Tootfinder

NetRoller: Interfacing General and Specialized Models for End-to-End Autonomous Driving
Ren Xin, Hongji Liu, Xiaodong Mei, Wenru Liu, Maosheng Ye, Zhili Chen, Jun Ma
https://arxiv.org/abs/2506.14589

NetRoller: Interfacing General and Specialized Models for End-to-End Autonomous Driving
Integrating General Models (GMs) such as Large Language Models (LLMs), with Specialized Models (SMs) in autonomous driving tasks presents a promising approach to mitigating challenges in data diversity and model capacity of existing specialized driving models. However, this integration leads to problems of asynchronous systems, which arise from the distinct characteristics inherent in GMs and SMs. To tackle this challenge, we propose NetRoller, an adapter that incorporates a set of novel mechan…

CRITICTOOL: Evaluating Self-Critique Capabilities of Large Language Models in Tool-Calling Error Scenarios
Shiting Huang, Zhen Fang, Zehui Chen, Siyu Yuan, Junjie Ye, Yu Zeng, Lin Chen, Qi Mao, Feng Zhao
https://arxiv.org/abs/2506.13977

CRITICTOOL: Evaluating Self-Critique Capabilities of Large Language Models in Tool-Calling Error Scenarios
The ability of large language models (LLMs) to utilize external tools has enabled them to tackle an increasingly diverse range of tasks. However, as the tasks become more complex and long-horizon, the intricate tool utilization process may trigger various unexpected errors. Therefore, how to effectively handle such errors, including identifying, diagnosing, and recovering from them, has emerged as a key research direction for advancing tool learning. In this work, we first extensively analyze t…

SmokeSVD: Smoke Reconstruction from A Single View via Progressive Novel View Synthesis and Refinement with Diffusion Models
Chen Li, Shanshan Dong, Sheng Qiu, Jianmin Han, Zan Gao, Kemeng Huang, Taku Komura
https://arxiv.org/abs/2507.12156

SmokeSVD: Smoke Reconstruction from A Single View via Progressive Novel View Synthesis and Refinement with Diffusion Models
Reconstructing dynamic fluids from sparse views is a long-standing and challenging problem, due to the severe lack of 3D information from insufficient view coverage. While several pioneering approaches have attempted to address this issue using differentiable rendering or novel view synthesis, they are often limited by time-consuming optimization and refinement processes under ill-posed conditions. To tackle above challenges, we propose SmokeSVD, an efficient and effective framework to progress…

Elk: Exploring the Efficiency of Inter-core Connected AI Chips with Deep Learning Compiler Techniques
Yiqi Liu, Yuqi Xue, Noelle Crawford, Jilong Xue, Jian Huang
https://arxiv.org/abs/2507.11506

Elk: Exploring the Efficiency of Inter-core Connected AI Chips with Deep Learning Compiler Techniques
To meet the increasing demand of deep learning (DL) models, AI chips are employing both off-chip memory (e.g., HBM) and high-bandwidth low-latency interconnect for direct inter-core data exchange. However, it is not easy to explore the efficiency of these inter-core connected AI (ICCA) chips, due to a fundamental tussle among compute (per-core execution), communication (inter-core data exchange), and I/O (off-chip data access). In this paper, we develop Elk, a DL compiler framework to maximiz…

Hangin’ with the ‘Boys: Giggle Tickle Time https://www.dallascowboys.com/podcast/hangin-with-the-boys-giggle-tickle-time

In May 2025, a high-profile lunch in Riyadh brought together Donald Trump,
the Crown Prince,
and a group of Silicon Valley billionaires and executives.
The guest list assembled by Elon Musk (Tesla, xAI, SpaceX) and arriving by their own jets:
Sam Altman (OpenAI), Jensen Huang (Nvidia), Alex Karp (Palantir), Dara Khosrowshahi (Uber), Ruth Porat (Alphabet/Google), Alexandr Wang (Scale AI), Travis Kalanick (Cloud Kitchens, former Uber CEO), Stephen Schwarzman (Blackstone…

Mad Kings and Saudi Princes
Award winning author and futurist Albert Bates writes about ecovillages, permaculture, negative emission technology, drawdown, and climate solutions.

Analyzing Common Electronic Structure Theory Algorithms for Distributed Quantum Computing
Grier M. Jones, Hans-Arno Jacobsen
https://arxiv.org/abs/2507.01902

Analyzing Common Electronic Structure Theory Algorithms for Distributed Quantum Computing
To move towards the utility era of quantum computing, many corporations have posed distributed quantum computing (DQC) as a framework for scaling the current generation of devices for practical applications. One of these applications is quantum chemistry, also known as electronic structure theory, which has been poised as a "killer application" of quantum computing, To this end, we analyze five electronic structure methods, found in common packages such as Tequila and ffsim, which can be easily…

Ego-centric Learning of Communicative World Models for Autonomous Driving
Hang Wang, Dechen Gao, Junshan Zhang
https://arxiv.org/abs/2506.08149 https://

Ego-centric Learning of Communicative World Models for Autonomous Driving
We study multi-agent reinforcement learning (MARL) for tasks in complex high-dimensional environments, such as autonomous driving. MARL is known to suffer from the \textit{partial observability} and \textit{non-stationarity} issues. To tackle these challenges, information sharing is often employed, which however faces major hurdles in practice, including overwhelming communication overhead and scalability concerns. By making use of generative AI embodied in world model together with its latent …

SpeechVerifier: Robust Acoustic Fingerprint against Tampering Attacks via Watermarking
Lingfeng Yao (University of Houston), Chenpei Huang (University of Houston), Shengyao Wang (Waseda University), Junpei Xue (Waseda University), Hanqing Guo (University of Hawaii at M\=anoa), Jiang Liu (Waseda University), Xun Chen (Independent Researcher), Miao Pan (University of Houston)

SpeechVerifier: Robust Acoustic Fingerprint against Tampering Attacks via Watermarking
With the surge of social media, maliciously tampered public speeches, especially those from influential figures, have seriously affected social stability and public trust. Existing speech tampering detection methods remain insufficient: they either rely on external reference data or fail to be both sensitive to attacks and robust to benign operations, such as compression and resampling. To tackle these challenges, we introduce SpeechVerifer to proactively verify speech integrity using only the …

Spatiotemporal Prediction of Electric Vehicle Charging Load Based on Large Language Models
Hang Fan, Mingxuan Li, Jingshi Cui, Zuhan Zhang, Wencai Run, Dunnan Liu
https://arxiv.org/abs/2506.03728

Spatiotemporal Prediction of Electric Vehicle Charging Load Based on Large Language Models
The rapid growth of EVs and the subsequent increase in charging demand pose significant challenges for load grid scheduling and the operation of EV charging stations. Effectively harnessing the spatiotemporal correlations among EV charging stations to improve forecasting accuracy is complex. To tackle these challenges, we propose EV-LLM for EV charging loads based on LLMs in this paper. EV-LLM integrates the strengths of Graph Convolutional Networks (GCNs) in spatiotemporal feature extraction w…