Knowledge catalogue
arXiv:2608.14757v1 Announce Type: cross Abstract: Whole slide image analysis is commonly formulated as multiple instance learning (MIL), where instance features are contextually updated and aggregated
arXiv:2506.02917v2 Announce Type: replace Abstract: We propose a training-free, Vision-Language Model (VLM)-guided approach for efficiently generating trajectories to facilitate target inspection plan
arXiv:2608.16739v1 Announce Type: new Abstract: Reinforcement learning algorithms for Large Language Models (LLMs) are largely distinguished by their variance reduction strategy. Group-relative method
arXiv:2504.06659v2 Announce Type: replace-cross Abstract: Despite advances in Preference Alignment (PA) for Large Language Models (LLMs), mainstream methods like reinforcement learning with human feed
arXiv:2608.14657v1 Announce Type: cross Abstract: Early identification of lung cancer risk is critical for timely intervention, yet existing prediction models are limited by their reliance on single d
arXiv:2603.07977v3 Announce Type: replace-cross Abstract: Machine Learning Interatomic Potentials (MLIPs) enable accurate large-scale atomistic simulations, yet improving their expressive capacity eff
arXiv:2412.04504v2 Announce Type: replace Abstract: As large language models (LLMs) grow in popularity for their diverse capabilities, improving the efficiency of their inference systems has become in
arXiv:2608.16181v1 Announce Type: cross Abstract: Recent advances in large language models have enabled a new class of agentic data science systems that allow users to complete complex data science wo
arXiv:2509.16586v2 Announce Type: replace Abstract: Recent advances have significantly improved our understanding of the sample complexity of learning in average-reward Markov decision processes (AMDP
The rise of neoclouds — AI-first cloud providers — is transforming enterprise infrastructure. The unique requirements of AI models and agents have paved the way for new collaborations between hardware
nobody takes Altman at his word anymore – nor should they. Altman's claim that OpenAI is slowing their frontier model training in order to focus on safety doesn't pass the sniff test.👃 For several yea
arXiv:2603.06952v2 Announce Type: replace Abstract: As graphs scale to billions of nodes and edges, graph Machine Learning workloads are constrained by the cost of multi-hop traversals over exponentia
arXiv:2608.16070v1 Announce Type: cross Abstract: Reliable global ocean forecasting is critical for climate monitoring, marine navigation, and extreme event early warning. Physics-based ocean forecast
arXiv:2608.14774v1 Announce Type: new Abstract: Modern sequence models heavily rely on massive memory footprints and large-batch stochastic optimization, barriers that restrict sample efficiency and c
arXiv:2608.15026v1 Announce Type: new Abstract: Post-training of vision-language-action (VLA) models typically relies on expert demonstrations and policy interaction trajectories. However, in long-hor
arXiv:2608.16477v1 Announce Type: new Abstract: AI-RAN brings large language model (LLM) serving close to mobile users, but cellular handover can separate an active request from its inference state: t
arXiv:2608.16425v1 Announce Type: new Abstract: Parallel reasoning improves the accuracy and robustness of large reasoning models by exploring multiple solution paths, but its computational cost grows
arXiv:2608.16578v1 Announce Type: new Abstract: AI agents increasingly operate as part of interacting systems rather than in isolation. As agents exchange information and jointly make decisions, their
arXiv:2510.07546v2 Announce Type: replace Abstract: We address the task of video style transfer with diffusion models, where the goal is to preserve the context of an input video while rendering it in
arXiv:2608.15705v1 Announce Type: new Abstract: Controllable text-to-image diffusion models can often follow the global layout of spatial conditions, yet still violate fine-grained structures such as
arXiv:2608.16407v1 Announce Type: cross Abstract: Point-of-interest (POI) recommendation models based on graph neural networks achieve strong performance by propagating collaborative signals over user
arXiv:2608.15583v1 Announce Type: new Abstract: While Text-to-Image (T2I) diffusion models have achieved remarkable success, precise spatial and orientational control in multi-object scenes remains a
arXiv:2608.14667v1 Announce Type: new Abstract: Large language model-based agents are increasingly deployed as collaborators in scientific discovery yet most current work focuses on the autonomous cap
arXiv:2608.16565v1 Announce Type: new Abstract: This cumulative habilitation thesis studies probabilistic circuits (PCs) as a powerful and tractable framework for reasoning and learning under uncertai
arXiv:2608.15112v1 Announce Type: new Abstract: Solving the time-dependent Schrodinger equation (TDSE) via traditional numerical methods is computationally intensive. Transformer models offer a compel
arXiv:2608.15037v1 Announce Type: cross Abstract: Audio-Text Foundation Models (ATMs) fail catastrophically under severe acoustic noise, yet existing adaptation strategies either rely on gradient-base
arXiv:2608.16168v1 Announce Type: cross Abstract: Large language model (LLM) agents increasingly use external memory systems to support personalization by drawing on long and evolving interaction hist
arXiv:2608.16664v1 Announce Type: cross Abstract: We study the Random Quadratic Form (RQF) on a sphere in the presence of random Brownian forcing. We show that the forcing does not effectively change
arXiv:2608.14685v1 Announce Type: new Abstract: Knowledge distillation (KD) is widely used to transfer the capabilities of large language models (LLMs) to smaller students, but existing objectives oft
arXiv:2608.16333v1 Announce Type: cross Abstract: On-policy distillation (OPD) aligns a student model with a teacher's logit distribution on student-generated trajectories. This approach has achieved
arXiv:2608.14697v1 Announce Type: new Abstract: Steganography in large language models offers a way to embed hidden messages within natural-sounding text. Existing token and logit-level methods typica
arXiv:2608.15012v1 Announce Type: cross Abstract: The rapid advancement of large language models (LLMs) has created a growing asymmetry in cybersecurity, where attack accelerates toward autonomous exe
arXiv:2608.14953v1 Announce Type: new Abstract: Recent advances in Large Language Models (LLMs) have opened opportunities to apply high-level code transformations to the field of code optimization, an
arXiv:2608.14728v1 Announce Type: cross Abstract: On-policy distillation (OPD) has emerged as an effective paradigm for transferring knowledge between language models, where a student is trained to al
arXiv:2608.14759v1 Announce Type: cross Abstract: Whole Slide Image (WSI) analysis has been widely studied for cancer diagnosis. Conventionally, a gigapixel WSI is divided into small patches and proce
arXiv:2607.18237v2 Announce Type: replace Abstract: Human visual similarity judgments are context-dependent. For example, two images may be similar in shape but distinct in color. Existing perceptual
arXiv:2608.14756v1 Announce Type: cross Abstract: Real-world EV charging data exhibit three interlocking pathologies: hardware fragmentation (network timeouts and billing resets split sessions), physi
arXiv:2608.15940v1 Announce Type: new Abstract: Modern encoder-decoder systems can produce fluent text even when their input contains no recoverable message. We study this failure in ASR and NMT throu
arXiv:2608.16394v1 Announce Type: new Abstract: Generating regulation-compliant test scenarios is essential for validating safety-critical automotive systems, yet Large Language Models (LLMs) struggle
arXiv:2604.02637v2 Announce Type: replace Abstract: As large language models (LLMs) become increasingly persuasive, there is concern that people's opinions and decisions may be influenced across vario
arXiv:2602.20839v3 Announce Type: replace Abstract: Training-free image editing with diffusion models is highly desirable yet is complex and remains a significant challenge. While recent optimisation-
arXiv:2608.16156v1 Announce Type: new Abstract: Long-horizon large language model (LLM) agents are typically optimized with sparse terminal outcomes, making fine-grained credit assignment across multi
arXiv:2608.16661v1 Announce Type: new Abstract: Hyperspectral reflectance spectroscopy enables non-destructive estimation of plant functional traits, yet current deep learning approaches process spect
arXiv:2608.15304v1 Announce Type: new Abstract: Frontier agentic systems powered by large language models (LLMs) exhibit human-like patterns of cognition. As these systems become deeply integrated acr
arXiv:2608.15799v1 Announce Type: new Abstract: In this paper, we focus on the dictionary of 2048 tokens used in Mimi semantic token codebook, the neural codec of the Moshi language model. We show tha
arXiv:2608.15708v1 Announce Type: new Abstract: We study a critical yet overlooked failure mode in Grounded Video Question Answering: question-invariant grounding, where models predict nearly identica
arXiv:2608.16515v1 Announce Type: cross Abstract: Retrieval-augmented generation (RAG) improves large language models by grounding generation in external evidence, but it also introduces a source trus
arXiv:2608.14940v1 Announce Type: new Abstract: Current agent evaluations score models on the state visible at the end of a stopped run which they count as one trial. However, interpreting the score a
arXiv:2608.14659v1 Announce Type: new Abstract: Large language models for code generation often produce incorrect solutions without reliable indicators of failure. We study whether uncertainty estimat
arXiv:2608.14854v1 Announce Type: new Abstract: While Multimodal Large Language Models (MLLMs) excel in general video understanding, their capability in fine-grained and motion-centric tasks remains l
arXiv:2608.14135v1 Announce Type: cross Abstract: Autonomous pursuit-evasion is a fundamental challenge for Unmanned Aerial Vehicles (UAVs), requiring rapid decision-making under tightly coupled dynam
arXiv:2608.14235v1 Announce Type: new Abstract: The development of reliable plant disease monitoring systems is constrained by limited longitudinal datasets capturing disease progression under natural
arXiv:2602.09430v2 Announce Type: replace-cross Abstract: Robotic laboratories play a critical role in autonomous scientific discovery by enabling scalable, continuous experimental execution. Recent v
arXiv:2608.14228v1 Announce Type: new Abstract: Life science knowledge graphs make large collections of structured data available through SPARQL, but each resource uses its own schema, identifiers, an
arXiv:2608.14146v1 Announce Type: new Abstract: Accurate polyp segmentation is critical for computer-aided colonoscopy, yet endoscopic images often contain low-contrast boundaries, mucosal texture int
arXiv:2608.14539v1 Announce Type: cross Abstract: Determining the biological sex of the individuals who created Upper Paleolithic hand stencils remains a challenging problem due to the absence of grou
arXiv:2608.14359v1 Announce Type: new Abstract: The sustainability constraints of FLaaS consumers pose significant challenges to maintaining carbon-feasible federated training in FLaaS environments. T
arXiv:2608.14011v1 Announce Type: cross Abstract: Generative recommendation autoregressively generates the semantic IDs of the target item, unifying preference modeling and index retrieval within the
arXiv:2608.14428v1 Announce Type: new Abstract: 3D object detection from LiDAR point clouds is a core problem in autonomous driving. Recent advances in self-supervised learning (SSL) enable scalable p
arXiv:2608.13698v1 Announce Type: new Abstract: Reinforcement Learning with Verifiable Rewards (RLVR), often optimized with Group Relative Policy Optimization (GRPO), has become a central recipe for i