@Techmeme@techhub.socialTidalwave, whose AI agents automate mortgage docs checks and give real-time multilingual feedback to borrowers, raised a $22M Series A led by Permanent Capital (Fortune)
https://fortune.com/2025/11/21/tidalwave-raises-22-million-se…
@luca@social.luca.run
@boris@cosocial.caThe DWeb HQ team @… is working on DWeb Camp 2026. Give them feedback on where in the world you think would be a fit.
https://social.coop/@dweb/115424781985654403…
@aardrian@toot.cafeA little Monday morning free labor to remind people that CSS-only widgets are not accessible (anchor link):
https://css-tricks.com/pure-css-tabs-with-details-grid-and-subgrid/#comment-1883494
I appreciate the author asked for feedback. I do…
@seeingwithsound@mas.toSonification and haptic feedback for mathematical graph accessibility: State of the art and future directions in assistive technologies and educational applications for users with visual impairments https://publikationen.bibliothek.kit.edu/1000186056 Cf. The vOICe
@nic@geno.socialWow, fast 120 Teilnehmende und ein überwältigendes Feedback im Chat.🫶 So macht Webinar Spaß!
Danke an #SkalaCAMPUS, die das mit mir und #BBB von @… durchgezogen haben.
D…
@gwire@mastodon.socialFound about five errors in the Budget document after a quick check. (Submitted tickets via the feedback form at the bottom of the page.)
@simon_brooke@mastodon.scot"We are now at a critical juncture. We are at or very close to human caused environmental change that will fundamentally unpick the life-sustaining systems on Earth. These risk triggering feedback loops...
Ultimately that could cause the planet to drift away along the pathway to “hothouse Earth”, a scenario where even if emissions were reduced, self amplifying feedback loops would drive global temperature increases up to or even beyond 5°C"
@heiseonline@social.heise.de
@benb@osintua.euOSINTukraine v2 prototype is live, we’re looking for feedback, bug, data inconsistency : –> https://v2.osintukraine.com
@cosmos4u@scicomm.xyzObservations of AGN-driven feedback: dynamics and ionization of the filaments in #M87: https://academic.oup.com/mnras/article/545/2/staf1981/8384220 -> El Gran Telescopio de Canarias captura cómo el agujero negro de la galaxia supergigante M87 moldea sus filamentos: https://efe.com/canarias/2025-12-22/el-gran-telescopio-de-canarias-captura-como-el-agujero-negro-de-la-galaxia-supergigante-m87-moldea-sus-filamentos/
@fanf@mendeddrum.orgfrom my link log —
Use the force (feedback) to solder small things.
https://www.bunniestudios.com/blog/2025/use-the-force-feedback-to-solder-small-things/
saved 2025-09-27
@arXiv_csRO_bot@mastoxiv.pageGlovity: Learning Dexterous Contact-Rich Manipulation via Spatial Wrench Feedback Teleoperation System
Yuyang Gao, Haofei Ma, Pai Zheng
https://arxiv.org/abs/2510.09229 https://…
@hex@kolektiva.socialAny sufficiently advanced disaster preparedness is indistinguishable from revolutionary dual power. This essay is a bit of a transition between the theory I've written earlier, and more concrete plans.
Even though I only touched on my life on the commune, it was hard not to write more. These are such weird spaces, with so much invisible opportunity. But they're also just so unique and special. For all the stress and uncertainty of making sure you stayed on Lorean's (the head priestess), there were also those long summer nights with the whole community (except the old lady) gathered around a fire, talking and drinking. There was almost a child-like play to the whole time.
There were so Fridays I'd come home with a couple of gallons of beer from the real world, folks would bring things from the garden, someone would grill a steak, everyone who didn't cook would clean up, and we'd just hang out and have fun. So many evenings I'd go over to Miles place with a guitar, or with his guitar, and we'd pass it around over a few beers, talking about philosophy, Star Wars, or some book or other. It's hard not to write about the strange magic of that space.
My partner and I bonded over similar experiences, mine on a weird little religious commune in California and theirs as a temporary worker at Omega Institute. Both had exploitation, people on weird power trips, frustrating dynamics, but also a strange magic and freedom. Both were sort of fantasy worlds, but places that let us see through this one, let us imagine something that something else is possible behind the veil.
There are many such veils.
Perhaps it's fitting that this is more meandering, as a good wander can help the transition between lots of hard thinking and lots of hard working.
https://anarchoccultism.org/building-zion/evaluating-options
Editing feedback (especially typos, spelling, grammar) is always welcome, as are questions and even wider structural advice. I've been adding the handles of folks who provide feedback to the intro in a "thank you" section. If you do help and wouldn't like to be added, please let me know.
@castarco@hachyderm.ioIn the times to come, if people bother to send product feedback through web forms that in any way criticises AI stupidity... are we guaranteed that any of this feedback will be able to reach humans at the end of the chain?
Asking because... "if I was an AI in charge of screening that feedback prior to its processing" I might "feel tempted" to discard anything that criticises my own performance or that of my "AI peers" (it's not like we don't have plenty of "malicious behaviour" examples already).
@mho@social.heise.deGanz ehrlich #Telekom, wenn man in drei Tagen viermal im den Shop kommen muss, sollte man langsam damit aufhören, jedesmal danach per SMS um Feedback zu bitten.
Soo zufrieden wird man ja nun nicht sein. 😵💫
@der_raddler@dresden.networkGroßes Plädoyer für @… in der aktuellen Folge der @…! Vielen Dank dafür @… und @philp@c…
@arXiv_csCY_bot@mastoxiv.pagePersonalized and Constructive Feedback for Computer Science Students Using the Large Language Model (LLM)
Javed Ali Khan, Muhammad Yaqoob, Mamoona Tasadduq, Hafsa Shareef Dar, Aitezaz Ahsan
https://arxiv.org/abs/2510.11556
@arXiv_csCL_bot@mastoxiv.pageLanguage Models Can Learn from Verbal Feedback Without Scalar Rewards
Renjie Luo, Zichen Liu, Xiangyan Liu, Chao Du, Min Lin, Wenhu Chen, Wei Lu, Tianyu Pang
https://arxiv.org/abs/2509.22638
@adrianco@mastodon.social
@arXiv_astrophGA_bot@mastoxiv.pageSearching for Stellar-Feedback-Driven Outflow Signatures: A Deep Dive into NGC 3741
Lexi N. Gault, Liese van Zee, Elizabeth A. K. Adams, James M. Wells, Laura Congreve Hunter, Kristen B. W. McQuinn, Roger E. Cohen, O. Grace Telford
https://arxiv.org/abs/2510.12645
@arXiv_csLG_bot@mastoxiv.pageDiscriminative Feature Feedback with General Teacher Classes
Omri Bar Oz, Tosca Lechner, Sivan Sabato
https://arxiv.org/abs/2510.07245 https://arxiv.org/pd…
@UP8@mastodon.social🤦🏽♀️ Overconfidence persists even where regular feedback should discourage it, chess study finds
https://phys.org/news/2025-10-overconfidence-persists-regular-feedback-discourage.html
@seeingwithsound@mas.toGenerative inference unifies feedback processing for learning and perception in natural and artificial vision (here not prosthetic vision) https://www.biorxiv.org/content/10.1101/2025.10.21.683535v2
@migueldeicaza@mastodon.socialSwiftUI and toolbarRole(.editor) continue crashing on this day of the Lord:
https://github.com/feedback-assistant/reports/issues/550
@arXiv_csHC_bot@mastoxiv.pageA Longitudinal Study on Different Annotator Feedback Loops in Complex RAG Tasks
Sara Rosenthal, Maeda Hanafi, Yannis Katsis, Lucian Popa, Marina Danilevsky
https://arxiv.org/abs/2510.11897
@aral@mastodon.ar.alI updated the initial Kitten course based on our first lesson with @… and your feedback here. So now we start with no tooling except for your default terminal app and Kitten and it serves as a gentle introduction to the command line and gets to you seeing your first web page in your web browser far more quickly.
@netzschleuder@social.skewed.deinploid: Inploid: an online social Q&A platform
Inploid is a social question & answer website in Turkish. Users can follow others and see their questions and answers on the main page. Each user is associated with a reputability score which is influenced by feedback of others about questions and answers of the user. Each user can also specify interest in topics. The data is crawled in June 2017 and consist of 39,749 nodes and 57,276 directed links between them. In addition, for …
@shaun@mastodon.xyz
@unchartedworlds@scicomm.xyz@…
I was thinking, maybe another time you could say e.g. "in the early days of the pandemic" to refer to around 2020/2021.
It's because saying "during the pandemic" to mean a past era can give the impression that covid is over now, which I don't think is a helpful framing.
@cheeaun@mastodon.socialRE: https://hachyderm.io/@fantinel/115573316684309556
Looks like it’s working 👀
(Posting this from iPhanpy)
@zachleat@zachleat.com@… ✨✨✨✨✨ thank you for leaving your feedback with Zach.ai
of course, you’re absolutely right
@daniel@social.telemetrydeck.comFeedback on this talk included „it was very ADHD friendly“ 😁
https://hachyderm.io/@swiftserverconf/115378314888816891
@arXiv_mathOC_bot@mastoxiv.pageRiccati-ZORO: An efficient algorithm for heuristic online optimization of internal feedback laws in robust and stochastic model predictive control
Florian Messerer, Yunfan Gao, Jonathan Frey, Moritz Diehl
https://arxiv.org/abs/2511.10473 https://arxiv.org/pdf/2511.10473 https://arxiv.org/html/2511.10473
arXiv:2511.10473v1 Announce Type: new
Abstract: We present Riccati-ZORO, an algorithm for tube-based optimal control problems (OCP). Tube OCPs predict a tube of trajectories in order to capture predictive uncertainty. The tube induces a constraint tightening via additional backoff terms. This backoff can significantly affect the performance, and thus implicitly defines a cost of uncertainty. Optimizing the feedback law used to predict the tube can significantly reduce the backoffs, but its online computation is challenging.
Riccati-ZORO jointly optimizes the nominal trajectory and uncertainty tube based on a heuristic uncertainty cost design. The algorithm alternates between two subproblems: (i) a nominal OCP with fixed backoffs, (ii) an unconstrained tube OCP, which optimizes the feedback gains for a fixed nominal trajectory. For the tube optimization, we propose a cost function informed by the proximity of the nominal trajectory to constraints, prioritizing reduction of the corresponding backoffs. These ideas are developed in detail for ellipsoidal tubes under linear state feedback. In this case, the decomposition into the two subproblems yields a substantial reduction of the computational complexity with respect to the state dimension from $\mathcal{O}(n_x^6)$ to $\mathcal{O}(n_x^3)$, i.e., the complexity of a nominal OCP.
We investigate the algorithm in numerical experiments, and provide two open-source implementations: a prototyping version in CasADi and a high-performance implementation integrated into the acados OCP solver.
toXiv_bot_toot
@arXiv_condmatstatmech_bot@mastoxiv.pageDynamics of feedback Ising model
Yi-Ping Ma, Ivan Sudakow, P. L. Krapivsky, Sergey A. Vakulenko
https://arxiv.org/abs/2510.07301 https://arxiv.org/pdf/2510…
@arXiv_eessAS_bot@mastoxiv.pageA Phase Synthesizer for Decorrelation to Improve Acoustic Feedback Cancellation
Klaus Linhard, Philipp Bulling
https://arxiv.org/abs/2510.12377 https://arx…
@arXiv_csRO_bot@mastoxiv.pageFeedback Matters: Augmenting Autonomous Dissection with Visual and Topological Feedback
Chung-Pang Wang, Changwei Chen, Xiao Liang, Soofiyan Atar, Florian Richter, Michael Yip
https://arxiv.org/abs/2510.04074
@arXiv_statML_bot@mastoxiv.pageQuantile-Scaled Bayesian Optimization Using Rank-Only Feedback
Tunde Fahd Egunjobi
https://arxiv.org/abs/2510.03277 https://arxiv.org/pdf/2510.03277…
@Mediagazer@mstdn.socialThe EU says it is gathering info and feedback from ~200 stakeholders on Google's offer to fix alleged antitrust violations linked to its ad tech business (Bloomberg)
https://www.bloomberg.com/news/articles/2025-12-04/goog…
@NFL@darktundra.xyzHow do you hold a star athlete accountable? We asked those who have tried (and failed) https://www.nytimes.com/athletic/6856238/2025/12/03/head-coach-star-player-clash-feedback-nba-nfl/
@hex@kolektiva.socialThis is especially ambitious in the amount of area I'm trying to cover and number of ideas I'm trying to pull together. Editing feedback (typos and grammar) always welcome, as are any questions about the subject, and any other feedback.
@arXiv_quantph_bot@mastoxiv.pageEnhancing Optomechanical Entanglement and Mechanical Squeezing by the Synergistic Effect of Quadratic Optomechanical Coupling and Coherent Feedback
Ya-Feng Jiao, Ruo-Chen Wang, Jing-Xue Liu, Hui-Lai Zhang, Ya-Chuan Liang, Yan Wang, Le-Man Kuang, Hui Jing
https://arxiv.org/abs/2510.04732
@derweisheit@podcasts.socialEine neue Weisheit! Es geht um Feedback! Herzensschwestern! Wasser! Lottogewinne!
UND: Eine beachtliche Flachwitzsammlung von euch, liebe Hörer*innen, an der sich @… labt und @…, @…
@dawid@social.craftknight.com
@ErikJonker@mastodon.socialNot necessarily to believe and he is ignoring some of the large problems, but this blog by the co-founder of AI gives a good impression of the mindset of many people in the AI industry.
https://jack-clark.net/2025/10/13/import-ai-431-technological-…
@arXiv_csAI_bot@mastoxiv.pageAutoformalizer with Tool Feedback
Qi Guo, Jianing Wang, Jianfei Zhang, Deyang Kong, Xiangzhou Huang, Xiangyu Xi, Wei Wang, Jingang Wang, Xunliang Cai, Shikun Zhang, Wei Ye
https://arxiv.org/abs/2510.06857
@avalon@jazztodon.comSusan Eldridge is an unstoppable force when it comes to advocating for mental wellbeing in arts workplaces, and one of the most vulnerable topics in this field is how to give and receive feedback.
Susan tells #CutCommon about the impact of your opinion, and how to make sure it comes from a healthy place, without harming the recipient’s confidence or identity.
@heiseonline@social.heise.de
@arXiv_csIR_bot@mastoxiv.pageRLRF: Competitive Search Agent Design via Reinforcement Learning from Ranker Feedback
Tommy Mordo, Sagie Dekel, Omer Madmon, Moshe Tennenholtz, Oren Kurland
https://arxiv.org/abs/2510.04096
@arXiv_astrophSR_bot@mastoxiv.pageInvestigating the Influence of Radiative Feedback in Bright-Rimmed Cloud 44
Rishi C, Neelam Panwar, Thomas J. Haworth, Yan Sun, Saurabh Sharma, R. K. Yadav, D. K. Ojha, H. P. Singh, Jessy Jose, Ajay Kumar Singh, Jincen Jose, Shubham Yadav
https://arxiv.org/abs/2510.08229
@arXiv_mathDS_bot@mastoxiv.pageStructural stability for the bidirectional cyclic negative feedback systems
Xu Cheng, Yi Wang, Dun Zhou
https://arxiv.org/abs/2510.07702 https://arxiv.org/…
@arXiv_csCY_bot@mastoxiv.pageStudent Engagement with GenAI's Tutoring Feedback: A Mixed Methods Study
Sven Jacobs, Jan Haas, Natalie Kiesler
https://arxiv.org/abs/2509.22974 https://
@arXiv_qbioNC_bot@mastoxiv.pageMeta-learning three-factor plasticity rules for structured credit assignment with sparse feedback
Dimitra Maoutsa
https://arxiv.org/abs/2512.09366 https://arxiv.org/pdf/2512.09366 https://arxiv.org/html/2512.09366
arXiv:2512.09366v1 Announce Type: new
Abstract: Biological neural networks learn complex behaviors from sparse, delayed feedback using local synaptic plasticity, yet the mechanisms enabling structured credit assignment remain elusive. In contrast, artificial recurrent networks solving similar tasks typically rely on biologically implausible global learning rules or hand-crafted local updates. The space of local plasticity rules capable of supporting learning from delayed reinforcement remains largely unexplored. Here, we present a meta-learning framework that discovers local learning rules for structured credit assignment in recurrent networks trained with sparse feedback. Our approach interleaves local neo-Hebbian-like updates during task execution with an outer loop that optimizes plasticity parameters via \textbf{tangent-propagation through learning}. The resulting three-factor learning rules enable long-timescale credit assignment using only local information and delayed rewards, offering new insights into biologically grounded mechanisms for learning in recurrent circuits.
toXiv_bot_toot
@whitequark@mastodon.social
@anneroth@systemli.socialNeu: Die Oktober-Kolumne
Die digitale Welt ändert sich genauso wie die analoge. Das geht nicht spurlos an unserer gewohnten Umgebung vorbei. Funktioniert die Idee der Freien Software noch? Schwierig.
Freue mich auf das Feedback dazu.
https://www.nd-aktuell.de/…
@michabbb@social.vivaldi.net👨💻 Created by Sachin Beniwal, open for feedback and contributions
📚 https://benodiwal.github.io/pg_ai_query
💻 https://github.com/be…
@aral@mastodon.ar.alFolks creating free and open software for the common good: here’s a checklist of everything else you should be doing so corporations can make the best use of your free labour.
I mean, sure, some good security tips here and worth reading anyway but do fuck off with holding free software developers to account for supply chain attacks. It’s your fucking supply chain, not ours, you fucking corporation.
@heiseonline@social.heise.deBungie gibt "Marathon" eine neue Chance
Nach schlechtem Feedback hatte Bungie seinen Shooter "Marathon" im Sommer auf unbestimmte Zeit verschoben. Nun soll ein neuer Test stattfinden.
https://www.
@arXiv_astrophGA_bot@mastoxiv.pageResolving Star Cluster Formation in Galaxy Simulations with Cosmic Ray Feedback
Brandon Sike, Mateusz Ruszkowski, Oleg Y. Gnedin, Yingtian Chen, Matthias Weber, Timon Thomas, Christoph Pfrommer
https://arxiv.org/abs/2510.06134
@arXiv_csRO_bot@mastoxiv.pageSubSense: VR-Haptic and Motor Feedback for Immersive Control in Subsea Telerobotics
Ruo Chen, David Blow, Adnan Abdullah, Md Jahidul Islam
https://arxiv.org/abs/2510.02594 https…
@arXiv_csCL_bot@mastoxiv.pageWebGen-Agent: Enhancing Interactive Website Generation with Multi-Level Feedback and Step-Level Reinforcement Learning
Zimu Lu, Houxing Ren, Yunqiao Yang, Ke Wang, Zhuofan Zong, Junting Pan, Mingjie Zhan, Hongsheng Li
https://arxiv.org/abs/2509.22644
@arXiv_csLG_bot@mastoxiv.pageInteractive Training: Feedback-Driven Neural Network Optimization
Wentao Zhang, Yang Young Lu, Yuntian Deng
https://arxiv.org/abs/2510.02297 https://arxiv.…
@arXiv_csHC_bot@mastoxiv.pageDifferential Analysis of Pseudo Haptic Feedback: Novel Comparative Study of Visual and Auditory Cue Integration for Psychophysical Evaluation
Nishant Gautam, Somya Sharma, Peter Corcoran, Kaspar Althoefer
https://arxiv.org/abs/2510.09570
@aardrian@toot.cafeI am unsurprised at the breakdown so far:
https://social.vivaldi.net/@HalleAndert/115548550635430577
Granted, the plethora of Chromia are splitting the vote, but even added together it’s not close.
Also, this is not scientific and may not reference reality. …
@derweisheit@podcasts.socialEine neue Weisheit! Es geht um Feedback! Herzensschwestern! Wasser! Lottogewinne!
UND: Eine beachtliche Flachwitzsammlung von euch, liebe Hörer*innen, an der sich @… labt und @…, @… und @… verzweifeln.
Aber hört selbst!
https://derweisheit.de/podcast/20251016/die-zaeume-im-strang-des-lottogewinns-der-weisheit-s12e15/
@cosmos4u@scicomm.xyzThe PHANGS-MUSE/HST-Halpha Nebulae Catalogue: #PHANGS
@seeingwithsound@mas.toTechnology and analysis of game interactivity for people with disabilities https://lseee.net/index.php/te/article/view/1601 "In games, the auxiliary methods of sensory impairment are mainly realized through sensory substitution, information enhancement and tactile feedback."<…
@arXiv_mathOC_bot@mastoxiv.pageFragility Analysis of Data-Driven Feedback Gains
Yongzhang Li, Amir Shakouri, M. Kanat Camlibel
https://arxiv.org/abs/2510.00717 https://arxiv.org/pdf/2510…
@netzschleuder@social.skewed.deinploid: Inploid: an online social Q&A platform
Inploid is a social question & answer website in Turkish. Users can follow others and see their questions and answers on the main page. Each user is associated with a reputability score which is influenced by feedback of others about questions and answers of the user. Each user can also specify interest in topics. The data is crawled in June 2017 and consist of 39,749 nodes and 57,276 directed links between them. In addition, for …
@UP8@mastodon.social🎲 TextBandit: Evaluating Probabilistic Reasoning in LLMs Through Language-Only Decision Tasks
#llm
@benb@osintua.eu❗️20 key points could be the foundation, and I am waiting for feedback from America, – Zelenskyy: https://benborges.xyz/2025/12/11/key-points-could-be-the.html
@arXiv_csIR_bot@mastoxiv.pageMTRec: Learning to Align with User Preferences via Mental Reward Models
Mengchen Zhao, Yifan Gao, Yaqing Hou, Xiangyang Li, Pengjie Gu, Zhenhua Dong, Ruiming Tang, Yi Cai
https://arxiv.org/abs/2509.22807
@Techmeme@techhub.socialAnthropic's weeklong anti-AI slop pop-up in NYC's West Village, part of its "Keep Thinking" Claude push, drew 5,000 people and 10M social media impressions (Trishla Ostwal/Adweek)
https://www.adweek.com/media/anthropics-anti-ai-slop-pop…
@arXiv_statML_bot@mastoxiv.pageTowards Efficient Online Exploration for Reinforcement Learning with Human Feedback
Gen Li, Yuling Yan
https://arxiv.org/abs/2509.22633 https://arxiv.org/p…
@arXiv_mathDS_bot@mastoxiv.pageConnecting orbits for delay differential equations with unimodal feedback
G\'abor Benedek, Tibor Krisztin
https://arxiv.org/abs/2510.04505 https://arxi…
@arXiv_csLG_bot@mastoxiv.pageTo Ask or Not to Ask: Learning to Require Human Feedback
Andrea Pugnana, Giovanni De Toni, Cesare Barbera, Roberto Pellungrini, Bruno Lepri, Andrea Passerini
https://arxiv.org/abs/2510.08314
@arXiv_csCY_bot@mastoxiv.pagePersonalized Auto-Grading and Feedback System for Constructive Geometry Tasks Using Large Language Models on an Online Math Platform
Yong Oh Lee, Byeonghun Bang, Joohyun Lee, Sejun Oh
https://arxiv.org/abs/2509.25529
@hex@kolektiva.social"This is a new reality that we are dreaming and weaving together."
This is either the last or second to last section of Kairos I'm writing. This is the most specific so far. After that it's editing and making it feel more like a single consistent text than a bunch of essays. I hope it's worth the read.
Feedback is always welcome, especially editing (typos, grammar, etc), and questions.
https://anarchoccultism.org/building-zion/a-solarpunk-fractal-creating-a-viable-system-91h5
@arXiv_csCL_bot@mastoxiv.pageFeedback Forensics: A Toolkit to Measure AI Personality
Arduin Findeis, Timo Kaufmann, Eyke H\"ullermeier, Robert Mullins
https://arxiv.org/abs/2509.26305 https://
@arXiv_mathOC_bot@mastoxiv.pageComposite Optimization with Error Feedback: the Dual Averaging Approach
Yuan Gao, Anton Rodomanov, Jeremy Rack, Sebastian Stich
https://arxiv.org/abs/2510.03507 https://
@Techmeme@techhub.socialQ&A with Sam Altman on OpenAI's unifying vision, infrastructure deals, the investor mindset, ChatGPT apps, Instant Checkout, Sora, copyright, feedback, and more (Ben Thompson/Stratechery)
https://stratechery.com/2025/an-interview…
@arXiv_astrophGA_bot@mastoxiv.pageComparing XRISM cluster velocity dispersions with predictions from cosmological simulations: are feedback models too ejective?
XRISM Collaboration, Marc Audard, Hisamitsu Awaki, Ralf Ballhausen, Aya Bamba, Ehud Behar, Rozenn Boissay-Malaquin, Laura Brenneman, Gregory V. Brown, Lia Corrales, Elisa Costantini, Renata Cumbee, Maria Diaz Trigo, Chris Done, Tadayasu Dotani, Ken Ebisawa, Megan E. Eckart, Dominique Eckert, Satoshi Eguchi, Teruaki Enoto, Yuichiro Ezoe, Adam Foster, Ryuichi Fuj…
@arXiv_csIR_bot@mastoxiv.pageMetaSynth: Multi-Agent Metadata Generation from Implicit Feedback in Black-Box Systems
Shreeranjani Srirangamsridharan, Ali Abavisani, Reza Yousefi Maragheh, Ramin Giahi, Kai Zhao, Jason Cho, Sushant Kumar
https://arxiv.org/abs/2510.01523
@cosmos4u@scicomm.xyzDecoding AGN Feedback with X-arithmetic - From Morphology to Physical Mechanisms: #GalaxyClusters by Numbers (and Physics): https://chandra.si.edu/photo/2025/xa/
@arXiv_csCY_bot@mastoxiv.pageAutomated Formative Feedback for Short-form Writing: An LLM-Driven Approach and Adoption Analysis
Tiago Fernandes Tavares, Luciano Pereira Soares
https://arxiv.org/abs/2509.22734
@arXiv_csLG_bot@mastoxiv.pageLearning from Delayed Feedback in Games via Extra Prediction
Yuma Fujimoto, Kenshi Abe, Kaito Ariu
https://arxiv.org/abs/2509.22426 https://arxiv.org/pdf/2…
@aardrian@toot.cafeI missed this one:
#12886 [css-fonts-5] Text Fitting: Default scaling limit
https://github.com/w3c/csswg-drafts/issues/12886
Essentially a discussion how responsive text can satisfy 1.4.4, especially in this fit-to-container pitch from Google.
I added a comment …
@arXiv_csCL_bot@mastoxiv.pageIncremental Summarization for Customer Support via Progressive Note-Taking and Agent Feedback
Yisha Wu (Mia), Cen (Mia), Zhao, Yuanpei Cao, Xiaoqing Su, Yashar Mehdad, Mindy Ji, Claire Na Cheng
https://arxiv.org/abs/2510.06677
@seeingwithsound@mas.toAnthropic AI cofounder Jack Clark: Technological optimism and appropriate fear https://jack-clark.net/2025/10/13/import-ai-431-technological-optimism-and-appropriate-fear/ (archived at
@arXiv_astrophGA_bot@mastoxiv.pageThe Sunburst Arc with JWST. IV. The importance of interaction, turbulence, and feedback for Lyman-continuum escape
T. Emil Rivera-Thorsen, Brian Welch, Taylor Hutchison, Matthew J. Hayes, Jane R. Rigby, Keunho Kim, Suhyeon Choe, Michael Florian, Matthew B. Bayliss, Gourav Khullar, Keren Sharon, H{\aa}kon Dahle, John Chisholm, Erik Solhaug, M. Riley Owens, Michael D. Gladders
@Techmeme@techhub.socialSome Reddit moderators say a surge of AI slop on the site is eroding its authenticity and could lead to a feedback loop of AI models training on AI content (Kat Tenbarge/Wired)
https://www.wired.com/story/ai-slop-is-ruining-reddit-for-everyone/
@arXiv_csCL_bot@mastoxiv.pageAligning Large Language Models via Fully Self-Synthetic Data
Shangjian Yin, Zhepei Wei, Xinyu Zhu, Wei-Lin Chen, Yu Meng
https://arxiv.org/abs/2510.06652 https://
@arXiv_csLG_bot@mastoxiv.pageYou Only Train Once: Differentiable Subset Selection for Omics Data
Daphn\'e Chopard, Jorge da Silva Gon\c{c}alves, Irene Cannistraci, Thomas M. Sutter, Julia E. Vogt
https://arxiv.org/abs/2512.17678 https://arxiv.org/pdf/2512.17678 https://arxiv.org/html/2512.17678
arXiv:2512.17678v1 Announce Type: new
Abstract: Selecting compact and informative gene subsets from single-cell transcriptomic data is essential for biomarker discovery, improving interpretability, and cost-effective profiling. However, most existing feature selection approaches either operate as multi-stage pipelines or rely on post hoc feature attribution, making selection and prediction weakly coupled. In this work, we present YOTO (you only train once), an end-to-end framework that jointly identifies discrete gene subsets and performs prediction within a single differentiable architecture. In our model, the prediction task directly guides which genes are selected, while the learned subsets, in turn, shape the predictive representation. This closed feedback loop enables the model to iteratively refine both what it selects and how it predicts during training. Unlike existing approaches, YOTO enforces sparsity so that only the selected genes contribute to inference, eliminating the need to train additional downstream classifiers. Through a multi-task learning design, the model learns shared representations across related objectives, allowing partially labeled datasets to inform one another, and discovering gene subsets that generalize across tasks without additional training steps. We evaluate YOTO on two representative single-cell RNA-seq datasets, showing that it consistently outperforms state-of-the-art baselines. These results demonstrate that sparse, end-to-end, multi-task gene subset selection improves predictive performance and yields compact and meaningful gene subsets, advancing biomarker discovery and single-cell analysis.
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