Knowledge catalogue
arXiv:2601.14958v3 Announce Type: replace-cross Abstract: The performance of Language Models (LMs) on low-resource, morphologically rich languages like Sinhala remains largely unexplored, particularly
arXiv:2605.07530v1 Announce Type: new Abstract: The Danish Technological Institute (DTI) focuses on transferring advanced technologies (including robots) to the industry and the public sector. One key
arXiv:2605.06898v1 Announce Type: new Abstract: At the heart of existing language model agents is a fixed orchestrator program responsible for the state transition between consecutive turns. This pape
arXiv:2605.07531v1 Announce Type: new Abstract: Black-Box Variational Inference (BBVI) typically relies on Stochastic Gradient Descent (SGD) to optimize the Evidence Lower Bound (ELBO). However, the s
arXiv:2602.07425v2 Announce Type: replace-cross Abstract: While adaptive gradient methods are the workhorse of modern machine learning, sign-based optimization algorithms such as Lion and Muon have re
arXiv:2605.07130v1 Announce Type: new Abstract: Being robust to the presence of outliers is crucial for applying clustering algorithms in practice. In the extit{robust k-Means} problem (i.e., k-Means
arXiv:2509.25584v2 Announce Type: replace Abstract: Vision-language models achieve incredible performance across a wide range of tasks, but their large size makes inference costly. Recent work has sho
arXiv:2601.22766v3 Announce Type: replace Abstract: Recent work has revealed a link between self-attention mechanisms in transformers and test-time kernel regression via the Nadaraya-Watson estimator,
arXiv:2605.07243v1 Announce Type: new Abstract: Speculative decoding accelerates LLM inference by drafting a tree of candidate continuations and verifying it in one target forward. Existing drafters f
arXiv:2605.07297v1 Announce Type: cross Abstract: Understanding why trained Transformers generalize well is a fundamental problem in modern machine learning theory, and complexity-based generalization
arXiv:2605.07420v1 Announce Type: cross Abstract: Pre-trained models with parameter-efficient fine-tuning (PEFT) have demonstrated promising potential for class-incremental learning (CIL), yet catastr
arXiv:2605.07182v1 Announce Type: new Abstract: Training a family of large language models (LLMs), either from scratch or via iterative compression, is prohibitively expensive and inefficient, requiri
arXiv:2605.06690v1 Announce Type: new Abstract: Recursive reasoning systems alternate between acquiring new evidence and refining an accumulated understanding. Two design choices are typically left im
arXiv:2605.06902v1 Announce Type: new Abstract: Adversarial robustness has been studied extensively for offline deep networks, but less is known about strict single-pass streaming neural learners. Thi
arXiv:2605.07274v1 Announce Type: new Abstract: Reinforcement learning from verifiable rewards (RLVR), especially with Group Relative Policy Optimization (GRPO), has shown strong potential for improvi
arXiv:2602.00924v2 Announce Type: replace Abstract: Sparse auto-encoders (SAEs) have re-emerged as a prominent method for mechanistic interpretability, yet they face two significant challenges: the no
arXiv:2501.09209v2 Announce Type: replace Abstract: Owing to recent advances in machine learning and the ability to harvest large amounts of data during robotic-assisted surgeries, surgical data scien
arXiv:2605.07639v1 Announce Type: new Abstract: Tacit knowledge plays a central role in human expertise, yet it remains difficult to capture, formalize, and reuse in machine-interpretable form. This c
arXiv:2605.07507v1 Announce Type: new Abstract: The exponential growth of academic publications has created an urgent need for automated tools capable of extracting structured knowledge from unstructu
arXiv:2605.06877v1 Announce Type: new Abstract: Adaptive control of Euler-Lagrange systems is challenging when friction is governed by a finite-horizon internal state that is not directly observable f
The inability of AI models to produce creative variation is a huge gap. The fact that they generate similar ideas limits their ability to do science & the same-y writing limits their usefulness in man
// The Memory Curse in LLM Agents // (bookmark it) Long histories apparently degrades agents as they become increasingly history-following and risk-minimizing. Across 7 LLMs and 4 social dilemma games
arXiv:2605.07093v1 Announce Type: cross Abstract: The Translation Tax is often treated as a scalar: translated benchmarks are assumed to inflate scores by preserving English-source cues. We audit this
This story originally appeared in The Algorithm, our weekly newsletter on AI. To get stories like this in your inbox first, sign up here. A few months before he was awarded the Nobel Prize in economic
arXiv:2605.07986v1 Announce Type: cross Abstract: AI measurement science has a wide variety of methodologies and measurements for comparing AI systems, resulting in what often appear to be 'apples-to-
arXiv:2605.07202v1 Announce Type: new Abstract: Transforming fragmented enterprise data into actionable insights remains a significant challenge for LLMs, constrained by complex database schemas, limi
arXiv:2605.07655v1 Announce Type: cross Abstract: Searching a multi-biometric database of a billion records for a country-level identity system requires pushing the limits of all aspects of a biometri
arXiv:2605.07412v1 Announce Type: cross Abstract: Although Global Navigation Satellite Systems (GNSS) provide a general solution for bike tracking outdoors, there still exist complex riding environmen
arXiv:2605.07143v1 Announce Type: new Abstract: Translation averaging aims to recover camera locations from pairwise relative translation directions and is a fundamental component of global Structure-
arXiv:2605.06718v1 Announce Type: cross Abstract: Malware and malware-based attacks are becoming more prevalent and complex. Attackers regularly come up with new techniques that have the ability to ev
arXiv:2605.07148v1 Announce Type: new Abstract: Decades of cognitive science establish that humans navigate environments by forming cognitive maps, defined as allocentric and topology-preserving repre
arXiv:2605.07359v1 Announce Type: new Abstract: Compared to RGB images, raw sensor data provides a richer representation of information, which is crucial for accurate recognition, particularly under c
arXiv:2509.11777v2 Announce Type: replace Abstract: Customer feedback in industrial forums offers rich but underexplored insights into real-world product experience. Yet systematic analysis remains ch
arXiv:2605.07451v1 Announce Type: new Abstract: Neural network verification is an active and rapidly maturing research area, with a growing ecosystem of solvers and tools. The VNN-LIB standard was int
arXiv:2605.07915v1 Announce Type: new Abstract: Tokenizers are a crucial component of latent diffusion models, as they define the latent space in which diffusion models operate. However, existing toke
arXiv:2605.06992v1 Announce Type: new Abstract: AI agents are increasingly deployed in multi-task settings, where the task to perform is specified at test time, and the agent must generalize to unseen
arXiv:2602.01752v2 Announce Type: replace Abstract: As large language models (LLMs) generate increasingly human-like text, watermarking has emerged as a promising solution for reliable attribution bey
32,000 views for my tweet; 2.2 million for the nonsense it critiques. so typical. lies and distortions beating debunking by a factor of roughly 100. Wanna get a million views? Make stuff up. Take a ti
So many people misremember (or never read) what I said in in 2022 in “Deep learning is hitting a wall”, which was neither about revenue or AI’s potential upper limits. Rather, it was an argument that
“Combined free cash flow across Microsoft, Alphabet, Amazon, Meta, and Oracle is projected to FALL more than -70%, to ~100 billion, by the end of …. AI capital expenditure is consuming nearly every do
arXiv:2605.04813v1 Announce Type: new Abstract: With the rapid development of cloud computing and Web services, Quality of Service (QoS) has become a key criterion for service selection and recommenda
arXiv:2605.04051v1 Announce Type: cross Abstract: In complex real-world settings, optimization is challenged by the presence of diverse models of differing fidelity. In many optimization problems, a s
arXiv:2605.04439v1 Announce Type: new Abstract: Deep neural networks enriched with structural information have been widely employed for facial expression recognition tasks. However, these methods ofte
arXiv:2605.03391v1 Announce Type: cross Abstract: Weighted first-order model counting (WFOMC) is a central task in lifted probabilistic inference: It asks for the weighted sum of all models of a first
arXiv:2605.04880v1 Announce Type: new Abstract: Recent research has revived and amplified interest in algorithms for undiscounted average reward reinforcement learning in infinite-horizon, non-episodi
arXiv:2605.04074v1 Announce Type: new Abstract: AI data centers experience rapid fluctuations in power demand due to the heterogeneity of computational tasks that they have to support. For example, th
arXiv:2605.04595v1 Announce Type: new Abstract: The rapid adoption of large language models (LLMs) has created significant challenges for efficient inference at scale. Unlike traditional workloads, LL
arXiv:2605.04076v1 Announce Type: new Abstract: U.S. financial institutions deploying AI-based fraud detection face a fragmented compliance landscape spanning four regulatory frameworks -- OCC Bulleti
arXiv:2504.13614v2 Announce Type: replace-cross Abstract: The rapid growth of the internet has made personalized recommendation systems indispensable. Graph-based sequential recommendation systems, po
arXiv:2605.05123v1 Announce Type: new Abstract: In offline-to-online reinforcement learning (O2O-RL), policies are first safely trained offline using previously collected datasets and then further fin
arXiv:2309.09550v4 Announce Type: replace-cross Abstract: The human brain can self-organize rich and diverse sparse neural pathways to incrementally master hundreds of cognitive tasks. However, most e
arXiv:2602.13670v2 Announce Type: replace Abstract: Class-incremental learning (CIL) with pre-trained models (PTMs) faces a critical trade-off between efficient adaptation and long-term stability. Whi
arXiv:2512.16455v3 Announce Type: replace-cross Abstract: The rapid growth of Artificial Intelligence and Machine Learning in scientific research has highlighted a gap between industry-standard MLOps
arXiv:2605.04193v1 Announce Type: cross Abstract: Inductive Logic Programming (ILP) aims to learn interpretable first-order rules from data, but existing symbolic and neuro-symbolic approaches struggl
arXiv:2605.04985v1 Announce Type: new Abstract: Deep learning models for medical image classification usually achieve promising results but typically rely on large, annotated datasets or standard tran
arXiv:2605.04754v1 Announce Type: new Abstract: Deep neural network (DNN) inference at the edge demands simultaneous improvements in accuracy, computational efficiency, and energy consumption. Approxi
Badass tank that beats your sports car in a drag race Only when you drive the Cybertruck do you realize how incredible it is: a bulletproof tank that moves like a million dollar sports car! Reason for
arXiv:2605.04556v1 Announce Type: cross Abstract: The Massive Sound Embedding Benchmark (MSEB) has emerged as a standard for evaluating the functional breadth of audio models. While initial baselines
arXiv:2605.04406v1 Announce Type: new Abstract: The integration of Symmetric Positive Definite (SPD) matrices into deep learning has historically relied on fixed algebraic Riemannian metrics. Analogou
arXiv:2605.03512v1 Announce Type: cross Abstract: The scope of AI safety and alignment work in generative artificial intelligence (GenAI) has so far mostly been limited to harms related to: (a) discri