Knowledge catalogue
arXiv:2604.17566v1 Announce Type: cross Abstract: Machine learning is becoming increasingly important for nonlinear system identification, including dynamical systems with spatially distributed output
arXiv:2604.18107v1 Announce Type: new Abstract: Vision-Language-Action models (VLAs) achieve remarkable performance in sequential decision-making but remain fragile to subtle environmental shifts, suc
arXiv:2510.07761v2 Announce Type: replace Abstract: Large language models (LLMs) now give reasoning before answering, excelling in tasks like multiple-choice question answering (MCQA). Yet, a concern
arXiv:2504.18269v2 Announce Type: replace Abstract: When generating images from prompts that include specific entities, the model must retain as much entity-specific knowledge as possible. However, th
arXiv:2509.07024v2 Announce Type: replace-cross Abstract: The Trapped Gyro-Landau Fluid (TGLF) model provides fast, accurate predictions of turbulent transport in tokamaks, but whole device simulation
arXiv:2604.18096v1 Announce Type: cross Abstract: LLMs are increasingly presented as collaborators in programming, design, writing, and analysis. Yet the practical experience of working with them ofte
arXiv:2604.16704v1 Announce Type: new Abstract: This study examines the role of artificial intelligence in translation, focusing on ChatGPT, specifically ChatGPT-4, and the extent to which human poste
Single-use plastics are a persistent source of environmental pollution, and the need to house a growing global population puts increasing pressure on resources such as timber. MIT engineers have an id
arXiv:2510.09378v2 Announce Type: replace Abstract: Recent efforts to accelerate LLM pretraining have focused on computationally-efficient approximations that exploit second-order structure. This rais
arXiv:2510.00546v5 Announce Type: replace Abstract: Large Reasoning Models (LRMs) allocate substantial inference-time compute to Chain-of-Thought (CoT) reasoning, improving performance on mathematics,
arXiv:2604.18352v1 Announce Type: cross Abstract: State-of-the-art Differentially Private (DP) synthetic data generators such as MST and AIM are widely used, yet tightly auditing their privacy guarant
arXiv:2502.15315v3 Announce Type: replace Abstract: Sparse Mixture-of-Experts (MoE) architectures have emerged as a promising approach to decoupling model capacity from computational cost. At the core
arXiv:2410.15368v2 Announce Type: replace-cross Abstract: We revisit FedExProx - a recently proposed distributed optimization method designed to enhance convergence properties of parallel proximal alg
arXiv:2604.16887v1 Announce Type: new Abstract: Robotic manipulators for aerospace applications require a delicate balance between lightweight construction and fault-tolerant operation to satisfy stri
arXiv:2509.18060v2 Announce Type: replace Abstract: Tibetan is a low-resource language with limited parallel speech corpora spanning its three major dialects (U-Tsang, Amdo, and Kham), limiting progre
arXiv:2603.28178v2 Announce Type: replace Abstract: 3D Scene Graph (3DSG) generation plays a pivotal role in spatial understanding and affordance perception. To mitigate generalization issues from dat
arXiv:2510.19410v2 Announce Type: replace Abstract: Identifying which text spans refer to entities - mention detection - is both foundational for information extraction and a known performance bottlen
arXiv:2509.11612v3 Announce Type: replace Abstract: Reservoir is an efficient network for time series processing. It is well known that network structure is one of the determinants of its performance.
arXiv:2509.15651v2 Announce Type: replace Abstract: Assessing the impact the training data on machine learning models is crucial for understanding the behavior of the model, enhancing the transparency
arXiv:2604.16834v1 Announce Type: cross Abstract: Privacy-preserving machine learning (PPML) has become increasingly important in applications where sensitive data must remain confidential. Homomorphi
arXiv:2604.18239v1 Announce Type: new Abstract: Preference optimization is widely used to align large language models (LLMs) with human preferences. However, many margin-based objectives suppress the
arXiv:2604.18089v1 Announce Type: new Abstract: Bayesian Deep Ensembles (BDEs) represent a powerful approach for uncertainty quantification in deep learning, combining the robustness of Deep Ensembles
arXiv:2604.00505v2 Announce Type: replace Abstract: Overparameterized neural networks often show a benign overfitting property in the sense of achieving excellent generalization behavior despite the n
arXiv:2604.18067v1 Announce Type: new Abstract: The miniaturisation of neural processing units (NPUs) and other low-power accelerators has enabled their integration into microcontroller-scale wearable
arXiv:2604.16536v1 Announce Type: new Abstract: Machine learning components are now central to AI-infused software systems, from recommendations and code assistants to clinical decision support. As re
arXiv:2604.18208v1 Announce Type: new Abstract: Symmetric objects are common in daily life and industry, yet their inherent orientation ambiguities that impede the training of deep learning networks f
arXiv:2604.17067v1 Announce Type: cross Abstract: Linear convergence of first-order methods is typically characterized by global optimization conditions whose constants reflect worst-case geometry of
arXiv:2603.19684v2 Announce Type: replace Abstract: Automatic tooth segmentation and identification from intra-oral scanned 3D models are fundamental problems in digital dentistry, yet most existing a
arXiv:2604.16542v1 Announce Type: cross Abstract: Safety guardrails have become an active area of research in AI safety, aimed at ensuring the appropriate behavior of large language models (LLMs). How
arXiv:2604.17156v1 Announce Type: new Abstract: Physics-informed neural networks (PINNs) have emerged as a promising framework for solving inverse problems governed by partial differential equations (
arXiv:2511.17699v2 Announce Type: replace Abstract: Counting is one of the fundamental abilities of large language models (LLMs) and large vision-language models (LVLMs). This paper examines how these
arXiv:2604.18389v1 Announce Type: new Abstract: Prompt sensitivity, which refers to how strongly the output of a large language model (LLM) depends on the exact wording of its input prompt, raises con
arXiv:2604.17565v1 Announce Type: new Abstract: Camera-controllable image editing aims to synthesize novel views of a given scene under varying camera poses while strictly preserving cross-view geomet
arXiv:2604.17472v1 Announce Type: new Abstract: Recent advances in 3D vision have led to specialized models for either 3D understanding (e.g., shape classification, segmentation, reconstruction) or 3D
arXiv:2602.11893v3 Announce Type: replace Abstract: We introduce a universal diffusion-based downscaling framework that lifts deterministic low-resolution weather forecasts into probabilistic high-res
arXiv:2402.02953v2 Announce Type: replace-cross Abstract: With the rapid advancement of machine learning (ML), ML-based Android malware detection has gained significant popularity due to its ability t
arXiv:2512.01015v2 Announce Type: replace Abstract: Neural oscillators, originating from second-order ordinary differential equations (ODEs), have demonstrated strong performance in stably learning ca
arXiv:2506.00065v2 Announce Type: replace Abstract: Multimodal language models (MLMs) increasingly demonstrate human-like communication, yet their use of everyday perspectival words remains poorly und
arXiv:2604.18035v1 Announce Type: new Abstract: Machine learning (ML) models trained to detect physical-layer threats on one optical fiber system often fail catastrophically when applied to a differen
arXiv:2509.16538v3 Announce Type: replace-cross Abstract: We propose VC-Inspector, a lightweight, open-source large multimodal model (LMM) for reference-free evaluation of video captions, with a focus
arXiv:2604.17801v1 Announce Type: new Abstract: Text-driven 3D scene editing has recently attracted increasing attention. Most existing methods follow a render-edit-optimize pipeline, where multi-view
arXiv:2604.17623v1 Announce Type: new Abstract: Kinematic rigs provide a structured interface for articulating 3D meshes, but they lack an inherent representation of the plausible manifold of joint co
arXiv:2505.23941v4 Announce Type: replace-cross Abstract: Large language models (LLMs) memorize a vast amount of prior knowledge from the Internet that helps them on downstream tasks but also may noto
arXiv:2512.01643v2 Announce Type: replace Abstract: Test-Time Training (TTT) has recently emerged as a promising direction for efficient sequence modeling. TTT reformulates attention operation as an o
arXiv:2510.17001v2 Announce Type: replace Abstract: Large language models (LLMs) often encode word-form variation (e.g., walk vs. walked) as linear directions in the embedding space. However, standard
arXiv:2604.18546v1 Announce Type: new Abstract: We propose a distributionally robust approach to risk-sensitive estimation of an unknown signal x from an observed signal y. The unknown signal and obse
arXiv:2603.19880v2 Announce Type: replace Abstract: Test-Time Reinforcement Learning (TTRL) enables Large Language Models (LLMs) to enhance reasoning capabilities on unlabeled test streams by deriving
arXiv:2508.16464v2 Announce Type: replace Abstract: Entities in discourse vary in salience: main participants, objects and locations stay prominent, while others are quickly forgotten, raising questio
arXiv:2604.17318v1 Announce Type: new Abstract: Vision-Language Models (VLMs) are increasingly used in clinical diagnostics, yet their robustness to adversarial attacks remains largely unexplored, pos
arXiv:2604.16787v1 Announce Type: new Abstract: We study how informal surface forms degrade NLI accuracy in ELECTRA-small (14M) and RoBERTa-large (355M) across four transforms applied to SNLI and Mult
arXiv:2604.16767v1 Announce Type: new Abstract: Audio platforms have evolved beyond entertainment. They have become central to public discourse, from podcasts and radio to WhatsApp voice notes and liv
arXiv:2601.04609v2 Announce Type: replace Abstract: Vision-language models (VLMs) are increasingly used to make visual content accessible via text-based descriptions. In current systems, however, desc
arXiv:2507.13868v2 Announce Type: replace Abstract: Vision-language models (VLMs) increasingly combine visual and textual information to perform complex tasks. However, conflicts between their interna
arXiv:2604.17031v1 Announce Type: new Abstract: The individuation problem for large language models asks which entities associated with them, if any, should be identified as minds. We approach this pr
arXiv:2604.17393v1 Announce Type: new Abstract: Automatic evaluation metrics are central to the development of machine translation systems, yet their robustness under domain shift remains unclear. Mos
arXiv:2604.16745v1 Announce Type: cross Abstract: Training-free token reduction methods for Vision Transformers (ToMe, ToFu, PiToMe, and MCTF) employ different scoring mechanisms, yet they share a clo
arXiv:2506.05760v2 Announce Type: replace Abstract: Recent advances in Large Language Models(LLMs) have enabled strong performance in long-form writing, but current training paradigms remain limited:
arXiv:2604.16917v1 Announce Type: new Abstract: Languages encode distinct abstractions and inductive priors, yet most large language models (LLMs) overlook this diversity by reasoning in a single domi
arXiv:2604.15453v1 Announce Type: cross Abstract: Tokenization is a key component of autoregressive (AR) generative models, converting raw data into more manageable units for modeling. Commonly, token
arXiv:2604.15907v1 Announce Type: new Abstract: Vine-inspired robots achieve large workspace coverage through tip eversion, enabling safe navigation in confined and cluttered environments. However, th