Knowledge catalogue
arXiv:2604.05158v2 Announce Type: replace Abstract: Large language models encode extensive world knowledge valuable for zero-shot named entity recognition. However, their causal attention mechanism, w
arXiv:2407.00809v3 Announce Type: replace Abstract: This paper introduces the Kernel Neural Operator (KNO), a provably convergent operator-learning architecture that utilizes compositions of deep kern
arXiv:2604.15141v1 Announce Type: new Abstract: Higher-order learning is fundamentally rooted in exploiting compositional features. It clearly hinges on enriching the representation by more elaborate
arXiv:2506.00433v4 Announce Type: replace Abstract: High-resolution image synthesis remains a core challenge in generative modeling, particularly in balancing computational efficiency with the preserv
arXiv:2604.01197v2 Announce Type: replace-cross Abstract: Learning quantum states from measurement data is a central problem in quantum information and computational complexity. In this work, we study
arXiv:2604.14931v1 Announce Type: cross Abstract: Concatenating quantum error correction codes scales error correction capability by driving logical error rates down double-exponentially across levels
arXiv:2501.11711v2 Announce Type: replace Abstract: The COVID-19 pandemic has claimed millions of lives, spurring the development of diverse forecasting models. In this context, the true utility of co
arXiv:2604.13348v1 Announce Type: new Abstract: We introduce CONCORD, a privacy-aware asynchronous assistant-to-assistant (A2A) framework that leverages collaboration between proactive speech-based AI
arXiv:2604.15216v1 Announce Type: cross Abstract: Mobility in urban and interurban areas, mainly by cars, is a day-to-day activity of many people. However, some of its main drawbacks are traffic jams
arXiv:2604.14574v1 Announce Type: new Abstract: With the rapid advancement of deep learning in image generation, facial forgery techniques have achieved unprecedented realism, posing serious threats t
arXiv:2604.14582v1 Announce Type: new Abstract: High-resolution (HR) land-cover mapping is often constrained by the high cost of dense HR annotations. We revisit this problem from the perspective of m
arXiv:2602.07529v3 Announce Type: replace Abstract: Large language models (LLMs) have demonstrated strong performance and rapid progress in a wide range of medical reasoning tasks. However, their sequ
arXiv:2604.14218v1 Announce Type: new Abstract: Hate speech detection in Devanagari-scripted social media memes presents compounded challenges: multimodal content structure, script-specific linguistic
arXiv:2604.15107v1 Announce Type: cross Abstract: Feature selection is a classical problem in statistics and machine learning, and it continues to remain an extremely challenging problem especially in
arXiv:2603.22564v2 Announce Type: replace Abstract: Understanding cellular trajectories via time-resolved single-cell transcriptomics is vital for studying development, regeneration, and disease. A ke
arXiv:2604.14208v1 Announce Type: cross Abstract: This work develops machine learning approaches to classify structured light wave beams developing random speckle disturbances as they propagate throug
arXiv:2604.14957v1 Announce Type: cross Abstract: Network security is a critical concern in the digital landscape of today, with users demanding secure browsing experiences and protection of their per
arXiv:2604.14565v1 Announce Type: new Abstract: Embodiment is a significant keyword in recent machine learning fields. This study focused on the passive nature of the body of a biped robot to generate
arXiv:2604.15076v1 Announce Type: new Abstract: To navigate a space, the brain makes an internal representation of the environment using different cells such as place cells, grid cells, head direction
arXiv:2604.14595v1 Announce Type: new Abstract: This position paper argues that recent progress with diversity in NLP is disproportionately concentrated on a small number of areas surrounding fairness
arXiv:2604.15063v1 Announce Type: new Abstract: Gradient inversion attacks threaten client privacy in federated learning by reconstructing training samples from clients' shared gradients. Gradients ag
arXiv:2604.14816v1 Announce Type: new Abstract: This paper presents an overview of the NTIRE 2026 Challenge on Video Saliency Prediction. The goal of the challenge participants was to develop automati
arXiv:2604.14501v1 Announce Type: new Abstract: We study the expressive power and limitations of multi-layer state-space models (SSMs). First, we show that multi-layer SSMs face fundamental limitation
arXiv:2604.14782v1 Announce Type: new Abstract: We propose a compositional method for constructing a complete 3D head avatar from a single image. Prior one-shot holistic approaches frequently fail to
arXiv:2603.09923v3 Announce Type: replace Abstract: The Exponential Moving Average (EMA) is a cornerstone of widely used optimizers such as Adam. However, existing theoretical analyses of Adam-style m
arXiv:2604.15242v1 Announce Type: new Abstract: We study the problem of learning minimax policies in zero-sum matrix games. Fiegel et al. (2025) recently showed that achieving last-iterate convergence
arXiv:2604.13037v1 Announce Type: cross Abstract: Mining multiple longest common subsequences (extit{MLCS}) from a set of sequences of three or more over a finite alphabet Sigma (a classical NP-hard p
arXiv:2604.14338v1 Announce Type: new Abstract: We introduce path-sampled integrated gradients (PS-IG), a framework that generalizes feature attribution by computing the expected value over baselines
arXiv:2604.14566v1 Announce Type: new Abstract: Accurate temperature estimation of pouch cells with indirect liquid cooling is essential for optimizing battery thermal management systems for transport
Ellie’s Pi Day post: https://mitadmissions.org/blogs/entry/pi-day-2026-food-institute/ How Ellie orchestrated the baking of 30 pies: https://mitadmissions.org/blogs/entry/behind-the-scenes-of-thirty-p
arXiv:2511.20645v2 Announce Type: replace Abstract: Latent-space modeling has been the standard for Diffusion Transformers (DiTs). However, it relies on a two-stage pipeline where the pretrained autoe
arXiv:2604.14241v1 Announce Type: cross Abstract: The classic paradigm of structural biology is that the sequence of a biomolecule (protein, nucleic acid, lipid, etc) determines its conformation (shap
arXiv:2604.14206v1 Announce Type: new Abstract: This paper proposes a machine learning assisted portfolio optimization framework designed for low data environments and regime uncertainty. We construct
arXiv:2604.14547v1 Announce Type: new Abstract: Objective: Post-traumatic epilepsy (PTE) is a debilitating neurological disorder that develops after traumatic brain injury (TBI). Early prediction of P
arXiv:2604.14463v1 Announce Type: new Abstract: Large language models (LLMs) emulate a consistent human-like behavior that can be shaped through activation-level interventions. This paradigm is conver
arXiv:2604.14796v1 Announce Type: cross Abstract: Proteins carry out biological functions through the coordinated action of groups of residues organized into structural arrangements. These arrangement
arXiv:2503.21970v3 Announce Type: replace Abstract: State-Space Models (SSMs) have attracted considerable attention in Image Restoration (IR) due to their ability to scale linearly sequence length whi
arXiv:2604.14193v1 Announce Type: new Abstract: Human 3D vision involves two distinct stages: an Experience Module, where stereo depth is extracted relative to fixation, and an Inference Module, where
arXiv:2602.20370v2 Announce Type: replace Abstract: The universal approximation theorem establishes that neural networks can approximate any continuous function on a compact set. Later works in approx
arXiv:2604.14487v1 Announce Type: new Abstract: Quantization is a natural complement to the sparse, event-driven computation of Spiking Neural Networks, reducing memory bandwidth and arithmetic cost f
arXiv:2604.14287v1 Announce Type: new Abstract: Tensor networks were developed in the context of many-body physics as compressed representations of multiparticle quantum states. These representations
arXiv:2604.14885v1 Announce Type: new Abstract: Autoregressive decoding in Large Language Models (LLMs) generates one token per step, causing high inference latency. Speculative decoding (SD) mitigate
arXiv:2506.13139v2 Announce Type: replace-cross Abstract: Modern Machine Learning (ML) and Deep Neural Networks (DNNs) often operate on high-dimensional data and rely on overparameterized models, wher
arXiv:2603.21094v3 Announce Type: replace Abstract: Human annotation is central to NLP evaluation, yet subjective tasks often exhibit substantial variability across annotators. While large language mo
arXiv:2601.20868v2 Announce Type: replace Abstract: Large Language Models (LLMs) have advanced the field of Combinatorial Optimization through automated heuristic generation. Instead of relying on man
arXiv:2604.14563v1 Announce Type: new Abstract: Vision Transformer (ViT)-based sparse multi-view 3D object detectors have achieved remarkable accuracy but still suffer from high inference latency due
arXiv:2604.14973v1 Announce Type: cross Abstract: A vision foundation model outputs an embedding vector for an image, which can be affected by common editing operations (e.g., JPEG compression, bright
arXiv:2507.18454v2 Announce Type: replace-cross Abstract: CPUs are critical for LLM serving due to their availability, cost efficiency, and edge applicability. However, efficient CPU serving is hinder
arXiv:2604.14810v1 Announce Type: cross Abstract: In online clustering problems, there is often a large amount of uncertainty over possible cluster assignments that cannot be resolved until more data
arXiv:2604.13180v1 Announce Type: new Abstract: Recent advances in agentic AI have enabled increasingly autonomous workflows, but existing systems still face substantial challenges in achieving reliab
arXiv:2604.14648v1 Announce Type: new Abstract: Video outpainting aims to expand the visible content of a video beyond the original frame boundaries while preserving spatial fidelity and temporal cohe
arXiv:2604.14865v1 Announce Type: new Abstract: Large Language Models (LLMs) are increasingly exposed to adaptive jailbreaking, particularly in high-stakes Chemical, Biological, Radiological, and Nucl
arXiv:2603.16869v2 Announce Type: replace Abstract: We introduce SegviGen, a framework that repurposes native 3D generative models for 3D part segmentation. Existing pipelines either lift strong 2D pr
arXiv:2604.15271v1 Announce Type: new Abstract: Reliable uncertainty estimation is critical for medical image segmentation, where automated contours feed downstream quantification and clinical decisio
arXiv:2509.12760v4 Announce Type: replace-cross Abstract: We introduce the Similarity-Distance-Magnitude (SDM) activation function, a more robust and interpretable formulation of the standard softmax
arXiv:2508.05015v2 Announce Type: replace Abstract: Large language models (LLMs) have shown strong reasoning capabilities when fine-tuned with reinforcement learning (RL). However, such methods requir
arXiv:2604.15259v1 Announce Type: new Abstract: Looped transformers promise test-time compute scaling by spending more iterations on harder problems, but it remains unclear which architectural choices
arXiv:2604.14927v1 Announce Type: cross Abstract: Many CAD learning pipelines discretize Boundary Representations (B-Reps) into triangle meshes, discarding analytic surface structure and topological a
arXiv:2604.15237v1 Announce Type: new Abstract: Reconstructing dense 3D geometry from continuous video streams requires stable inference under a constant memory budget. Existing O(1) frameworks primar
arXiv:2604.15285v1 Announce Type: cross Abstract: We study post-training interpretability for Support Vector Machines (SVMs) built from truncated orthogonal polynomial kernels. Since the associated re