Knowledge catalogue
Elon Musk shared a post expressing that having children contributes to happiness and citing commentary from @ZubyMusic about the need to culturally elevate the status of parenting. The post appears to
arXiv:2605.10790v1 Announce Type: new Abstract: Diffusion models have achieved remarkable success, yet their training remains inefficient due to a severe optimization bottleneck, which we term Represe
arXiv:2605.08377v1 Announce Type: new Abstract: In many practical applications it is important to build symmetries into neural network architectures. Consider the important case of permutation symmetr
arXiv:2605.08360v1 Announce Type: new Abstract: Modern AI is opening the door to collective decision-making in which participants express their views as free-form text rather than voting on a fixed se
arXiv:2605.10653v1 Announce Type: new Abstract: Embodied artificial intelligence is rapidly moving from research into real-world systems such as autonomous vehicles, mobile robots, and industrial mach
arXiv:2605.10332v1 Announce Type: new Abstract: Embodied agents can benefit from skills that guide object search, action execution, and state changes across diverse environments. Since embodied enviro
arXiv:2601.22449v2 Announce Type: replace Abstract: Intrinsic Motivation (IM) aims to train agents without external rewards, enabling useful behavior to emerge from the agent's interaction with its en
arXiv:2605.09187v1 Announce Type: new Abstract: Understanding how linguistic structure emerges in language models is central to interpreting what these systems learn from data and how much supervision
arXiv:2602.01194v2 Announce Type: replace Abstract: Long-term weather forecasting is critical for socioeconomic planning and disaster preparedness. While recent approaches employ finetuning to extend
arXiv:2605.06663v2 Announce Type: replace Abstract: Large language models are typically deployed as monolithic systems, requiring the full model even when applications need only a narrow subset of cap
arXiv:2605.08847v1 Announce Type: new Abstract: In the context of today's high-pressure, aging society, the demand for large-scale emotional models capable of providing empathetic support is more crit
arXiv:2605.09509v1 Announce Type: cross Abstract: The problem of predicting unobserved entries in a binary matrix, known as 1-bit matrix completion, has found diverse applications in fields such as re
arXiv:2605.09395v1 Announce Type: new Abstract: In this paper, we propose the first VLnderline{extbf{M}} nderline{extbf{a}}gentic nderline{extbf{r}}easoning framework for few-nderline{extbf{s}}hot mul
arXiv:2605.10198v1 Announce Type: cross Abstract: Erasing specific concepts from text-to-image diffusion models is essential for avoiding the generation of copyrighted and explicit content. Closed-for
arXiv:2605.09495v1 Announce Type: cross Abstract: Machine learning-based simulators offer the potential to model the dynamics of complex systems more efficiently than classical approaches, while retai
arXiv:2605.09826v1 Announce Type: new Abstract: Theory of Mind (ToM), the ability to track others epistemic state, makes humans efficient collaborators. AI agents need the same capacity in multi agent
arXiv:2604.16838v2 Announce Type: replace-cross Abstract: We present enclawed, a hard-fork hardening framework built on the OpenClaw AI assistant gateway. enclawed targets deployments that need attest
arXiv:2605.09570v1 Announce Type: new Abstract: With the rapid growth of mobile robotics and embedded intelligence, there is an increasing demand for efficient on-device data processing on edge platfo
arXiv:2605.08645v1 Announce Type: cross Abstract: Energy-based models (EBMs) provide a powerful and flexible way of learning a joint probability distribution over data by constructing an energy surfac
arXiv:2511.08644v3 Announce Type: replace-cross Abstract: This paper presents a detailed comparative analysis of the performance of three major Python data manipulation libraries - Pandas, Polars, and
arXiv:2605.10556v1 Announce Type: new Abstract: As large language models span dense, mixture-of-experts, and state-space architectures and are deployed on heterogeneous accelerators under increasingly
arXiv:2605.10907v1 Announce Type: cross Abstract: The dominant paradigm for AI agents is an 'on-the-fly' loop in which agents synthesize plans and execute actions within seconds or minutes in response
arXiv:2605.08910v1 Announce Type: cross Abstract: The new wave of adversarial attacks that utilize gradient-related vulnerabilities in neural network-based classifiers makes Network Intrusion Detectio
arXiv:2605.08572v1 Announce Type: new Abstract: Diffusion models for multi-agent trajectory prediction are limited by iterative denoising, which causes inference latency that hinders their use in time
arXiv:2601.15065v2 Announce Type: replace Abstract: CLIP-based foreground-background (FG-BG) decomposition methods have demonstrated remarkable effectiveness in improving few-shot out-of-distribution
arXiv:2605.09745v1 Announce Type: cross Abstract: Large language models (LLMs) achieve remarkable generative performance, yet their output quality is dependent on the decoding strategy. While sampling
arXiv:2605.09505v1 Announce Type: new Abstract: Epilepsy diagnosis and treatment require evidence-intensive reasoning across heterogeneous clinical knowledge, including biosignal patterns, genetic mec
arXiv:2605.10410v1 Announce Type: new Abstract: Large language models can score well on named game-theory benchmarks while failing on the same strategic computation once semantic cues are removed. We
arXiv:2605.09278v1 Announce Type: new Abstract: Multi-agent debate (MAD) systems increasingly rely on shared memory to support long-horizon reasoning, but this convenience opens a critical vulnerabili
arXiv:2605.10910v1 Announce Type: cross Abstract: We consider the problem of synthesizing Clifford quantum circuits for devices with all-to-all qubit connectivity. We approach this task as a reinforce
arXiv:2507.18847v3 Announce Type: replace-cross Abstract: We propose a new volumetric grasp model that is equivariant to rotations around the vertical axis, leading to a significant improvement in sam
arXiv:2505.22919v3 Announce Type: replace Abstract: Existing benchmarks for evaluating the clinical reasoning capabilities of large language models (LLMs) often lack a clear definition of 'clinical re
arXiv:2605.09982v1 Announce Type: new Abstract: Recent advancements in Vision-Language Models (VLMs) enable large language models (LLMs) to process high-resolution images, significantly improving real
arXiv:2602.08617v2 Announce Type: replace Abstract: Scaling Federated Learning (FL) to billion-parameter models forces a challenging trade-off between privacy, scalability, and model utility. Existing
arXiv:2411.05516v3 Announce Type: replace Abstract: Autonomous Underwater Vehicles (AUVs) have advanced significantly in obstacle detection and path planning through sonar, cameras, and learning-based
arXiv:2605.09181v1 Announce Type: new Abstract: Retinal image-based eye tracking is widely used in ophthalmic imaging and vision science, and is a promising path to deliver higher gaze accuracy than t
arXiv:2509.18484v2 Announce Type: replace-cross Abstract: Estimating causal effects on networks is challenging because treatments may affect both treated units and their neighbors, while network homop
arXiv:2601.21484v2 Announce Type: replace Abstract: Reinforcement Learning (RL) post-training alignment for language models is effective, but also costly and unstable in practice, owing to its complic
arXiv:2605.09429v1 Announce Type: cross Abstract: Are low-attention visual tokens truly redundant in vision-language reasoning? Existing pruning methods often assume so, ranking visual tokens by shall
arXiv:2605.08549v1 Announce Type: new Abstract: Conversational AI is increasingly personalized around users' preferences, histories, goals, and knowledge, but much less around how users interpret and
arXiv:2605.09137v1 Announce Type: new Abstract: Breast density is a key factor that influences mammography interpretation and is a major source of heterogeneity in multicenter datasets. Such heterogen
arXiv:2605.09042v1 Announce Type: new Abstract: Evaluating pragmatic reasoning in large language models (LLMs) remains challenging because model behavior can vary depending on evaluation methods. Prev
Even @haider1 sees that Mythos has been overhyped. mythos is pretty on par with gpt-5.5 and while gpt-5.5 is currently SOTA, it's not anything like what anthropic describes mythos as it's pretty obvio
arXiv:2605.08685v1 Announce Type: cross Abstract: We propose a new class of waveform foundation models that departs from conventional sequence based representations by modeling physiological time seri
arXiv:2508.13434v2 Announce Type: replace-cross Abstract: Time series forecasting is vital in diverse sectors such as energy and transportation, where non-stationary dynamics are deeply intertwined wi
arXiv:2605.10402v1 Announce Type: cross Abstract: A finite presentation of a finite group is called `just finite' if removing any relation from R results in a presentation for an infinite group. It ha
arXiv:2510.06371v2 Announce Type: replace-cross Abstract: Large-scale multimodal models achieve strong results on tasks like Visual Question Answering (VQA), but they are often limited when queries re
arXiv:2605.09935v1 Announce Type: new Abstract: With the rapid development of deep generative models, forged facial images are massively exploited for illegal activities. Although existing synthetic f
arXiv:2605.09192v1 Announce Type: new Abstract: Agent skills can remarkably improve task success rates by using human-written procedural documents, but their quality is difficult to assess without env
arXiv:2603.09465v3 Announce Type: replace-cross Abstract: Vision-Language-Action models have shown great promise for autonomous driving, yet they suffer from degraded perception after unfreezing the v
arXiv:2605.09018v1 Announce Type: cross Abstract: We introduce Evolutionary Ensemble (EvE), a decentralized framework that organizes existing, highly capable coding agents into a live, co-evolving sys
arXiv:2605.09924v1 Announce Type: new Abstract: Recent advancements in Neural Machine Translation (NMT) have significantly improved translation quality. However, the increasing size and complexity of
arXiv:2605.10663v1 Announce Type: new Abstract: Experience-driven self-evolving agents aim to overcome the static nature of large language models by distilling reusable experience from past interactio
arXiv:2605.08769v1 Announce Type: new Abstract: Large language model (LLM)-based multi-agent systems have shown strong potential on complex tasks through agent specialization, tool use, and collaborat
arXiv:2605.09777v1 Announce Type: cross Abstract: Gradient-based preference optimization methods for large language model (LLM) alignment suffer from preference collapse, converging to narrow behavior
arXiv:2605.10343v1 Announce Type: cross Abstract: Streaming video understanding demands more than watching longer videos: assistants must decide when to speak in real time, balancing responsiveness ag
arXiv:2605.10613v1 Announce Type: cross Abstract: We introduce a technique that enables Neural-ODEs to approximate arbitrary velocity fields with a priori planted fixed-points. Specifically, a recipe
arXiv:2605.10680v1 Announce Type: new Abstract: This paper proposes a paradigm shift linking machine unlearning directly to the structure of the data distributions rather than a mere update of the neu
arXiv:2605.08285v1 Announce Type: new Abstract: Autoregressive scientific forecasters often enforce physical or structural constraints by repairing each predicted state before feeding it back into the
Marina Temkin / TechCrunch: Exaforce, which uses AI agents to detect and thwart cyberattacks, raised a 125M Series B at a 725M valuation, bringing its total funding to $200M — As bad actors weaponize