Tootfinder

SCIENCE WARRIORS: From Antarctica — STRAIGHT INTO BATTLE! Polar defenders showed their double front: https://benborges.xyz/2025/11/13/science-warriors-from-antarctica-straight.html

Open space on the first floor of the new Computing and Information Science Building
#photo #photography #cornell

Series B, Episode 01 - Redemption
BLAKE: Zen, identify the hostiles.
CALLY: Forty-two seconds.
BLAKE: Zen! [Zen burbles]
AVON: The information must be bypassing the translator systems.
CALLY: Thirty-five seconds.
https://blake.torpidity.net/m/201/97 B7B3

"What about the developers who start their careers during the 2020s? How will they deal with the ever-present issue of comparing floating-point values in a world that prefers TikTok to books? Well, they will probably ask an LLM how to do that, and not even bother about this issue anymore. Welcome to the future!"
ht…

The Smartest Concept In Computer Science
Every decade, programmers new to the industry must ingest a non-negligible amount of information in order to be proficient in their jobs. For the past five decades, however, this basic set of information has changed substantially, generating as a result a quite varied set of priorities in the minds of age-diverse programming teams.

Quantum Fisher information matrices from R\'enyi relative entropies
Mark M. Wilde
https://arxiv.org/abs/2510.02218 https://arxiv.org/pdf/2510.02218

Quantum Fisher information matrices from Rényi relative entropies
Quantum generalizations of the Fisher information are important in quantum information science, with applications in high energy and condensed matter physics and in quantum estimation theory, machine learning, and optimization. One can derive a quantum generalization of the Fisher information matrix in a natural way as the Hessian matrix arising in a Taylor expansion of a smooth divergence. Such an approach is appealing for quantum information theorists, given the ubiquity of divergences in qua…

List Decoding Reed--Solomon Codes in the Lee, Euclidean, and Other Metrics
Chris Peikert, Alexandra Veliche Hostetler
https://arxiv.org/abs/2510.11453 https://

List Decoding Reed--Solomon Codes in the Lee, Euclidean, and Other Metrics
Reed--Solomon error-correcting codes are ubiquitous across computer science and information theory, with applications in cryptography, computational complexity, communication and storage systems, and more. Most works on efficient error correction for these codes, like the celebrated Berlekamp--Welch unique decoder and the (Guruswami--)Sudan list decoders, are focused on measuring error in the Hamming metric, which simply counts the number of corrupted codeword symbols. However, for some applica…

I think the root of the “AI” evil is when AI researchers in the 1960s recognized that they outrageously underestimated the complexity of the human mind.
They became humiliated by their promises that AGI was just a few years away—and then went full goblin mode that’s lasting to this day.
Some of the OG researchers took it quite badly that they stalled and weren’t in the limelight anymore.
‣ Marvin Minsky (co-founder of MIT AI lab and arguably the most important early AI bro) went on to visit Epstein’s island multiple times.
‣ Karl Steinbuch, who came up with the German term for computer science ("Informatik")—who also was a literal Nazi (and likely war criminal) in World War II—later wrote articles in ultra-right magazines about things like “equal rights rob women of their children”.
‣ John McCarthy (inventor of Lisp, co-authored document that coined the term “Artificial Intelligence”) was a staunch Republican who years later claimed (in a serious article) that “thermostats have beliefs”.
[one moment, I am receiving more information]
‣ There’s a second Epstein Island AI pioneer? Who also was Chief Learning Officer at… Trump University? That would be Roger Schank (founded one of the first AI companies in the 1980s AI boom, it even had an IPO. Of course the 1980s AI bubble burst).
Obviously all of the above received all the awards in computer science and are very revered people.

If you want to spend time on AI you can best spend it on lectures like this. No hype, just science, but in this case also very practical.
#AI

Stanford CS230 | Autumn 2025 | Lecture 7: Agents, Prompts, and RAG.
For more information about Stanford’s Artificial Intelligence professional and graduate programs, visit: https://stanford.io/aiNovember 11, 2025This lecture ...

Preparing students for the quantum information revolution: Interdisciplinary teaching, curriculum development, and advising in quantum information science and engineering
Fargol Seifollahi, Chandralekha Singh
https://arxiv.org/abs/2510.00150

Preparing students for the quantum information revolution: Interdisciplinary teaching, curriculum development, and advising in quantum information science and engineering
As the field of quantum information science and engineering (QISE) continues its rapid growth, there are increasing concerns about the workforce demands and the necessity of preparing students for quantum-related careers. Given the interdisciplinary nature of the field, it is necessary to offer diverse educational opportunities to ensure that students are well-prepared for careers emerging from the second quantum revolution. In this paper, we present our findings from a qualitative study involv…

User Prompting Strategies and ChatGPT Contextual Adaptation Shape Conversational Information-Seeking Experiences
Haoning Xue, Yoo Jung Oh, Xinyi Zhou, Xinyu Zhang, Berit Oxley
https://arxiv.org/abs/2509.25513

User Prompting Strategies and ChatGPT Contextual Adaptation Shape Conversational Information-Seeking Experiences
Conversational AI, such as ChatGPT, is increasingly used for information seeking. However, little is known about how ordinary users actually prompt and how ChatGPT adapts its responses in real-world conversational information seeking (CIS). In this study, a nationally representative sample of 937 U.S. adults engaged in multi-turn CIS with ChatGPT on both controversial and non-controversial topics across science, health, and policy contexts. We analyzed both user prompting strategies and the com…

ZKProphet: Understanding Performance of Zero-Knowledge Proofs on GPUs
Tarunesh Verma (Computer Science and Engineering, University of Michigan, USA), Yichao Yuan (Computer Science and Engineering, University of Michigan, USA), Nishil Talati (Computer Science and Engineering, University of Michigan, USA), Todd Austin (Computer Science and Engineering, University of Michigan, USA)

ZKProphet: Understanding Performance of Zero-Knowledge Proofs on GPUs
Zero-Knowledge Proofs (ZKP) are protocols which construct cryptographic proofs to demonstrate knowledge of a secret input in a computation without revealing any information about the secret. ZKPs enable novel applications in private and verifiable computing such as anonymized cryptocurrencies and blockchain scaling and have seen adoption in several real-world systems. Prior work has accelerated ZKPs on GPUs by leveraging the inherent parallelism in core computation kernels like Multi-Scalar Mul…

Computing moment polytopes - with a focus on tensors, entanglement and matrix multiplication
Maxim van den Berg, Matthias Christandl, Vladimir Lysikov, Harold Nieuwboer, Michael Walter, Jeroen Zuiddam
https://arxiv.org/abs/2510.08336

Computing moment polytopes - with a focus on tensors, entanglement and matrix multiplication
Tensors are fundamental in mathematics, computer science, and physics. Their study through algebraic geometry and representation theory has proved very fruitful in the context of algebraic complexity theory and quantum information. In particular, moment polytopes have been understood to play a key role. In quantum information, moment polytopes (also known as entanglement polytopes) provide a framework for the single-particle quantum marginal problem and offer a geometric characterization of ent…

Efficient Learning-based Graph Simulation for Temporal Graphs
Sheng Xiang, Chenhao Xu, Dawei Cheng, Xiaoyang Wang, Ying Zhang
https://arxiv.org/abs/2510.05569 https://

Efficient Learning-based Graph Simulation for Temporal Graphs
Graph simulation has recently received a surge of attention in graph processing and analytics. In real-life applications, e.g. social science, biology, and chemistry, many graphs are composed of a series of evolving graphs (i.e., temporal graphs). While most of the existing graph generators focus on static graphs, the temporal information of the graphs is ignored. In this paper, we focus on simulating temporal graphs, which aim to reproduce the structural and temporal properties of the observed…

LLM-based Multi-Agent Blackboard System for Information Discovery in Data Science
Alireza Salemi, Mihir Parmar, Palash Goyal, Yiwen Song, Jinsung Yoon, Hamed Zamani, Hamid Palangi, Tomas Pfister
https://arxiv.org/abs/2510.01285

LLM-based Multi-Agent Blackboard System for Information Discovery in Data Science
The rapid advancement of Large Language Models (LLMs) has opened new opportunities in data science, yet their practical deployment is often constrained by the challenge of discovering relevant data within large heterogeneous data lakes. Existing methods struggle with this: single-agent systems are quickly overwhelmed by large, heterogeneous files in the large data lakes, while multi-agent systems designed based on a master-slave paradigm depend on a rigid central controller for task allocation …

The #AGIT2026 call for papers is out:
Short paper submission deadline: March 1, 2026
#GISChat

AGIT Submissions - AGIT
Call for Submissions 2026 We encourage researchers, scholars, practitioners, and professionals in all facets of Geographic Information Science and Technology, and applications thereof, to present

LLM-Based Information Extraction to Support Scientific Literature Research and Publication Workflows
Samy Ateia, Udo Kruschwitz, Melanie Scholz, Agnes Koschmider, Moayad Almohaishi
https://arxiv.org/abs/2510.04749

LLM-Based Information Extraction to Support Scientific Literature Research and Publication Workflows
The increasing volume of scholarly publications requires advanced tools for efficient knowledge discovery and management. This paper introduces ongoing work on a system using Large Language Models (LLMs) for the semantic extraction of key concepts from scientific documents. Our research, conducted within the German National Research Data Infrastructure for and with Computer Science (NFDIxCS) project, seeks to support FAIR (Findable, Accessible, Interoperable, and Reusable) principles in scienti…

Crosslisted article(s) found for cs.GT. https://arxiv.org/list/cs.GT/new
[1/1]:
- Data as Commodity: a Game-Theoretic Principle for Information Pricing
Pasquale Casaburi, Giovanni Piccioli, Pierpaolo Vivo

Exploiting higher-order correlation functions for photon-statistics-based characterization and reconstruction of arbitrary Gaussian states
Philip Heinzel, Ren\'e Sondenheimer
https://arxiv.org/abs/2510.09083

Exploiting higher-order correlation functions for photon-statistics-based characterization and reconstruction of arbitrary Gaussian states
Gaussian states are an essential building block for various applications in quantum optics and quantum information science, yet the precise relation between their second- and third-order correlation functions remains not fully explored. We discuss connections between these correlation functions by constructing an explicit decomposition formula for arbitrary sixth-order moments of ladder operators for general Gaussian states and demonstrate how the derived relations enable state classification f…

Crosslisted article(s) found for cs.IR. https://arxiv.org/list/cs.IR/new
[1/1]:
- LLM-based Multi-Agent Blackboard System for Information Discovery in Data Science
Salemi, Parmar, Goyal, Song, Yoon, Zamani, Palangi, Pfister

Crosslisted article(s) found for cond-mat.mtrl-sci. https://arxiv.org/list/cond-mat.mtrl-sci/new
[1/1]:
- Reliable End-to-End Material Information Extraction from the Literature with Source-Tracked Multi...
Xin Wang, Anshu Raj, Matthew Luebbe, Haiming Wen, Shuozhi Xu, Ku…

The ParticipACTION website has a wealth of information, for both individuals and communities, to help Canadians get more active.
https://www.participaction.com/the-science/benefits-and-guidelines/adults-18-to-64/

List Recoverable Codes: The Good, the Bad, and the Unknown (hopefully not Ugly)
Nicolas Resch, S. Venkitesh
https://arxiv.org/abs/2510.07597 https://arxiv.…

List Recoverable Codes: The Good, the Bad, and the Unknown (hopefully not Ugly)
List recovery is a fundamental task for error-correcting codes, vastly generalizing unique decoding from worst-case errors and list decoding. Briefly, one is given ''soft information'' in the form of input lists S_1,...,S_n of bounded size, and one argues that there are not too many codewords that agree a lot with this soft information. This general problem appears in many guises, both within coding theory and in theoretical computer science more broadly. In this article we survey recent resu…

A Modular Theory of Subjective Consciousness for Natural and Artificial Minds
Micha\"el Gillon
https://arxiv.org/abs/2510.01864 https://arxiv.org/pdf/…

A Modular Theory of Subjective Consciousness for Natural and Artificial Minds
Understanding how subjective experience arises from information processing remains a central challenge in neuroscience, cognitive science, and AI research. The Modular Consciousness Theory (MCT) proposes a biologically grounded and computationally explicit framework in which consciousness is a discrete sequence of Integrated Informational States (IISs). Each IIS is a packet of integrated information tagged with a multidimensional density vector that quantifies informational richness. Its magnit…

A curated list of UI clients for accessing the ActivityPub Fediverse social network.
https://delightful.coding.social/delightful_fediverse_clients

delightful fediverse clients
Delightful curated lists of free software, open science and information sources.

Optimal Computation from Fluctuation Responses
Jinghao Lyu, Kyle J. Ray, James P. Crutchfield
https://arxiv.org/abs/2510.03900 https://arxiv.org/pdf/2510.0…

Optimal Computation from Fluctuation Responses
The energy cost of computation has emerged as a central challenge at the intersection of physics and computer science. Recent advances in statistical physics -- particularly in stochastic thermodynamics -- enable precise characterizations of work, heat, and entropy production in information-processing systems driven far from equilibrium by time-dependent control protocols. A key open question is then how to design protocols that minimize thermodynamic cost while ensur- ing correct outcomes. To …

REFER: Mitigating Bias in Opinion Summarisation via Frequency Framed Prompting
Nannan Huang, Haytham M. Fayek, Xiuzhen Zhang
https://arxiv.org/abs/2509.15723 https://

REFER: Mitigating Bias in Opinion Summarisation via Frequency Framed Prompting
Individuals express diverse opinions, a fair summary should represent these viewpoints comprehensively. Previous research on fairness in opinion summarisation using large language models (LLMs) relied on hyperparameter tuning or providing ground truth distributional information in prompts. However, these methods face practical limitations: end-users rarely modify default model parameters, and accurate distributional information is often unavailable. Building upon cognitive science research demo…

GRNND: A GPU-Parallel Relative NN-Descent Algorithm for Efficient Approximate Nearest Neighbor Graph Construction
Xiang Li (Nanjing University), Qiong Chang (Institute of Science Tokyo), Yun Li (Nanjing University), Jun Miyazaki (Institute of Science Tokyo)
https://arxiv.org/abs/2510.02774

GRNND: A GPU-Parallel Relative NN-Descent Algorithm for Efficient Approximate Nearest Neighbor Graph Construction
Relative Nearest Neighbor Descent (RNN-Descent) is a state-of-the-art algorithm for constructing sparse approximate nearest neighbor (ANN) graphs by combining the iterative refinement of NN-Descent with the edge-pruning rules of the Relative Neighborhood Graph (RNG). It has demonstrated strong effectiveness in large-scale search tasks such as information retrieval and related tasks. However, as the amount and dimensionality of data increase, the complexity of graph construction in RNN-Descent r…

From Service-Oriented Computing to Metaverse Services: A Framework for Inclusive and Immersive Learning for Neurodivergent Students
Rachid Hamadi (School of Computer Science and Engineering UNSW Sydney Australia), Abdelmounaam Rezgui (School of Information Technology Illinois State University Normal IL USA), Ali Darejeh (School of Computer Science and Engineering UNSW Sydney Australia)

From Service-Oriented Computing to Metaverse Services: A Framework for Inclusive and Immersive Learning for Neurodivergent Students
The metaverse offers immersive and adaptive learning environments for neurodivergent students to thrive and reach their full potential. In this paper, we propose a generic framework that leverages metaverse services as an evolution beyond traditional service-oriented computing, enabling more interactive, personalized, and engaging educational experiences. By integrating AI-driven adaptability, multimodal interaction, and privacy-first service design, the framework ensures that learning remains …

Series B, Episode 04 - Horizon
CALLY: [V.O.] Right.
AVON: Orac, have you obtained any further information on the planet Horizon?
ORAC: No.
AVON: Why not?
ORAC: Is that a serious question?
AVON: No.
https://blake.torpidity.net/m/204/406 B7B2

Similarity as Thermodynamic Work: Between Depth and Diversity -- from Information Distance to Ugly Duckling
Kentaro Imafuku
https://arxiv.org/abs/2509.16236 https://

Similarity as Thermodynamic Work: Between Depth and Diversity -- from Information Distance to Ugly Duckling
Defining similarity is a fundamental challenge in information science. Watanabe's Ugly Duckling Theorem highlights diversity, while algorithmic information theory emphasizes depth through Information Distance. We propose a statistical-mechanical framework that treats program length as energy, with a temperature parameter unifying these two aspects: in the low-temperature limit, similarity approaches Information Distance; in the high-temperature limit, it recovers the indiscriminability of the U…

Fundamental Costs of Noise-Robust Quantum Control: Speed Limits and Complexity
Junkai Zeng, Xiu-Hao Deng
https://arxiv.org/abs/2510.07183 https://arxiv.org…

Fundamental Costs of Noise-Robust Quantum Control: Speed Limits and Complexity
Noise is ubiquitous in quantum systems and is a major obstacle for the advancement of quantum information science. Noise-robust quantum control achieves high-fidelity operations by engineering the evolution path so that first-order noise contributions cancel at the final time. Such dynamical error correction typically incurs a time overhead beyond standard quantum speed limits. We derive general lower bounds on control complexity that quantify this overhead for quasi-static coherent noise under…

Augmenting LLMs for General Time Series Understanding and Prediction
Felix Parker, Nimeesha Chan, Chi Zhang, Kimia Ghobadi
https://arxiv.org/abs/2510.01111 https://

Augmenting LLMs for General Time Series Understanding and Prediction
Time series data is fundamental to decision-making in many crucial domains including healthcare, finance, and environmental science. However, analyzing this data often requires incorporating unstructured contextual information, answering domain-specific questions, and generating natural language explanations -- capabilities that traditional time series models lack due to their inability to process text. While Large Language Models (LLMs) excel at contextual reasoning and knowledge integration, …

LeakageDetector 2.0: Analyzing Data Leakage in Jupyter-Driven Machine Learning Pipelines
Owen Truong, Terrence Zhang, Arnav Marchareddy, Ryan Lee, Jeffery Busold, Michael Socas, Eman Abdullah AlOmar
https://arxiv.org/abs/2509.15971

LeakageDetector 2.0: Analyzing Data Leakage in Jupyter-Driven Machine Learning Pipelines
In software development environments, code quality is crucial. This study aims to assist Machine Learning (ML) engineers in enhancing their code by identifying and correcting Data Leakage issues within their models. Data Leakage occurs when information from the test dataset is inadvertently included in the training data when preparing a data science model, resulting in misleading performance evaluations. ML developers must carefully separate their data into training, evaluation, and test sets t…

Atrium of New Computing and Information Science Building With Workers Still Finishing Up
#photo #photography #buildings

Crosslisted article(s) found for cs.GT. https://arxiv.org/list/cs.GT/new
[1/1]:
- Beyond Majority Voting: LLM Aggregation by Leveraging Higher-Order Information
Rui Ai, Yuqi Pan, David Simchi-Levi, Milind Tambe, Haifeng Xu

Series D, Episode 05 - Animals
ARDUS: I, I cannot say, Commissioner.
SERVALAN: Very well, I await your information.
ARDUS: I think it would be improper of me to release it unless authorised by the High Council.
https://blake.torpidity.net/m/405/196 B7B5

Dynamics of Quantum Entanglement Between Photon and Phonon Modes in a Coulomb-coupled Optomechanical Cavity Magnonic Systems
Muhib Ullah, Muhammad Idrees, Said Mikki
https://arxiv.org/abs/2510.02767

Dynamics of Quantum Entanglement Between Photon and Phonon Modes in a Coulomb-coupled Optomechanical Cavity Magnonic Systems
Quantum entanglement is a fundamental phenomenon in quantum information science and a crucial resource for quantum technologies such as precision sensing, secure communication, and computation. In hybrid cavity magno-optomechanical systems, entanglement among cavity photons, magnons, and phonons enables manipulation of quantum states across different modes via radiation pressure-based light-matter interaction. We propose a hybrid optomechanical cavity-magnonic setup with Yttrium iron garnet (YI…

Plaque by the new computer and information science building commemorating the old baseball field that used to be at the site
#photo #photography #cornell

First Workshop on Building Innovative Research Systems for Digital Libraries (BIRDS 2025)
Christin Katharina Kreutz, Hermann Kroll
https://arxiv.org/abs/2509.26001 https://

First Workshop on Building Innovative Research Systems for Digital Libraries (BIRDS 2025)
We propose the first workshop on Building Innovative Research Systems for Digital Libraries (BIRDS) to take place at TPDL 2025 as a full-day workshop. BIRDS addresses practitioners working in digital libraries and GLAMs as well as researchers from computational domains such as data science, information retrieval, natural language processing, and data modelling. Our interdisciplinary workshop focuses on connecting members of both worlds. One of today's biggest challenges is the increasing inform…

Series C, Episode 03 - Volcano
ZEN: That information is not available.
VILA: Very helpful.
CALLY: Avon, those Federation assessment teams were always followed up by invasion and colonization.
AVON: Usually.
https://blake.torpidity.net/m/303/60 B7B2

Latent learning: episodic memory complements parametric learning by enabling flexible reuse of experiences
Andrew Kyle Lampinen, Martin Engelcke, Yuxuan Li, Arslan Chaudhry, James L. McClelland
https://arxiv.org/abs/2509.16189

Latent learning: episodic memory complements parametric learning by enabling flexible reuse of experiences
When do machine learning systems fail to generalize, and what mechanisms could improve their generalization? Here, we draw inspiration from cognitive science to argue that one weakness of machine learning systems is their failure to exhibit latent learning -- learning information that is not relevant to the task at hand, but that might be useful in a future task. We show how this perspective links failures ranging from the reversal curse in language modeling to new findings on agent-based navig…

The new Computing and Information Science has custom video screens that 𝑎𝑐𝑡𝑢𝑎𝑙𝑙𝑦 𝑤𝑜𝑟𝑘 unlike most on campus
#photo #photography #cornell

Nonclassical phonon pair
Yu Wang, Zhen Shen, Mai Zhang, Zhi-Peng Shi, Hong-Yi Kuang, Shuai Wan, Fang-Wen Sun, Guang-Can Guo, Chun-Hua Dong
https://arxiv.org/abs/2509.24429 https…

Nonclassical phonon pair
Quantum-correlated photon pairs are crucial resources for modern quantum information science. Similarly, the reliable generation of nonclassical phonon pairs is vital for advancing engineerable solid-state quantum devices and hybrid quantum networks based on phonons. Here, we present a novel approach to generate quantum-correlated phonon pairs in a suspended silicon microstructure initialized in its motional ground state. By simultaneously implementing red- and blue-detuned laser pulses, equiva…

Series C, Episode 13 - Terminal
ZEN: The information you require has been stored via a coded route, and retrieval is not possible without the correct security command sequence.
CALLY: And that sequence is known only to Avon, I imagine.
ZEN: Confirmed.
https://blake.torpidity.net/m/313/100

Assessing metadata privacy in neuroimaging
Emilie Kibsgaard, Anita Sue Jwa, Christopher J Markiewicz, David Rodriguez Gonzalez, Judith Sainz Pardo, Russell A. Poldrack, Cyril R. Pernet
https://arxiv.org/abs/2509.15278

Assessing metadata privacy in neuroimaging
The ethical and legal imperative to share research data without causing harm requires careful attention to privacy risks. While mounting evidence demonstrates that data sharing benefits science, legitimate concerns persist regarding the potential leakage of personal information that could lead to reidentification and subsequent harm. We reviewed metadata accompanying neuroimaging datasets from six heterogeneous studies openly available on OpenNeuro, involving participants across the lifespan, f…

Near-Optimal Simultaneous Estimation of Quantum State Moments
Xiao Shi, Jiyu Jiang, Xian Wu, Jingu Xie, Hongshun Yao, Xin Wang
https://arxiv.org/abs/2509.24842 https://

Near-Optimal Simultaneous Estimation of Quantum State Moments
Estimating nonlinear properties such as Rényi entropies and observable-weighted moments serves as a central strategy for spectrum spectroscopy, which is fundamental to property prediction and analysis in quantum information science, statistical mechanics, and many-body physics. However, existing approaches are susceptible to noise and require significant resources, making them challenging for near-term quantum hardware. In this work, we introduce a framework for resource-efficient simultaneous…

Series C, Episode 11 - Moloch
TARRANT: It knows too much about me. [Scene shifts to the computer room, where Avon and Dayna, hands bound, are being interrogated by Grose and Lector. Two stormtroopers are at the back of the room.]
GROSE: [O.O.V.] It isn't the information we want, as such. It's the fun of extracting it. We can be very crude like that, can't we, Lector?