Tootfinder

contiguous_usa: Contiguous states (USA)
A network of contiguous states in the USA, in which each state is a node and two nodes are connected if they share a land-based geographic border. The dataset includes the lower 48 states, and the District of Columbia.
This network has 49 nodes and 107 edges.
Tags: Transportation, Roads, Unweighted

Coinbase invests in Indian crypto exchange CoinDCX to expand into India and the Middle East; CEO Sumit Gupta said the deal values CoinDCX at $2.45B post-money (Danny Park/The Block)
https://www.theblock.co/post/374683/coinbase-invests-coindcx

Context Misleads LLMs: The Role of Context Filtering in Maintaining Safe Alignment of LLMs
Jinhwa Kim, Ian G. Harris
https://arxiv.org/abs/2508.10031 https://

Context Misleads LLMs: The Role of Context Filtering in Maintaining Safe Alignment of LLMs
While Large Language Models (LLMs) have shown significant advancements in performance, various jailbreak attacks have posed growing safety and ethical risks. Malicious users often exploit adversarial context to deceive LLMs, prompting them to generate responses to harmful queries. In this study, we propose a new defense mechanism called Context Filtering model, an input pre-processing method designed to filter out untrustworthy and unreliable context while identifying the primary prompts contai…

Learning with Incomplete Context: Linear Contextual Bandits with Pretrained Imputation
Hao Yan, Heyan Zhang, Yongyi Guo
https://arxiv.org/abs/2510.09908 https://

Learning with Incomplete Context: Linear Contextual Bandits with Pretrained Imputation
The rise of large-scale pretrained models has made it feasible to generate predictive or synthetic features at low cost, raising the question of how to incorporate such surrogate predictions into downstream decision-making. We study this problem in the setting of online linear contextual bandits, where contexts may be complex, nonstationary, and only partially observed. In addition to bandit data, we assume access to an auxiliary dataset containing fully observed contexts--common in practice si…

DCP: Addressing Input Dynamism In Long-Context Training via Dynamic Context Parallelism
Chenyu Jiang, Zhenkun Cai, Ye Tian, Zhen Jia, Yida Wang, Chuan Wu
https://arxiv.org/abs/2510.10620

DCP: Addressing Input Dynamism In Long-Context Training via Dynamic Context Parallelism
Context parallelism has emerged as a key technique to support long-context training, a growing trend in generative AI for modern large models. However, existing context parallel methods rely on static parallelization configurations that overlook the dynamic nature of training data, specifically, the variability in sequence lengths and token relationships (i.e., attention patterns) across samples. As a result, these methods often suffer from unnecessary communication overhead and imbalanced comp…

Retrospective Sparse Attention for Efficient Long-Context Generation
Seonghwan Choi, Beomseok Kang, Dongwon Jo, Jae-Joon Kim
https://arxiv.org/abs/2508.09001 https://

Retrospective Sparse Attention for Efficient Long-Context Generation
Large Language Models (LLMs) are increasingly deployed in long-context tasks such as reasoning, code generation, and multi-turn dialogue. However, inference over extended contexts is bottlenecked by the Key-Value (KV) cache, whose memory footprint grows linearly with sequence length and dominates latency at each decoding step. While recent KV cache compression methods identify and load important tokens, they focus predominantly on input contexts and fail to address the cumulative attention erro…

sp_primary_school: Primary school dynamic contacts (2009)
Two temporal networks of contacts among students and teachers at a primary school in Lyon, France, on consecutive days of in October 2009. Each network accumulates all contacts over the course of a single day; contacts were sampled at 20-second intervals.
This network has 242 nodes and 125773 edges.
Tags: Social, Offline, Weighted, Temporal, Metadata

sp_infectious: Art exhibit dynamic contacts (2011)
This dataset contains the daily dynamic contact networks collected during the Infectious SocioPatterns event that took place at the Science Gallery in Dublin, Ireland, during the artscience exhibition INFECTIOUS: STAY AWAY. Each file in the downloadable package contains a tab-separated list representing the active contacts during 20-second intervals of one day of data collection. Each line has the form “t i j“, where i and j are the a…

sp_infectious: Art exhibit dynamic contacts (2011)
This dataset contains the daily dynamic contact networks collected during the Infectious SocioPatterns event that took place at the Science Gallery in Dublin, Ireland, during the artscience exhibition INFECTIOUS: STAY AWAY. Each file in the downloadable package contains a tab-separated list representing the active contacts during 20-second intervals of one day of data collection. Each line has the form “t i j“, where i and j are the a…

sp_kenyan_households: Kenyan households contacts (2012)
A network of proximity contacts measured between members of 5 households of rural Kenya, between April 24 and May 12, 2012.
This network has 47 nodes and 32643 edges.
Tags: Social, Offline, Unweighted, Temporal, Metadata
https://networks.skewed.…