Tootfinder

cintestinalis: Tadpole larva brain (C. intestinalis)
Entire connectivity matrix for the complete brain of a larva of Ciona intestinalis. Each directed edge represents a synaptic connection from pre-synaptic cell i to post-synaptic cell j (may not be a neuron). Edge weights represent the cumulative depth of presynaptic contacts in µm.
This network has 205 nodes and 2903 edges.
Tags: Biological, Connectome, Weighted

Order Optimal Regret Bounds for Sharpe Ratio Optimization in the Bandit Setting
Mohammad Taha Shah, Sabrina Khurshid, Gourab Ghatak
https://arxiv.org/abs/2508.13749 https://

Order Optimal Regret Bounds for Sharpe Ratio Optimization in the Bandit Setting
In this paper, we investigate the problem of sequential decision-making for Sharpe ratio (SR) maximization in a stochastic bandit setting. We focus on the Thompson Sampling (TS) algorithm, a Bayesian approach celebrated for its empirical performance and exploration efficiency, under the assumption of Gaussian rewards with unknown parameters. Unlike conventional bandit objectives focusing on maximizing cumulative reward, Sharpe ratio optimization instead introduces an inherent tradeoff between a…

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Event-LAB: Towards Standardized Evaluation of Neuromorphic Localization Methods
Adam D. Hines, Alejandro Fontan, Michael Milford, Tobias Fischer
https://arxiv.org/abs/2509.14516

Event-LAB: Towards Standardized Evaluation of Neuromorphic Localization Methods
Event-based localization research and datasets are a rapidly growing area of interest, with a tenfold increase in the cumulative total number of published papers on this topic over the past 10 years. Whilst the rapid expansion in the field is exciting, it brings with it an associated challenge: a growth in the variety of required code and package dependencies as well as data formats, making comparisons difficult and cumbersome for researchers to implement reliably. To address this challenge, we…

The Sum Leaks More Than Its Parts: Compositional Privacy Risks and Mitigations in Multi-Agent Collaboration
Vaidehi Patil, Elias Stengel-Eskin, Mohit Bansal
https://arxiv.org/abs/2509.14284

The Sum Leaks More Than Its Parts: Compositional Privacy Risks and Mitigations in Multi-Agent Collaboration
As large language models (LLMs) become integral to multi-agent systems, new privacy risks emerge that extend beyond memorization, direct inference, or single-turn evaluations. In particular, seemingly innocuous responses, when composed across interactions, can cumulatively enable adversaries to recover sensitive information, a phenomenon we term compositional privacy leakage. We present the first systematic study of such compositional privacy leaks and possible mitigation methods in multi-agent…

Evaluating Identity Leakage in Speaker De-Identification Systems
Seungmin Seo, Oleg Aulov, Afzal Godil, Kevin Mangold
https://arxiv.org/abs/2508.14012 https://

Evaluating Identity Leakage in Speaker De-Identification Systems
Speaker de-identification aims to conceal a speaker's identity while preserving intelligibility of the underlying speech. We introduce a benchmark that quantifies residual identity leakage with three complementary error rates: equal error rate, cumulative match characteristic hit rate, and embedding-space similarity measured via canonical correlation analysis and Procrustes analysis. Evaluation results reveal that all state-of-the-art speaker de-identification systems leak identity information.…

On estimation of weighted cumulative residual Tsallis entropy
Siddhartha Chakraborty, Asok K. Nanda
https://arxiv.org/abs/2510.12442 https://arxiv.org/pdf/…

On estimation of weighted cumulative residual Tsallis entropy
Recently, weighted cumulative residual Tsallis entropy has been introduced in the literature as a generalization of weighted cumulative residual entropy. We study some new properties of weighted cumulative residual Tsallis entropy measure. Next, we propose some non-parametric estimators of this measure. Asymptotic properties of these estimators are discussed. Performance of these estimators are compared by mean squared error. Non-parametric estimators for weighted cumulative residual entropy me…

Leptogenesis from Dark Matter Coannihilation
Simran Arora, Debasish Borah, Arnab Dasgupta, P. S. Bhupal Dev, Devabrat Mahanta
https://arxiv.org/abs/2509.12333 https://

Leptogenesis from Dark Matter Coannihilation
We propose a minimal extension of the type-I seesaw model to realise leptogenesis from the co-annihilation of dark sector particles. The type-I seesaw model is extended with a singlet fermion and two singlet scalars charged under a $Z_{2}$ symmetry. The $Z_{2}$-odd singlet scalar is the dark matter candidate. Here the usual type-I seesaw mechanism generates neutrino mass, and a net lepton asymmetry is generated from the co-annihilation of the dark matter and the $Z_2$-odd singlet fermion. The $…

Depth-Breadth Synergy in RLVR: Unlocking LLM Reasoning Gains with Adaptive Exploration
Zhicheng Yang, Zhijiang Guo, Yinya Huang, Yongxin Wang, Dongchun Xie, Yiwei Wang, Xiaodan Liang, Jing Tang
https://arxiv.org/abs/2508.13755

Depth-Breadth Synergy in RLVR: Unlocking LLM Reasoning Gains with Adaptive Exploration
Reinforcement Learning with Verifiable Reward (RLVR) has emerged as a powerful paradigm for unlocking reasoning capabilities in large language models, yet its full potential is hindered by two under-explored dimensions: Depth-the hardest problem a model can sample; Breadth-the number of instances consumed in a single iteration. We dissect the popular GRPO algorithm and reveal a systematic bias: the cumulative-advantage disproportionately weights samples with medium accuracy, while down-weightin…

cintestinalis: Tadpole larva brain (C. intestinalis)
Entire connectivity matrix for the complete brain of a larva of Ciona intestinalis. Each directed edge represents a synaptic connection from pre-synaptic cell i to post-synaptic cell j (may not be a neuron). Edge weights represent the cumulative depth of presynaptic contacts in µm.
This network has 205 nodes and 2903 edges.
Tags: Biological, Connectome, Weighted