Knowledge catalogue
arXiv:2609.35536v1 Announce Type: new Abstract: Multimodal large language models (MLLMs) can explain deepfake verdicts in natural language, but such explanations are not necessarily visually grounded
arXiv:2609.34309v1 Announce Type: new Abstract: Executable programs offer explicit control over how images and videos are constructed, but generating runnable code is only the beginning of visual crea
arXiv:2609.34702v1 Announce Type: new Abstract: Future Mars missions will require rover autonomy that can operate across unstructured terrain, changing illumination, atmospheric dust, and limited comm
arXiv:2609.32445v1 Announce Type: cross Abstract: Direct Preference Optimization (DPO) aligns language models by optimizing over sequence-level sums of token-wise implicit reward differences. However,
arXiv:2609.35515v1 Announce Type: cross Abstract: Scientific discovery requires not only recovering mathematical laws that describe observable behavior, but also identifying the mechanisms that genera
arXiv:2609.34420v1 Announce Type: new Abstract: Graph Transformers (GTs) offer superior representation capabilities by overcoming the depth limitations and over-smoothing issues of traditional Graph N
arXiv:2609.32313v1 Announce Type: new Abstract: Memory lets an embodied agent reuse past experience, yet retaining useful information does not ensure that the agent can apply it when conditions change
arXiv:2609.32839v1 Announce Type: cross Abstract: Recommender systems rely heavily on heterogeneous behavioral feedback to infer user preference. Although abundant, these signals are imperfect measure
arXiv:2609.33893v1 Announce Type: new Abstract: Large audio-language models (LALMs) produce fluent responses about audio but often hallucinate by making plausible yet ungrounded claims. Existing audio
arXiv:2609.33431v1 Announce Type: new Abstract: We release MoGround, a vision-language dataset spanning four visual domains in which the answer to every question is guaranteed to be available from exa
arXiv:2609.33347v1 Announce Type: new Abstract: World models can be studied as experimental systems with response laws of their own. We introduce MultiEcho, a framework for estimating these laws throu
arXiv:2609.32876v1 Announce Type: new Abstract: State-of-the-art pathology foundation models, trained on millions of histology tiles, can fail to preserve tissue similarity when comparisons cross slid
arXiv:2609.35291v1 Announce Type: cross Abstract: Modern AI models are aligned through post-training to adapt them to downstream tasks. Recent work shows that fine-tuning language models on narrow tas
arXiv:2609.34276v1 Announce Type: new Abstract: Frontier models can now perform well on individual embodied navigation tasks through multi-round multimodal reasoning with simple tools. Across successi
arXiv:2510.13602v3 Announce Type: replace-cross Abstract: Decoding throughput improvements from larger inference batches are limited by GPU memory, which is largely consumed by the key-value (KV) cach
arXiv:2609.32316v1 Announce Type: new Abstract: Traffic lights are a key regulatory signal for autonomous driving at urban intersections, yet existing traffic signal perception is still predominantly
arXiv:2609.34391v1 Announce Type: cross Abstract: AIVC (AI Virtual Cell) is a learned simulator of cellular behavior across conditions. Predicting how a cell population responds transcriptionally to a
arXiv:2609.34759v1 Announce Type: new Abstract: Recent vision-language models (VLMs) have advanced vision-and-language navigation (VLN), enabling models to predict navigation actions from visual obser
arXiv:2609.33236v1 Announce Type: new Abstract: Training-free online video anomaly detection (VAD) with frozen multimodal language models faces two coupled challenges: extracting reliable current-wind
arXiv:2609.35303v1 Announce Type: new Abstract: Reinforcement learning with verifiable rewards (RLVR) via Group-Relative Policy Optimization (GRPO) is widely used for multi-turn VLM agent training, ye
arXiv:2602.12029v2 Announce Type: replace Abstract: Multi-agent systems increasingly orchestrate multiple specialized language models to solve complex real-world problems, often invoking them over a s
Prompt engineering in Amazon Quick shapes how accurately its AI-powered features respond to your requests. Part 1 of a two-part series covers the foundational principles and reusable frameworks (speci
arXiv:2609.34222v1 Announce Type: new Abstract: Payload forces must be accommodated during locomotion, while leash forces can specify desired motion. We investigate whether a shared three-dimensional
arXiv:2609.34426v1 Announce Type: new Abstract: This paper addresses the problem of safe offline reinforcement learning, which involves training a policy to satisfy safety constraints using an offline
arXiv:2609.33796v1 Announce Type: cross Abstract: Translating natural-language access-control requirements into policies requires careful reasoning about permissions, constraints, and exceptions, and
arXiv:2609.34170v1 Announce Type: new Abstract: Flow-based vision-language-action (VLA) models are highly effective for generalist robot manipulation, yet their reliance on computationally expensive V
arXiv:2609.32155v1 Announce Type: new Abstract: Sequential manipulation requires a robot to track what has already happened, even when the current scene no longer reveals it. Policies with explicit hi
arXiv:2609.32669v1 Announce Type: cross Abstract: Attention weights depend on token counts, which change with the representation of a signal. We study refinement symmetry: splitting a representation w
arXiv:2609.33304v1 Announce Type: new Abstract: Personal Graphical User Interface (GUI) agents rely on interaction history to infer what a user wants from ambiguous instructions and to anticipate recu
arXiv:2609.33217v1 Announce Type: new Abstract: Generative models learn visual structure through denoising, yet their internal states are entangled with both noise level and network depth, making it d
arXiv:2609.34563v1 Announce Type: new Abstract: Latent visual reasoning (LVR) enables multimodal large language models (MLLMs) to perform intermediate computation in continuous latent tokens rather th
arXiv:2609.35086v1 Announce Type: new Abstract: In this work, we study portfolio optimization under the stochastic discount factor (SDF) framework by learning market state representations that capture
arXiv:2609.32590v1 Announce Type: new Abstract: Work on memory for multimodal agents optimizes what is written, updated and retrieved. Between retrieval and the answer, however, is a stage that multim
arXiv:2609.34863v1 Announce Type: new Abstract: Multimodal large language models (MLLMs) have approached image segmentation by reasoning about visual content and predicting target locations. Their gen
arXiv:2609.34556v1 Announce Type: cross Abstract: Transformers process tokens without any inherent notion of order, making positional encoding a fundamental requirement rather than an architectural re
arXiv:2609.31918v1 Announce Type: new Abstract: We introduce ROTE (RollOut Testing of Exact memorization), a benchmarking protocol for evaluating symbolic memorization of neural architectures. We stud
arXiv:2609.33518v1 Announce Type: new Abstract: Recent 3D large multimodal models (3D-LMMs) rely on a visual bottleneck to compress complex 3D scene evidence into a limited number of visual tokens com
arXiv:2609.33179v1 Announce Type: cross Abstract: Constrained-option scoring reads a model's probabilities for a fixed set of permitted answers, so it returns a score even when the model is about to w
arXiv:2609.34235v1 Announce Type: new Abstract: Medical image segmentation remains challenging in practical deployment, as models often struggle to generalize beyond the distributions covered by their
arXiv:2603.02772v2 Announce Type: replace Abstract: Motion planners tuned under nominal conditions can fail when execution conditions change. Existing differentiable adaptation methods provide efficie
arXiv:2609.34801v1 Announce Type: new Abstract: Foundation models are increasingly adapted to individual users, but an apparent personalization gain can simply reflect a stronger population model. Thi
arXiv:2609.32529v1 Announce Type: new Abstract: Open-prompt detectors allow users to specify targets with text, visual exemplars, or both. We argue that existing designs underuse the visual modality i
arXiv:2609.34111v1 Announce Type: cross Abstract: The rapid advancement of artificial intelligence has observed increased application in predicting vehicle interior noise levels within the automotive
arXiv:2609.33408v1 Announce Type: new Abstract: In Integrated Sensing and Communications (ISAC), radar sensing must operate under chirp subsampling with up to 90% missing data. An attention-based base
arXiv:2609.34363v1 Announce Type: new Abstract: Recent omni-modal models demonstrate strong perception of audio and visual inputs, yet often struggle to connect what they hear with what they see at th
arXiv:2609.35224v1 Announce Type: new Abstract: Masked-diffusion language models fill in masked positions in parallel and in no fixed order. Most practical text watermarks assume left-to-right generat
arXiv:2609.33200v1 Announce Type: new Abstract: On-policy self-distillation trains reasoning models on their own trajectories using dense token distribution guidance from a privileged teacher with acc
arXiv:2609.35219v1 Announce Type: new Abstract: Relational deep learning models database rows and foreign-key links as a heterogeneous graph for prediction from record attributes and relational contex
this is insane, my bonsai 2 27b build is trending on hugging face, first page of text generation, with 20,964 downloads in its first week. it's prismml's ternary bonsai 2 with the qwen 3.8 mtp head gr
arXiv:2407.08983v2 Announce Type: replace-cross Abstract: Trustworthiness and interpretability are inextricably linked concepts for LLMs. The more interpretable an LLM is, the more trustworthy it beco
arXiv:2609.34834v1 Announce Type: new Abstract: Transform-based methods provide an effective framework for point cloud attribute compression by representing attributes as transform coefficients. Intro
arXiv:2609.33000v1 Announce Type: cross Abstract: Predicting how drivers, vehicles, and road scenes interact and evolve together is central to driver monitoring. Prior work models in-cabin activity or
arXiv:2609.35214v1 Announce Type: cross Abstract: Cardiac model personalisation requires inferring mechanical parameters that are not directly measurable in vivo. Ultrafast ultrasound shear wave elast
arXiv:2609.35450v1 Announce Type: new Abstract: Physical contact often determines how a humanoid should respond during loco-manipulation, yet vision and proprioception alone are often insufficient to
arXiv:2609.35483v1 Announce Type: new Abstract: Many learning tasks map an input distribution to an output distribution. A natural way to model such an operator is to transform each input sample using
Decision models Ollama now supports decision models through /v1/systemone, based on TypeSafe’s Jev API. Decision models return choices, probabilities, and scores instead of text. Use them for tasks su
arXiv:2609.33325v1 Announce Type: new Abstract: Visual backbones have evolved from Convolutional Neural Networks (CNNs) with local aggregation to Vision Transformers (ViTs) with global interactions, S
arXiv:2609.33104v1 Announce Type: new Abstract: While action-conditioned video prediction provides an intuitive world model for robotics, purely data-driven predictors often suffer from compounding er
arXiv:2602.14143v2 Announce Type: replace Abstract: When a model solves a problem on one attempt and fails it on the next, what separates the two is rarely the final answer token; it is the trajectory
arXiv:2609.33487v1 Announce Type: new Abstract: EEG foundation models hold promise for scalable brain-signal decoding across clinical and cognitive neuroscience applications, yet their pre-training pi