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@arXiv_csLG_bot@mastoxiv.page
2026-02-25 16:08:18

Replaced article(s) found for cs.LG. arxiv.org/list/cs.LG/new
[5/6]:
- Watermarking Degrades Alignment in Language Models: Analysis and Mitigation
Apurv Verma, NhatHai Phan, Shubhendu Trivedi
arxiv.org/abs/2506.04462 mastoxiv.page/@arXiv_csCL_bot/
- Sensory-Motor Control with Large Language Models via Iterative Policy Refinement
J\^onata Tyska Carvalho, Stefano Nolfi
arxiv.org/abs/2506.04867 mastoxiv.page/@arXiv_csAI_bot/
- ICE-ID: A Novel Historical Census Dataset for Longitudinal Identity Resolution
de Carvalho, Popov, Kaatee, Correia, Th\'orisson, Li, Bj\"ornsson, Sigur{\dh}arson, Dibangoye
arxiv.org/abs/2506.13792 mastoxiv.page/@arXiv_csAI_bot/
- Feedback-driven recurrent quantum neural network universality
Lukas Gonon, Rodrigo Mart\'inez-Pe\~na, Juan-Pablo Ortega
arxiv.org/abs/2506.16332 mastoxiv.page/@arXiv_quantph_b
- Programming by Backprop: An Instruction is Worth 100 Examples When Finetuning LLMs
Cook, Sapora, Ahmadian, Khan, Rocktaschel, Foerster, Ruis
arxiv.org/abs/2506.18777 mastoxiv.page/@arXiv_csAI_bot/
- Stochastic Quantum Spiking Neural Networks with Quantum Memory and Local Learning
Jiechen Chen, Bipin Rajendran, Osvaldo Simeone
arxiv.org/abs/2506.21324 mastoxiv.page/@arXiv_csNE_bot/
- Enjoying Non-linearity in Multinomial Logistic Bandits: A Minimax-Optimal Algorithm
Pierre Boudart (SIERRA), Pierre Gaillard (Thoth), Alessandro Rudi (PSL, DI-ENS, Inria)
arxiv.org/abs/2507.05306 mastoxiv.page/@arXiv_statML_bo
- Characterizing State Space Model and Hybrid Language Model Performance with Long Context
Saptarshi Mitra, Rachid Karami, Haocheng Xu, Sitao Huang, Hyoukjun Kwon
arxiv.org/abs/2507.12442 mastoxiv.page/@arXiv_csAR_bot/
- Is Exchangeability better than I.I.D to handle Data Distribution Shifts while Pooling Data for Da...
Ayush Roy, Samin Enam, Jun Xia, Won Hwa Kim, Vishnu Suresh Lokhande
arxiv.org/abs/2507.19575 mastoxiv.page/@arXiv_csCV_bot/
- TASER: Table Agents for Schema-guided Extraction and Recommendation
Nicole Cho, Kirsty Fielding, William Watson, Sumitra Ganesh, Manuela Veloso
arxiv.org/abs/2508.13404 mastoxiv.page/@arXiv_csAI_bot/
- Morphology-Aware Peptide Discovery via Masked Conditional Generative Modeling
Nuno Costa, Julija Zavadlav
arxiv.org/abs/2509.02060 mastoxiv.page/@arXiv_qbioBM_bo
- PCPO: Proportionate Credit Policy Optimization for Aligning Image Generation Models
Jeongjae Lee, Jong Chul Ye
arxiv.org/abs/2509.25774 mastoxiv.page/@arXiv_csCV_bot/
- Multi-hop Deep Joint Source-Channel Coding with Deep Hash Distillation for Semantically Aligned I...
Didrik Bergstr\"om, Deniz G\"und\"uz, Onur G\"unl\"u
arxiv.org/abs/2510.06868 mastoxiv.page/@arXiv_csIT_bot/
- MoMaGen: Generating Demonstrations under Soft and Hard Constraints for Multi-Step Bimanual Mobile...
Chengshu Li, et al.
arxiv.org/abs/2510.18316 mastoxiv.page/@arXiv_csRO_bot/
- A Spectral Framework for Graph Neural Operators: Convergence Guarantees and Tradeoffs
Roxanne Holden, Luana Ruiz
arxiv.org/abs/2510.20954 mastoxiv.page/@arXiv_statML_bo
- Breaking Agent Backbones: Evaluating the Security of Backbone LLMs in AI Agents
Bazinska, Mathys, Casucci, Rojas-Carulla, Davies, Souly, Pfister
arxiv.org/abs/2510.22620 mastoxiv.page/@arXiv_csCR_bot/
- Uncertainty Calibration of Multi-Label Bird Sound Classifiers
Raphael Schwinger, Ben McEwen, Vincent S. Kather, Ren\'e Heinrich, Lukas Rauch, Sven Tomforde
arxiv.org/abs/2511.08261 mastoxiv.page/@arXiv_csSD_bot/
- Two-dimensional RMSD projections for reaction path visualization and validation
Rohit Goswami (Institute IMX and Lab-COSMO, \'Ecole polytechnique f\'ed\'erale de Lausanne)
arxiv.org/abs/2512.07329 mastoxiv.page/@arXiv_physicsch
- Distribution-informed Online Conformal Prediction
Dongjian Hu, Junxi Wu, Shu-Tao Xia, Changliang Zou
arxiv.org/abs/2512.07770 mastoxiv.page/@arXiv_statML_bo
- Coupling Experts and Routers in Mixture-of-Experts via an Auxiliary Loss
Ang Lv, Jin Ma, Yiyuan Ma, Siyuan Qiao
arxiv.org/abs/2512.23447 mastoxiv.page/@arXiv_csCL_bot/
toXiv_bot_toot

@hex@kolektiva.social
2026-02-28 10:20:01

As salty as I am about it, there's also another way to think about this. For anyone who still has connections to folks on the right (which is perhaps unlikely for anyone on this server, I digress), the cult that has consumed them thrives on isolation and grievance.
The words "you were right" have the potential to cut through the programming and open up an opportunity for reconnection. The modern conspiratorial cult of the Right has been built partially around people who were told they were wrong or were crazy. In the vast majority of cases, they were wrong and even when they were right they completely misunderstood why, but we'll skip that for now. Liberals making fun of them (even the times when they definitely earned it) has pushed them further and further into their ideological hole.
The thing about those words, "you were right," in this context is that the way they offer reconnection also requires them to take one little step of betraying their ideology to accept them. So they must choose between maintaining allegiance to a pedophile or finally getting to feel superior after years of living in an illusion of persecution.
Under the ideology of the Right, admitting one is wrong is a weakness. It is admitting defeat. They have to "own the libs" by saying things, things that they know aren't true, in order to feel dominant. But these things are often so absurd that they end up being made fun of, feeling even more weak and pathetic, reinforcing their fear and alienation.
Offering what they're looking for can offer a way out, but only if they're willing to start to recognize the thing they've supported for what it is.
And they were right about some things. They were right that Bill Gates was a terrible person. I've had plenty of liberals defend him based on his philanthropy washing, but he's awful and always has been. The Epstein links make that blatant. They intuitively recognized him and didn't trust him, even if they were wildly off base about *how and why* he shouldn't be trusted... Even if their correct mistrust was leveraged into one of the most destructive conspiracy theories ever (vaccine denial and COVID vaccine avoidance).
They were right about Bill Clinton. He was always shady as fuck. Sure, the people who attacked him at the time turned out to be even more shady but that's not the point right now. He was connected to Epstein and that was always creepy as fuck.
And the Epstein thing was an open secret that liberals ignored for a long time. It was seen as some weird thing that right wing nutjobs believed about the Clintons. But it was true. Not all of it, and there has always been an antisemitic element to the right wing interpretation or Epstein stuff, but his whole pedophile conspiracy was always kind of real.
The whole "Illuminati"/deep state thing is a vast oversimplification, an attempt to make comprehensible an incredibly complex set of interlocking and emergent behaviors. But Epstein did very much want to remake the world, to create a new world order, and he absolutely played a part in it.
The Right wing nutjobs talked about global authoritarianism, Blackhawks flying over American cities, masked men with guns disarming and executing legal gun owners in the streets. That's all happening right now.
The "FEMA concentration camps" are not actually that far off. ICE and FEMA are sister agencies, both under DHS. I'd be more than happy to call that one "close enough" in order to hear some MAGA admit that ICE is, in fact, building concentration camps.
There was always a huge millennialist element to these things. They tended to be connected to "the antichrist." It was absurd, especially for me as someone who no longer identifies as a Christian. But I'll even acquiess that to a degree. The "the number of the Beast" is 666. That's just the sum of the Hebrew spelling of "Nero." Revelations focuses a lot on Nero coming back to life after his death. His death that involved a head wound, thus the line from Revelation 13:3:
> And I saw one of his heads as if it had been mortally wounded, and his deadly wound was healed. And all the world marveled and followed the beast.
The parallels between Trump and Nero are easy to draw, and Trump's ear wound feels pretty on-the-nose for this. I don't believe in "prophecy" in this way. I think that there are patterns, and useful patterns can become encoded in beleif systems. But I will, again, happily call this one "close enough" for anyone on that side willing to also acknowledge it. I'm happy to meet on that common ground, because anyone who accepts it must recognize that their duty is to fight against it.
A lot of these correct nuggets are embedded in a framework of religious extremism and antisemitism. The vast majority of the beliefs holding these together are wildly wrong and incredibly toxic. But by giving some room to feel validated, listened to, understood, can give some room to admit things that were wrong.
Cult de-programming starts with an opening. People have to talk through their own thoughts, hear their own inconsistencies. Guiding questions can help them untangle these things for themselves. And it all starts by having enough room to feel safe, to not feel cornered, to not feel stupid. Admitting mistakes means being vulnerable, and the MAGA cult is built on fear. It's built on exploiting vulnerability and locking it away.
De-programming takes a long time. It's not easy. It takes patience. But every person who comes out does so with a powerful perspective, a deep understanding, that can be turned back against it. The best people at getting people out of cults are former members. Some of the most dedicated antifa are former fascists who understood their mistakes and dedicate their lives to fixing them.

@arXiv_csLG_bot@mastoxiv.page
2025-12-22 11:50:19

Crosslisted article(s) found for cs.LG. arxiv.org/list/cs.LG/new
[1/3]:
- Optimizing Text Search: A Novel Pattern Matching Algorithm Based on Ukkonen's Approach
Xinyu Guan, Shaohua Zhang
arxiv.org/abs/2512.16927 mastoxiv.page/@arXiv_csDS_bot/
- SpIDER: Spatially Informed Dense Embedding Retrieval for Software Issue Localization
Shravan Chaudhari, Rahul Thomas Jacob, Mononito Goswami, Jiajun Cao, Shihab Rashid, Christian Bock
arxiv.org/abs/2512.16956 mastoxiv.page/@arXiv_csSE_bot/
- MemoryGraft: Persistent Compromise of LLM Agents via Poisoned Experience Retrieval
Saksham Sahai Srivastava, Haoyu He
arxiv.org/abs/2512.16962 mastoxiv.page/@arXiv_csCR_bot/
- Colormap-Enhanced Vision Transformers for MRI-Based Multiclass (4-Class) Alzheimer's Disease Clas...
Faisal Ahmed
arxiv.org/abs/2512.16964 mastoxiv.page/@arXiv_eessIV_bo
- Probing Scientific General Intelligence of LLMs with Scientist-Aligned Workflows
Wanghan Xu, et al.
arxiv.org/abs/2512.16969 mastoxiv.page/@arXiv_csAI_bot/
- PAACE: A Plan-Aware Automated Agent Context Engineering Framework
Kamer Ali Yuksel
arxiv.org/abs/2512.16970 mastoxiv.page/@arXiv_csAI_bot/
- A Women's Health Benchmark for Large Language Models
Elisabeth Gruber, et al.
arxiv.org/abs/2512.17028 mastoxiv.page/@arXiv_csCL_bot/
- Perturb Your Data: Paraphrase-Guided Training Data Watermarking
Pranav Shetty, Mirazul Haque, Petr Babkin, Zhiqiang Ma, Xiaomo Liu, Manuela Veloso
arxiv.org/abs/2512.17075 mastoxiv.page/@arXiv_csCL_bot/
- Disentangled representations via score-based variational autoencoders
Benjamin S. H. Lyo, Eero P. Simoncelli, Cristina Savin
arxiv.org/abs/2512.17127 mastoxiv.page/@arXiv_statML_bo
- Biosecurity-Aware AI: Agentic Risk Auditing of Soft Prompt Attacks on ESM-Based Variant Predictors
Huixin Zhan
arxiv.org/abs/2512.17146 mastoxiv.page/@arXiv_csCR_bot/
- Application of machine learning to predict food processing level using Open Food Facts
Arora, Chauhan, Rana, Aditya, Bhagat, Kumar, Kumar, Semar, Singh, Bagler
arxiv.org/abs/2512.17169 mastoxiv.page/@arXiv_qbioBM_bo
- Systemic Risk Radar: A Multi-Layer Graph Framework for Early Market Crash Warning
Sandeep Neela
arxiv.org/abs/2512.17185 mastoxiv.page/@arXiv_qfinRM_bo
- Do Foundational Audio Encoders Understand Music Structure?
Keisuke Toyama, Zhi Zhong, Akira Takahashi, Shusuke Takahashi, Yuki Mitsufuji
arxiv.org/abs/2512.17209 mastoxiv.page/@arXiv_csSD_bot/
- CheXPO-v2: Preference Optimization for Chest X-ray VLMs with Knowledge Graph Consistency
Xiao Liang, Yuxuan An, Di Wang, Jiawei Hu, Zhicheng Jiao, Bin Jing, Quan Wang
arxiv.org/abs/2512.17213 mastoxiv.page/@arXiv_csCV_bot/
- Machine Learning Assisted Parameter Tuning on Wavelet Transform Amorphous Radial Distribution Fun...
Deriyan Senjaya, Stephen Ekaputra Limantoro
arxiv.org/abs/2512.17245 mastoxiv.page/@arXiv_condmatmt
- AlignDP: Hybrid Differential Privacy with Rarity-Aware Protection for LLMs
Madhava Gaikwad
arxiv.org/abs/2512.17251 mastoxiv.page/@arXiv_csCR_bot/
- Practical Framework for Privacy-Preserving and Byzantine-robust Federated Learning
Baolei Zhang, Minghong Fang, Zhuqing Liu, Biao Yi, Peizhao Zhou, Yuan Wang, Tong Li, Zheli Liu
arxiv.org/abs/2512.17254 mastoxiv.page/@arXiv_csCR_bot/
- Verifiability-First Agents: Provable Observability and Lightweight Audit Agents for Controlling A...
Abhivansh Gupta
arxiv.org/abs/2512.17259 mastoxiv.page/@arXiv_csMA_bot/
- Warmer for Less: A Cost-Efficient Strategy for Cold-Start Recommendations at Pinterest
Saeed Ebrahimi, Weijie Jiang, Jaewon Yang, Olafur Gudmundsson, Yucheng Tu, Huizhong Duan
arxiv.org/abs/2512.17277 mastoxiv.page/@arXiv_csIR_bot/
- LibriVAD: A Scalable Open Dataset with Deep Learning Benchmarks for Voice Activity Detection
Ioannis Stylianou, Achintya kr. Sarkar, Nauman Dawalatabad, James Glass, Zheng-Hua Tan
arxiv.org/abs/2512.17281 mastoxiv.page/@arXiv_csSD_bot/
- Penalized Fair Regression for Multiple Groups in Chronic Kidney Disease
Carter H. Nakamoto, Lucia Lushi Chen, Agata Foryciarz, Sherri Rose
arxiv.org/abs/2512.17340 mastoxiv.page/@arXiv_statME_bo
toXiv_bot_toot

@arXiv_csLG_bot@mastoxiv.page
2025-12-22 11:50:31

Crosslisted article(s) found for cs.LG. arxiv.org/list/cs.LG/new
[2/3]:
- Sharp Structure-Agnostic Lower Bounds for General Functional Estimation
Jikai Jin, Vasilis Syrgkanis
arxiv.org/abs/2512.17341 mastoxiv.page/@arXiv_statML_bo
- Timely Information Updating for Mobile Devices Without and With ML Advice
Yu-Pin Hsu, Yi-Hsuan Tseng
arxiv.org/abs/2512.17381 mastoxiv.page/@arXiv_csNI_bot/
- SWE-Bench : A Framework for the Scalable Generation of Software Engineering Benchmarks from Open...
Wang, Ramalho, Celestino, Pham, Liu, Sinha, Portillo, Osunwa, Maduekwe
arxiv.org/abs/2512.17419 mastoxiv.page/@arXiv_csSE_bot/
- Perfect reconstruction of sparse signals using nonconvexity control and one-step RSB message passing
Xiaosi Gu, Ayaka Sakata, Tomoyuki Obuchi
arxiv.org/abs/2512.17426 mastoxiv.page/@arXiv_statML_bo
- MULTIAQUA: A multimodal maritime dataset and robust training strategies for multimodal semantic s...
Jon Muhovi\v{c}, Janez Per\v{s}
arxiv.org/abs/2512.17450 mastoxiv.page/@arXiv_csCV_bot/
- When Data Quality Issues Collide: A Large-Scale Empirical Study of Co-Occurring Data Quality Issu...
Emmanuel Charleson Dapaah, Jens Grabowski
arxiv.org/abs/2512.17460 mastoxiv.page/@arXiv_csSE_bot/
- Behavioural Effects of Agentic Messaging: A Case Study on a Financial Service Application
Olivier Jeunen, Schaun Wheeler
arxiv.org/abs/2512.17462 mastoxiv.page/@arXiv_csIR_bot/
- Linear Attention for Joint Power Optimization and User-Centric Clustering in Cell-Free Networks
Irched Chafaa, Giacomo Bacci, Luca Sanguinetti
arxiv.org/abs/2512.17466 mastoxiv.page/@arXiv_eessSY_bo
- Translating the Rashomon Effect to Sequential Decision-Making Tasks
Dennis Gross, J{\o}rn Eirik Betten, Helge Spieker
arxiv.org/abs/2512.17470 mastoxiv.page/@arXiv_csAI_bot/
- Alternating Direction Method of Multipliers for Nonlinear Matrix Decompositions
Atharva Awari, Nicolas Gillis, Arnaud Vandaele
arxiv.org/abs/2512.17473 mastoxiv.page/@arXiv_eessSP_bo
- TwinSegNet: A Digital Twin-Enabled Federated Learning Framework for Brain Tumor Analysis
Almustapha A. Wakili, Adamu Hussaini, Abubakar A. Musa, Woosub Jung, Wei Yu
arxiv.org/abs/2512.17488 mastoxiv.page/@arXiv_csCV_bot/
- Resource-efficient medical image classification for edge devices
Mahsa Lavaei, Zahra Abadi, Salar Beigzad, Alireza Maleki
arxiv.org/abs/2512.17515 mastoxiv.page/@arXiv_eessIV_bo
- PathBench-MIL: A Comprehensive AutoML and Benchmarking Framework for Multiple Instance Learning i...
Brussee, Valkema, Weijer, Doeleman, Schrader, Kers
arxiv.org/abs/2512.17517 mastoxiv.page/@arXiv_csCV_bot/
- HydroGym: A Reinforcement Learning Platform for Fluid Dynamics
Christian Lagemann, et al.
arxiv.org/abs/2512.17534 mastoxiv.page/@arXiv_physicsfl
- When De-noising Hurts: A Systematic Study of Speech Enhancement Effects on Modern Medical ASR Sys...
Chondhekar, Murukuri, Vasani, Goyal, Badami, Rana, SN, Pandia, Katiyar, Jagadeesh, Gulati
arxiv.org/abs/2512.17562 mastoxiv.page/@arXiv_csSD_bot/
- Enabling Disaggregated Multi-Stage MLLM Inference via GPU-Internal Scheduling and Resource Sharing
Lingxiao Zhao, Haoran Zhou, Yuezhi Che, Dazhao Cheng
arxiv.org/abs/2512.17574 mastoxiv.page/@arXiv_csDC_bot/
- SkinGenBench: Generative Model and Preprocessing Effects for Synthetic Dermoscopic Augmentation i...
N. A. Adarsh Pritam, Jeba Shiney O, Sanyam Jain
arxiv.org/abs/2512.17585 mastoxiv.page/@arXiv_eessIV_bo
- MAD-OOD: A Deep Learning Cluster-Driven Framework for an Out-of-Distribution Malware Detection an...
Tosin Ige, Christopher Kiekintveld, Aritran Piplai, Asif Rahman, Olukunle Kolade, Sasidhar Kunapuli
arxiv.org/abs/2512.17594 mastoxiv.page/@arXiv_csCR_bot/
- Confidence-Credibility Aware Weighted Ensembles of Small LLMs Outperform Large LLMs in Emotion De...
Menna Elgabry, Ali Hamdi
arxiv.org/abs/2512.17630 mastoxiv.page/@arXiv_csCL_bot/
- Generative Multi-Objective Bayesian Optimization with Scalable Batch Evaluations for Sample-Effic...
Madhav R. Muthyala, Farshud Sorourifar, Tianhong Tan, You Peng, Joel A. Paulson
arxiv.org/abs/2512.17659 mastoxiv.page/@arXiv_statML_bo
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@arXiv_csGR_bot@mastoxiv.page
2026-01-21 08:13:41

Copy-Trasform-Paste: Zero-Shot Object-Object Alignment Guided by Vision-Language and Geometric Constraints
Rotem Gatenyo, Ohad Fried
arxiv.org/abs/2601.14207 arxiv.org/pdf/2601.14207 arxiv.org/html/2601.14207
arXiv:2601.14207v1 Announce Type: new
Abstract: We study zero-shot 3D alignment of two given meshes, using a text prompt describing their spatial relation -- an essential capability for content creation and scene assembly. Earlier approaches primarily rely on geometric alignment procedures, while recent work leverages pretrained 2D diffusion models to model language-conditioned object-object spatial relationships. In contrast, we directly optimize the relative pose at test time, updating translation, rotation, and isotropic scale with CLIP-driven gradients via a differentiable renderer, without training a new model. Our framework augments language supervision with geometry-aware objectives: a variant of soft-Iterative Closest Point (ICP) term to encourage surface attachment and a penetration loss to discourage interpenetration. A phased schedule strengthens contact constraints over time, and camera control concentrates the optimization on the interaction region. To enable evaluation, we curate a benchmark containing diverse categories and relations, and compare against baselines. Our method outperforms all alternatives, yielding semantically faithful and physically plausible alignments.
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@arXiv_csLG_bot@mastoxiv.page
2026-02-25 10:39:51

Does Order Matter : Connecting The Law of Robustness to Robust Generalization
Himadri Mandal, Vishnu Varadarajan, Jaee Ponde, Aritra Das, Mihir More, Debayan Gupta
arxiv.org/abs/2602.20971 arxiv.org/pdf/2602.20971 arxiv.org/html/2602.20971
arXiv:2602.20971v1 Announce Type: new
Abstract: Bubeck and Sellke (2021) pose as an open problem the connection between the law of robustness and robust generalization. The law of robustness states that overparameterization is necessary for models to interpolate robustly; in particular, robust interpolation requires the learned function to be Lipschitz. Robust generalization asks whether small robust training loss implies small robust test loss. We resolve this problem by explicitly connecting the two for arbitrary data distributions. Specifically, we introduce a nontrivial notion of robust generalization error and convert it into a lower bound on the expected Rademacher complexity of the induced robust loss class. Our bounds recover the $\Omega(n^{1/d})$ regime of Wu et al.\ (2023) and show that, up to constants, robust generalization does not change the order of the Lipschitz constant required for smooth interpolation. We conduct experiments to probe the predicted scaling with dataset size and model capacity, testing whether empirical behavior aligns more closely with the predictions of Bubeck and Sellke (2021) or Wu et al.\ (2023). For MNIST, we find that the lower-bound Lipschitz constant scales on the order predicted by Wu et al.\ (2023). Informally, to obtain low robust generalization error, the Lipschitz constant must lie in a range that we bound, and the allowable perturbation radius is linked to the Lipschitz scale.
toXiv_bot_toot