Tootfinder

JPMorgan's recent data access deal with Plaid, if extended to other aggregators, could allow Plaid to use its cost advantage to further consolidate market share (Marc Rubinstein/Bloomberg)

With the impending arrival of Typhoon #OpongPH (#Bualoi) and the recent protests against corruption in government-funded flood control projects, the recent release of the 2025 World Risk Report, with its topical focus on flooding, is very timely.
And to the surprise of no one, the

Causal Understanding by LLMs: The Role of Uncertainty
Oscar Lithgow-Serrano, Vani Kanjirangat, Alessandro Antonucci
https://arxiv.org/abs/2509.20088 https://

Causal Understanding by LLMs: The Role of Uncertainty
Recent papers show LLMs achieve near-random accuracy in causal relation classification, raising questions about whether such failures arise from limited pretraining exposure or deeper representational gaps. We investigate this under uncertainty-based evaluation, testing whether pretraining exposure to causal examples improves causal understanding >18K PubMed sentences -- half from The Pile corpus, half post-2024 -- across seven models (Pythia-1.4B/7B/12B, GPT-J-6B, Dolly-7B/12B, Qwen-7B). We an…

A survey of moment bounds for $\zeta(s)$: from Heath Brown's work to the present
Alexandra Florea
https://arxiv.org/abs/2509.20335 https://arxiv.org/pd…

A survey of moment bounds for $ζ(s)$: from Heath Brown's work to the present
In this expository article, we review some of the ideas behind the work of Heath-Brown (D.~R. Heath-Brown, {\em Fractional moments of the Riemann Zeta Function}, J. Lond. Math. Soc., (2), {\bf 24}, no.1, (1981), 65--78) on upper and lower bounds for moments of the Riemann zeta-function, as well as the impact this work had on subsequent developments in the field. We survey recent results on the topic, which essentially recover the expected rate of growth for all moments - unconditionally for sma…

Q-Palette: Fractional-Bit Quantizers Toward Optimal Bit Allocation for Efficient LLM Deployment
Deokjae Lee, Hyun Oh Song
https://arxiv.org/abs/2509.20214 https://

Q-Palette: Fractional-Bit Quantizers Toward Optimal Bit Allocation for Efficient LLM Deployment
We study weight-only post-training quantization (PTQ), which quantizes the weights of a large language model (LLM) without retraining, using little or no calibration data. Weight-only PTQ is crucial for reducing the memory footprint and latency of LLM inference, especially in memory-bound, small-batch inference scenarios, such as personalized inference on edge devices. Despite its importance, irregular weight distributions with heavy-tailed outliers in LLMs complicate quantization, recently mot…

InterQnet: A Heterogeneous Full-Stack Approach to Co-designing Scalable Quantum Networks
Joaquin Chung, Daniel Dilley, Ely Eastman, Alvin Gonzales, Kara Hokenstad, Md Shariful Islam, Varun Jorapur, Joseph Petrullo, Andy C. Y. Li, Bikun Li, Vasileios Niaouris, Anirudh Ramesh, Ansh Singal, Caitao Zhan, Michael Bishof, Eric Chitambar, Jacob P. Covey, Alan Dibos, Xu Han, Liang Jiang, Prem Kumar, Jeffrey Larson, Zain H. Saleem, Rajkumar Kettimuthu

InterQnet: A Heterogeneous Full-Stack Approach to Co-designing Scalable Quantum Networks
Quantum communications have progressed significantly, moving from a theoretical concept to small-scale experiments to recent metropolitan-scale demonstrations. As the technology matures, it is expected to revolutionize quantum computing in much the same way that classical networks revolutionized classical computing. Quantum communications will also enable breakthroughs in quantum sensing, metrology, and other areas. However, scalability has emerged as a major challenge, particularly in terms of…

Identifying and Addressing User-level Security Concerns in Smart Homes Using "Smaller" LLMs
Hafijul Hoque Chowdhury, Riad Ahmed Anonto, Sourov Jajodia, Suryadipta Majumdar, Md. Shohrab Hossain
https://arxiv.org/abs/2509.19485

Identifying and Addressing User-level Security Concerns in Smart Homes Using "Smaller" LLMs
With the rapid growth of smart home IoT devices, users are increasingly exposed to various security risks, as evident from recent studies. While seeking answers to know more on those security concerns, users are mostly left with their own discretion while going through various sources, such as online blogs and technical manuals, which may render higher complexity to regular users trying to extract the necessary information. This requirement does not go along with the common mindsets of smart ho…

"…poll in April showed a majority of Americans suddenly wanted to restrict that presidency in ways they hadn’t before…percentage who said a president has 'too much' power rose from 32% last year (under Biden) all the way up to 54% (under Trump)."
"All of which suggests Americans-or at least a majority of them-see something extraordinary happening right now."
"Republicans…try to diminish… 'No Kings' rallies…something that’s very real to l…

Republicans are ridiculing ‘No Kings.’ A striking new poll shows Americans aren’t laughing
New polling reinforces how much the thrust of the “No Kings” message has resonated with much of the American public.

From Text to Talk: Audio-Language Model Needs Non-Autoregressive Joint Training
Tianqiao Liu, Xueyi Li, Hao Wang, Haoxuan Li, Zhichao Chen, Weiqi Luo, Zitao Liu
https://arxiv.org/abs/2509.20072

From Text to Talk: Audio-Language Model Needs Non-Autoregressive Joint Training
Recent advances in large language models have attracted significant interest in extending their capabilities to multimodal scenarios, particularly for speech-in speech-out conversational systems. However, existing multimodal models handling interleaved audio and text, such as MOSHI require complex multi stage training pipelines, incurring substantial computational costs. Moreover, these models uniformly apply autoregressive generation to both text and audio tokens, overlooking a fundamental asy…

CorIL: Towards Enriching Indian Language to Indian Language Parallel Corpora and Machine Translation Systems
Soham Bhattacharjee, Mukund K Roy, Yathish Poojary, Bhargav Dave, Mihir Raj, Vandan Mujadia, Baban Gain, Pruthwik Mishra, Arafat Ahsan, Parameswari Krishnamurthy, Ashwath Rao, Gurpreet Singh Josan, Preeti Dubey, Aadil Amin Kak, Anna Rao Kulkarni, Narendra VG, Sunita Arora, Rakesh Balbantray, Prasenjit Majumdar, Karunesh K Arora, Asif Ekbal, Dipti Mishra Sharma

CorIL: Towards Enriching Indian Language to Indian Language Parallel Corpora and Machine Translation Systems
India's linguistic landscape is one of the most diverse in the world, comprising over 120 major languages and approximately 1,600 additional languages, with 22 officially recognized as scheduled languages in the Indian Constitution. Despite recent progress in multilingual neural machine translation (NMT), high-quality parallel corpora for Indian languages remain scarce, especially across varied domains. In this paper, we introduce a large-scale, high-quality annotated parallel corpus covering 1…