Knowledge catalogue
arXiv:2509.25741v3 Announce Type: replace-cross Abstract: Test-time training (TTT) enhances model performance by explicitly updating designated parameters prior to each prediction to adapt to the test
arXiv:2609.08055v1 Announce Type: cross Abstract: Many scientific graphs attach several variables to each node, so a single scalar edge weight cannot describe direction-dependent interactions. We mode
arXiv:2609.07314v1 Announce Type: cross Abstract: Rotary position embedding (RoPE) uses each token's integer position to determine the rotation applied inside attention. This works well for local toke
arXiv:1706.04580v3 Announce Type: replace Abstract: The design and organization of complex robotic systems traditionally requires laborious trial-and-error processes to ensure both hardware and softwa
arXiv:2609.07108v1 Announce Type: new Abstract: Speculative decoding accelerates rollout generation, which dominates the cost of reinforcement learning (RL) post-training. Online co-training can furth
arXiv:2609.06591v1 Announce Type: new Abstract: Developing unified physics-based humanoid controllers that can navigate complex 3D scenes and manipulate objects remains a longstanding challenge. Exist
I've got an refurb Dell R740 running Proxmox that I put a Tesla T4 in, mainly to run some CTC local transcription work, but thought it would be fun to try DS4 when it came out, and it was appalling at
arXiv:2609.01647v1 Announce Type: new Abstract: The creation of telescope bibliographies is a crucial part of assessing the scientific impact of observatories and ensuring reproducibility in astronomy
arXiv:2609.02772v1 Announce Type: new Abstract: Low-resource authorship style transfer (LAST) aims to rewrite text into the style of an arbitrary target author using only a few reference examples whil
arXiv:2609.00950v1 Announce Type: new Abstract: Executing long-term tasks in dynamic environments requires embodied agents to maintain robust and adaptive 3D scene representations. However, most exist
arXiv:2609.00578v1 Announce Type: new Abstract: Large Language Models (LLMs) can solve complex problems, but their misuse in high-risk domains can lead to severe consequences. Model providers therefor
arXiv:2607.19313v2 Announce Type: replace-cross Abstract: Reinforcement learning with verifiable rewards (RLVR) improves reasoning in large language models. Yet, typical RLVR approaches fail on diffic
arXiv:2608.29016v1 Announce Type: new Abstract: Large language models (LLMs) have demonstrated considerable promise in program generation for small-scale and conventional application development; howe
arXiv:2608.30880v1 Announce Type: new Abstract: Generalizable embodied manipulation remains difficult to achieve through pretraining alone, due to unseen physical conditions in the real world. We argu
arXiv:2608.27465v1 Announce Type: new Abstract: As large language models (LLMs) are increasingly used for everyday decision-making advice, whether a model shifts the direction of its advice according
arXiv:2608.26235v1 Announce Type: new Abstract: Accuracy-only benchmarking of reasoning-capable large language models misses a central deployment question: when do extended thinking tokens earn their
arXiv:2608.24115v1 Announce Type: cross Abstract: Multimodal large language models (MLLMs) can integrate long visual histories, reason under partial observability, and infer behavior from a few exampl
arXiv:2607.02486v2 Announce Type: replace Abstract: Descriptor-free visual localization eliminates high-dimensional descriptor storage, preserves scene privacy, and simplifies map maintenance, yet its
arXiv:2608.22137v1 Announce Type: new Abstract: Modern language models and agents increasingly require persistent memory for complete codebases, long interaction histories, and heterogeneous enterpris
arXiv:2608.22089v1 Announce Type: cross Abstract: Modern software systems require earlier and more scalable vulnerability severity assessment to reduce exposure to high-impact security flaws. Security
arXiv:2608.22538v1 Announce Type: new Abstract: Policy-governed agents must interpret case evidence while following an authorized procedure. We present extsc{Stage}, an executable-graph framework that
arXiv:2608.23252v1 Announce Type: cross Abstract: As Retrieval-Augmented Generation (RAG) shifts toward diverse portfolio generation, it is stymied by two critical bottlenecks: flawed measurement of e
arXiv:2501.14728v2 Announce Type: replace-cross Abstract: While generative artificial intelligence (GenAI) models have achieved significant success, their misuse for generating deceptive content raise
I run local models on a 128GB Strix Halo and restart llama.cpp fairly often while testing builds, backends and model parameters. The annoying part is long-running agent sessions. Hermes/OpenCode sessi
arXiv:2603.30025v2 Announce Type: replace Abstract: Automated fact-checking pipelines typically begin with a filtering stage that decides which claims are worth verifying, given that the later evidenc
arXiv:2608.15071v1 Announce Type: new Abstract: Learning from experience is critical for developing capable, self-improving large language model (LLM) agents. Existing methods typically extract knowle
arXiv:2608.14804v1 Announce Type: new Abstract: Large language models (LLMs) have become the dominant interface of clinical artificial intelligence, yet the interface they expose (text in, text out, o
arXiv:2608.15279v1 Announce Type: new Abstract: 4D occupancy forecasting models the spatio-temporal evolution of 3D scenes and is crucial for autonomous driving, especially for corner-case simulation.
arXiv:2601.10267v2 Announce Type: replace Abstract: Separate Source-Channel Coding (SSCC) remains attractive for text transmission due to its modularity and compatibility with mature entropy coders an
arXiv:2608.15930v1 Announce Type: new Abstract: Foundation GUI agents can automate complex digital tasks, but deployment is hindered by scarce and biased training data, ambiguous prompts, and unreliab
arXiv:2608.13589v1 Announce Type: cross Abstract: As human space exploration returns to the Moon, astronauts need rapid access to procedural information during extravehicular activities (EVAs), where
arXiv:2608.14136v1 Announce Type: cross Abstract: Octree-based anchor Gaussian Splatting has emerged as a scalable representation for city-scale novel view synthesis, where multi-level anchors adaptiv
arXiv:2608.13767v1 Announce Type: new Abstract: Analog IC layout design remains a labor-intensive iterative process dominated by simulation-driven refinement. Although end-to-end layout generators acc
arXiv:2608.12695v1 Announce Type: new Abstract: Self-supervised electrocardiogram (ECG) models are often trained on a few seconds of ECG signal and, increasingly, on discretized token sequences. It re
arXiv:2603.24060v3 Announce Type: replace Abstract: Despite the promise of Vision-Language-Action (VLA) models as generalist robotic controllers, their robustness against perceptual noise and environm
arXiv:2608.11671v1 Announce Type: new Abstract: Vision-Language-Action (VLA) models can follow instructions and manipulate objects, but their performance often collapses out of distribution (OOD), whe
arXiv:2602.11410v2 Announce Type: replace Abstract: Click-through rate (CTR) prediction is fundamental to online advertising systems. While Deep Learning Recommendation Models (DLRMs) with explicit fe
arXiv:2608.09348v1 Announce Type: new Abstract: Density estimation underlies many unsupervised tasks on tabular data such as anomaly detection, out-of-distribution detection, and data augmentation. Al
arXiv:2608.09779v1 Announce Type: cross Abstract: Answering complex conditional questions using Large Language Models (LLMs) and Retrieval-Augmented Generation (RAG) remains a challenge, particularly
arXiv:2608.09551v1 Announce Type: new Abstract: In the era of large language models (LLMs), attackers often manipulate natural language to elicit unsafe or harmful outputs, creating a new natural lang
arXiv:2608.09228v1 Announce Type: cross Abstract: On-Policy Self-Distillation (OPSD) is commonly interpreted as the transfer of privileged information: a teacher observes the verified solution to the
arXiv:2608.09630v1 Announce Type: cross Abstract: Unsupervised domain adaptation is a widespread set of methods that leverages the knowledge of a labeled source domain to train a model to perform well
arXiv:2608.08569v1 Announce Type: new Abstract: Recent advancements in Speech Large Language Models have demonstrated remarkable capabilities in understanding complex audio tasks. Despite this progres
arXiv:2608.05166v1 Announce Type: new Abstract: We present an evaluation of cognitive bias expression in state-of-the-art instruction-tuned LLMs under realistic multi-turn interaction settings. Our wo
arXiv:2608.05326v1 Announce Type: cross Abstract: Autoregressive large language model inference is increasingly constrained by the memory footprint of the Key-Value (KV) cache. A dominant line of work
arXiv:2608.05879v1 Announce Type: new Abstract: Text-driven 3D generation has advanced rapidly in creating large-scale outdoor environments and detailed indoor scenes, but these domains are usually sy
arXiv:2608.05228v1 Announce Type: new Abstract: The 'decompose-then-verify' paradigm for LLM factuality evaluation faces a fundamental trade-off: atomic facts, i.e., one sentence conveying one unit of
arXiv:2512.03590v3 Announce Type: replace Abstract: Existing talking-head generation methods primarily target open-ended generation rather than bridging two existing video segments. In this paper, we
arXiv:2608.03565v1 Announce Type: new Abstract: While modern tabular learners excel at capturing statistical patterns, they frequently operate in a semantic vacuum, treating textual features as discre
arXiv:2608.00065v1 Announce Type: cross Abstract: Terminology-intensive retrieval, especially in medical settings, depends on preserving multi-word entities, abbreviations, numerical constraints, and
arXiv:2608.02183v1 Announce Type: new Abstract: Automatic sleep staging is a critical role in sleep disorder diagnosis, sleep quality assessment, and long-term health monitoring; however, existing app
arXiv:2607.28651v1 Announce Type: cross Abstract: Collaboration supports learning and problem-solving, but its effectiveness depends on cognitive engagement during discourse. This study applies an ext
arXiv:2607.26404v1 Announce Type: new Abstract: Graph Neural Networks (GNN) facilitate effective prediction on graph data such as molecules, media networks and neural network blueprints. GNNs facilita
arXiv:2607.27084v1 Announce Type: new Abstract: Scientific images are the core elements of presenting experimental conclusions, elaborating system architecture, and supporting comparative arguments in
I am building a domain-specific AI agent powered by Ollama (using the qwen:2.5 model). For data retrieval, the agent utilizes multiple search APIs: DuckDuckGo Search (DDGS), Tavily, Serper, and Google
arXiv:2607.20587v1 Announce Type: cross Abstract: Modern power systems increasingly require probabilistic forecasts amid interacting uncertainties from renewable intermittency, flexible demand, market
arXiv:2512.02473v2 Announce Type: replace-cross Abstract: Video world models have attracted significant attention for their ability to produce high-fidelity future visual observations conditioned on p
arXiv:2603.22510v2 Announce Type: replace-cross Abstract: Large language models are increasingly used in scholarly work, yet it remains unclear whether their productivity gains are accompanied by chan
arXiv:2607.07721v1 Announce Type: new Abstract: Retrieval-Augmented Generation (RAG) and agentic frameworks have advanced enterprise AI considerably, yet agents remain fundamentally reactive: they wai
arXiv:2607.06919v1 Announce Type: new Abstract: Artificial intelligence (AI) analysis of micro-ultrasound (muUS) has shown promise for prostate cancer (PCa) detection. However, most existing AI method