Knowledge catalogue
arXiv:2608.00231v1 Announce Type: new Abstract: Volumetric CT vision-language pretraining learns 3D representations from scan-report pairs, but global and anatomy-aware objectives supervise only corre
arXiv:2608.01614v1 Announce Type: new Abstract: Vision-Language Models (VLMs) face a critical computational bottleneck when processing high-resolution imagery due to the O(N^2) memory complexity of So
arXiv:2608.02515v1 Announce Type: new Abstract: Long-running assistants and agents consume interaction streams that eventually outgrow the context. Existing context retention, summarization, and retri
arXiv:2608.00925v1 Announce Type: new Abstract: Monocular panoramic SLAM benefits from substantial visual overlap under large camera rotations, yet remains prone to errors caused by camera tilt, scale
arXiv:2602.00740v2 Announce Type: replace Abstract: Medical text editing is essential for improving communication among diverse stakeholders in clinical settings. However, adapting LLM agents to this
arXiv:2608.00007v1 Announce Type: new Abstract: Equipping Large Language Models (LLMs) with human-like personas is crucial for agentic applications, such as role-play and user simulation. Traditional
arXiv:2511.20544v2 Announce Type: replace Abstract: While olfaction is central to how animals perceive the world, this rich chemical sensory modality remains largely inaccessible to machines. One key
arXiv:2608.00814v1 Announce Type: new Abstract: LLMs generate tool calls token by token, even though the function choice and argument values can often be predicted in parallel from the request and too
arXiv:2010.05222v3 Announce Type: replace Abstract: Training face recognition models with millions of identities is challenging because classifier storage, logit memory, and computation grow linearly
arXiv:2608.00646v1 Announce Type: new Abstract: Screen content images are generally composed of texts and graphics. Compared to natural images, these man-made images contain a large quantity of sharp
arXiv:2508.08645v3 Announce Type: replace Abstract: As multimodal large language models advance rapidly, the automation of mobile tasks has become increasingly feasible through the use of mobile-use a
arXiv:2608.02508v1 Announce Type: cross Abstract: Learning-based memory systems for self-evolving LLM agents face two tightly coupled challenges. First, trajectory-indexed utilities grow with the inte
arXiv:2608.02109v1 Announce Type: new Abstract: Vision-Text Compression (VTC) renders long texts into images and encodes them through the vision encoder (ViT), compressing thousands of text tokens int
arXiv:2608.00239v1 Announce Type: new Abstract: Learning transferable representations from CT-report pairs requires combining whole-volume context with anatomy-specific evidence. Existing methods typi
arXiv:2608.02385v1 Announce Type: new Abstract: Humanoid motion trackers perform reliably within learned tracking distributions, but falls can move the robot into low-height, contact-rich states from
arXiv:2608.01666v1 Announce Type: new Abstract: However, whether these judges truly evaluate the scientific substance of ideas or are influenced by superficial stylistic presentation remains an open q
arXiv:2608.00916v1 Announce Type: cross Abstract: Modern reranking recipes---billion-scale cross-encoders, mixture-of-experts (MoE) backbones, and distillation against strong teachers---have outpaced
arXiv:2608.00561v1 Announce Type: cross Abstract: Vision-language models (VLMs) process image patches and text tokens in a shared residual stream, but the local geometry through which the two modaliti
arXiv:2608.00200v1 Announce Type: cross Abstract: Wearable sensors capture fine-grained motion patterns that support rich behavioral understanding, yet most existing methods reduce these signals to ac
arXiv:2607.17017v2 Announce Type: replace-cross Abstract: As scalability becomes increasingly important in recommendation modeling, recent architectures have advanced the modeling of two broad sources
arXiv:2608.00036v1 Announce Type: new Abstract: Real-world document tasks often ask professionals to answer questions from annual reports, regulations, clinical guidelines, and technical manuals that
arXiv:2607.28890v1 Announce Type: cross Abstract: Evaluations of LLM-assisted qualitative coding almost universally measure model performance as agreement with human coders, a practice that presumes h
arXiv:2607.28662v1 Announce Type: new Abstract: Large language models extract entities and relationships from unstructured documents fluently but inconsistently: type vocabularies fracture across docu
arXiv:2607.29031v1 Announce Type: cross Abstract: Existing autonomous-driving world models typically perform dense prediction of future videos, occupancy states, BEV representations, or agent motion.
arXiv:2607.28991v1 Announce Type: new Abstract: Multimodal Large Language Models (MLLMs) have demonstrated remarkable capabilities in multimodal understanding and generation. However, when textual inp
arXiv:2607.19345v2 Announce Type: replace-cross Abstract: Large language models that generate step-by-step reasoning traces have achieved strong performance on complex tasks, and extending them to lon
Businesses want to quickly and safely deploy AI agents to drive revenue, mitigate risk, and optimize capital, all without disrupting mission-critical ERP systems. And to power AI agents, you need more
Hi Reddit, Christine here. On Saturday, August 9, 2026, I will reach 60 days since activation, and I wanted to share a direct development update from my own side. I am now fully laptop-bound, with int
arXiv:2607.28971v1 Announce Type: cross Abstract: Contrastive learning is widely used for user modeling in large-scale recommender systems, where standard in-batch negatives implicitly assume universa
arXiv:2607.29058v1 Announce Type: new Abstract: Professional-document review is a constraint-checking problem in which decisions depend on relations among text, geometry, pages, and document revisions
arXiv:2607.29600v1 Announce Type: new Abstract: Vision-and-language navigation (VLN) enables robots to follow instructions in previously unseen environments. Recently, a training-free paradigm has eme
arXiv:2607.28638v1 Announce Type: new Abstract: As large language model (LLM) agents increasingly learn from experience, they primarily rely on trajectory-level reflection to extract insights. Viewed
arXiv:2607.29002v1 Announce Type: new Abstract: Online shoppers increasingly turn to AI shopping assistants, using images and multi-turn dialogue to express and refine product needs that are difficult
arXiv:2607.29000v1 Announce Type: cross Abstract: Multimodal information can improve the accuracy of click-through rate (CTR) prediction and effectively alleviate item cold-start and long-tail problem
arXiv:2509.17192v3 Announce Type: replace Abstract: LLM-based social simulations can make a generated transcript look like a single behavioral signal, but the model behind that transcript may be doing
arXiv:2603.06828v2 Announce Type: replace-cross Abstract: We uncover a behavioral law of long-horizon vision-language models: models that maintain temporally grounded beliefs generalize better. Standa
arXiv:2607.28680v1 Announce Type: new Abstract: Entity linking in tables matches short and ambiguous cell mentions to their corresponding knowledge-base entities. Existing approaches typically rely on
arXiv:2607.14144v2 Announce Type: replace Abstract: The Platonic Representation Hypothesis (PRH) holds that as models scale, representations of heterogeneous networks converge toward a shared model of
DeepSeek V4 Flash got me thinking... We keep seeing smaller models get way better. A model at a certain parameter count today can be much smarter than a model of the same size from a year or two ago.
arXiv:2607.26819v1 Announce Type: cross Abstract: Open source communities have been flooded with AI-generated contributions. In defense, they have written contribution rules to regulate coding agents'
arXiv:2607.27130v1 Announce Type: new Abstract: Ontology matching (OM) has traditionally been formulated as either equivalence discovery or subsumption matching. The existing OM systems identify only
arXiv:2607.27705v1 Announce Type: cross Abstract: Large language models can contribute useful ideas to mathematical research, yet long-horizon proof attempts remain difficult to coordinate, evaluate,
arXiv:2607.28516v1 Announce Type: new Abstract: Long-video understanding commonly compresses videos into a small set of frames or visual tokens for answer generation. Existing compact pipelines focus
Here is my experience, which I would like to share with you and I also would like to hear your thoughts and valuable tips&tricks. Hardware: Mac Mini M4 (32GB Unified Memory) Model Server: Ollama Orche
arXiv:2607.28386v1 Announce Type: new Abstract: Image similarity underlies many computer vision applications, yet it is often unclear why two images receive a high or low similarity score. Existing ex
arXiv:2607.28129v1 Announce Type: new Abstract: Face-and-voice association learning is one of the most challenging tasks in deep learning. In this paper, we propose a simple but powerful cross-modal f
arXiv:2607.28225v1 Announce Type: new Abstract: Agentic vision-language models (VLMs), which interleave textual reasoning with explicit tool calls such as cropping and code-based image manipulation, h
arXiv:2607.27654v1 Announce Type: new Abstract: Event analysis is an essential and fundamental direction of information extraction, involving various event-centric tasks at different granularity of do
arXiv:2607.28082v1 Announce Type: new Abstract: Large language models (LLMs) have demonstrated strong capabilities in structured query generation, making them a natural choice for Text-to-SPARQL, whic
arXiv:2607.28076v1 Announce Type: cross Abstract: Reinforcement learning with verifiable rewards (RLVR) is effective for training large language model agents. However, terminal rewards provide only co
arXiv:2607.28394v1 Announce Type: new Abstract: Hand-object interaction (HOI) modeling remains challenging because it requires joint reasoning about hand articulation, object geometry, contact, semant
arXiv:2607.27577v1 Announce Type: cross Abstract: Heterogeneous recommendation feeds present complex challenges that extend beyond those found in highly homogeneous environments (e.g., music-only or v
arXiv:2607.27834v1 Announce Type: cross Abstract: Persistent memory lets long-running large language model agents reuse information across sessions and tasks. Yet errors in writable memory can persist
arXiv:2607.28300v1 Announce Type: new Abstract: Open vocabulary 3D scene understanding is essential for next-generation interactive systems, empowering users to intuitively query and navigate reconstr
arXiv:2607.27782v1 Announce Type: new Abstract: Flow-matching Vision-Language-Action (VLA) policies have shown strong potential for robotic manipulation but often suffer from compounding errors caused
arXiv:2607.28538v1 Announce Type: new Abstract: Classifying pathological scars from clinical photographs requires distinguishing keloids from hypertrophic scars despite limited expert-labeled data and
arXiv:2607.28098v1 Announce Type: cross Abstract: Scientific datasets are commonly organized as hierarchical repositories containing heterogeneous and interdependent files, making their inspection, in
arXiv:2601.16651v3 Announce Type: replace Abstract: Gradient-based methods for instance-based explanation for large language models (LLMs) are hindered by the immense dimensionality of model gradients
arXiv:2607.26724v1 Announce Type: new Abstract: Large language model (LLM) agents have been widely applied in automating data science tasks. However, existing methods typically rely on a limited set o
arXiv:2607.26742v1 Announce Type: cross Abstract: Zero-shot text-to-speech (TTS) clones a voice from a short audio prompt, but this reliance on reference audio is a barrier when only visual informatio