams: Effects on Collective Intelligence and Performance
Thuy Ngoc Nguyen, Anita Williams Woolley, Cleotilde Gonzalez
https://arxiv.org/abs/2509.09314

Measuring Implicit Spatial Coordination in Teams: Effects on Collective Intelligence and Performance
Coordinated teamwork is essential in fast-paced decision-making environments that require dynamic adaptation, often without an opportunity for explicit communication. Although implicit coordination has been extensively considered in the existing literature, the majority of work has focused on co-located, synchronous teamwork (such as sports teams) or, in distributed teams, primarily on coordination of knowledge work. However, many teams (firefighters, military, law enforcement, emergency respon…

Civil Society in the Loop: Feedback-Driven Adaptation of (L)LM-Assisted Classification in an Open-Source Telegram Monitoring Tool
Milena Pustet, Elisabeth Steffen, Helena Mihaljevi\'c, Grischa Stanjek, Yannis Illies
https://arxiv.org/abs/2507.06734

Civil Society in the Loop: Feedback-Driven Adaptation of (L)LM-Assisted Classification in an Open-Source Telegram Monitoring Tool
The role of civil society organizations (CSOs) in monitoring harmful online content is increasingly crucial, especially as platform providers reduce their investment in content moderation. AI tools can assist in detecting and monitoring harmful content at scale. However, few open-source tools offer seamless integration of AI models and social media monitoring infrastructures. Given their thematic expertise and contextual understanding of harmful content, CSOs should be active partners in co-dev…

Avoiding Over-Personalization with Rule-Guided Knowledge Graph Adaptation for LLM Recommendations
Fernando Spadea, Oshani Seneviratne
https://arxiv.org/abs/2509.07133 https://…

Avoiding Over-Personalization with Rule-Guided Knowledge Graph Adaptation for LLM Recommendations
We present a lightweight neuro-symbolic framework to mitigate over-personalization in LLM-based recommender systems by adapting user-side Knowledge Graphs (KGs) at inference time. Instead of retraining models or relying on opaque heuristics, our method restructures a user's Personalized Knowledge Graph (PKG) to suppress feature co-occurrence patterns that reinforce Personalized Information Environments (PIEs), i.e., algorithmically induced filter bubbles that constrain content diversity. These …

Multiple Timescale Dynamics of Network Adaptation with Constraints
Erik Andreas Martens, Christian Bick
https://arxiv.org/abs/2507.06359 https://

Multiple Timescale Dynamics of Network Adaptation with Constraints
Adaptive network dynamical systems describe the co-evolution of dynamical quantities on the nodes as well as dynamics of the network connections themselves. For dense networks of many nodes, the resulting dynamics are typically high-dimensional. Here we consider adaptive dynamical systems subject to constraints on network adaptation: Asymptotically, the adaptive dynamics of network connections evolve on a low-dimensional subset of possible connectivity. Such dimension reduction may be intrinsic…

Crosslisted article(s) found for stat.CO. https://arxiv.org/list/stat.CO/new
[1/1]:
- Measuring General Associations in Time Series: An Adaptation and Empirical Evaluation of the CODE...
Juan Pablo Monta\~no, Mario E. Arrieta-Prieto

Community-Centered Spatial Intelligence for Climate Adaptation at Nova Scotia's Eastern Shore
Gabriel Spadon, Oladapo Oyebode, Camilo M. Botero, Tushar Sharma, Floris Goerlandt, Ronald Pelot
https://arxiv.org/abs/2509.01845

Community-Centered Spatial Intelligence for Climate Adaptation at Nova Scotia's Eastern Shore
This paper presents an overview of a human-centered initiative aimed at strengthening climate resilience along Nova Scotia's Eastern Shore. This region, a collection of rural villages with deep ties to the sea, faces existential threats from climate change that endanger its way of life. Our project moves beyond a purely technical response, weaving together expertise from Computer Science, Industrial Engineering, and Coastal Geography to co-create tools with the community. By integrating generat…

Crosslisted article(s) found for nlin.AO. https://arxiv.org/list/nlin.AO/new
[1/1]:
- Recursive Hierarchical Networks and the Law of Functional Evolution: A Universal Framework for Co...
Hui Li, Yanxin Li

DriftMoE: A Mixture of Experts Approach to Handle Concept Drifts
Miguel Aspis, Sebasti\'an A. Cajas Ord\'onez, Andr\'es L. Su\'arez-Cetrulo, Ricardo Sim\'on Carbajo
https://arxiv.org/abs/2507.18464

DriftMoE: A Mixture of Experts Approach to Handle Concept Drifts
Learning from non-stationary data streams subject to concept drift requires models that can adapt on-the-fly while remaining resource-efficient. Existing adaptive ensemble methods often rely on coarse-grained adaptation mechanisms or simple voting schemes that fail to optimally leverage specialized knowledge. This paper introduces DriftMoE, an online Mixture-of-Experts (MoE) architecture that addresses these limitations through a novel co-training framework. DriftMoE features a compact neural r…

Scaling the human niche
Marcus J. Hamilton
https://arxiv.org/abs/2506.20902 https://arxiv.org/pdf/2506.20902

Scaling the human niche
The human niche represents the intersection of biological, ecological, cultural, and technological processes that have co-evolved to shape human adaptation and societal complexity. This paper explores the human niche through the lens of macroecological scaling theory, seeking to define and quantify the dimensions along which human ecological strategies have diversified. By leveraging concepts from classic niche theory, niche construction, and complex adaptive systems, I develop a framework for …

Non-exchangeable mean-field theory for adaptive weights: propagation of chaos and graphon sampling lemma
Datong Zhou
https://arxiv.org/abs/2506.13587 https…

Non-exchangeable mean-field theory for adaptive weights: propagation of chaos and graphon sampling lemma
We develop a mean-field theory for large, non-exchangeable particle (agent) systems where the states and interaction weights co-evolve in a coupled system of SDEs. A first main result is a generalization of the propagation of chaos. The weight adaptation in the SDEs makes the classical approach of using a static probability space as the continuum limit for the agent labels inadequate. We address this by introducing a label space endowed with a filtration that captures the stochasticity. While t…

Crosslisted article(s) found for nlin.AO. https://arxiv.org/list/nlin.AO/new
[1/1]:
- Spatial Super-Infection and Co-Infection Dynamics in Networks
Alyssa Yu, Laura P. Schaposnik