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Tailoring AI-Driven Reading Scaffolds to the Distinct Needs of Neurodiverse Learners
Soufiane Jhilal, Eleonora Pasqua, Caterina Marchesi, Riccardo Corradi, Martina Galletti
https://arxiv.org/abs/2603.28370 https://arxiv.org/pdf/2603.28370 https://arxiv.org/html/2603.28370
arXiv:2603.28370v1 Announce Type: new
Abstract: Neurodiverse learners often require reading supports, yet increasing scaffold richness can sometimes overload attention and working memory rather than improve comprehension. Grounded in the Construction-Integration model and a contingent scaffolding perspective, we examine how structural versus semantic scaffolds shape comprehension and reading experience in a supervised inclusive context. Using an adapted reading interface, we compared four modalities: unmodified text, sentence-segmented text, segmented text with pictograms, and segmented text with pictograms plus keyword labels. In a within-subject pilot with 14 primary-school learners with special educational needs and disabilities, we measured reading comprehension using standardized questions and collected brief child- and therapist-reported experience measures alongside open-ended feedback. Results highlight heterogeneous responses as some learners showed patterns consistent with benefits from segmentation and pictograms, while others showed patterns consistent with increased coordination costs when visual scaffolds were introduced. Experience ratings showed limited differences between modalities, with some apparent effects linked to clinical complexity, particularly for perceived ease of understanding. Open-ended feedback of the learners frequently requested simpler wording and additional visual supports. These findings suggest that no single scaffold is universally optimal, reinforcing the need for calibrated, adjustable scaffolding and provide design implications for human-AI co-regulation in supervised inclusive reading contexts.
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#TIL: "Anders als arabischer Text, der von rechts nach links gelesen wird, liest man die #arabisch​e Brailleschrift gemäß den internationalen Konventionen von links nach rechts."
– aus dem newsletter 4/2026 des @…

In recent interviews, Sam Altman said AI's adoption faces more resistance than he expected, while Jensen Huang warned the "doomer narrative" may be winning (David Streitfeld/New York Times)
https://www.nytimes.com/20…

Un interesante estudio cuyos resultados cuestionan el paradigma de bajar impuestos para generar crecimiento económico.
https://revistaecociencias.cl/2026/04/23/reducir-impuestos-i…

Replaced article(s) found for cs.CL. https://arxiv.org/list/cs.CL/new
[4/5]:
- Retrieving Climate Change Disinformation by Narrative
Upravitelev, Solopova, Jakob, Sahitaj, M\"oller, Schmitt
https://arxiv.org/abs/2603.22015 https://mastoxiv.page/@arXiv_csCL_bot/116283633674519408
- PaperVoyager : Building Interactive Web with Visual Language Models
Dasen Dai, Biao Wu, Meng Fang, Wenhao Wang
https://arxiv.org/abs/2603.22999 https://mastoxiv.page/@arXiv_csCL_bot/116289015432093128
- Continual Robot Skill and Task Learning via Dialogue
Weiwei Gu, Suresh Kondepudi, Anmol Gupta, Lixiao Huang, Nakul Gopalan
https://arxiv.org/abs/2409.03166 https://mastoxiv.page/@arXiv_csRO_bot/113089412115632702
- Shifting Perspectives: Steering Vectors for Robust Bias Mitigation in LLMs
Zara Siddique, Irtaza Khalid, Liam D. Turner, Luis Espinosa-Anke
https://arxiv.org/abs/2503.05371 https://mastoxiv.page/@arXiv_csLG_bot/114136994263573386
- SkillFlow: Scalable and Efficient Agent Skill Retrieval System
Fangzhou Li, Pagkratios Tagkopoulos, Ilias Tagkopoulos
https://arxiv.org/abs/2504.06188 https://mastoxiv.page/@arXiv_csAI_bot/114306773220502860
- Large Language Models for Computer-Aided Design: A Survey
Licheng Zhang, Bach Le, Naveed Akhtar, Siew-Kei Lam, Tuan Ngo
https://arxiv.org/abs/2505.08137 https://mastoxiv.page/@arXiv_csLG_bot/114504972217393639
- Structured Agent Distillation for Large Language Model
Liu, Kong, Dong, Yang, Li, Tang, Yuan, Niu, Zhang, Zhao, Lin, Huang, Wang
https://arxiv.org/abs/2505.13820 https://mastoxiv.page/@arXiv_csLG_bot/114544636506163783
- VLM-3R: Vision-Language Models Augmented with Instruction-Aligned 3D Reconstruction
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https://arxiv.org/abs/2505.20279 https://mastoxiv.page/@arXiv_csCV_bot/114578817567171199
- Learning to Diagnose Privately: DP-Powered LLMs for Radiology Report Classification
Bhattacharjee, Tian, Rubin, Lo, Merchant, Hanson, Gounley, Tandon
https://arxiv.org/abs/2506.04450 https://mastoxiv.page/@arXiv_csCR_bot/114635189706505648
- L-MARS: Legal Multi-Agent Workflow with Orchestrated Reasoning and Agentic Search
Ziqi Wang, Boqin Yuan
https://arxiv.org/abs/2509.00761 https://mastoxiv.page/@arXiv_csAI_bot/115140304787881576
- Your Models Have Thought Enough: Training Large Reasoning Models to Stop Overthinking
Han, Huang, Liao, Jiang, Lu, Zhao, Wang, Zhou, Jiang, Liang, Zhou, Sun, Yu, Xiao
https://arxiv.org/abs/2509.23392 https://mastoxiv.page/@arXiv_csAI_bot/115293169353788311
- Person-Centric Annotations of LAION-400M: Auditing Bias and Its Transfer to Models
Leander Girrbach, Stephan Alaniz, Genevieve Smith, Trevor Darrell, Zeynep Akata
https://arxiv.org/abs/2510.03721 https://mastoxiv.page/@arXiv_csCV_bot/115332690912652473
- Agentic Context Engineering: Evolving Contexts for Self-Improving Language Models
Zhang, Hu, Upasani, Ma, Hong, Kamanuru, Rainton, Wu, Ji, Li, Thakker, Zou, Olukotun
https://arxiv.org/abs/2510.04618 https://mastoxiv.page/@arXiv_csLG_bot/115332999596603375
- Mitigating Premature Exploitation in Particle-based Monte Carlo for Inference-Time Scaling
Giannone, Xu, Nayak, Awhad, Sudalairaj, Xu, Srivastava
https://arxiv.org/abs/2510.05825 https://mastoxiv.page/@arXiv_csLG_bot/115338159696513898
- Complete asymptotic type-token relationship for growing complex systems with inverse power-law co...
Pablo Rosillo-Rodes, Laurent H\'ebert-Dufresne, Peter Sheridan Dodds
https://arxiv.org/abs/2511.02069 https://mastoxiv.page/@arXiv_physicssocph_bot/115496283627867809
- ViPRA: Video Prediction for Robot Actions
Sandeep Routray, Hengkai Pan, Unnat Jain, Shikhar Bahl, Deepak Pathak
https://arxiv.org/abs/2511.07732 https://mastoxiv.page/@arXiv_csRO_bot/115535941444003568
- AISAC: An Integrated multi-agent System for Transparent, Retrieval-Grounded Scientific Assistance
Chandrachur Bhattacharya, Sibendu Som
https://arxiv.org/abs/2511.14043
- VideoARM: Agentic Reasoning over Hierarchical Memory for Long-Form Video Understanding
Yufei Yin, Qianke Meng, Minghao Chen, Jiajun Ding, Zhenwei Shao, Zhou Yu
https://arxiv.org/abs/2512.12360 https://mastoxiv.page/@arXiv_csCV_bot/115729238732682644
- RadImageNet-VQA: A Large-Scale CT and MRI Dataset for Radiologic Visual Question Answering
L\'eo Butsanets, Charles Corbi\`ere, Julien Khlaut, Pierre Manceron, Corentin Dancette
https://arxiv.org/abs/2512.17396 https://mastoxiv.page/@arXiv_csCV_bot/115762705911757243
- Measuring all the noises of LLM Evals
Sida Wang
https://arxiv.org/abs/2512.21326 https://mastoxiv.page/@arXiv_csLG_bot/115779597137785637
toXiv_bot_toot

There are all sorts of complex practices and laws around churches, because "religious freedom" is really a minefield. It's not that the state cares about the law, but that the narrative of the US is deeply intertwined with the narrative of "religious freedom" and "escaping religious persecution." (I probably don't need to tell anyone that the people "escaping religious persecution" were some of the absolute worst humans on the planet who were not being persecuted but wanted to be free to persecute others... but I digress.)
It is not aligning with the law that matters, nor any other sort of legal justification for their authority. Authority comes from a complex memetic fabric of woven ideas. This fabric can be attacked, these threads can be pulled out, and eventually the fabric unravels and the authority collapses.
When central authority collapses, dual power institutions pick up the pieces. They replace the faltering authority. Today, as the US government is frantically burning itself down, corporations and churches are the two most developed institutions prepared to fill that void,

In the chaos of life, I find tranquility in World War II's intricate narratives through books and films — an endless source of timeless lessons. 📚✈️ How do you recharge after a tough week? Share your rejuvenation strategies. #HistoryBuff #WeekendWindDown

I sometimes use self deprecation to tell you "don't llisten to me, I'm no academic...or am I? I don't think I am one!"
so that you won't listen to me if you have strong feelings about what I talk about.
But who makes the real waves from their ripples if nobody gets upset or interested in what you gotta say?
Right?
Neurodiversity so far are only small ripples but when are they going to turn into waves?
💡

Owl City has been an interesting recurring character for a long time. He fundamentally makes kinkade music, but in many instances there's something unintentionally captivating about watching a sheltered autistic christian man trying and failing to emulate the sterile neurotypical inhumanity of late 00s commercials, attempts at sincerely becoming what the squeakiest cleanest presenting factions want giving birth to an entirely different surreal alternate reality from the one he professes …

“It’s a new regime,” Trump said in a Fox Business interview that aired on Wednesday,
referring to Iran’s new leaders.
“We find them pretty reasonable to be honest with you, by comparison pretty reasonable.”
It was the latest instance of Trump’s trying to spin a “regime change” accomplishment in Iran,
even though analysts believe the war may have only increased the internal sway of Iran’s Islamic Revolutionary Guards Corps
-- the hard-line military force that ha…

Trump’s Portrayal of the War in Iran Collides With Reality
President Trump is confronting a crisis that is not bending to his narrative of a “pretty reasonable” new regime in Iran and all-but-assured victory for the United States.