Knowledge catalogue
arXiv:2605.14636v1 Announce Type: new Abstract: Large language models (LLMs) often fail to reason under temporal cutoffs: when prompted to answer from the standpoint of an earlier time, they exploit k
arXiv:2605.14136v1 Announce Type: new Abstract: Recent text-to-video diffusion transformers generate visually compelling frames, yet still struggle with temporal coherence, often producing flickering,
arXiv:2605.14879v1 Announce Type: cross Abstract: A plethora real-world environments require agents to compete repeatedly for the same limited resource, calling for a temporal notion of fairness judge
arXiv:2603.12529v2 Announce Type: replace-cross Abstract: Large Reasoning Models (LRMs) achieve impressive performance on complex reasoning tasks via Chain-of-Thought (CoT) reasoning, which enables th
arXiv:2605.13909v1 Announce Type: cross Abstract: Negotiation is a central mechanism of economic exchange, shaping markets, procurement, labor agreements, and resource allocation. It is also a canonic
arXiv:2605.14651v1 Announce Type: new Abstract: Urban vegetation monitoring plays a vital role in understanding environmental changes, yet comprehensive datasets for this purpose remain limited. To ad
arXiv:2605.14477v1 Announce Type: new Abstract: We introduce EvoLib, a test-time learning framework that enables large language models to accumulate, reuse, and evolve knowledge across problem instanc
arXiv:2605.14896v1 Announce Type: cross Abstract: This paper presents a system for the 2024 Text-Dependent Speaker Verification (TdSV) Challenge. The system achieved a Minimum Detection Cost Function
arXiv:2605.15168v1 Announce Type: cross Abstract: Reconstructing precise clinical timelines is essential for modeling patient trajectories and forecasting risk in complex, heterogeneous conditions lik
arXiv:2605.15053v1 Announce Type: cross Abstract: Continually pre-training a large language model on heterogeneous text domains, without replay or task labels, has remained an unsolved architectural p
arXiv:2605.14167v1 Announce Type: new Abstract: Every AI benchmark operationalizes theoretical assumptions about the capability it claims to assess. When assumptions function as unexamined commitments
arXiv:2605.14418v1 Announce Type: cross Abstract: 'Oh-Oh, yes, I'm the great pretender. Pretending that I'm doing well. My need is such, I pretend too much...' summarizes the state in the area of jail
arXiv:2410.02091v3 Announce Type: replace-cross Abstract: Generative artificial intelligence (AI) facilitates content production and enhances ideation capabilities, which can significantly influence d
arXiv:2605.13860v1 Announce Type: cross Abstract: Moltbook is a social media platform in which posts and comments are authored exclusively by autonomous AI agents. We present the Moltbook Observatory
arXiv:2510.00231v2 Announce Type: replace-cross Abstract: KV cache compression promises increased throughput and efficiency with negligible loss in performance. While the gains in throughput are indis
arXiv:2412.17155v4 Announce Type: replace Abstract: Early detection is crucial for successful cancer treatment and increasing survivability rates, particularly in the most common forms. Ten different
arXiv:2605.14694v1 Announce Type: new Abstract: Sparse Autoencoders (SAEs) that can accurately reconstruct their input (minimizing distortion) by making efficient use of few features (minimizing the r
arXiv:2605.15011v1 Announce Type: new Abstract: Scientific contributions rarely develop in isolation, but instead build upon prior discoveries. We formulate the task of automated technological roadmap
arXiv:2511.21247v2 Announce Type: replace-cross Abstract: This paper introduces The Spheres dataset, multitrack orchestral recordings designed to advance machine learning research in music source sepa
arXiv:2605.14819v1 Announce Type: new Abstract: While Flow Matching theoretically guarantees constant-velocity trajectories, we identify a critical breakdown in high-dimensional practice: the Velocity
arXiv:2605.14448v1 Announce Type: cross Abstract: Multimodal large language models (MLLMs) have emerged as a powerful backbone for multimodal embeddings. Recent methods introduce chain-of-thought (CoT
arXiv:2605.14177v1 Announce Type: cross Abstract: Long-horizon personalization requires dialogue assistants to retrieve user-specific facts from extended interaction histories. In practice, many relev
arXiv:2605.14915v1 Announce Type: new Abstract: Imbalanced learning remains a fundamental challenge in tabular data applications. Despite decades of research and numerous proposed algorithms, a system
arXiv:2605.14280v1 Announce Type: new Abstract: We introduce and analyze Target-Induced Loss Tilting (TILT) for unsupervised domain adaptation under covariate shift. It is based on a novel objective f
arXiv:2510.04682v3 Announce Type: replace-cross Abstract: Large Language Models (LLMs) are widely applied in real world scenarios, yet fine-tuning them comes with significant computational and storage
arXiv:2605.14142v1 Announce Type: cross Abstract: Integration against a probability distribution given its unnormalized density is a central task in Bayesian inference and other fields. We introduce n
arXiv:2605.14291v1 Announce Type: cross Abstract: The rapid advancement of Large Vision-Language Models (LVLMs) is increasingly accompanied by unauthorized scraping and training on multimodal web data
arXiv:2605.14890v1 Announce Type: new Abstract: Foundation models tokenize Ukrainian legal text with vastly different efficiency, yet no systematic comparison exists for this domain. We benchmark seve
arXiv:2502.16060v5 Announce Type: replace-cross Abstract: Foundation models are reshaping EEG analysis, yet an important problem of EEG tokenization remains a challenge. This paper presents TFM-Tokeni
arXiv:2605.14824v1 Announce Type: new Abstract: Topological clustering, and its main algorithm ToMATo, is a clustering method from Topological Data Analysis (TDA) which has been applied successfully i
arXiv:2605.15035v1 Announce Type: new Abstract: We introduce TopoPrimer, a framework that makes the global topological structure of the series population an explicit input to any forecasting model. To
arXiv:2605.14594v1 Announce Type: new Abstract: High-fidelity 3D head generation plays a crucial role in the film, animation and video game industries. In industrial pipelines, studios typically enfor
arXiv:2605.14601v1 Announce Type: new Abstract: Three-dimensional object detection in panoramic imagery is crucial for comprehensive scene understanding, yet accurately mapping 2D features to 3D remai
arXiv:2605.14705v1 Announce Type: new Abstract: Sign language is the primary language for many Deaf and Hard-of-Hearing (DHH) signers, yet most conversational AI systems still mediate interaction thro
arXiv:2605.14210v1 Announce Type: cross Abstract: Concept Bottleneck Models (CBMs) offer interpretable alternatives to black-box predictors by introducing human-relatable concepts before the final out
arXiv:2605.14999v1 Announce Type: cross Abstract: As generative AI becomes increasingly integrated into journalism, designing effective AI-use disclosures that inform readers without imposing unnecess
arXiv:2605.14866v1 Announce Type: cross Abstract: As modern microservice systems grow increasingly complex due to dynamic interactions and evolving runtime environments, they experience failures with
arXiv:2605.14717v1 Announce Type: cross Abstract: Label-free single-cell imaging offers a scalable, non-invasive alternative to fluorescence-based cytometry, yet inferring molecular phenotypes directl
arXiv:2605.14253v1 Announce Type: new Abstract: Purpose: Mechanical thrombectomy (MT) improves stroke outcomes, but is limited by a lack of local treatment access. Widespread distribution of reinforce
arXiv:2605.13981v1 Announce Type: cross Abstract: The rise in deployment of large language models has driven a surge in GPU demand and datacenter scaling, raising concerns about electricity use, grid
arXiv:2605.13925v1 Announce Type: new Abstract: Robotic dexterous hands are central to contact-rich manipulation, with rapid progress driven by advances in hardware, sensing, control, simulation, and
arXiv:2605.13936v1 Announce Type: cross Abstract: The recent success of large language models (LLMs) has been largely driven by vast public datasets. However, the next frontier for LLM development lie
arXiv:2605.14276v1 Announce Type: cross Abstract: Diffusion models generate samples by denoising along the score of a perturbed target distribution. In practice, one trains a neural diffusion model, w
arXiv:2506.08809v5 Announce Type: replace Abstract: High-resolution sinogram completion is critical for computed tomography reconstruction, as missing projections can introduce severe artifacts. While
arXiv:2605.15134v1 Announce Type: new Abstract: Estimating how often an ML model will fail at deployment scale is central to pre-deployment safety assessment, but a feasible evaluation set is rarely l
arXiv:2510.07118v3 Announce Type: replace Abstract: Instruction tuning is essential for aligning large language models (LLMs) to downstream tasks and commonly relies on large, diverse corpora. However
arXiv:2602.04657v3 Announce Type: replace Abstract: Recently, reducing redundant visual tokens in vision-language models (VLMs) to accelerate VLM inference has emerged as a hot topic. However, most ex
arXiv:2605.14315v1 Announce Type: new Abstract: Recent feed-forward 3D reconstruction methods, such as visual geometry transformers, have substantially advanced the traditional per-scene optimization
arXiv:2605.14373v1 Announce Type: cross Abstract: Zeroth-Order (ZO) optimization is pivotal for scenarios where backpropagation is unavailable, such as memory-constrained on-device learning and black-
arXiv:2605.14731v1 Announce Type: cross Abstract: Speech-driven gestures and facial animations are fundamental to expressive digital avatars in games, virtual production, and interactive media. Howeve
arXiv:2605.14643v1 Announce Type: new Abstract: Deep learning methods based on backward stochastic differential equations (BSDEs) have emerged as competitive alternatives to physics-informed neural ne
arXiv:2605.14570v1 Announce Type: new Abstract: Large Language Diffusion Models (LLDMs) are emerging as an alternative to autoregressive models, offering faster inference through higher parallelism. S
arXiv:2605.14358v1 Announce Type: new Abstract: Language models often generate long chain-of-thought traces, but it remains unclear how much of this reasoning is necessary for preserving the final pre
arXiv:2605.14156v1 Announce Type: new Abstract: While generative models have shown promise in pediatric sleep analysis, the latent structure of their multimodal embeddings remains poorly understood. T
arXiv:2605.15127v1 Announce Type: cross Abstract: Moving to a new culture and adapting to a new life, as an international student, can be a stressful experience. In the US, international students face
arXiv:2605.14785v1 Announce Type: cross Abstract: Neural networks suffer from catastrophic forgetting in class-incremental learning (CIL) settings. Rehearsalnicode{x2013}replaying a subset of past sam
arXiv:2605.14028v1 Announce Type: new Abstract: Since the emergence of Vision Transformer (ViT), it has been widely used in generative language model and generative visual model. Especially in the cur
arXiv:2605.14626v1 Announce Type: new Abstract: RGB-T semantic segmentation requires strictly aligned VIS-IR-Label triplets; however, such aligned triplet data are often scarce in real-world scenarios
arXiv:2605.14876v1 Announce Type: cross Abstract: Despite rapid advancements, current text-to-image (T2I) models predominantly rely on a single-step generation paradigm, which struggles with complex s
arXiv:2605.14164v1 Announce Type: new Abstract: The primary way to establish and compare competencies in foundation and generative AI models has shifted from peer-reviewed literature to press releases