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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
Cybersecurity researchers are calling attention to a new campaign where threat actors are abusing
"FortiGate Next-Generation Firewall"
(NGFW) appliances as entry points to breach victim networks.
The activity involves the exploitation of recently disclosed security vulnerabilities or weak credentials
to extract configuration files containing service account credentials and network topology information
The security outfit said the campaign has singled ou…
A spate of big developments emerged yesterday regarding Anthropic, its status as a "supply chain threat," and what that means for organizations that have deployed the company's tech as well as what it means for the company.
Don't miss today's Metacurity for a précis on these critical developments and other top infosec news you should know, including
--FBI warns of phishing campaigns impersonating city and county officials,
--TX governor warns health ag…
A federal judge ruled the DOD violated his earlier order undoing most of the Pentagon's press pass policy and failed to reinstate NYT reporters' credentials (Associated Press)
https://apnews.com/article/pentagon-press-nyt-new-york-…
"Hegseth never explains how it is possible that the president and his “Deal Team Six” are saving US taxpayers money while at the same time asking US taxpayers to fund a $1.5 trillion military budget that would be over 50% more than the 2025 US defense budget and more than four times the money spent on defense by China,"
Hegseth Lampooned for Absurd Video Claiming $1.5 Trillion Pentagon Budget Puts 'American Taxpayer First' | Common Dreams
https://www.commondreams.org/news/pete-hegseth-pentagon-budget
RE: https://vox.ominous.net/@occult/116103841606429399
Extra points for anyone who can explain what she is *doing* here! I cannot ...
When spokesperson was < 4 years old the 1 time NATO activated Article 5 collective defense in defense of USA, and the spokesperson is foundationally incapable of being a good spokesperson, they'll say this:
"Pentagon Press Secretary Kingsley Wilson told the BBC that despite 'everything' the US has done for its Nato allies, 'they were not there for us'."
Kegsbreath & Orange Failure's prints are all over it; their bad thinking gets people …
Still, there are some other things Hypercard did we’d do well to study, even with full-scale tools. Off the top of my head:
- It richly rewarded unguided exploration. Unsuccessful experimentation had a way of leading to paths forward, not just dead ends.
- Much of it worked by direct manipulation: if you want the thing there, you put the thing there. (Unity and Godot both sort of kind of do some descendant of this, but not with the same discoverability and transparency.)
- There was a rich library of good starting points, modifiable examples.
- An empty but functioning new project had essentially zero boilerplate. You didn’t have to have 15 files and hundreds of lines of code to get a blank page.
- Its UI made it easy-ish for newcomers to ask “What can I do with this thing here?” Modern autocomplete and inline docs kind of sort of approximate this, but in practice only for people who already have tool expertise.
- HyperTalk (the programming language) is tricky to write (it’s a p-lang), but it’s remarkably easy to read. You can peer at it with very limited knowledge and make educated guesses about its semantics, and those guesses will be mostly correct. (HyperTalk syntax tends to get the most attention when people talk about this, I think at the expense of the other things above.)
OpenAI is amending its hastily arranged deal to supply artificial intelligence to the US Department of War (DoW) after the ChatGPT owner’s chief executive admitted it looked 🔸“opportunistic and sloppy”.
The contract prompted fears the San Francisco startup’s AI could be used for ♦️domestic mass surveillance but its boss, Sam Altman, said on Monday night the startup would explicitly bar its technology from being used for that purpose
or being deployed by defence department intelli…