Tootfinder

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…

Ian Sinclair on the SAFER report from Climate Majority.
"Could ‘strategic adaptation’ combat climate breakdown and the surging hard right?" | Morning Star https://morningstaronline.co.uk/article/could-strategic-adap…

Co-Alignment: Rethinking Alignment as Bidirectional Human-AI Cognitive Adaptation
Yubo Li, Weiyi Song
https://arxiv.org/abs/2509.12179 https://arxiv.org/pd…

Co-Alignment: Rethinking Alignment as Bidirectional Human-AI Cognitive Adaptation
Current AI alignment through RLHF follows a single directional paradigm that AI conforms to human preferences while treating human cognition as fixed. We propose a shift to co-alignment through Bidirectional Cognitive Alignment (BiCA), where humans and AI mutually adapt. BiCA uses learnable protocols, representation mapping, and KL-budget constraints for controlled co-evolution. In collaborative navigation, BiCA achieved 85.5% success versus 70.3% baseline, with 230% better mutual adaptation an…

EgoBridge: Domain Adaptation for Generalizable Imitation from Egocentric Human Data
Ryan Punamiya, Dhruv Patel, Patcharapong Aphiwetsa, Pranav Kuppili, Lawrence Y. Zhu, Simar Kareer, Judy Hoffman, Danfei Xu
https://arxiv.org/abs/2509.19626

EgoBridge: Domain Adaptation for Generalizable Imitation from Egocentric Human Data
Egocentric human experience data presents a vast resource for scaling up end-to-end imitation learning for robotic manipulation. However, significant domain gaps in visual appearance, sensor modalities, and kinematics between human and robot impede knowledge transfer. This paper presents EgoBridge, a unified co-training framework that explicitly aligns the policy latent spaces between human and robot data using domain adaptation. Through a measure of discrepancy on the joint policy latent featu…

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 …

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…

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

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

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…

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