"It places page cache pages in a writable scatterlist, separated from the legitimate write region by nothing more than an offset boundary. The design assumes every AEAD algorithm will confine its writes to the intended destination, but nothing in the API enforces this, and nothing documents it as a requirement.
Unfortunately, one AEAD algorithm breaks this silent invariant."
"No other standard AEAD algorithm in the kernel [uses memory that doesn't belong to it as a scratch pad]. GCM, CCM, and regular authenc all confine their writes to the legitimate output area. authencesn alone writes past the boundary."
I'm actually amazed that there's only one bug here. Somehow almost everyone just managed to do the right thing, despite no mechanism enforcing it and no documentation describing it. That's just amazing. It's a testament to the skill of those developers, despite an incredibly bad design.
#copyfail
A newly expanded policy from the Trump administration could require travelers from five World Cup-qualified countries to front a bond of up to $15,000 in order to enter the United States for the tournament.
Visa bonds operate like security deposits:
a one-time payment meant to be refunded after a traveler exits the US under the terms of their visa.
The amounts generally run between $5,000 and $15,000, and are required for passport holders from certain countries to enter the…
> The Commission copied and pasted an amendment suggested by Microsoft and the lobby group Digital Europe. The aim: To prevent NGOs from obtaining information on energy-hungry data centers in the face of growing resistance
https://algorithmwatch.org/en/copy-pas…
Society has lost its direction. We are now driving blind, and the reason is reductionism. We’ve become so obsessed with breaking the world into tiny parts that we’ve forgotten how the whole thing actually hangs together.
https://www.ocrampal.com/too-narrow-to-be-
Closed-Loop Integrated Sensing, Communication, and Control for Efficient Drone Flight
Jingli Li, Yiyan Ma, Bo Ai, Wei Chen, Weijie Yuan, Qingqing Cheng, Tongyang Xu, Guoyu Ma, Mi Yang, Yunlong Lu, Wenwei Yue, Christos Masouros, Zhangdui Zhong
https://arxiv.org/abs/2603.29220 https://arxiv.org/pdf/2603.29220 https://arxiv.org/html/2603.29220
arXiv:2603.29220v1 Announce Type: new
Abstract: Low-altitude wireless networks (LAWN) require drones to follow specific trajectories controlled by ground base stations (GBSs). However, given complex low-altitude channel conditions and limited spectrum and power resources, sensing errors and wireless link unreliability cannot be ignored, leading to trajectory deviations that threaten flight safety. To address this issue, this paper proposes an integrated sensing-communication-control (ISCC) closed-loop trajectory tracking approach, aiming to reveal the coupling mechanisms among communication, sensing, and control during drone flight. In detail, we incorporate sensing errors in trajectory state estimation, packet losses in control command transmission, and finite blocklength transmission effects into the closed-loop dynamics. First, through theoretical analysis, we identify the dominant role of the time-frequency resources allocated to control in ensuring system stability and derive a lower bound on the resources required to guarantee stable operation. Second, to minimize tracking error, we formulate a time-frequency resource allocation optimization problem for the sensing, communication, and control components, subject to constraints on communication rate and closed-loop stability. Accordingly, a solution algorithm based on successive convex approximation is proposed. Third, simulation results indicate that once stability is ensured, system performance is primarily determined by sensing accuracy, with the trajectory tracking error exhibiting an approximately linear dependence on the position error bound. Finally, it is shown that the proposed ISCC scheme avoids trajectory divergence under FBL transmission compared with ISCC designs ignoring control packet loss, and could achieve decimeter-level average tracking accuracy, reducing the error to only 17.37% of that observed in the baseline global navigation satellite system scheme.
toXiv_bot_toot
ALGORITHM OR ALLY? AI, GLOBAL ENGLISH, AND THE FUTURE OF LANGUAGE LEARNING https://call-for-papers.sas.upenn.edu/cfp/2026/02/28/algorithm-or-ally-ai-global-english-and-the-future-of-language-learning
Crosslisted article(s) found for cs.CL. https://arxiv.org/list/cs.CL/new
[1/2]:
- Bridge-RAG: An Abstract Bridge Tree Based Retrieval Augmented Generation Algorithm With Cuckoo Fi...
Li, Liu, Zong, Tao, Dai, Ren, Liu, Jiang, Yang
https://arxiv.org/abs/2603.26668 https://mastoxiv.page/@arXiv_csIR_bot/116322781593134028
- SRAG: RAG with Structured Data Improves Vector Retrieval
Shalin Shah, Srikanth Ryali, Ramasubbu Venkatesh
https://arxiv.org/abs/2603.26670 https://mastoxiv.page/@arXiv_csIR_bot/116322784870180864
- LITTA: Late-Interaction and Test-Time Alignment for Visually-Grounded Multimodal Retrieval
Seonok Kim
https://arxiv.org/abs/2603.26683 https://mastoxiv.page/@arXiv_csIR_bot/116322841916406330
- Agentic AI for Human Resources: LLM-Driven Candidate Assessment
Yuksel, Anees, Elneima, Hewavitharana, Al-Badrashiny, Sawaf
https://arxiv.org/abs/2603.26710 https://mastoxiv.page/@arXiv_csIR_bot/116322937601675587
- SEAR: Schema-Based Evaluation and Routing for LLM Gateways
Zecheng Zhang, Han Zheng, Yue Xu
https://arxiv.org/abs/2603.26728 https://mastoxiv.page/@arXiv_csDB_bot/116322627580095245
- SleepVLM: Explainable and Rule-Grounded Sleep Staging via a Vision-Language Model
Guifeng Deng, Pan Wang, Jiquan Wang, Shuying Rao, Junyi Xie, Wanjun Guo, Tao Li, Haiteng Jiang
https://arxiv.org/abs/2603.26738 https://mastoxiv.page/@arXiv_csCV_bot/116322739676378309
- Aesthetic Assessment of Chinese Handwritings Based on Vision Language Models
Chen Zheng, Yuxuan Lai, Haoyang Lu, Wentao Ma, Jitao Yang, Jian Wang
https://arxiv.org/abs/2603.26768 https://mastoxiv.page/@arXiv_csCV_bot/116323078149576728
- Learning to Select Visual In-Context Demonstrations
Eugene Lee, Yu-Chi Lin, Jiajie Diao
https://arxiv.org/abs/2603.26775 https://mastoxiv.page/@arXiv_csLG_bot/116322648878995047
- CRISP: Characterizing Relative Impact of Scholarly Publications
Hannah Collison, Benjamin Van Durme, Daniel Khashabi
https://arxiv.org/abs/2603.26791 https://mastoxiv.page/@arXiv_csDL_bot/116322621679820997
- GroupRAG: Cognitively Inspired Group-Aware Retrieval and Reasoning via Knowledge-Driven Problem S...
Xinyi Duan, Yuanrong Tang, Jiangtao Gong
https://arxiv.org/abs/2603.26807 https://mastoxiv.page/@arXiv_csIR_bot/116322959557860848
- In your own words: computationally identifying interpretable themes in free-text survey data
Jenny S Wang, Aliya Saperstein, Emma Pierson
https://arxiv.org/abs/2603.26930 https://mastoxiv.page/@arXiv_csCY_bot/116322780637316287
- Multilingual Stutter Event Detection for English, German, and Mandarin Speech
Felix Haas, Sebastian P. Bayerl
https://arxiv.org/abs/2603.26939 https://mastoxiv.page/@arXiv_csSD_bot/116322704289189130
- FormalProofBench: Can Models Write Graduate Level Math Proofs That Are Formally Verified?
Ravi, Ying, Nesterov, Krishnan, Uskuplu, Xia, Aswedige, Nashold
https://arxiv.org/abs/2603.26996 https://mastoxiv.page/@arXiv_csAI_bot/116322625941412681
- PHONOS: PHOnetic Neutralization for Online Streaming Applications
Waris Quamer, Mu-Ruei Tseng, Ghady Nasrallah, Ricardo Gutierrez-Osuna
https://arxiv.org/abs/2603.27001 https://mastoxiv.page/@arXiv_eessAS_bot/116322763598554193
- ChartNet: A Million-Scale, High-Quality Multimodal Dataset for Robust Chart Understanding
Jovana Kondic, et al.
https://arxiv.org/abs/2603.27064 https://mastoxiv.page/@arXiv_csCV_bot/116323214468792735
- daVinci-LLM:Towards the Science of Pretraining
Qin, Liu, Mi, Xie, Huang, Si, Lu, Feng, Wu, Liu, Luo, Hou, Guo, Qiao, Liu
https://arxiv.org/abs/2603.27164 https://mastoxiv.page/@arXiv_csAI_bot/116322653467105951
- LightMover: Generative Light Movement with Color and Intensity Controls
Zhou, Wang, Kim, Shu, Yu, Hold-Geoffroy, Chaturvedi, Wu, Lin, Cohen
https://arxiv.org/abs/2603.27209 https://mastoxiv.page/@arXiv_csCV_bot/116323263295656104
- Self-evolving AI agents for protein discovery and directed evolution
Tan, Zhang, Li, Yu, Zhong, Zhou, Dong, Hong
https://arxiv.org/abs/2603.27303 https://mastoxiv.page/@arXiv_csAI_bot/116322838641595927
- Inference-Time Structural Reasoning for Compositional Vision-Language Understanding
Amartya Bhattacharya
https://arxiv.org/abs/2603.27349 https://mastoxiv.page/@arXiv_csCV_bot/116323280006044500
- LLM Readiness Harness: Evaluation, Observability, and CI Gates for LLM/RAG Applications
Alexandre Cristov\~ao Maiorano
https://arxiv.org/abs/2603.27355 https://mastoxiv.page/@arXiv_csAI_bot/116322987708962414
- Heterogeneous Debate Engine: Identity-Grounded Cognitive Architecture for Resilient LLM-Based Eth...
Jakub Mas{\l}owski, Jaros{\l}aw A. Chudziak
https://arxiv.org/abs/2603.27404 https://mastoxiv.page/@arXiv_csAI_bot/116322999177460352
toXiv_bot_toot
ALGORITHM OR ALLY? AI, GLOBAL ENGLISH, AND THE FUTURE OF LANGUAGE LEARNING https://call-for-papers.sas.upenn.edu/cfp/2026/02/28/algorithm-or-ally-ai-global-english-and-the-future-of-language-learning
ALGORITHM OR ALLY? AI, GLOBAL ENGLISH, AND THE FUTURE OF LANGUAGE LEARNING https://call-for-papers.sas.upenn.edu/cfp/2026/02/28/algorithm-or-ally-ai-global-english-and-the-future-of-language-learning