Knowledge catalogue
arXiv:2603.25685v2 Announce Type: replace-cross Abstract: Action-conditioned robot world models generate future video frames of the manipulated scene given a robot action sequence, offering a promisin
arXiv:2609.04516v1 Announce Type: cross Abstract: Recent work on controllable music generation has focused on autoregressive models, leaving diffusion-based systems comparatively underexplored. We pre
arXiv:2609.04265v1 Announce Type: new Abstract: Existing post-hoc explainers for machine learning classifiers primarily focus on feature attribution, assigning importance scores to individual features
arXiv:2609.05249v1 Announce Type: cross Abstract: As spaceborne computing systems increasingly rely on neural network (NN) accelerators, the opacity of commercial, black-box architectures severely res
arXiv:2609.05039v1 Announce Type: cross Abstract: Quantum software development is iterative and error-prone. Noisy hardware and repeated re-execution make experiment tracking, provenance, and reproduc
arXiv:2509.21889v3 Announce Type: replace Abstract: Existing evaluations of foundation models predominantly focus on output correctness, treating interaction as a static exchange of information. Howev
arXiv:2211.12337v4 Announce Type: replace Abstract: The theory of magnitude provides a mathematical framework for quantifying and maximizing diversity. We apply this framework to formulate quality-div
arXiv:2609.04649v1 Announce Type: new Abstract: We present DiT Readout (ReaDiT) Guidance, a lightweight framework for controlling generation with Diffusion Transformer (DiT) models via their internal
arXiv:2609.04432v1 Announce Type: cross Abstract: Transferable coarse-grained (CG) force fields compress chemical space: by aggregating atoms into a reduced set of interaction beads, models such as MA
arXiv:2609.04561v1 Announce Type: new Abstract: Whisper is a widely used foundation model for automatic speech recognition (ASR), but its generative decoder can produce fluent hallucinated transcripts
arXiv:2609.05382v1 Announce Type: cross Abstract: We propose Ref-GeNVS, a training-free, reflection-aware method for generative novel view synthesis (NVS) in mirror scenes. Existing multi-view diffusi
arXiv:2602.19381v2 Announce Type: replace-cross Abstract: We establish a regularity theorem for second-order elliptic PDEs on R^{d} in spectral Barron spaces. Under mild ellipticity and smallness assu
arXiv:2609.05059v1 Announce Type: cross Abstract: Whether repeated identical buying questions exhaust a language model's brand recommendations depends on retrieval. Across 300 question-engine cells (5
arXiv:2609.04583v1 Announce Type: new Abstract: The representation chosen for a mathematical operation can affect both its algebraic form and its empirical learning difficulty. We study this phenomeno
arXiv:2609.04493v1 Announce Type: new Abstract: We present ResLearn-XR, a residual learning framework for predicting eXtended Reality (XR) network traffic and estimating Quality-of-Experience (QoE) ri
arXiv:2609.04526v1 Announce Type: new Abstract: Merging a LoRA adapter into its base model is standard deployment practice: it removes the runtime adapter's per-forward overhead and leaves a single st
arXiv:2609.04261v1 Announce Type: cross Abstract: Self-supervised pretraining has transformed language and vision, but its value for molecular graph neural networks remains contested. We ask whether p
arXiv:2609.04767v1 Announce Type: new Abstract: Large language models (LLMs) increasingly rely on information retrieval (IR) systems, such as Retrieval-Augmented Generation (RAG), to incorporate domai
arXiv:2609.05263v1 Announce Type: cross Abstract: We investigate the best L_2 approximation of mixed Sobolev spaces by shallow neural networks with n neurons and general activation functions. We first
arXiv:2609.04344v1 Announce Type: cross Abstract: Sparse autoencoders (SAEs) are widely used to interpret language model activations, but SAE training and latent labelling are typically repeated for e
arXiv:2609.04710v1 Announce Type: cross Abstract: Inferring cellular dynamics from unpaired single-cell snapshots requires modeling both state transitions and population growth or death. Unbalanced dy
arXiv:2603.20686v2 Announce Type: replace-cross Abstract: Recent advancements in text-to-speech technologies enable generating high-fidelity synthetic speech nearly indistinguishable from real human v
arXiv:2609.04545v1 Announce Type: cross Abstract: Robots interacting with people must recognize not only explicit commands, but also social cues such as invitations, refusals, and unavailability. In r
arXiv:2609.04902v1 Announce Type: cross Abstract: Can we recover the 3D poses of multiple people using only sound? This paper presents the first attempt to estimate multi-person 3D poses solely from a
arXiv:2609.04264v1 Announce Type: new Abstract: Latent world models have become increasingly popular as a method to predict and plan in latent space rather than pixel space. Recent architectures, such
arXiv:2609.04435v1 Announce Type: cross Abstract: This paper proposes a spatio-temporal edge-movement direction pixel (STEMPix) for generating compact direction-aware edge movement information inside
arXiv:2609.04817v1 Announce Type: new Abstract: Strict nonlinear normal modes provide very regular families of oscillations within conservative mechanical systems. However, a strict normal mode will g
arXiv:2609.04705v1 Announce Type: cross Abstract: Passive cooling eliminates the energy overhead and mechanical failure modes of fans, making it attractive for edge deployment, yet sustained Deep Neur
arXiv:2609.04984v1 Announce Type: new Abstract: In the field of computer vision and graphics, high-quality reconstruction of the human body in static scenes has been achieved in recent years by a sing
arXiv:2609.04404v1 Announce Type: new Abstract: ASR systems sometimes produce fluent text that is unrelated to the speech they receive. We view these hallucinations as one possible consequence of a br
arXiv:2603.29566v2 Announce Type: replace Abstract: We study polynomial group convolutional neural networks (PGCNNs) for an arbitrary finite group G. In particular, we introduce a new mathematical fra
arXiv:2609.05335v1 Announce Type: cross Abstract: Public vulnerability databases collect rich information about known software flaws, including their weakness types, affected components, and related p
This is insane! GPT-6 Astra built this beautiful math animation in one go! (🔉 sound on) 'Jaw-on-the-floor' moment. I've not been able to get anything close to this with previous AI models. True person
arXiv:2603.27520v2 Announce Type: replace Abstract: In video diffusion transformers, visual patch tokens maintain explicit correspondence to space and time. We hypothesize that their channel dimension
arXiv:2609.04911v1 Announce Type: new Abstract: Interactive visual world models must distinguish observation from physical intervention. Camera motion reveals new surfaces, whereas intervention change
arXiv:2508.06207v3 Announce Type: replace Abstract: Back-support exoskeletons mitigate musculoskeletal strain, yet current systems rely on reactive sensing and lack context-aware assistance modulation
arXiv:2503.08904v3 Announce Type: replace Abstract: The recent developments in data-driven methods have paved the way to new methodologies to provide accurate state reconstruction of engineering syste
arXiv:2609.04637v1 Announce Type: cross Abstract: Audio Large Language Models (Audio LLMs) have advanced in audio understanding, yet they can still predict the answer by reasoning from textual cues or
arXiv:2609.04575v1 Announce Type: cross Abstract: Modern fine-grained Mixture-of-Experts (MoE) models route each token to a small number of experts and renormalize their router probabilities. We show
arXiv:2609.05091v1 Announce Type: new Abstract: Industrial anomaly detection must handle two distinct defect families: structural anomalies, which manifest as local texture corruptions, and logical an
arXiv:2609.04912v1 Announce Type: cross Abstract: Reliability evaluation of deep neural networks under hardware faults commonly relies on fault injection, but exhaustive campaigns are intractable for
arXiv:2609.05337v1 Announce Type: new Abstract: We present a Hessian-based approach to numerically continue periodic orbits in dynamical systems. A loop (periodic orbit candidate) is parametrized as a
arXiv:2609.04755v1 Announce Type: new Abstract: We construct a corpus of 1,262 verse-commentary (urai) pairs from five Classical Tamil source sections, ranging from technical grammatical prose to mode
arXiv:2609.04434v1 Announce Type: new Abstract: Hybrid language models combine attention with a fixed-size recurrent state, but the role of each channel remains unclear. We introduce two cache-level i
arXiv:2609.04766v1 Announce Type: cross Abstract: Generative AI research has increasingly evaluated factuality, citation, coverage, and report structure. Yet passing such local checks does not by itse
arXiv:2609.04806v1 Announce Type: new Abstract: Financial large language models are increasingly deployed for summarization of reports and disclosures, where numerical hallucination poses significant
arXiv:2609.05370v1 Announce Type: cross Abstract: Decompilation recovers high-level source from compiled machine code and serves as a foundation for security tasks such as vulnerability detection and
arXiv:2609.04490v1 Announce Type: new Abstract: Quantization is widely used to reduce the computational and memory demands of neural-network inference. In recurrent networks, however, the quantized st
Banger paper from Microsoft and Cornell. If you have looked at thinking tokens and decided you cannot afford the context, read this one. (bookmark it) A pause token buys the model extra compute for ea
How we think about the “wiki incident,” where our agents wrote to several internet sites: it’s past time for us to define standards for when and how we share misalignment incidents, not just misalignm
arXiv:2609.03687v1 Announce Type: new Abstract: Coreference resolution is an important task in contextual reasoning. In this paper, we investigate the mechanism for representing and retrieving singula
arXiv:2609.03621v1 Announce Type: new Abstract: Making science computable requires representations of both scientific knowledge and the physical world in which scientific claims are tested. A computab
arXiv:2609.03758v1 Announce Type: new Abstract: Soft eversion robots, also known as vine robots, have attracted growing interest for navigation and inspection tasks, including minimally invasive medic
arXiv:2511.02657v2 Announce Type: replace Abstract: We investigate robust federated learning, where a group of workers collaboratively train a shared model under the orchestration of a central server
arXiv:2609.03809v1 Announce Type: new Abstract: Energy consumption is one of the largest operational expenditure items for mobile network operators, yet site-level energy inefficiencies such as faulty
arXiv:2609.03788v1 Announce Type: cross Abstract: Isolated Sign Language Recognition (ISLR) is conventionally cast as closed-set classification over gloss labels, which cannot generalize to signs unse
arXiv:2602.23996v2 Announce Type: replace Abstract: Masked Image Generation Models (MIGMs) have achieved great success, yet their efficiency is hampered by the multiple steps of bi-directional attenti
arXiv:2609.03635v1 Announce Type: new Abstract: Clinical trials in the UK can cost up to {pounds}1.3 million, with approximately 90% drug failure rate. Toxicity is a major contributing factor in drug
arXiv:2206.05215v2 Announce Type: replace Abstract: K-Means clustering algorithm is one of the most commonly used clustering algorithms because of its simplicity and efficiency. K-Means clustering alg
arXiv:2604.17000v1 Announce Type: cross Abstract: The growing reliance on large-scale speech data has made privacy protection a critical concern. However, existing anonymization approaches often degra