Knowledge catalogue
arXiv:2610.12007v1 Announce Type: cross Abstract: Flow-based vision-language-action (VLA) models generate action chunks for temporally coherent robot motion, but chunked control creates a fundamental
arXiv:2609.32123v2 Announce Type: replace Abstract: Time-series diagnostic systems rarely rely on retrieving relevant historical cases, and when they do, retrieval is evaluated only indirectly through
arXiv:2610.11245v1 Announce Type: cross Abstract: KV-cache entries are stored before their future queries are known, but each decoding query needs precision in different places. We study this mismatch
arXiv:2610.10906v1 Announce Type: new Abstract: Human communities are governed by normative systems: shared standards that produce extit{norms} dictating acceptable behavior, enforced through communit
arXiv:2609.12035v3 Announce Type: replace Abstract: Cardiovascular diagnosis and treatment rest on integrating complementary modalities, such as electrocardiogram, echocardiography, and chest X-rays,
arXiv:2610.10845v1 Announce Type: cross Abstract: A large language model can only use the text that fits in its context window, and it recomputes its internal key-value (KV) state for a prompt every t
arXiv:2610.12249v1 Announce Type: cross Abstract: We study real-time motion planning in dynamic hazard fields through a controlled comparison between classical planning and learning-based methods. Rat
arXiv:2610.12343v1 Announce Type: new Abstract: Large Multi-modality Models (LMMs) have made significant progress in visual understanding and generation, but still face challenges in visual editing, p
arXiv:2610.11332v1 Announce Type: new Abstract: Structured pruning reduces the deployment cost of reasoning language models, but the resulting capability degradation can hinder subsequent on-policy di
arXiv:2610.11334v1 Announce Type: new Abstract: In LLM-based agent systems, failures can originate from early steps whose effects propagate through subsequent interactions, making their origins diffic
arXiv:2610.12090v1 Announce Type: new Abstract: Latent reasoning enables vision-language-action (VLA) models to transform multimodal observations into task-relevant internal states before generating c
arXiv:2610.11004v1 Announce Type: cross Abstract: Reconstructing spatially resolved plasma dynamics from few sensors is essential for diagnostics, reduced-order modelling and control, yet remains diff
arXiv:2610.11834v1 Announce Type: new Abstract: We study the sample complexity of noisy one-bit compressed sensing for signals drawn from a prior distribution. By characterizing the effective distribu
arXiv:2610.10623v1 Announce Type: cross Abstract: Looped Language Models (LoopLMs) offer a parameter efficient approach to scaling reasoning by reusing shared parameters across recurrent computation s
arXiv:2610.12176v1 Announce Type: new Abstract: Recursive self-improvement (RSI) of a model on non-verifiable tasks, such as open-ended research, faces a supervision bottleneck when its outputs exceed
arXiv:2610.11058v1 Announce Type: new Abstract: In autonomous driving, understanding scene topology - the connectivity between lanes and traffic elements - is critical for safe path planning and motio
arXiv:2610.11415v1 Announce Type: cross Abstract: Prediction aggregation aims to combine information from multiple predictors into a more informative one. We study this question in the setting of cali
arXiv:2610.10991v1 Announce Type: new Abstract: Image retrieval methods often rely on a single global semantic descriptor extracted from an image, e.g., the [CLS] token in vision transformers. However
arXiv:2610.12133v1 Announce Type: new Abstract: Many key-value (KV) caches are compressed before anyone knows what will be asked of them: a document cached for retrieval, a prompt prefix shared across
arXiv:2610.11876v1 Announce Type: new Abstract: Generative models can now synthesize highly realistic images, simultaneously increasing the risks of misinformation and visual forgery. Therefore, detec
arXiv:2610.11956v1 Announce Type: cross Abstract: A generated video of a task the robot is about to perform is useful guidance only if it depicts the phase the robot is actually in. We show that tempo
arXiv:2610.11287v1 Announce Type: cross Abstract: Long-horizon agents compact their history to continue within a finite context window, but a textual summary alone may not support every subsequent dec
arXiv:2610.11039v1 Announce Type: new Abstract: Active robot vision requires controlling the camera to reveal task-relevant information that is hidden from the current viewpoint. For example, determin
arXiv:2610.12185v1 Announce Type: new Abstract: Reliable robotic manipulation requires timely intervention to correct emerging deviations and restore progress after execution errors. However, recovery
arXiv:2610.12233v1 Announce Type: cross Abstract: Recursive self-improvement, the participation of AI systems in improving their own capabilities, is beginning to move from theoretical prospect to pra
arXiv:2610.11519v1 Announce Type: new Abstract: Reinforcement learning with verifiable rewards (RLVR) and on-policy distillation (OPD) have become two main paradigms for post-training reasoning models
arXiv:2610.12231v1 Announce Type: new Abstract: Learning from demonstration has enabled impressive robot behaviors. A common choice for policy learning is to use diffusion or flow matching (Flow-Polic
arXiv:2610.11257v1 Announce Type: new Abstract: Learned representations can lose latent degrees of freedom successively, suggesting a cascade of transitions whose underlying stability principle remain
arXiv:2610.11529v1 Announce Type: new Abstract: Self-distillation can improve reasoning without a separately trained, more capable teacher, but its effectiveness depends on how the self-teacher gains
arXiv:2610.11374v1 Announce Type: cross Abstract: CLIP serves as a foundational vision-language model and the de facto vision encoder for downstream VLMs such as LLaVA. Post-training offers a lightwei
arXiv:2602.07878v2 Announce Type: replace-cross Abstract: LLM inference is inherently expensive, even a modest slowdown can translate into substantial operating costs and severe availability risks. Re
arXiv:2610.10933v1 Announce Type: cross Abstract: Spiking language models face a tradeoff between representing continuous semantic features over short temporal windows and retaining costly nonlinear a
arXiv:2603.07233v2 Announce Type: replace Abstract: Predicting transcriptional responses to genetic perturbations is fundamental to functional genomics and therapeutic discovery. Recent deep learning
arXiv:2610.11641v1 Announce Type: new Abstract: Handcrafted minutiae detection algorithms remain fundamental to biometric science and forensic practice due to their full auditability, adherence to int
arXiv:2610.11924v1 Announce Type: new Abstract: Media-bridged time series forecasting is expanding to encompass traditional 'multivariate' and emerging 'multimodal' (e.g., through textual assistance).
arXiv:2610.11416v1 Announce Type: cross Abstract: Vision-Language-Action (VLA) models have emerged as a prominent framework for complex robotic manipulation, building on the strong semantic understand
arXiv:2610.10858v1 Announce Type: cross Abstract: Analog/RF circuits remain the critical interface between digital computation and the physical world, and emerging standards from Wi-Fi 7 to 6G place s
arXiv:2610.11187v1 Announce Type: new Abstract: Single-view 3D reconstructors often produce plausible meshes that disagree with the input view, especially near depth discontinuities and self-occlusion
arXiv:2610.10947v1 Announce Type: new Abstract: Reward hacking, where a model exploits an evaluation signal without completing the intended task, threatens the reliability of deployed language model s
arXiv:2610.12333v1 Announce Type: cross Abstract: Accurate simulation of rigid-body interactions is essential for predictive physical world models. Despite recent progress in modeling object dynamics,
arXiv:2610.11164v1 Announce Type: new Abstract: We release Ride-Hailing, a large-scale ride-hailing time series dataset synthesized from DiDi's marketplace data across 200 spatial areas. Ride-Hailing
arXiv:2610.12244v1 Announce Type: new Abstract: Isolation Forest (IF) is a widely used baseline for unsupervised anomaly detection. Recent studies provide a closed-form expression for the infinite-for
arXiv:2610.11942v1 Announce Type: new Abstract: A login screen that appears after a tap on Sign in is expected; the same screen after a tap on View order is an attack. For GUI agents, safety is theref
arXiv:2610.11387v1 Announce Type: cross Abstract: Symbolic regression combines structural search with numerical fitting, but aggregate fit scores do not describe how the remaining error varies across
arXiv:2610.11370v1 Announce Type: new Abstract: Retrieval-augmented generation (RAG) grounds language models in external corpora. Agentic RAG enables iterative search, yet exposes the model to isolate
arXiv:2610.11352v1 Announce Type: new Abstract: Language models (LMs) are commonly trained with Reinforcement Learning with Verifiable Rewards (RLVR) to enhance their reasoning capabilities. However,
arXiv:2602.14032v2 Announce Type: replace Abstract: Enhancing the generalization of robotic learning in diverse unseen environments remains a fundamental challenge. Existing approaches often rely on l
arXiv:2610.11480v1 Announce Type: cross Abstract: Embodied coding agents can combine modular robot skills with frozen end-to-end policies, yet effective composition requires anticipating which policy
arXiv:2610.12424v1 Announce Type: cross Abstract: A generalist robot should not only perform diverse tasks but also improve through experience, turning what it learns during execution into capabilitie
arXiv:2610.11906v1 Announce Type: cross Abstract: We consider the problem of learning linear dynamical systems under adversarial contamination from a single trajectory of length T. While identificatio
arXiv:2610.12444v1 Announce Type: new Abstract: Quantizing AdamW's optimizer states reduces persistent storage, but quantization errors propagate through the moment recurrences and perturb subsequent
Friday, October 16, 2026 Can AI design new life forms? In 2025, Stanford University PhD student Samuel King came up with a preliminary answer when he used a generative AI model to propose genetic blue
arXiv:2610.11775v1 Announce Type: new Abstract: Sparse Mixture of Experts (MoE) models scale more efficiently than dense models by routing tokens to modular expert networks that are only active for pr
arXiv:2510.25257v2 Announce Type: replace Abstract: Real-time object detection has achieved substantial progress through meticulously designed architectures and optimization strategies. However, the p
I have rtx 4090 and 64 gb of ddr4 ram. This is enough to fit smaller quants of qfn, but I found them not great and switched back to qwen3.8. However, there are option that bother me a lot, buy huawei
arXiv:2610.12469v1 Announce Type: new Abstract: Instruction-guided image editors have become highly capable, yet improving them further still depends on human-edited training pairs or external reward
arXiv:2610.11593v1 Announce Type: cross Abstract: Coding agents produce large, tangled patches that mix multiple development purposes, making the code hard to review and maintain. Commit untangling of
HauhauCS ships their uncensored Qwen3.8-27B as GGUF only. NInfer, a C++/CUDA wanted its own format. Now the same model that ran at 91.6 tok/s / 131K under llama.cpp does: 262K context (the model's ful
arXiv:2610.11608v1 Announce Type: new Abstract: Cross-view geo-localization (CVGL) aims to estimate geographic locations by matching images captured from different viewpoints, such as drone and satell
arXiv:2606.18096v2 Announce Type: replace-cross Abstract: Structured State Space Models (SSMs), including the S4 and S4D architectures, have recently emerged as powerful alternatives to attention-base