Knowledge catalogue
arXiv:2609.00387v1 Announce Type: cross Abstract: Large language models extracting knowledge graphs from text capture only explicitly stated facts, often leaving semantically related entities disconne
arXiv:2609.00472v1 Announce Type: cross Abstract: We provide an expository introduction on the importance of higher-arity tensor operations to deep learning. Then, we conduct a novel empirical investi
arXiv:2609.01282v1 Announce Type: cross Abstract: Vision Transformer (ViT) design has become increasingly diverse, with backbones combining convolutional stems, windowed, linear, or multi-axis attenti
arXiv:2504.20584v2 Announce Type: replace-cross Abstract: This work presents an RGB-D imaging-based approach to marker-free hand-eye calibration using a novel implementation of the iterative closest p
arXiv:2609.00251v1 Announce Type: new Abstract: As people increasingly interact with LLM assistants in daily life, continually adapting to individual preferences has become essential for effective lon
arXiv:2609.00563v1 Announce Type: cross Abstract: Animals and robots encounter mud at the water-land interface. Like sand, mud can stay solid or flow like a fluid. Unlike sand, the yield strength of m
arXiv:2609.00998v1 Announce Type: cross Abstract: Word Sense Disambiguation has advanced rapidly for English and a handful of well-resourced modern languages, but it continues to assume the existence
arXiv:2609.01375v1 Announce Type: cross Abstract: Large Vision-Language Models (LVLMs) have achieved strong multimodal performance, yet ensuring the factual correctness of generated content remains ch
arXiv:2609.00311v1 Announce Type: new Abstract: The inverse kinematics of generic 3R robots has been investigated through multiple approaches, mainly algebraic methods involving the solution of certai
arXiv:2603.03229v3 Announce Type: replace Abstract: The shock response spectrum (SRS) is widely used to characterize the response of single-degree-of-freedom (SDOF) systems to transient accelerations.
arXiv:2609.00449v1 Announce Type: cross Abstract: Neuroevolution of Augmenting Topologies (NEAT) and its advanced version, Evolvable-Substrate HyperNEAT (ES-HyperNEAT), have shown great potential in d
arXiv:2609.00513v1 Announce Type: new Abstract: Retrieval-Augmented Generation (RAG) mitigates large language models (LLMs) hallucinations, yet conventional dense retrieval struggles with the complex
arXiv:2609.00756v1 Announce Type: new Abstract: The Mutual Reinforcement Effect (MRE) asks whether a fine, span-level and a coarse, document-level task help each other when one model handles both. We
arXiv:2511.04093v2 Announce Type: replace Abstract: Large language models (LLMs) excel at reasoning but struggle with knowledge-intensive questions due to limited context and parametric knowledge. How
arXiv:2609.00082v1 Announce Type: cross Abstract: LLMs acquire vast amounts of knowledge during pre-training, but often lack the specialized knowledge needed to answer questions from niche sources suc
arXiv:2609.01532v1 Announce Type: new Abstract: Logit-based knowledge distillation (KD) is used to train smaller language models (LMs) via supervision from stronger teachers, but whether its benefits
arXiv:2609.01048v1 Announce Type: cross Abstract: Across the full Pythia suite (160M-12B, eight checkpoints, four task families), a linear probe can read a target variable from the residual stream as
arXiv:2609.00416v1 Announce Type: new Abstract: Probing studies have established that syntactic information is decodable in early and middle transformer layers, but what happens to that information in
arXiv:2609.01117v1 Announce Type: new Abstract: Chain-of-thought reasoning unfolds in discrete token space: each step is committed as text, errors propagate, and eliciting good traces presupposes trac
arXiv:2609.00332v1 Announce Type: cross Abstract: Generative models for implied volatility surfaces must produce outputs that satisfy static no-arbitrage constraints. We study these constraints in lat
arXiv:2609.01507v1 Announce Type: cross Abstract: Compressed context is usually carried as human-readable text or as rendered images that must be decoded, even when its consumer is a language model. W
arXiv:2609.01430v1 Announce Type: cross Abstract: Decision trees are among the most widely used models in machine learning, largely due to their transparent decision logic, making them well-suited for
arXiv:2609.01072v1 Announce Type: cross Abstract: Post-hoc calibration corrects reported confidence, yet a multiclass calibrator can also change the associated top-1 prediction. Accuracy captures only
arXiv:2609.00155v1 Announce Type: cross Abstract: Latent language identification is often used to argue that multilingual language models route computation through language-specific states, such as En
arXiv:2609.00093v1 Announce Type: new Abstract: Imbalanced time series classification is often addressed by changing the training distribution, objective, logits, or final threshold. These interventio
arXiv:2609.00241v1 Announce Type: new Abstract: Long documents often distribute important information across extensive narrative passages and multiple tables, making faithful summarization particularl
arXiv:2609.00792v1 Announce Type: cross Abstract: Dominant audio classification pipelines rely either on compact handcrafted summaries or on fixed time-frequency frontends such as log-mel representati
arXiv:2602.16365v2 Announce Type: replace-cross Abstract: Flexible endoscopic continuum manipulators offer high dexterity and access to complex anatomy, but nonlinear hysteresis limits feedforward con
arXiv:2609.00715v1 Announce Type: cross Abstract: Feedback-based coding schemes have demonstrated substantial performance gains over today's open-loop coding schemes. Unfortunately, these gains are us
arXiv:2609.00748v1 Announce Type: new Abstract: A transformer carries each token's state from layer to layer, and the whole vocabulary carried together forms a cloud that moves with depth. We ask whet
arXiv:2609.01423v1 Announce Type: new Abstract: Image style is a highly abstract, human-constructed concept shaped by a range of visual factors and intrinsically entangled with content, yet a unified
arXiv:2609.00873v1 Announce Type: new Abstract: Diffusion language models (DLMs) have recently emerged as an alternative modeling paradigm to autoregressive LMs, offering advantages such as parallel g
arXiv:2609.01316v1 Announce Type: cross Abstract: Retrieval over visually rich documents has a representation problem: important content often lives in tables, charts, figures, and layout relations th
arXiv:2609.00742v1 Announce Type: new Abstract: As AI video generators achieve cinematic realism, reliable detection becomes essential for safeguarding digital trust. We identify cross-scale coupling
arXiv:2609.00073v1 Announce Type: new Abstract: Malaria remains a significant global health burden, necessitating continuous research efforts to understand its complex molecular mechanisms, epidemiolo
arXiv:2603.03517v2 Announce Type: replace-cross Abstract: General-purpose large language models (LLMs) that rely on in-context learning do not reliably deliver the scientific understanding and perform
arXiv:2609.01090v1 Announce Type: new Abstract: Scientific knowledge about AI models is produced faster than the community can organize it. Every few months a new foundation model reshapes the field a
arXiv:2609.00564v1 Announce Type: cross Abstract: At the water-land interface, amphibious fishes encounter wet flowable substrates made of granular solid-water mixtures, which can stay solid or flow l
arXiv:2609.00696v1 Announce Type: new Abstract: Graph data across diverse domains can expose valuable relational information to unauthorized representation learning, creating a pressing need for prote
arXiv:2502.20115v4 Announce Type: replace Abstract: Causal discovery is a difficult problem that typically relies on strong assumptions on the data-generating model, such as non-Gaussianity. In practi
arXiv:2609.01390v1 Announce Type: new Abstract: Unmanned Aerial Vehicles (UAVs) have emerged as a promising platform for firefighting operations due to their flexibility, low operational cost, and abi
arXiv:2609.01408v1 Announce Type: new Abstract: A fundamental challenge in artificial intelligence is the transformation of observations into explicit symbolic representations suitable for abstraction
arXiv:2609.00367v1 Announce Type: cross Abstract: Large Language Models are increasingly deployed for sophisticated data engineering tasks such as generating structured queries from natural language,
arXiv:2609.00066v1 Announce Type: cross Abstract: NVFP4 is an efficient microscaling format for low-bit inference, but activation outliers can still degrade quantization accuracy within NVFP4 blocks.
arXiv:2609.01032v1 Announce Type: cross Abstract: Automated mining of formal specifications is vital for verifying real-time systems. However, existing passive learning approaches remain restricted to
arXiv:2506.12454v2 Announce Type: replace-cross Abstract: What fundamentally distinguishes an adversarial attack from a misclassification due to limited model expressivity or finite data? In this work
arXiv:2609.00923v1 Announce Type: new Abstract: While feed-forward 3D reconstruction (3R) offers efficient end-to-end modeling, its application in large-scale UAV mapping is hindered by the prohibitiv
arXiv:2609.01410v1 Announce Type: cross Abstract: Generative data augmentation is widely used to mitigate class imbalance, yet its theoretical effect on downstream generalization remains poorly unders
arXiv:2609.01311v1 Announce Type: new Abstract: We extend recent work establishing an equivalence between one-layer transformers and nearest-neighbor classifiers in the binary setting to the multiclas
arXiv:2609.00751v1 Announce Type: new Abstract: Conventional soft robot actuators excel in compliance, but their uncontrolled deformations compromise accuracy and hinder scaling to multi-degree-of-fre
arXiv:2609.01249v1 Announce Type: cross Abstract: Invisible watermarks are typically evaluated against predefined perturbations such as compression, blur, noise, cropping, and denoising. Public founda
arXiv:2510.13302v4 Announce Type: replace-cross Abstract: Computational stylometry studies writing style through quantitative textual patterns, enabling applications such as authorship attribution, id
arXiv:2609.00734v1 Announce Type: new Abstract: Standard supervised fine-tuning (SFT) assigns the same explicit loss weight to every expert demonstration, regardless of the model's changing competence
arXiv:2505.13299v2 Announce Type: replace-cross Abstract: This paper considers the estimation of quantiles via a smoothed version of the stochastic gradient descent (SGD) algorithm. By smoothing the s
arXiv:2609.00471v1 Announce Type: cross Abstract: This paper introduces a structural taxonomy for constrained non-convex optimization based on the signature of Lagrange multipliers at KKT stationary p
arXiv:2609.01495v1 Announce Type: cross Abstract: Security evaluations of decentralized federated learning (DFL) typically focus on how Byzantine participants behave, while largely overlooking which p
arXiv:2609.01068v1 Announce Type: new Abstract: The heavy-tailed distribution of output lengths in Large Language Model (LLM) serving poses major challenges for resource provisioning and cluster sched
arXiv:2609.01135v1 Announce Type: new Abstract: Minimum Bayes Risk (MBR) decoding enables high-quality text generation by selecting the hypothesis that maximizes a utility metric over sampled pseudo-r
arXiv:2609.00611v1 Announce Type: cross Abstract: Foundation models are increasingly being pursued in particle and nuclear physics, but existing approaches remain strongly tied to individual experimen
arXiv:2602.22006v4 Announce Type: replace Abstract: Accurate relative localization is critical for multi-robot cooperation. In robot groups, measurements from different robots arrive asynchronously an