arXiv:2603.09980v1 Announce Type: new Abstract: LLM unlearning is essential for mitigating safety, copyright, and privacy concerns in pre-trained large language models (LLMs). Compared to preference alignment, it offers a more explicit way by removing undesirable knowledge characterized by specific unlearning datasets. In previous works, gradient ascent (GA) and its variants have shown promise for implementing unlearning, yet their untargeted nature results in unintended degradation of general capabilities, incomplete removal of knowledge, and the generation of incoherent responses, among […]

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arXiv:2604.06206v1 Announce Type: new Abstract: This study seeks to uncover evidence of a latent structure in evolved human culture as it is refracted through contemporary large language models (LLMs). Drawing on parallel responses from six leading generative models to a prompt which asks directly what their training corpora reveal about human culture and behavior, we identify a robust cross-model consensus on a limited set of recurring cultural themes. The themes include narrative meaning-making, affect-first cognition, coalition psychology, status […]

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arXiv:2601.00090v1 Announce Type: new Abstract: Contemporary text-to-image models exhibit a surprising degree of mode collapse, as can be seen when sampling several images given the same text prompt. While previous work has attempted to address this issue by steering the model using guidance mechanisms, or by generating a large pool of candidates and refining them, in this work we take a different direction and aim for diversity in generations via noise optimization. Specifically, we show that a simple […]

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arXiv:2601.08006v1 Announce Type: new Abstract: The Rosenbluth-Fokker-Planck (RFP) equation describes Coulomb collisional dynamics within and across species in plasmas. It belongs to the broader class of anisotropic-diffusion-advection equations, whose numerical approximation is highly-nontrivial due to its nonlinearity, stiffness, and structural properties such as conservation and entropy dissipation (hence with the Maxwellian distribution as the equilibrium state). In this paper, we propose a structure-preserving penalization scheme for the stiff, single-species RFP equation. The scheme features three novel components: 1) […]

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Neural operator learning accelerates PDE solution by approximating operators as mappings between continuous function spaces. Yet in many engineering settings, varying geometry induces discrete structural changes, including topological changes, abrupt changes in boundary conditions or boundary types, and changes in the computational domain, which break the smooth-variation premise. Here we introduce Discrete Solution Operator Learning (DiSOL), a complementary paradigm that learns discrete solution procedures rather than continuous function-space operators. DiSOL factorizes the solver into learnable stages that mirror […]

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hey! NeurIPS releases these yearly challenges called MyoChallenges, that focus on human musculoskeletal research using RL. This is the official playlist (by MyoSuite) which has an overview of what to expect: https://youtube.com/playlist?list=PLq492wGha2Iwi8B7OOg5muUmIaqTnSmu8&si=QgAmv9ZvdWc9_tip The challenge would be released around July and I wanted to create a team and learn as much from the past challenges as possible till then! Hit me up if you’re interested!! anyway, thanks! submitted by /u/snailinyourmailpart2 [link] [comments]

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arXiv:2604.18741v1 Announce Type: new Abstract: Older adults living with multiple chronic conditions (MCC) can considerably benefit from collecting and reflecting on their health data. Many older adults collect their health data using various approaches, such as digital tools or handwritten notebooks. However, in these approaches, the act of collecting data does not itself yield insights; sensemaking and reflection happen only if individuals later review their accumulated records. The daily process of data collection thus offers limited opportunity for […]

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arXiv:2602.07453v1 Announce Type: cross Abstract: Decision tree ensembles are widely used in critical domains, making robustness and sensitivity analysis essential to their trustworthiness. We study the feature sensitivity problem, which asks whether an ensemble is sensitive to a specified subset of features — such as protected attributes — whose manipulation can alter model predictions. Existing approaches often yield examples of sensitivity that lie far from the training distribution, limiting their interpretability and practical value. We propose a data-aware […]

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Why migrating from YOLO to Grounding DINO was a total grind against CPU caches — and why the “Magic Optimization” button is a lie In my previous post (Python is a Video Latency Suicide Note: How I Hit 29 FPS with Zero-Copy C++ ONNX), I detailed how I murdered the Global Interpreter Lock. By mapping hardware Luma (Y) natively into a Zero-Copy C++ pipeline, I drove YOLOv8 to a blistering 29+ FPS on a standard CPU. I had […]

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arXiv:2603.03293v1 Announce Type: new Abstract: Retrieval augmented generation (RAG) reduces hallucinations and factual errors in large language models (LLMs) by conditioning generation on retrieved external knowledge. Recent search agents further cast RAG as an autonomous, multi-turn information-seeking process. However, existing methods often accumulate irrelevant or noisy documents and rely on sparse reinforcement learning signals. We propose textbf{S}elf-textbf{E}volving textbf{Search}, a Self-Evolving Search agent that improves online search behavior through three components, memory purification, atomic query training, and dense rewards. […]

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