Knowledge catalogue
arXiv:2610.21170v1 Announce Type: new Abstract: Agent memory architectures define how LLM agents store and reuse experience across tasks, but they are typically fixed after deployment, limiting their
arXiv:2610.21492v1 Announce Type: new Abstract: Language-model agents can accumulate interaction feedback, but reliably turning that feedback into reusable experience remains challenging. Existing met
arXiv:2610.20967v1 Announce Type: cross Abstract: State-of-the-art text-to-music models increasingly rely on scaling model capacity, data, and compute, while still learning musical structure directly
arXiv:2603.14313v2 Announce Type: replace Abstract: Federal Open Market Committee (FOMC) statements are a major source of monetary-policy information, and even subtle changes in their wording can move
arXiv:2610.22314v1 Announce Type: new Abstract: With people increasingly turning to AI chatbots for mental health support, the need to evaluate these tools is becoming more pressing. Recent benchmarks
arXiv:2602.08901v2 Announce Type: replace Abstract: Large language models (LLMs) can memorize and reproduce training sequences verbatim, raising privacy and copyright concerns. Existing mitigation met
arXiv:2610.22276v1 Announce Type: new Abstract: Spatial intelligence lets an agent perceive and act in the physical world, and many of its judgments must be made at interaction rates: which spatial re
arXiv:2610.20998v1 Announce Type: new Abstract: Passive human videos of everyday activity offer robots access to a massive amount of human experience without requiring a demonstration for each task. H
arXiv:2610.22177v1 Announce Type: cross Abstract: Feed-forward transformer models have recently achieved strong performance in multi-view 3D reconstruction, but they still rely primarily on appearance
arXiv:2610.21292v1 Announce Type: new Abstract: Maintaining small path-tracking errors under moving reference frames is essential for robotic manipulation tasks with tight execution tolerances, such a
arXiv:2610.20965v1 Announce Type: new Abstract: Large multimodal models have achieved remarkable progress by scaling model capacity and training on massive paired multimodal data. However, this paradi
arXiv:2606.19911v2 Announce Type: replace Abstract: Decentralized deployment of LLM agents with diverse capabilities motivates infrastructure for knowledge sharing across heterogeneous agent populatio
arXiv:2610.21522v1 Announce Type: new Abstract: Specialized vision-language-action (VLA) policies acquire strong domain-specific skills, yet consolidating these skills into one shared policy remains c
arXiv:2610.21573v1 Announce Type: new Abstract: Decision trees can make vision-language model (VLM) classification interpretable by replacing a single prediction with a sequence of root-to-leaf questi
arXiv:2610.20797v1 Announce Type: new Abstract: Reliable absolute heading estimation is critical for road-vehicle navigation, particularly when magnetic, inertial, or GNSS-based references are degrade
arXiv:2610.21786v1 Announce Type: new Abstract: Understanding human communication is a long-standing goal in psychological research. People express themselves through many verbal and nonverbal channel
arXiv:2610.22320v1 Announce Type: new Abstract: Recent advancements in diffusion models have significantly improved text-to-image generation, enabling their application to visual storytelling, where c
arXiv:2610.22355v1 Announce Type: new Abstract: Generative visual verifiers play a crucial role in advancing multimodal intelligence. To enhance the robustness of visual verifiers in complex multimoda
arXiv:2610.20687v1 Announce Type: new Abstract: Residual context is key to unlocking the speed-quality Pareto frontier of diffusion language models (DLMs) by carrying forward predictive distributions
arXiv:2610.21983v1 Announce Type: new Abstract: Learning from experience requires recovering reusable knowledge and determining when it applies to a new situation. We introduce OntoAct, a hypothesis-b
arXiv:2610.21345v1 Announce Type: new Abstract: Agentic knowledge graph question answering(KGQA) relies on sequences of executable operations, but trajectory-level rewards obscure their individual con
arXiv:2508.12834v2 Announce Type: replace-cross Abstract: Stochastic gradient descent (SGD), one of the most fundamental optimization algorithms in machine learning (ML), can be recast through a conti
arXiv:2610.20753v1 Announce Type: cross Abstract: Large language models (LLMs) trained on vast amounts of data have demonstrated impressive performance over a range of tasks, but future performance ga
arXiv:2602.18891v2 Announce Type: replace-cross Abstract: Advances in large language models (LLMs) are rapidly transforming scientific work, yet empirical evidence on how these systems reshape researc
arXiv:2610.21979v1 Announce Type: cross Abstract: End-to-end full-page document parsing must preserve the content and structure of heterogeneous elements, yet otherwise accurate outputs can contain lo
arXiv:2610.21493v1 Announce Type: new Abstract: Dense associative memory underlies modern attention mechanisms; however, Modern Hopfield Networks (MHNs) with linear kernels yield overlapping attractor
arXiv:2610.20973v1 Announce Type: new Abstract: Geo-foundation models (GFMs) are typically evaluated by downstream task predictive performance, revealing little about which physical features they are
arXiv:2610.20925v1 Announce Type: cross Abstract: Robust adaptive control is vital for keeping robots safe while actively reducing uncertainty in unfamiliar settings. However, traditional methods are
arXiv:2610.21308v1 Announce Type: cross Abstract: Consumer phones and wearables increasingly collect physiological time-series data, yet most mobile systems treat these streams as summaries or text ra
arXiv:2610.21046v1 Announce Type: cross Abstract: Peer review is under strain from the growth of submissions, and review comments written by humans or AI can misstate what a paper says. We introduce R
arXiv:2610.22158v1 Announce Type: new Abstract: Recent advances in large language models (LLMs) have substantially improved Text-to-SQL performance, yet existing NL2SQL systems largely assume that use
arXiv:2610.20808v1 Announce Type: new Abstract: Recent literature shows that unsafe tool calls can often accompany textual refusals in language models. To investigate this hidden vulnerability, we int
arXiv:2610.20796v1 Announce Type: cross Abstract: Health-misinformation analysis presents a dual challenge: misleading claims can affect public well-being, while systems designed to detect them may ex
arXiv:2602.02573v2 Announce Type: replace-cross Abstract: Modern neural architectures are largely developed as distinct empirical constructions, despite recurring principles across convolutional, atte
arXiv:2610.22056v1 Announce Type: new Abstract: Traditional reinforcement learning (RL) algorithms face significant limitations in handling long-term tasks with sparse rewards. Recent advancements hav
I was running Unsloth IQ4_XS for a while and while it provided me with a healthy 96K context on 20GB vram, I did notice all the issues it had during draft and all the time and local tokens wasted tryi
arXiv:2610.22150v1 Announce Type: new Abstract: In Rhetorical Structure Theory (RST), each edge of a discourse tree carries a nuclearity label, marking which span holds the central content, and a rela
arXiv:2610.20781v1 Announce Type: cross Abstract: Image-to-text (I2T) understanding and text-to-image (T2I) generation are dual tasks in unified multimodal models (UMMs). Reconstructive post-training
arXiv:2610.21366v1 Announce Type: new Abstract: Residential design aims to transform existing homes into functional living spaces while satisfying diverse design preferences and physical constraints.
arXiv:2610.21121v1 Announce Type: cross Abstract: Real-time mobile volumetric video enables immersive free-viewpoint experiences, but delivering high-quality 3D content under constrained bandwidth and
arXiv:2610.22051v1 Announce Type: cross Abstract: In the best-arm identification problem, an algorithm adaptively selects arms to find the arm of the largest mean reward. Successive Rejects (SR) is a
arXiv:2610.20811v1 Announce Type: new Abstract: Generative gameplay requires more than immediate responses to player actions.Actions should have persistent consequences, while the generated world shou
arXiv:2610.21709v1 Announce Type: cross Abstract: Robust covariance estimation under entry-wise contamination and missing data is challenging in various signal processing applications. Under the Stude
arXiv:2610.21086v1 Announce Type: new Abstract: Camera-to-robot pose estimation from a monocular image is significant for vision-based robot navigation and control. However, recovering 3D structural f
arXiv:2610.21791v1 Announce Type: new Abstract: Large language model (LLM) agents have demonstrated strong capabilities in complex tasks, including code generation, tool use, and multi-hop question an
arXiv:2610.21214v1 Announce Type: new Abstract: Rubric-based evaluation is widely used to assess large language model (LLM) outputs by decomposing quality into multiple criteria and assigning each a b
arXiv:2610.21049v1 Announce Type: new Abstract: Learned dynamical models can accurately predict trajectories while producing unreliable sensitivities when differentiated or transposed, limiting their
arXiv:2610.21367v1 Announce Type: new Abstract: Test-time adaptation (TTA) mitigates the performance degradation of source-pretrained models on unlabeled test data under distribution shifts. Recent ba
arXiv:2610.21029v1 Announce Type: cross Abstract: As autonomous agents become widely deployed, coordinating access to shared computing resources and services will become increasingly important. Recent
arXiv:2406.18397v4 Announce Type: replace-cross Abstract: In this article, we introduce the novel concept of the second maximum of a Gaussian random field defined on a Riemannian manifold: Once the fi
arXiv:2610.21850v1 Announce Type: new Abstract: Generating 3D assets with explicit parts requires two properties at once: geometric detail inside each part and structural coherence across the whole ob
arXiv:2610.22029v1 Announce Type: cross Abstract: Self-evolving language models train on tasks they generate, so every round must decide which updated model the next round inherits. Current loops such
arXiv:2610.22243v1 Announce Type: new Abstract: AI agents take actions through tools. Some actions are sensitive, so guards that supervise them must themselves be evaluated reliably. Yet the data used
arXiv:2610.20866v1 Announce Type: cross Abstract: Realistic traffic simulation and autonomous planning both depend on modeling interactions among heterogeneous road users whose dynamics, constraints,
arXiv:2610.21087v1 Announce Type: cross Abstract: We identify a theoretical incompatibility between 3D point cloud masked autoencoding and SE(3)-equivariant networks. We prove that standard masking tr
arXiv:2610.22337v1 Announce Type: new Abstract: Robotic harvesting requires precise manipulation to ensure accurate positioning of the end-effector, especially in a constraint workspace. This study in
arXiv:2610.20863v1 Announce Type: cross Abstract: Unsupervised speech denoising methods such as MixIT and noisy-target training can be trained directly on noisy recordings, avoiding the sim-to-real ga
arXiv:2610.21129v1 Announce Type: new Abstract: Diffusion Transformers (DiTs) dominate video generation but suffer from high inference cost due to the quadratic complexity of self-attention. Sparse at
arXiv:2610.21621v1 Announce Type: new Abstract: Recent Vision-Language Models (VLMs) enable automated preference labeling for robotic manipulation, yet their deployment remains impractical since each
arXiv:2610.22347v1 Announce Type: new Abstract: Few-step autoregressive video generators enable streaming generation, yet they run every token through the final denoising step, regardless of how much