Knowledge catalogue
arXiv:2609.03106v1 Announce Type: new Abstract: Scaling laws in modern deep learning describe how held-out loss improves as model capacity, training data, and compute increase, often following power-l
arXiv:2506.10226v3 Announce Type: replace-cross Abstract: Synthetic data generation is increasingly used in machine learning for training and data augmentation. Yet, current strategies often rely on e
arXiv:2609.04183v1 Announce Type: cross Abstract: Weakly-Supervised Dense Video Captioning aims to localize and describe multiple events in untrimmed videos given only an ordered set of event-level ca
arXiv:2609.03055v1 Announce Type: new Abstract: Domestic robots rely on rich perception to operate in private homes, but privacy risk persists even when raw sensor data remain local. Structured repres
arXiv:2609.03820v1 Announce Type: cross Abstract: Long-video language models cannot look at every frame: an hour sampled once per second is 3,600 images, and a system keeps only a small fixed slice of
arXiv:2609.03265v1 Announce Type: new Abstract: Existing graph-clustering methods typically improve clustering performance by optimizing model parameters and node representations. Effective means of f
arXiv:2609.03689v1 Announce Type: new Abstract: Histopathological subtyping relies on the recognition of characteristic histological patterns. These patterns may be expressed by individual tissue stru
arXiv:2609.03834v1 Announce Type: new Abstract: Knowledge graphs describe reality in crisp assertions, while the systems now consuming them, foundation models and autonomous agents, reason natively in
arXiv:2504.02694v3 Announce Type: replace-cross Abstract: We study counterfactual regression, which maps features to outcomes under hypothetical scenarios that differ from those observed in the data.
arXiv:2609.03142v1 Announce Type: cross Abstract: Vision-Language-Action (VLA) policies fuse multimodal sensory inputs, but training on limited and homogeneous robot demonstrations encourages spurious
arXiv:2609.04159v1 Announce Type: cross Abstract: Large language model (LLM) agents are increasingly proposed as autonomous SOC analysts, but two limitations make them unreliable at enterprise scale:
arXiv:2609.04108v1 Announce Type: cross Abstract: Reinforcement learning with verifiable rewards (RLVR) and on-policy distillation (OPD) have emerged as two dominant methods for post-training reasonin
arXiv:2609.03235v1 Announce Type: new Abstract: Large language models (LLMs) face severe memory bottlenecks in long-context inference due to the linearly growing size of key-value (KV) caches. Existin
arXiv:2609.03981v1 Announce Type: new Abstract: Brain-MRI inpainting replaces a masked region of a scan with synthesized, anatomically plausible healthy tissue, so that analysis tools built for health
arXiv:2609.03047v1 Announce Type: cross Abstract: Libraries and archives manage large collections with limited staff and computing budgets, yet common benchmarks do not systematically test their bibli
arXiv:2511.10866v2 Announce Type: replace-cross Abstract: This paper proposes a Short-Window Sliding Learning framework for real-time violence detection in CCTV footages. Unlike conventional long-vide
arXiv:2609.03695v1 Announce Type: new Abstract: Sign language dictionaries are essential resources for sign language learners, yet automatically retrieving a sign from a dictionary, given only a query
arXiv:2609.03377v1 Announce Type: new Abstract: Proteins are fundamental to biological processes, with their function determined by the complex interplay between the amino acid sequence and the three-
arXiv:2609.03753v1 Announce Type: new Abstract: As large language models (LLMs) become increasingly capable, the long-term value of AI systems depends not only on solving individual requests, but also
arXiv:2504.21719v3 Announce Type: replace-cross Abstract: Sionna is an open-source, GPU-accelerated library that, as of version 0.14, incorporates a ray tracer, Sionna RT, for simulating radio wave pr
arXiv:2609.02941v1 Announce Type: cross Abstract: Speech emotion recognition (SER) faces two fundamental challenges: scarcity of labeled data and inter-speaker variability, both of which hinder genera
Claude Code is absolutely horrendous at managing docx XML structures and it's obscene that the best these frontier companies can do are skills. Skills are prone to breaking, burn tokens (hello busines
arXiv:2510.15869v4 Announce Type: replace Abstract: Synthesizing large-scale, explorable, and geometrically accurate 3D urban scenes is a challenging yet valuable task for immersive and embodied appli
arXiv:2609.03109v1 Announce Type: new Abstract: Current AI agents compellingly describe slides. However, AI-assisted slide editing requires more than understanding: the output must retain layout, styl
So much discussion on the emergent behaviors of agent swarms. This is a great read from Google DeepMind if you are tracking this research topic. https://x.com/omarsar0/status/2095873020778991918?s=20
arXiv:2609.03085v1 Announce Type: new Abstract: There is growing interest in adopting CLIP-style vision--language model (VLM) pretraining for mammography. However, models that directly employ the stan
sounds like the government testing standard fail to adequately factor in monitorability. that’s bad. OpenAI announced its latest model, Astra today. When asked during a briefing if they followed the n
Bloomberg: Sources: Abu Dhabi-based AI company G42 is exploring selling a majority stake to US companies, hoping to safeguard access to advanced AI chips beyond April 2027 — Executives at Abu Dhabi-ba
Bloomberg: Sources: London-based AI infrastructure startup Nscale is in talks to raise as much as 3.5B in financing, including 2B from Nvidia, ahead of a planned IPO — Nscale, a cloud computing firm f
Robbie Whelan / Wall Street Journal: Sources: US negotiators used the promise of access to Nvidia's chips for an Armenian data center to broker a preliminary Armenia-Azerbaijan peace deal last year —
When Google's Finance Engineering team needed to modernize their legacy data layer, they chose Spanner, a globally distributed, strongly consistent, multi-model database with high availability capabil
arXiv:2510.01984v4 Announce Type: replace Abstract: Quadruped mammals coordinate sagittal spinal bending with axial extension and compression during dynamic locomotion. Yet most robotic quadrupeds use
arXiv:2609.03813v1 Announce Type: new Abstract: Real-world image super-resolution (SR) increasingly relies on Diffusion Transformer (DiT) backbones, whose internal activations can be dominated by a sm
arXiv:2609.03931v1 Announce Type: new Abstract: Generating complete 3D scenes from sparse, unconstrained views is a fundamental challenge in 3D vision which requires reasoning beyond observed content
arXiv:2609.03923v1 Announce Type: new Abstract: In online meeting delegation, LLM agents fail to recognize when to speak. With no structured way to track stances, coverage, and floor, they miss the mo
arXiv:2606.16276v3 Announce Type: replace Abstract: As large language models (LLMs) are increasingly deployed in real-world applications, alignment is no longer governed by a single universal notion o
arXiv:2609.03495v1 Announce Type: new Abstract: This paper examines the relationship between the parameters of autoencoder models and the statistical properties of the data on which they are trained.
arXiv:2609.03401v1 Announce Type: cross Abstract: We first prove spectral convergence of the random feature method (RFM) for multidimensional targets in Sobolev, Gevrey, ultra-analytic, and bandlimite
arXiv:2603.15834v2 Announce Type: replace-cross Abstract: We introduce a spectral hierarchy of cosmic-web classifications obtained by applying simple scale-weighting kernels to the density field befor
arXiv:2609.03236v1 Announce Type: new Abstract: Tool-using LLM agents spend wall-clock time not only on model inference but also in serial action--observation turns, where each tool call, environment
arXiv:2609.03376v1 Announce Type: cross Abstract: Retrieval-Augmented Generation (RAG) has made dense retrieval over large document collections a standard building block. Organizations increasingly ou
arXiv:2609.04063v1 Announce Type: new Abstract: Group Relative Policy Optimization (GRPO) is widely studied for reinforcement learning with verifiable rewards, where its advantage estimator assigns ea
arXiv:2609.03639v1 Announce Type: new Abstract: Training-free, camera-controlled novel view synthesis from a single image using pre-trained video diffusion models often becomes unstable under large ca
arXiv:2609.04026v1 Announce Type: new Abstract: Bundle Adjustment (BA) is a cornerstone of 3D computer vision and has benefited from decades of advances in sparse optimization and numerical methods. I
arXiv:2609.03874v1 Announce Type: new Abstract: Retrieval Augmented Generation (RAG) is a key component for generating accurate and hallucination free answers using Large Language Models (LLMs). LLMs
arXiv:2609.03447v1 Announce Type: new Abstract: Large-scale 3D surface reconstruction from aerial imagery is fundamental to geospatial mapping and urban modeling. Recent advances in 3D Gaussian Splatt
arXiv:2609.02965v1 Announce Type: cross Abstract: Dynamic networks are being applied in many domains, from social media to logistics systems, each with their own set of special characteristics. A mode
arXiv:2609.03414v1 Announce Type: cross Abstract: Audio enhancement in real-world scenarios involves complex distortion couplings and requires personalized enhancement. Existing solutions struggle to
arXiv:2609.02981v1 Announce Type: new Abstract: Artificial intelligence is changing the form of applied English materials from fixed paper sequences to adaptive learning systems that can diagnose lear
arXiv:2606.19264v2 Announce Type: replace-cross Abstract: The knowledge encoded in large language models (LLMs) can serve as a substrate for structured reasoning over variables describing a complex wo
arXiv:2609.04066v1 Announce Type: cross Abstract: Reinforcement learning from human feedback (RLHF) has emerged as a powerful yet sample-inefficient approach for learning reward models from human pref
arXiv:2609.02963v1 Announce Type: cross Abstract: Lead optimization in structure-based drug design aims to improve target binding while avoiding unintended interactions with off-target pockets. Howeve
arXiv:2609.03382v1 Announce Type: cross Abstract: Predicting storm surge induced by landfalling tropical cyclones is crucial for flood mitigation and coastal risk management. Traditionally, physics-ba
arXiv:2609.03602v1 Announce Type: new Abstract: World models (WMs) have demonstrated strong potential for end-to-end autonomous driving by learning predictive representations of future scene dynamics.
arXiv:2609.03806v1 Announce Type: new Abstract: Scalable Vector Graphics (SVG) generation is attracting increasing attention as generative models improve in expressiveness and controllability. Progres
arXiv:2609.04167v1 Announce Type: cross Abstract: Repository-level software engineering benchmarks have significantly advanced the evaluation of coding agents, but existing benchmarks primarily measur
arXiv:2609.03215v1 Announce Type: new Abstract: Writing proficiency manifests in how students develop content, organize ideas, choose words, and use language. Despite growing interest in LLM-based stu
arXiv:2609.03697v1 Announce Type: cross Abstract: In the natural sciences, symmetries and cause-effect relationships are ubiquitous. Yet for complex machine-learning tasks, like world-modeling in rein
arXiv:2609.03702v1 Announce Type: new Abstract: General-purpose code embeddings power tools for code search, classification, and retrieval. Compact transformer encoders for code typically rely on eith
arXiv:2609.03395v1 Announce Type: cross Abstract: Large Language Models (LLMs) have shown strong performance on table question answering, yet their accuracy often degrades as table size increases. We