Tootfinder

Crosslisted article(s) found for cs.IT. https://arxiv.org/list/cs.IT/new
[2/2]:
- What Limits Does Quantization Place on Dense Top-$k$ Retrieval? A Theoretical Study
Koki Okajima, Tsukasa Yoshida
https://arxiv.org/abs/2606.11780 https://mastoxiv.page/@arXiv_csIR_bot/116730357313656261
- Quadratic APN Functions in Dimension 8 via Gr\"obner Basis Search in a Self-Equivalence Subspace
Oleksandr Kuznetsov
https://arxiv.org/abs/2606.11967 https://mastoxiv.page/@arXiv_csCR_bot/116730499833561826
- Game-Theoretic Latent Space Alignment for Multi-user Semantic MIMO Communications
Giuseppe Di Poce, Mattia Merluzzi, Emilio Calvanese Strinati, Paolo Di Lorenzo
https://arxiv.org/abs/2606.12005 https://mastoxiv.page/@arXiv_csGT_bot/116730408631687040
- An iterative Ising decoder for quantum error correction codes
Liu, Zeng, Li, Liu, Huang, Liu, Wang, Wu, Lao
https://arxiv.org/abs/2606.12301 https://mastoxiv.page/@arXiv_quantph_bot/116730537187964618
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Crosslisted article(s) found for q-bio.NC. https://arxiv.org/list/q-bio.NC/new
[1/1]:
- Linear equivalence of nonlinear recurrent neural networks
David G. Clark
https://arxiv.org/abs/2604.23489 https://mastoxiv.page/@arXiv_condmatdisnn_bot/116481282507557078
- Robust and Clinically Reliable EEG Biomarkers: A Cross Population Framework for Generalizable Par...
Rasmussen, Wang, Rizk, Pallab, Stuwart, Mancini, Singh, Santosh
https://arxiv.org/abs/2604.23933 https://mastoxiv.page/@arXiv_csLG_bot/116481525299245475
- Solution of a large nonlinear recurrent neural network at fixed connectivity
Albert J. Wakhloo
https://arxiv.org/abs/2604.24141 https://mastoxiv.page/@arXiv_condmatdisnn_bot/116481288377505225
- From Players to Participants: Citizen Science and Video Games to Understand Cognition
Syrine Salouhou, Edgar Dubourg, Maxwell Scott-Slade, Hugo Spiers, Antoine Coutrot
https://arxiv.org/abs/2604.24321 https://mastoxiv.page/@arXiv_csHC_bot/116481458253846078
- Persistent and anti-persistent stride-to-stride fluctuations: an ARFIMA decomposition consistent ...
Philippe Terrier
https://arxiv.org/abs/2604.24365 https://mastoxiv.page/@arXiv_qbioQM_bot/116481261245226483
- Cortex-Inspired Continual Learning: Unsupervised Instantiation and Recovery of Functional Task Ne...
Kevin McKee, Thomas Hazy, Yicong Zheng, Zacharie Bugaud, Thomas Miconi
https://arxiv.org/abs/2604.24637 https://mastoxiv.page/@arXiv_csLG_bot/116481549483167923
- Homology-based Morphometry of Brain Atrophy: Methods and Applications
Donato Quiccione, Mariam Pirashvili, Nathan Broomhead, Sean J. Fallon
https://arxiv.org/abs/2604.24714 https://mastoxiv.page/@arXiv_mathAT_bot/116481250432129653
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Fairness and Strategy-Proofness in Automated Market Makers
Frank M. V. Feys
https://arxiv.org/abs/2606.04959 https://arxiv.org/pdf/2606.04959 https://arxiv.org/html/2606.04959
arXiv:2606.04959v1 Announce Type: new
Abstract: No deployed automated market maker lets its liquidity providers vote on the trading function. We show this is structural, not an oversight. On the weighted-product family with $n \geq 3$ assets, no aggregation rule is at once fair and strategy-proof. Arrovian fairness forces a unique form, the weighted Aitchison centroid, the weighted geometric mean of the providers' preferred pools. But fairness forces mean-type aggregation and strategy-proofness forces median-type, and the only rule that is both is a single-provider dictator. The obstruction is sharp: it vanishes at $n = 2$, where a fair strategy-proof rule exists. Under the Frongillo--Papireddygari--Waggoner equivalence, the centroid is Genest's logarithmic opinion pool, and the impossibility transfers to externally Bayesian pooling.
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On Agentic Behavioral Modeling
Dirk Ostwald, Rasmus Bruckner, Franziska Us\'ee, Belinda Fleischmann, Joram Soch, Sean Mulready
https://arxiv.org/abs/2604.27894 https://arxiv.org/pdf/2604.27894 https://arxiv.org/html/2604.27894
arXiv:2604.27894v1 Announce Type: new
Abstract: Integrating theoretical neuroscience, decision theory, and probabilistic inference offers a promising route to understanding human cognition, yet concrete methodological bridges between agentic AI models and behavioral data analysis remain formally underdeveloped. We advance this synthesis under the framework of agentic behavioral modeling (ABM), which treats artificial agents as latent, generative hypotheses about cognitive mechanisms and evaluates them by their statistical adequacy in explaining human behavior. After outlining its conceptual foundations, we apply the framework to two minimal laboratory paradigms: a binary perceptual contrast-discrimination task and a symmetric two-armed bandit learning task. We formalize each task-agent-data system as a joint probability model, derive explicit conditional log-likelihoods for behavioral inference, validate different model variants using model and parameter recovery simulations, and evaluate them in light of empirical data. Using these minimal examples, we provide an agent-centric interpretation of the psychometric function, derive optimal policies for both tasks, and show the equivalence between Rescorla-Wagner learning and Bayesian inference in symmetric bandits. More broadly, this work may serve as a conceptual and practical foundation for applying ABM to cognitive behavioral science.
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