Knowledge catalogue
arXiv:2606.01339v1 Announce Type: cross Abstract: Long-term time-series forecasting needs models that are accurate yet efficient enough for commodity hardware. Lightweight linear forecasters are remar
arXiv:2603.12037v2 Announce Type: replace Abstract: Foundation models based on prior-data fitted networks (PFNs) have shown strong empirical performance in causal inference by framing the task as an i
arXiv:2606.02167v1 Announce Type: new Abstract: Engineers designing production systems need to verify that a given layout supports all required production sequences. Automated planning techniques can
arXiv:2606.00857v1 Announce Type: cross Abstract: Accurate and reliable vehicle trajectory prediction is essential for safe autonomous driving. Recent studies have incorporated safety risk into trajec
arXiv:2606.00083v1 Announce Type: cross Abstract: Reinforcement learning relies on accurate reward functions, which are often hand-crafted or even unavailable in real-world applications, such as robot
arXiv:2606.00728v1 Announce Type: new Abstract: As Large Language Models (LLMs) are increasingly deployed in long-term interactions with users, empathy has become an increasingly important capability.
arXiv:2602.04861v2 Announce Type: replace-cross Abstract: Machine Learning Interatomic Potentials (MLIPs) sometimes fail to reproduce the physical smoothness of the quantum potential energy surface (P
arXiv:2606.02268v1 Announce Type: new Abstract: Geometric analysis fundamentally distinguishes between extit{extrinsic} and extit{intrinsic} perspectives. The dominant paradigm in current 3D represent
arXiv:2602.06841v4 Announce Type: replace Abstract: Over the last decade, Explainable AI has primarily focused on interpreting individual model predictions, producing post-hoc explanations that relate
arXiv:2603.00021v2 Announce Type: replace Abstract: Recent NLP systems commonly represent documents as linear token sequences. Although this captures sequential order, it can hinder modeling long-rang
arXiv:2508.01815v2 Announce Type: replace-cross Abstract: Text-to-SPARQL maps natural-language questions to executable SPARQL queries over RDF knowledge graphs. While standard evaluations often fix th
arXiv:2606.00054v1 Announce Type: cross Abstract: Recent progress in generalizable embodied control has been driven by large-scale pretraining of Vision-Language-Action (VLA) models. However, most exi
arXiv:2606.02559v1 Announce Type: cross Abstract: Post-training compression of Large Language Models (LLMs) removes entire architectural components, either deleting them or replacing them with fitted
arXiv:2510.03690v4 Announce Type: replace Abstract: Real-world graph datasets often arise from mixtures of populations, where graphs are generated by multiple distinct underlying distributions. In thi
arXiv:2606.00336v1 Announce Type: new Abstract: We propose Parameterized Diffusion Policy (PDP), a framework for learning diffusion policies conditioned on low-dimensional, continuous parameters embed
arXiv:2602.11453v2 Announce Type: replace-cross Abstract: In information retrieval (IR), learning-to-rank (LTR) methods have traditionally limited themselves to discriminative machine learning approac
arXiv:2606.01136v1 Announce Type: new Abstract: Single-score translation metrics can conflate legitimate variation with error, a problem especially acute for classical languages where multiple defensi
arXiv:2606.01374v1 Announce Type: new Abstract: Observable performance is commonly used to characterize biological systems. In adaptive systems, however, similar performances may arise from distinct o
This post describes OpenAI's public equity investing plugin for Codex, which enables users to convert investment questions into analytical models through natural language processing. The plugin likely
arXiv:2606.00202v1 Announce Type: cross Abstract: Standard machine learning pipelines often admit many near-optimal models. These 'Rashomon sets' pose a range of challenges and opportunities for uncer
arXiv:2606.01123v1 Announce Type: new Abstract: Preference-based reinforcement learning (PbRL) avoids explicit reward engineering by learning from pairwise human preference feedback. Existing offline
arXiv:2511.12081v2 Announce Type: replace-cross Abstract: Despite massive investments in scale, deep models for click-through rate (CTR) prediction often exhibit rapidly diminishing returns -- a stark
arXiv:2512.05277v3 Announce Type: replace-cross Abstract: Vision-Language Models (VLMs) are increasingly deployed as the perception and reasoning backbone of autonomous agents acting in the wild, with
arXiv:2603.04828v2 Announce Type: replace Abstract: Pre-training data detection for LLMs is essential for addressing copyright concerns and mitigating benchmark contamination. Existing methods mainly
arXiv:2606.00357v1 Announce Type: new Abstract: Training strong large language models (LLMs) requires high-quality supervision, which is often scarce. Recent work shows that paired preference data fro
arXiv:2602.03018v2 Announce Type: replace Abstract: Outlier detection (OD) is widely used in practice; but its effective deployment on new tasks is hindered by lack of labeled outliers, which makes al
arXiv:2606.02575v1 Announce Type: new Abstract: Autoregressive world models have emerged as a powerful paradigm for interactive video generation, allowing users to navigate dynamically generated envir
arXiv:2606.00694v1 Announce Type: new Abstract: Short-term anticipation in egocentric video requires more than recognizing the current scene: a system must infer which object the camera wearer will co
arXiv:2509.04631v2 Announce Type: replace Abstract: Transductive conformal prediction addresses the simultaneous prediction for multiple data points. Given a desired confidence level, the objective is
arXiv:2606.01008v1 Announce Type: cross Abstract: We present a benchmark for evaluating AI models and agents on real-world formal software verification tasks. We first scrape 11,039 property-based tes
arXiv:2606.02251v1 Announce Type: cross Abstract: Robust state estimation is central to robotic autonomy, yet classical Kalman filters struggle with frequency-dependent disturbances and model mismatch
arXiv:2606.01873v1 Announce Type: new Abstract: LLM-as-Aligner has emerged as a prevalent pre-training paradigm for Text-Attributed Graphs(TAGS), aligning graph and text modalities into a shared embed
arXiv:2606.00886v1 Announce Type: new Abstract: Accurate segmentation is crucial for autonomous spacecraft, as it directly affects downstream tasks related to 3D situational awareness. The harsh illum
This post discusses how the model, application, and infrastructure layers of AI are competing to commoditize each other, suggesting a dynamic where each layer is attempting to move upstream or downstr
arXiv:2606.01660v1 Announce Type: new Abstract: Pre-propagation graph neural networks (PPGNNs) push all graph-dependent computation into a preprocessing step and train only on the resulting dense hop
arXiv:2605.13136v2 Announce Type: replace Abstract: Distilling multi-step reasoning abilities from large language models (LLMs) into compact student models remains challenging due to noisy rationales,
arXiv:2606.02424v1 Announce Type: cross Abstract: Histology-based single-cell spatial transcriptomics (ST) estimation aims to predict gene expression for individual cells from histopathological images
arXiv:2506.11903v5 Announce Type: replace Abstract: Advances in transformer-based language models have highlighted the benefits of language-specific pre-training on high-quality corpora. In this conte
According to every product demo from the last four years, planning a trip is a killer use case for AI. Just tell it where you're going, they all promise, and your chatbot / agent / other buzzword will
arXiv:2606.00110v1 Announce Type: new Abstract: Achieving robust generalization from limited data is a central challenge in embodied intelligence. Prevailing methods fail by regressing absolute coordi
arXiv:2606.01981v1 Announce Type: new Abstract: Vehicle re-identification focuses on retrieving images of the same vehicle from a gallery given a query image. Upon closer inspection of commonly used d
arXiv:2510.06028v3 Announce Type: replace Abstract: This paper provides data-dependent bounds on the expected error of the Gibbs algorithm in the overparameterized interpolation regime, where low trai
arXiv:2511.01064v3 Announce Type: replace-cross Abstract: Variational inference (VI) approximates a target density p by the best match q in a family of tractable distributions. The best variational ap
Generate a complete storyboard from a single prompt, then animate it in Seedance 2.0 using a reference image. This ComfyUI workflow uses LLMs to expand prompts, create storyboard scenes, and format ev
arXiv:2606.00514v1 Announce Type: cross Abstract: Generative modeling and self-supervised representation learning (SSL) optimize structurally different objectives: generative training rewards distribu
arXiv:2606.00136v1 Announce Type: cross Abstract: The proliferation of adversarial synthetic content, accelerated by Generative AI (GenAI) is rendering traditional reactive detection methods ineffecti
arXiv:2510.12049v4 Announce Type: replace-cross Abstract: We quantify the short-term impact of Generative Artificial Intelligence (GenAI) on sales performance through a series of large-scale randomize
arXiv:2606.00803v1 Announce Type: cross Abstract: Reconstructing the three-dimensional distribution of dark matter from weak-lensing observations is a central but highly ill-posed inverse problem in c
arXiv:2606.00933v1 Announce Type: new Abstract: Coordinating multiple robots in shared environments requires generating feasible trajectories for each agent while accounting for interactions among age
arXiv:2606.01461v1 Announce Type: new Abstract: Developing effective anticancer therapeutics remains challenging due to tumor heterogeneity and the absence of well-defined molecular targets across can
arXiv:2606.00860v1 Announce Type: cross Abstract: Self-report questionnaires remain the prevailing tool for probing the psychological states of persona-conditioned agents (PC-Agents). However, classic
arXiv:2606.00248v1 Announce Type: new Abstract: Vector Symbolic Algebras (VSAs) enable robust neurosymbolic reasoning by encoding symbolic information into high-dimensional distributed representations
arXiv:2606.00139v1 Announce Type: cross Abstract: Curvature-penalized geodesic models have proven their effectiveness in image segmentation by computing globally optimal curves. Unfortunately, these m
arXiv:2606.00140v1 Announce Type: cross Abstract: While the rapid adoption of multimodal generative models offers immense potential, it has also increased the risks of harmful content synthesis, deepf
arXiv:2606.02248v1 Announce Type: new Abstract: Large language models solve complex problems by generating lengthy chains of explicit reasoning tokens. While effective, this makes reasoning expensive,
arXiv:2606.02436v1 Announce Type: new Abstract: Video world models aim to simulate controllable visual environments, but long-horizon rollouts depend on what the model remembers after observations lea
arXiv:2603.11946v2 Announce Type: replace-cross Abstract: Probabilistic circuits (PCs) enable exact and tractable inference but employ data independent mixture weights that limit their ability to capt
arXiv:2606.00447v1 Announce Type: cross Abstract: Open-vocabulary 3D scene segmentation usually assumes RGB-D video, calibrated multi-view imagery, or a reconstructed mesh. GeoSAM-3D studies a lighter
This post references getting hair and makeup services or advice from Sara Normous, who is identified as a tech style icon. The tweet likely documents a personal experience or behind-the-scenes moment
arXiv:2506.16114v3 Announce Type: replace-cross Abstract: Generative recommendations (GR), which usually include item tokenizers and generative Large Language Models (LLMs), have demonstrated remarkab