Knowledge catalogue
arXiv:2503.17239v3 Announce Type: replace-cross Abstract: Fine-tuning large language models (LLMs) is a common practice to adapt generalist models to specialized domains. However, recent studies show
arXiv:2604.20930v1 Announce Type: cross Abstract: Internal Safety Collapse (ISC) is a failure mode in which frontier LLMs, when executing legitimate professional tasks whose correct completion structu
arXiv:2604.21681v1 Announce Type: new Abstract: We present Sapiens2, a model family of high-resolution transformers for human-centric vision focused on generalization, versatility, and high-fidelity o
arXiv:2604.21515v1 Announce Type: new Abstract: ASPIC-style structured argumentation frameworks provide a formal basis for reasoning in artificial intelligence by combining internal argument structure
arXiv:2511.18264v3 Announce Type: replace Abstract: Existing satellite video tracking methods often struggle with generalization, requiring scenario-specific training to achieve satisfactory performan
arXiv:2604.21174v1 Announce Type: cross Abstract: The Gaussian scale parameter (epsilon) is central to the behavior of Gaussian Kolmogorov--Arnold Networks (KANs), yet its role in deep edge-based arch
arXiv:2411.17061v2 Announce Type: replace Abstract: The Vision Transformer (ViT) has achieved notable success in computer vision, with its variants widely validated across various downstream tasks, in
arXiv:2509.19318v2 Announce Type: replace-cross Abstract: While robotic perception has advanced rapidly in vision and touch, enabling robots to reason about indoor fungal contamination from weak, diff
arXiv:2512.19995v2 Announce Type: replace-cross Abstract: Large language models increasingly expose reasoning traces, yet their underlying cognitive structure and steps remain difficult to identify an
arXiv:2604.20943v1 Announce Type: new Abstract: We present SCM (Sleep-Consolidated Memory), a research preview of a memory architecture for large language models that draws on neuroscientific principl
arXiv:2604.21592v1 Announce Type: new Abstract: Recent breakthroughs in 3D generative modeling have yielded remarkable progress in static shape synthesis, yet high-fidelity dynamic 4D generation remai
arXiv:2604.20934v1 Announce Type: cross Abstract: Software-Defined Networking (SDN) is another technology that has been developing in the last few years as a relevant technique to improve network prog
arXiv:2505.16737v2 Announce Type: replace-cross Abstract: Large language models (LLMs) have achieved remarkable success across many applications, but their ability to generate harmful content raises s
arXiv:2604.21931v1 Announce Type: cross Abstract: How can we tell whether a video has been sped up or slowed down? How can we generate videos at different speeds? Although videos have been central to
arXiv:2604.21523v1 Announce Type: cross Abstract: Large Vision-Language Models (VLMs) are increasingly used to evaluate outputs of other models, for image-to-text (I2T) tasks such as visual question a
arXiv:2604.21926v1 Announce Type: new Abstract: Understanding human activities and their surrounding environments typically relies on visual perception, yet cameras pose persistent challenges in priva
arXiv:2604.21130v1 Announce Type: new Abstract: Autonomous Unmanned Aerial Vehicles (UAVs) have revolutionized industries through their versatility with applications including aerial surveillance, sea
arXiv:2604.21414v1 Announce Type: new Abstract: Existing text-to-SQL synthesis pipelines still conflate executability with semantic validity: syntactic checks and execution-based validation can retain
arXiv:2602.11569v2 Announce Type: replace Abstract: Population synthesis is essential for individual-level simulation in transport planning and socio-economic analysis, yet remains challenging due to
arXiv:2604.21782v1 Announce Type: new Abstract: We present the shared task on narrative similarity and narrative representation learning - NSNRL (pronounced 'nass-na-rel'). The task operationalizes na
arXiv:2604.21571v1 Announce Type: new Abstract: Current model training approaches incorporate user information directly into shared weights, making individual data removal computationally infeasible w
arXiv:2604.21076v1 Announce Type: cross Abstract: Medication reconciliation at clinical handoffs is a high-stakes, error-prone process. Large language models are increasingly proposed to assist with t
arXiv:2604.21016v1 Announce Type: cross Abstract: When training neural networks with full-batch gradient descent (GD) and step size eta, the largest eigenvalue of the Hessian -- the sharpness S(oldsym
arXiv:2603.07961v3 Announce Type: replace Abstract: Scene Graph Generation (SGG) structures visual scenes as graphs of objects and their relations. While Multimodal Large Language Models (MLLMs) have
arXiv:2604.20937v1 Announce Type: new Abstract: Video Large Language Models (Video LLMs) incur high inference latency due to a large number of visual tokens provided to LLMs. To address this, training
arXiv:2508.06283v2 Announce Type: replace Abstract: 3D Scene Graphs integrate both metric and semantic information, yet their structure remains underutilized for improving path planning efficiency and
arXiv:2604.21693v1 Announce Type: new Abstract: Simultaneous localization and mapping (SLAM) is a foundational state estimation problem in robotics in which a robot accurately constructs a map of its
arXiv:2510.26615v3 Announce Type: replace Abstract: Retrieval-augmented generation (RAG) extends large language models (LLMs) with external knowledge, but it must balance limited effective context, re
arXiv:2604.21139v1 Announce Type: new Abstract: Language models must bind entities to the attributes they possess and maintain several such binding relationships within a context. We study how multipl
arXiv:2601.10003v2 Announce Type: replace Abstract: Constructing Knowledge Graphs (KGs) from unstructured text provides a structured framework for knowledge representation and reasoning, yet current L
arXiv:2604.21231v1 Announce Type: cross Abstract: Efficient inference for on-device Large Language Models (LLMs) remains challenging due to limited hardware resources and the high cost of the prefill
arXiv:2604.21221v1 Announce Type: new Abstract: We introduce Sparse Forcing, a training-and-inference paradigm for autoregressive video diffusion models that improves long-horizon generation quality w
arXiv:2604.21356v1 Announce Type: new Abstract: High-quality digital terrain models derived from airborne laser scanning (ALS) data are essential for a wide range of geospatial analyses, and their gen
arXiv:2604.21284v1 Announce Type: new Abstract: MemPalace is an open-source AI memory system that applies the ancient method of loci (memory palace) spatial metaphor to organize long-term memory for l
arXiv:2604.21190v1 Announce Type: new Abstract: Understanding visual scenes requires not only recognizing objects but also reasoning about their spatial relationships. Unlike general vision-language t
arXiv:2603.15055v2 Announce Type: replace-cross Abstract: We present a theory-guided generalized Bayesian methodology for spatio-temporal raster data, which we use to train an ensemble of stochastic f
arXiv:2604.21094v1 Announce Type: new Abstract: Graph Neural Networks (GNNs) excel on relational data, but standard benchmarks unrealistically assume the graph is centrally available. In practice, set
arXiv:2604.20887v1 Announce Type: cross Abstract: The spectral kernel field equation R[k] = T[k] lacks a conservation-law analog. We prove (i) the fixed-point flow is strictly volume-expanding (tr DF
arXiv:2510.04371v2 Announce Type: replace Abstract: AI agents are increasingly deployed in complex, interactive environments, yet their runtime remains a major bottleneck for training, evaluation, and
arXiv:2604.20849v1 Announce Type: cross Abstract: Retrieval-augmented generation over semi-structured sources such as HTML is constrained by a mismatch between document structure and the flat, sequenc
arXiv:2604.21214v1 Announce Type: cross Abstract: Text-to-SQL models have significantly improved with the adoption of Large Language Models (LLMs), leading to their increasing use in real-world applic
arXiv:2604.21700v1 Announce Type: cross Abstract: The growing application of large language models (LLMs) in safety-critical domains has raised urgent concerns about their security. Many recent studie
arXiv:2601.09056v3 Announce Type: replace-cross Abstract: Stylometry--the identification of an author through analysis of a text's style (i.e., authorship attribution)--serves many constructive purpos
arXiv:2604.21282v1 Announce Type: cross Abstract: Automated code vulnerability detection is critical for software security, yet existing approaches face a fundamental trade-off between detection accur
arXiv:2604.21043v1 Announce Type: cross Abstract: This paper examines the strategic use of language in contemporary artificial intelligence (AI) discourse, focusing on the widespread adoption of metap
arXiv:2506.12721v2 Announce Type: replace Abstract: Scaling test-time compute has emerged as an effective strategy for improving the performance of large language models. However, existing methods typ
arXiv:2503.10475v4 Announce Type: replace Abstract: In this paper, we present Stratified Topological Autonomy for Long-Range Coordination (STALC), a hierarchical planning approach for multi-robot coor
arXiv:2604.16395v2 Announce Type: replace-cross Abstract: Context retrieval systems for LLM inference face a critical challenge: high retrieval latency creates a fundamental tension between waiting fo
arXiv:2604.21748v1 Announce Type: cross Abstract: Long-term conversational agents need memory systems that capture relationships between events, not merely isolated facts, to support temporal reasonin
arXiv:2604.21090v1 Announce Type: cross Abstract: AI governance programmes increasingly rely on natural language prompts to constrain and direct AI agent behaviour. These prompts function as executabl
arXiv:2603.21697v2 Announce Type: replace-cross Abstract: Multimodal Large Language Models (MLLMs) extend text-only LLMs with visual reasoning, but also introduce new safety failure modes under visual
arXiv:2604.21689v1 Announce Type: cross Abstract: Creative face stylization aims to render portraits in diverse visual idioms such as cartoons, sketches, and paintings while retaining recognizable ide
arXiv:2604.21052v1 Announce Type: cross Abstract: We build on the Visual Autoregressive Modeling (VAR) framework and formulate style transfer as conditional discrete sequence modeling in a learned lat
arXiv:2604.21335v1 Announce Type: cross Abstract: Sub-token routing offers a finer control axis for transformer efficiency than the coarse units used in most prior work, such as tokens, pages, heads,
arXiv:2604.21211v1 Announce Type: new Abstract: Current text anonymization evaluation relies on span-based metrics that fail to capture what an adversary could actually infer, and assumes a single dat
arXiv:2604.21395v1 Announce Type: cross Abstract: We prove that empirical risk minimisation (ERM) imposes a necessary geometric constraint on learned representations: any encoder that minimises superv
arXiv:2511.03325v3 Announce Type: replace Abstract: Video Question Answering (VideoQA) in the surgical domain aims to enhance intraoperative understanding by enabling AI models to reason over temporal
arXiv:2503.16416v2 Announce Type: replace Abstract: LLM-based agents represent a paradigm shift in AI, enabling autonomous systems to plan, reason, and use tools while interacting with dynamic environ
arXiv:2604.21346v1 Announce Type: new Abstract: Vision--language models (VLMs) often fail on abstract visual reasoning benchmarks such as Bongard problems, raising the question of whether the main bot
arXiv:2604.21801v1 Announce Type: cross Abstract: Recent advances in deep learning for remote sensing rely heavily on large annotated datasets, yet acquiring high-quality ground truth for geometric, r