Knowledge catalogue
arXiv:2609.10767v1 Announce Type: cross Abstract: We develop an exact almost-sure learning theory for smooth parametric models trained by regularly weighted empirical risk minimization on long-range d
I wish I could spend $15k on my own homelab hardware, but no this is for work lol. Like the title says, we're looking to run DSV4 Flash (and similar tier models) locally at good speeds, both for token
Looking for tools to build/run an engine to do STT-LLM-TTS basically. I know how to run all of them separately, and if need be I can build one myself, but I was curious if there is already an open sou
What is happening in maths right now will happen to any hard industry with verifiable problems and high value rewards. Take microchip design, nuclear or 100s of other industrial processes that are val
arXiv:2602.17655v3 Announce Type: replace Abstract: Language Identification (LID) is an important component of many multilingual natural language processing pipelines, where it facilitates corpus cura
arXiv:2609.10263v1 Announce Type: new Abstract: Persistent language agents need stored experience to remain available across time, while each answer requires evidence suited to a particular question.
arXiv:2512.10147v2 Announce Type: replace Abstract: extbf{Motivation:} Pre-trained protein language models (PLMs) such as ESM-2 have become the default representation for biological sequence tasks, bu
arXiv:2609.11166v1 Announce Type: new Abstract: Rapid evaluation of many simultaneous road-link disruptions requires a practical compromise between exact spectral recomputation and local approximation
arXiv:2609.10726v1 Announce Type: new Abstract: Hazardous robotic exploration requires robots to map spatial risks, such as unsafe terrain, radiation, fire, mines, or structural damage, while operatin
arXiv:2609.11235v1 Announce Type: new Abstract: Test-time adaptation (TTA) offers many ways to update a deployed model without labels, but choosing the wrong update can make a strong source model wors
arXiv:2609.10961v1 Announce Type: new Abstract: P300 brain-computer interface (BCI) spellers can provide hands-free communication for people with severe motor impairments. Modern pipelines combine mul
arXiv:2609.11067v1 Announce Type: new Abstract: Large language models are increasingly used as judges to measure social bias in text, yet the passages they judge are often noisy, containing typos, inf
arXiv:2609.10750v1 Announce Type: cross Abstract: LLM agents increasingly rely on external skills retrieved at runtime, making skill selection from large repositories a critical challenge. We present
When you build a @NousResearch Hermes agent you can direct your entire AI second brain to any AI model Local or any API Open or closed You just type /model and switch Your model switching costs become
arXiv:2609.09707v1 Announce Type: new Abstract: Supervised fine-tuning (SFT) applies a uniform cross-entropy loss to all target tokens, even though different tokens provide unequal learning signals fo
arXiv:2609.11772v1 Announce Type: cross Abstract: Cross-cultural understanding has become increasingly important in today's highly connected, cross-national world. The success of LLM-based technologie
arXiv:2609.11198v1 Announce Type: cross Abstract: Generative AI and the practice of 'vibe coding' are changing how archaeologists carry out computational research, but their effects on the discipline'
arXiv:2609.11716v1 Announce Type: cross Abstract: Post-training quantization compresses large language models (LLMs) by storing their weights at reduced precision, and each quantized weight introduces
arXiv:2609.10221v2 Announce Type: new Abstract: Reinforcement learning over a frozen reasoner has become a common recipe for teaching a policy which external tools to invoke. We show that this recipe
arXiv:2609.09856v1 Announce Type: cross Abstract: The rapid adoption of generative AI tools such as ChatGPT has sparked intense debate about their risks and opportunities for education, as well as the
arXiv:2609.11548v1 Announce Type: new Abstract: Autoregressive video world models enable interactive, long-horizon exploration, but flexible control remains challenging. Exploring a source video from
arXiv:2609.09428v1 Announce Type: new Abstract: Evaluating the quality of explanations produced by explainable AI (XAI) methods remains challenging because existing approaches often rely on subjective
arXiv:2609.11274v1 Announce Type: cross Abstract: Recently, large language model (LLM) based ASR models have achieved significant progress, yet they generally lack support for multi-speaker scenarios,
arXiv:2609.11310v1 Announce Type: new Abstract: We address few-shot object detection with vision-language models (VLMs) in out-of-domain settings such as aerial, industrial, and medical imagery, using
arXiv:2609.10643v1 Announce Type: new Abstract: Natural load-bearing patterns such as leaf venation, trabecular bone, and spider webs achieve high stiffness per unit mass, yet classical topology optim
arXiv:2609.11642v1 Announce Type: cross Abstract: Neural audio codecs are a fundamental component of modern speech generation systems. While recent codecs achieve increasingly low bitrates, reducing f
I'm currently building my system around 3060s, but I might be able to get a 4060 for a nice deal. At first it seemed like a no brainer, but turns out the 4060 has lower memory bandwidth. In a system t
arXiv:2609.10187v1 Announce Type: new Abstract: In this work, we introduce language-aligned motion representations for domain-generalizable UPDRS-Gait severity estimation, aiming to learn semantically
arXiv:2609.09964v1 Announce Type: new Abstract: Large Language Models (LLMs) have achieved remarkable progress across natural language processing (NLP) tasks, yet their capabilities degrade sharply fo
arXiv:2609.09457v1 Announce Type: cross Abstract: Quantum state tomography is a fundamental technique for estimating the state of a quantum system from measured data and plays a crucial role in evalua
arXiv:2609.06654v1 Announce Type: new Abstract: Computational modeling of emotion has long faced a tension between descriptive, 'snapshot-based' appraisal models and granular, signal-driven architectu
arXiv:2609.10339v1 Announce Type: new Abstract: A confidence-aware multimodal fusion framework (CAMF) is proposed to realize reliable human intention prediction for industrial human-robot collaboratio
arXiv:2603.15038v2 Announce Type: replace-cross Abstract: A novel convolutional autoencoder and neural ODE (CAE-NODE) framework is proposed for a reduced-order model (ROM) applied to transient 2D coun
arXiv:2609.09137v1 Announce Type: new Abstract: Robotic Process Automation (RPA) is widely used to reduce administrative burden in United States hospitals, yet an estimated 30-50% of RPA initiatives u
arXiv:2609.09403v1 Announce Type: new Abstract: In the past decade, Bayesian optimization (BO) has emerged as a powerful and adaptable framework for automatic controller tuning and robot learning. Thi
arXiv:2609.10299v1 Announce Type: new Abstract: Sparse autoencoders (SAEs) are increasingly used to recover interpretable features from neural-network activations, yet systematic feature co-occurrence
arXiv:2109.02355v2 Announce Type: replace-cross Abstract: The last decade of progress in machine learning (ML), especially the deep learning era, has raised a number of scientific questions that chall
arXiv:2609.07075v1 Announce Type: new Abstract: Retrieval-augmented generation (RAG) is commonly evaluated by whether the final answer is correct. That test is insufficient: an answer can match its re
arXiv:2505.23215v2 Announce Type: replace-cross Abstract: We propose a framework for generative modeling of continuous-time processes from irregularly and asynchronously recorded data. It is based on
arXiv:2609.09910v1 Announce Type: new Abstract: The small-sample-size (SSS) problem remains a fundamental challenge in machine learning when labeled data are scarce due to cost, accessibility, or ethi
arXiv:2609.10357v1 Announce Type: new Abstract: Time-series foundation models are evaluated almost exclusively on public archives that predate them, so a strong score cannot be separated from having s
arXiv:2609.08016v1 Announce Type: new Abstract: Multi-agent debate, in which several LLMs exchange arguments before answering, is widely assumed to improve answer quality by surfacing genuine disagree
arXiv:2603.25259v2 Announce Type: replace Abstract: Research on mobile manipulation systems that physically interact with humans has expanded rapidly in recent years, opening the way to tasks which co
A person with mid single digit years of industry experience and months at one company and negligible external footprint quits that job voluntarily and ends up on a press tour that would be the envy of
arXiv:2609.10529v1 Announce Type: new Abstract: We prove the gap-entropy conjecture for fixed-confidence best-arm identification with independent unit-variance Gaussian arms, means in [0,1], and a uni
arXiv:2609.09796v1 Announce Type: new Abstract: The DIRECT algorithm is a deterministic global optimization method known for its versatility and balanced exploration-exploitation strategy. However, DI
arXiv:2609.07719v1 Announce Type: new Abstract: Medical imaging artificial intelligence (AI) is commonly developed as separate mappings from radiographs to diagnostic outputs or from clinical descript
arXiv:2609.09650v1 Announce Type: new Abstract: Reinforcement learning (RL) has demonstrated considerable potential for autonomous driving decision-making. However, its deployment in urban autonomous
arXiv:2609.09986v1 Announce Type: cross Abstract: Consistent submodular maximization studies the tradeoff between solution quality and stability when elements arrive over time. For a monotone submodul
arXiv:2609.06050v1 Announce Type: cross Abstract: For a compact set KsubsetC, let artheta(K) be the infimum of the planar areas of the unit lemniscates of all monic polynomials with zeros in K, allowi
arXiv:2609.10084v1 Announce Type: cross Abstract: Generalized zero-shot learning (GZSL) has emerged as an important paradigm for visual recognition systems that must generalize to classes that were no
arXiv:2609.09547v1 Announce Type: new Abstract: Sample size determination for machine learning (ML) prediction models is challenging because conventional power analysis typically requires the predicto
arXiv:2609.09211v1 Announce Type: cross Abstract: The Davis-Kahan theorem is a fundamental tool in spectral analysis, providing quantitative control over the distance between the eigenspaces of a symm
arXiv:2609.10099v1 Announce Type: new Abstract: Molecule generation has emerged as a powerful computational tool for de novo drug design, enabling the exploration of chemical space beyond the limits o
arXiv:2609.08592v1 Announce Type: new Abstract: Evaluating tool-augmented LLM agents requires diverse, realistic user inputs yet most evaluation frameworks use flat role descriptions ('you are an angr
arXiv:2609.07586v1 Announce Type: new Abstract: Software repositories contain vast amounts of data on code contributions, bug reports, and project activities, yet this information remains challenging
arXiv:2609.10400v1 Announce Type: new Abstract: The coordination of Automated Guided Vehicles (AGVs) in high-density industrial environments represents a critical challenge within Logistics 4.0, as tr
arXiv:2609.06573v1 Announce Type: new Abstract: Recent studies report that automated red-teaming finds more vulnerabilities, at lower cost, than human red-teaming on standard AI safety benchmarks, and
arXiv:2609.10108v1 Announce Type: new Abstract: Aging clocks quantify biological aging and help characterize individual health status. What protein interactions are important for accurate aging clocks
arXiv:2609.06708v1 Announce Type: cross Abstract: Large language models are transforming many industries with their text generation abilities. However, their outputs can be easily tampered with, creat