Knowledge catalogue
arXiv:2608.21919v1 Announce Type: new Abstract: Recent work has proposed that reasoning and memorization in language models can be characterized by a single representation direction, including methods
arXiv:2608.22294v1 Announce Type: new Abstract: World models for physical interaction are typically trained to predict future observations or latent features; however, a planning-oriented model must a
arXiv:2608.22920v1 Announce Type: new Abstract: Multi-behavior recommendation (MBR) leverages auxiliary behavioral signals, such as clicks and add-to-cart, to enhance target behavior prediction like p
arXiv:2608.21979v1 Announce Type: new Abstract: Charitable food agencies play an important role in alleviating food insecurity by distributing donated food to people in need. However, they rely on ad
arXiv:2508.05206v2 Announce Type: replace Abstract: Online advertising systems typically use a cascaded architecture to manage massive requests and candidate volumes, where the ranking stages allocate
arXiv:2608.21431v1 Announce Type: new Abstract: Knowledge-based Visual Question Answering aims to answer questions about an image by integrating external knowledge with visual and textual information.
arXiv:2608.23136v1 Announce Type: new Abstract: Poor illumination obscures small, low-contrast defects in UAV bridge imagery, reducing the reliability and operational flexibility of automated inspecti
arXiv:2608.22376v1 Announce Type: new Abstract: Large language models generate tokens sequentially, but can they execute multiple tasks concurrently while forming each token? Broader attention allocat
arXiv:2608.23172v1 Announce Type: new Abstract: Large-scale vision-language models (VLMs) have demonstrated remarkable versatility across a wide range of multimodal tasks. However, understanding humor
arXiv:2608.21860v1 Announce Type: cross Abstract: Chain-of-Thought (CoT) reasoning has significantly enhanced the multi-step problem-solving capabilities of large language models (LLMs) by introducing
arXiv:2608.22807v1 Announce Type: new Abstract: A rapidly growing range of sequential data tasks, such as identifying trend reversals in financial markets, auto-segmenting video and audio recordings,
arXiv:2608.23446v1 Announce Type: new Abstract: We study the problem of formally explaining why a candidate was not selected by a given tournament rule, by identifying sub-tournaments in which the can
arXiv:2608.23429v1 Announce Type: cross Abstract: Diffusion Transformers (DiTs) have shown strong performance in high-fidelity image generation, but their sampling process remains computationally inte
arXiv:2608.21473v1 Announce Type: new Abstract: Quantification, estimating class prevalences in bags of unlabeled instances is vital in domains where aggregate statistics are more important than indiv
arXiv:2606.09590v2 Announce Type: replace Abstract: Medical language models (LMs) can memorize and reproduce protected health information, but privacy evaluations often focus on recovery of training t
arXiv:2608.22003v1 Announce Type: new Abstract: Data-Free Knowledge Distillation (DFKD) preserves privacy by transferring knowledge without real data access. However, existing generator-based DFKD met
arXiv:2608.22967v1 Announce Type: new Abstract: Practical molecular inverse design is rarely a one-shot generation problem; it often takes the form of closed-loop candidate-pool enrichment, where unde
arXiv:2510.16927v2 Announce Type: replace Abstract: The optimization landscape of Transformer models remains poorly understood despite their widespread adoption. While recent studies have derived curv
arXiv:2608.21457v1 Announce Type: new Abstract: Unmanned aerial vehicle (UAV) object detection is critical for applications such as target search, where accurate detection of small objects in complex
arXiv:2608.23011v1 Announce Type: cross Abstract: Graph-based retrieval-augmented generation (RAG) provides a scalable paradigm for long-video understanding, but existing systems typically inherit a f
arXiv:2608.23205v1 Announce Type: new Abstract: Large Reasoning Models (LRMs) have revolutionized reasoning in LLMs, and the increasing public availability of reasoning traces creates valuable opportu
arXiv:2608.22064v1 Announce Type: new Abstract: We present our solution for the MOSEv2 track of the 8th Large-scale Video Object Segmentation (LSVOS) Challenge at ECCV 2026. The challenge evaluates ro
arXiv:2608.21464v1 Announce Type: cross Abstract: We demonstrate that structured distortion of training data - which we term complexity induction - can induce compositional generalization in a standar
arXiv:2411.09686v4 Announce Type: replace-cross Abstract: Regressing a function F on R^d without incurring the statistical and computational curse of dimensionality requires exploitable structure. Com
arXiv:2608.23551v1 Announce Type: cross Abstract: Recent advances in continuous diffusion and flow-based language models (LMs) have achieved performance competitive with discrete LMs. However, existin
arXiv:2608.23164v1 Announce Type: cross Abstract: Counterfactual (CF) explanations for time-series classifiers are usually evaluated one example at a time: what minimal edit flips this single window's
arXiv:2608.23391v1 Announce Type: cross Abstract: Structured data exists in many forms (tables, knowledge graphs, charts, and time series), and converting it into text may involve different generation
arXiv:2608.22420v1 Announce Type: cross Abstract: Decoding perceived speech from non-invasive brain recordings has garnered significant attention in recent years due to its wide range of potential app
arXiv:2411.05625v2 Announce Type: replace-cross Abstract: We propose a new approach to falsify causal discovery algorithms without ground truth, which is based on testing the causal model on a variabl
arXiv:2608.21462v1 Announce Type: cross Abstract: Due to the selection of their training data, large language models (LLMs) perform best on standard-language inputs from languages using the Latin alph
arXiv:2608.22277v1 Announce Type: new Abstract: Deep learning surrogates for forecasting chaotic dynamical systems suffer from catastrophic error accumulation over long-term autoregressive rollouts. T
arXiv:2608.22289v1 Announce Type: new Abstract: Unmanned aerial vehicles (UAVs) increasingly require robust visual localization in GNSS-denied environments. A common solution estimates UAV poses by ma
arXiv:2608.22126v1 Announce Type: cross Abstract: Physics reasoning requires constructing a consistent model of the underlying physical system rather than relying solely on symbolic or formula-based m
arXiv:2608.22923v1 Announce Type: cross Abstract: Dense IoT networks require reliable communication despite limited spectrum and substantial multi-user interference while maintaining manageable receiv
arXiv:2606.18395v2 Announce Type: replace-cross Abstract: The output combiner of a Doherty power amplifier (PA) integrates load modulation, impedance matching, and phase compensation within a single n
arXiv:2608.21784v1 Announce Type: new Abstract: Few-step text-to-image models increasingly replace slower generators, yet acceleration can silently change distributions over unspecified attributes eve
arXiv:2201.02352v3 Announce Type: replace Abstract: Mobility, which is a basic property for a mechanism has to be analyzed to find the degrees of freedom. A quick method for calculation of degrees of
arXiv:2608.23114v1 Announce Type: cross Abstract: Predicting transcriptome-wide responses to unseen genetic perturbations remains a major computational challenge because accurate prediction requires r
arXiv:2608.23304v1 Announce Type: new Abstract: Proprioception in musculoskeletal humanoids is typically estimated primarily from muscle sensing, while the role of cutaneous deformation around joints
arXiv:2608.22028v1 Announce Type: new Abstract: This paper presents a conceptual design for a proactive human assisting robot system capable of recognizing human activities and responding proactively.
arXiv:2608.23363v1 Announce Type: cross Abstract: Audio-visual deepfake detection is an actively studied topic, where one of the main challenges is to develop detectors able to generalize across deepf
arXiv:2606.17904v2 Announce Type: replace Abstract: Language models increasingly serve as advisory systems in maintenance operations. To prevent hallucination, recent systems ground these models in pr
arXiv:2608.22368v1 Announce Type: new Abstract: While linear attention is a compelling mechanism for high-resolution object detection due to its reduced cost for global token mixing, converting the So
arXiv:2608.15934v1 Announce Type: cross Abstract: Different shape representations facilitate different computations. Surface representations, in particular meshes, are often used for modeling, whereas
arXiv:2511.05575v2 Announce Type: replace Abstract: Diffusion-based approaches have recently achieved strong results in face swapping, offering improved visual quality over traditional GAN-based metho
arXiv:2608.22819v1 Announce Type: new Abstract: Text-conditioned image-to-video (I2V) generation has advanced rapidly, yet generating videos with multiple subjects remains challenging. A model must si
arXiv:2608.22065v1 Announce Type: cross Abstract: Whether the slow solar wind originates from one coronal source or two distinct channels remains a central open question in heliophysics. Resolving thi
arXiv:2608.22085v1 Announce Type: new Abstract: Synthetic clinical data generation with large language models addresses the scarcity that limits cancer staging research, but oncology hallucinations ar
arXiv:2608.21364v1 Announce Type: cross Abstract: Both human and AI systems that process narrative or long-form content operate incrementally: input is received over time, and internal representations
arXiv:2608.21448v1 Announce Type: cross Abstract: DNA methylation provides a stable record of cellular identity, capturing epigenetic programs that distinguish specialized cell states despite a shared
arXiv:2603.16459v2 Announce Type: replace Abstract: Diffusion large language models (D-LLMs) have emerged as a promising alternative to auto-regressive models due to their iterative refinement capabil
arXiv:2608.21998v1 Announce Type: new Abstract: Regional climate risk assessment is critical for applications such as infrastructure design, disaster forecasting, and insurance resource allocation. Ho
arXiv:2608.21755v1 Announce Type: new Abstract: Long-horizon agents need memory that identifies relevant experience, resolves revisions, and exposes checkable provenance. We present ECHO (Embodied Con
arXiv:2608.21424v1 Announce Type: new Abstract: Interactive video generation and editing are becoming increasingly important for creative design. In this report, we introduce EditStream: a unified fra
arXiv:2608.22201v1 Announce Type: cross Abstract: We introduce a new class of regression models for scan statistics on real-valued signals. These allow for improved fitting of non-stationary signals t
arXiv:2608.22721v1 Announce Type: cross Abstract: Machine-learning malware detectors often achieve high clean-data accuracy, but operational triage also requires evidence about uncertainty, novelty, r
arXiv:2608.21697v1 Announce Type: new Abstract: Generating expressive facial behavior in virtual humans (VHs) remains a central challenge in affective computing and character animation. This paper pre
arXiv:2608.21463v1 Announce Type: new Abstract: The study employed an Artificial Neural Network in combination with the optimized Adaptive Moment Estimation (Adam) algorithm, currently the only AQI fo
arXiv:2608.21420v1 Announce Type: new Abstract: Thematic analysis (TA) has long been regarded as an inherently human, reflexive, and interpretive process. However, the extent to which LLM-generated TA
arXiv:2608.22652v1 Announce Type: cross Abstract: LLMs are increasingly used for code generation, yet they frequently hallucinate non-existent software packages, creating exploitable entry points into