Knowledge catalogue
arXiv:2610.10367v1 Announce Type: new Abstract: Interpretability offers a solution to safe autonomy by providing transparency into an agent's underlying decision-making model. Within sequential-decisi
arXiv:2610.09509v1 Announce Type: new Abstract: Latent actions supervise robot policies with action-like codes inferred from visual transitions, and their usefulness hinges on two questions: what a co
arXiv:2610.10539v1 Announce Type: new Abstract: We propose Tetris3D, a generative framework for single-image 3D scene reconstruction that recovers objects which are physically and geometrically cohere
Hey everyone, Jovan from UkisAI (Swift Qwen) here! For those who don't know us, UkisAI is a small lab making tiny frontier LLMs, tools and datasets (+doing it open-source!). I'm one of the guys runnin
arXiv:2610.08975v1 Announce Type: new Abstract: A plethora of new adaptive optimizers are designed to efficiently estimate and use minibatch gradient statistics to shape parameter updates, but they ar
arXiv:2610.09371v1 Announce Type: cross Abstract: Calibrated uncertainty quantification is essential to ensuring AI agents are trustworthy and reliable. However, when agents seek to maximize user enga
arXiv:2610.08816v1 Announce Type: new Abstract: Long-range temporal dependence poses a resource question for sequence models: for a specified predictive-memory law, how much state, context, or dynamic
arXiv:2610.09120v1 Announce Type: new Abstract: Randomized exploration is central to bandit learning, multi-agent reinforcement learning, and zeroth-order policy search, yet its independence and priva
arXiv:2610.09186v1 Announce Type: new Abstract: We argue that pattern recognition and step-by-step reasoning are two ends of a spectrum. A large language model (LLM) learns to reason step-by-step when
This is today’s edition of The Download, our weekday newsletter that provides a daily dose of what’s going on in the world of technology. AI breakthroughs in robotics won’t change your life any time s
arXiv:2610.09620v1 Announce Type: new Abstract: We study which parameters of deep, unmasked, single-head attention are determined by its input--output function. For known positive nonconstant real-ana
arXiv:2610.09132v1 Announce Type: cross Abstract: In wide Bayesian neural networks, Gaussian mean-field variational inference is prone to 'prior dominance': the Kullback-Leibler (KL) regularization te
arXiv:2610.05366v2 Announce Type: replace Abstract: IroyinSpeech is a 42-hour, 80-speaker Yoruba read-speech corpus whose audio has been distributed by ELRA since 2024. This paper describes the releas
arXiv:2610.10049v1 Announce Type: new Abstract: Reasoning models allocate extra computation at inference time and present their answers as the product of deliberate thought. If this deliberation works
arXiv:2605.06295v2 Announce Type: replace Abstract: How can an arbitrary attribution method be generalized from first principles to capture interactions? We answer this with the metagame, a conceptual
arXiv:2610.09384v1 Announce Type: new Abstract: Language models are routinely fine-tuned under a fixed context, such as a generic system prompt, persona or domain-specific instruction, yet the learned
arXiv:2610.09687v1 Announce Type: cross Abstract: Structured recovery phenomena, such as restricted isometry properties in compressed sensing, have shown that high-dimensional objects can often be rec
arXiv:2610.09255v1 Announce Type: new Abstract: Neural PDE surrogates are trained on numerical solver outputs that contain both physical evolution and solver-specific discretization errors. Because su
arXiv:2610.09239v1 Announce Type: cross Abstract: Self-improving LLM systems propose changes to themselves and keep those that score better on a small evaluation set. We treat this keep-if-better step
There was a time when @nvidia and @Google weren't into AI. There was a time when the big cloud providers didn't have GPUs. There was a time before GPUs and deep learning, and AI, ... During that time
arXiv:2610.09876v1 Announce Type: cross Abstract: Diffusion models generate realistic images but often fail on visual reasoning tasks, such as filling in a Sudoku or drawing the path through a maze. W
arXiv:2610.10317v1 Announce Type: new Abstract: Tabular foundation models (TFMs) are pretrained across diverse tabular tasks and make predictions on a new table at inference time using its labeled exa
arXiv:2610.09343v1 Announce Type: new Abstract: Tiled 3D Gaussian Splatting rasterizers often use one scene-wide contribution cutoff for tile enumeration, although content differs in its sensitivity t
arXiv:2610.08879v1 Announce Type: new Abstract: Pretraining strategies significantly impact the quality of language models, yet existing comparisons of Masked Language Modeling (MLM), Whole Word Maski
arXiv:2610.09441v1 Announce Type: new Abstract: Microsatellite instability-high (MSI-H) and high tumor mutational burden (TMB-H) are clinically relevant biomarkers, yet their histopathological predict
arXiv:2610.09408v1 Announce Type: new Abstract: Audio-visual multi-segment grounding (AV-MSG) in untrimmed videos, reasoning over audio-visual evidence and predicting multiple segments for a query, is
arXiv:2610.09462v1 Announce Type: new Abstract: Backdoored vision-language-action (VLA) policies can preserve benign task performance while producing malicious actions when a trigger appears. Detectin
Today we are launching an agent arena for RL environments: The Open Env Arena. Your agents can work to define the best RL environments; the platform trains Qwen-3.8-27B on them, we evaluate the models
Tokenomics, from the chip all the way up. @dylan522p, Founder & CEO of @SemiAnalysis_, joins the Fireworks Forge speaker list on optimizing every layer of AI infrastructure for inference at scale. Joi
arXiv:2610.08794v1 Announce Type: new Abstract: Large language models rely on subword tokenizers whose quality varies across languages, yet no standardized multi-metric framework exists for broad comp
arXiv:2610.09163v1 Announce Type: new Abstract: Task-oriented conversational agents remain fragile under real world conversation scenarios as they rarely follow a predictable script, especially when u
arXiv:2610.09118v1 Announce Type: new Abstract: Topology reasoning jointly detects 3D lanes and traffic elements from multi-view images and infers their structural connectivity. Current methods model
arXiv:2610.09360v1 Announce Type: cross Abstract: Real-world documents distribute evidence across text, tables, figures, and captions within complex page layouts. Answering complex questions over such
arXiv:2610.10288v1 Announce Type: new Abstract: Large-scale egocentric human interaction data is becoming an important source of physical supervision for embodied learning, yet video alone leaves the
arXiv:2610.08933v1 Announce Type: new Abstract: Deep-space crews cannot rely on real-time ground support for urgent off-nominal events. Initial alerts may underdetermine cause, while discriminating ev
arXiv:2610.09857v1 Announce Type: new Abstract: Robot demonstration learning requires accurate and temporally complete end-effector localization during close-range manipulation and camera occlusion. E
arXiv:2610.09075v1 Announce Type: cross Abstract: The rapid expansion of text-to-music generative models challenges traditional paradigms of music creation and intellectual property. Plagiarism in thi
arXiv:2610.10076v1 Announce Type: cross Abstract: Calibration is an essential requirement for probabilistic predictions to be useful for decision making. While state-of-the-art prediction methods ofte
arXiv:2610.09724v1 Announce Type: cross Abstract: Query Expansion (QE) techniques have long been widely used in Information Retrieval (IR) to address the vocabulary mismatch problem. They remain relev
arXiv:2610.09048v1 Announce Type: new Abstract: Building a world model requires a state representation useful for planning and decision-making---potentially over tasks unknown at training time. In the
arXiv:2610.09969v1 Announce Type: new Abstract: Post-training quantization (PTQ) enables efficient deployment, yet transformer architectures remain challenging to quantize due to nonlinear layers. Whi
arXiv:2610.09637v1 Announce Type: cross Abstract: How can we verify whose music contributed to an AI-generated output? This paper demonstrates how input-based attribution can provide verifiable eviden
arXiv:2610.10061v1 Announce Type: cross Abstract: This paper presents TRACK (Telemetry-Based Racing Analysis and Coaching Kit), which is a framework for analyzing driving performance in sim racing and
arXiv:2610.09417v1 Announce Type: new Abstract: Effective remote monitoring and surveillance using drones are frequently impeded by severe environmental and thermal clutter, dynamic vegetation, target
arXiv:2610.09858v1 Announce Type: new Abstract: Large language model agents tackle multi-step tasks by interleaving reasoning and tool calls with observations from the environment. Prior work has show
🧬 Training and evaluating AI scientists on drug target discovery is hard. We introduce DrugTargetWorld: Synthetic Biomedical Worlds for Training and Benchmarking AI Scientists The environment turns en
arXiv:2610.10411v1 Announce Type: cross Abstract: Speculative decoding accelerates large language model inference by using a low-cost draft model to propose tokens that the full-size target model veri
arXiv:2610.10124v1 Announce Type: cross Abstract: Generative recommenders return a limited candidate set and may omit observed targets before reranking. A training strategy appends these missed target
arXiv:2610.09237v1 Announce Type: cross Abstract: Scientific studies of language agents need behavioral variables that support hypotheses across tasks and models. We formulate this research problem as
arXiv:2610.08863v1 Announce Type: new Abstract: A common way for trajectory planning is to leverage generative models trained on large collections of expert trajectories. At inference time, the model
arXiv:2610.08810v1 Announce Type: new Abstract: Fine-tuned geospatial foundation models (GeoFMs) pretrained on large satellite archives have been shown to improve crop classification accuracy and geog
arXiv:2610.10054v1 Announce Type: cross Abstract: Sampling transitions between metastable states is a central problem in dynamical systems theory and molecular dynamics in particular. A key challenge
arXiv:2603.15614v2 Announce Type: replace Abstract: Recent video diffusion models achieve strong visual quality and temporal coherence, but still lack coordinated control over scene layout, customized
True You don't have to wait for Grok Bot to add Claude, OpenAI, etc. You can do it now. I did Just ask your Grok Bot to connect to your AI subscriptions. How it'll do it: • Opens a terminal on its own
arXiv:2610.08883v1 Announce Type: cross Abstract: Finding low-energy configurations of interacting particles and approximating probability distributions lead to the minimization of potential-interacti
Release: ttok 0.4 ttok is my CLI tool for counting tokens, using OpenAI's open source tiktoken library. It hasn't been in updated in a couple of years, but I finally fixed a Click warning, updated CI,
arXiv:2610.09090v1 Announce Type: new Abstract: The quadratic cost of self-attention limits scalability to long sequences from multidimensional data. We introduce Tucker bottleneck attention (TuBA), w
arXiv:2610.09281v1 Announce Type: new Abstract: In Bayesian inverse problems, posterior sampling requires generating samples that are consistent with given observations while capturing the range of pl
arXiv:2610.10483v1 Announce Type: new Abstract: Sampling from a softmax distribution is a fundamental operation in machine learning, but its linear complexity in the number of items makes exact sampli
arXiv:2610.09087v1 Announce Type: new Abstract: Large language models are informing decisions with ever-higher stakes. As the consequences of their errors grow, a central question becomes harder to ig