Knowledge catalogue
arXiv:2605.24749v1 Announce Type: cross Abstract: Reward modeling is not only a prediction problem: in KL-regularized policy optimization, the learned reward is exponentiated to define the deployed po
arXiv:2605.24009v1 Announce Type: cross Abstract: Convective available potential energy (CAPE) is an important variable for forecasting severe weather and understanding deep convection and precipitati
arXiv:2605.24647v1 Announce Type: new Abstract: Personalized dialogue requires more than recalling explicit user histories: systems also need to infer hidden user states that evolve through interactio
arXiv:2603.00857v2 Announce Type: replace-cross Abstract: MultiPUFFIN is a domain-informed multimodal foundation model for predicting thermophysical properties of small molecules, addressing a critica
arXiv:2605.26064v1 Announce Type: cross Abstract: We present Paris 2.0, the first video generation model pre-trained through decentralized computation. Its training recipe builds upon Paris 1.0 (arXiv
arXiv:2601.20539v3 Announce Type: replace Abstract: Large Language Models (LLMs) have enabled automated heuristic design (AHD) for combinatorial optimization problems (COPs), but existing frameworks'
arXiv:2605.25085v1 Announce Type: cross Abstract: We study the rate-distortion limits of online KV cache compression in autoregressive language models, formulating it as sequential Wyner-Ziv source co
arXiv:2605.24850v1 Announce Type: new Abstract: Evaluating whether large language models (LLMs) capture the structure of natural language beyond local fluency remains an open challenge. Existing evalu
arXiv:2605.23915v1 Announce Type: cross Abstract: The Intelligent Driver Model (IDM) is a cornerstone of Adaptive Cruise Control (ACC), valued for its interpretable parameters and effectiveness in car
arXiv:2605.24371v1 Announce Type: cross Abstract: CT report generation (CTRG) requires models to summarize three-dimensional anatomical context and pathological findings from hundreds of axial slices.
arXiv:2509.05614v3 Announce Type: replace-cross Abstract: Pruning is a typical acceleration technique for compute-bound models by removing computation on unimportant values. Recently, it has been appl
arXiv:2605.24564v1 Announce Type: new Abstract: Backtesting large language models (LLMs) on historical financial data is unreliable because pre-training cuts off after the events happened. An LLM trai
arXiv:2509.12194v2 Announce Type: replace Abstract: Differential diagnosis is an iterative process that integrates patient information with broader medical knowledge. Clinical case series such as the
arXiv:2605.24697v1 Announce Type: cross Abstract: Diffusion large language models promise faster generation by refining many token positions in parallel, but this parallelism introduces a hidden contr
arXiv:2605.25044v1 Announce Type: new Abstract: Learning universal policies from cross-embodied data remains a fundamental challenge in robotics. Although Vision-Language-Action (VLA) models are pre-t
arXiv:2605.23234v1 Announce Type: new Abstract: Assessing the spatial fairness of predictive models involves establishing whether they are statistically penalizing (favoring) individuals associated wi
A developer created a local Model Context Protocol (MCP) memory server that integrates Ollama to provide AI coding assistants with persistent memory capabilities while maintaining complete privacy and
arXiv:2605.23275v1 Announce Type: new Abstract: In this paper, we propose Diffusion Domain Expansion (DDE), a method that efficiently extends pre-trained diffusion models to generate larger objects an
arXiv:2512.22597v2 Announce Type: replace Abstract: Exploring molecular energy landscapes and identifying ground-state conformations are central challenges in computational chemistry. However, generat
arXiv:2511.02239v2 Announce Type: replace-cross Abstract: Learning generalizable policies for robotic manipulation increasingly relies on large-scale models that map language instructions to actions (
arXiv:2605.22967v1 Announce Type: new Abstract: When Masked Diffusion Models (MDMs) generate sequences through iterative refinement, the rich internal computation over masked positions is discarded, f
arXiv:2605.23856v1 Announce Type: new Abstract: Robot policy learning benefits from world-action models that capture environment dynamics, but pixel-level prediction entangles dynamics with nuisance f
arXiv:2605.23345v1 Announce Type: new Abstract: Interactive world models for first-person shooter (FPS) games must resolve high-frequency overlapping control signals at every frame without disrupting
arXiv:2503.12868v2 Announce Type: replace Abstract: Learning-based medical image registration has matched the accuracy of conventional methods while offering superior computational efficiency. However
arXiv:2510.07869v4 Announce Type: replace Abstract: Underwater environments pose unique challenges for robotic navigation and manipulation. While existing research has primarily focused on task-specif
arXiv:2510.03271v2 Announce Type: replace Abstract: Decision boundary, the subspace of inputs where a machine learning model assigns equal classification probabilities to two classes, is pivotal in re
arXiv:2605.21568v1 Announce Type: new Abstract: In this work, we extend the Equilibrium Propagation framework to skew-gradient systems and show an equivalence between deep Energy-Based Models and Hami
arXiv:2605.22532v1 Announce Type: new Abstract: Linear properties are ubiquitous in the representations of language models; however, testing them experimentally remains a challenging task. This work f
six years ago world (cognitive) models were the centerpiece of my essay The Next Decade in AI. their time is finally coming. Demis Hassabis on the limit in today’s AI: language can describe the world,
World models have existed for years (though not in LLMs); I take them to be explicit representation of objects, places, events, mechanisms etc you can reason over. Chess computers have them (board, pi
arXiv:2605.21642v1 Announce Type: new Abstract: Vision-language models (VLMs) are increasingly augmented with continuous or latent non-textual tokens intended to support 'visual thinking.' Despite imp
arXiv:2605.22185v1 Announce Type: new Abstract: Recent advancements in Multimodal Large Language Models (MLLMs) have demonstrated impressive capabilities in general visual understanding. However, thei
arXiv:2605.21931v1 Announce Type: new Abstract: Recent Video Large Language Models (Video-LLMs) have demonstrated strong capabilities in video reasoning through reinforcement learning (RL). However, e
arXiv:2507.23773v3 Announce Type: replace-cross Abstract: What does it mean to plan? Current agentic systems, whether scaffolded workflows or end-to-end policies, rely on reactive decision-making: sel
Streaming vision-language models (VLMs) continuously generate responses given an instruction prompt and an online stream of input frames. This is a core mechanism for real-time visual assistants. Exis
arXiv:2602.10455v2 Announce Type: replace-cross Abstract: Driven by scaling laws, recommender systems increasingly rely on larger-scale models to capture complex feature interactions and user behavior
arXiv:2605.21308v1 Announce Type: new Abstract: State Space Models (SSMs) have emerged as a powerful and efficient alternative to Transformers, demonstrating linear-time complexity and exceptional seq
arXiv:2601.03019v4 Announce Type: replace-cross Abstract: DNA language models are increasingly used to represent genomic sequence, yet their effectiveness depends critically on how raw nucleotides are
arXiv:2603.14698v3 Announce Type: replace Abstract: Unmanned aerial vehicles (UAVs) operating in cluttered environments require efficient and accurate impact modeling to maintain stability post collis
arXiv:2510.06824v2 Announce Type: replace Abstract: To drive progress in science and engineering, large language models (LLMs) must be able to process large amounts of numerical data and solve long ca
arXiv:2603.16513v3 Announce Type: replace Abstract: Structured data is widely used in domains such as healthcare, finance, and scientific data management. Recent studies on structured data foundation
arXiv:2605.21362v1 Announce Type: new Abstract: Jailbreak attacks expose a persistent gap between the intended safety behavior of aligned large language models and their behavior under adversarial pro
arXiv:2605.21280v1 Announce Type: new Abstract: High-fidelity EEG generation is critical for alleviating data scarcity and addressing privacy constraints in large-scale neural modeling. Despite recent
arXiv:2605.20549v1 Announce Type: new Abstract: Modern vision models achieve strong performance on standard benchmarks, yet their aggregate accuracy reveals little about which scene properties drive t
arXiv:2605.20299v1 Announce Type: new Abstract: Generative sequence models are often trained to plan motion in physical domains, from robotics to mechanical simulations. When constructing a dataset to
arXiv:2605.21031v1 Announce Type: new Abstract: This article presents a novel SOFA based finite element method for the soft body modeling and the corresponding dynamic simulation and control of a pneu
arXiv:2605.21388v1 Announce Type: new Abstract: Despite the remarkable empirical success of generative models, the available theory on their statistical accuracy in scientific computing remains largel
arXiv:2605.20941v1 Announce Type: new Abstract: We present PaintCopilot, a co-creative neural painting assistant that models painting as an open-ended autoregressive artistic behavior conditioned on e
arXiv:2510.00171v2 Announce Type: replace-cross Abstract: We investigate quantum reservoir computing (QRC) using a hybrid qubit-boson system described by the Jaynes-Cummings (JC) Hamiltonian and its d
arXiv:2605.20300v1 Announce Type: new Abstract: In this paper, we propose a robust subspace-constrained quadratic model (SCQM) for learning low-dimensional structure from high-dimensional data. Buildi
arXiv:2605.20908v1 Announce Type: new Abstract: Concept-based (CB) models provide interpretability and support test-time human intervention, while standard neural networks (NN) offer strong task perfo
Ten years ago, I walked into Tesla as an intern — January 2015, back when all we built was the Model S. I never imagined I'd one day lead the Fremont factory where we are building some of the most ama
arXiv:2605.20502v1 Announce Type: cross Abstract: We address out-of-distribution (OOD) detection across the full spectrum of distribution shifts -- global domain changes, semantic divergence, texture
arXiv:2605.18900v1 Announce Type: cross Abstract: One of the main causes of death around the globe is malaria. Researchers have sought to develop predictive models for malaria outbreaks based on meteo
arXiv:2605.19950v1 Announce Type: new Abstract: Humans infer emotions by integrating observed multimodal cues with expectations about how affective states may unfold. Existing multimodal large languag
arXiv:2605.19698v1 Announce Type: cross Abstract: Text-to-image diffusion models are increasingly developed through open-source reuse and repeated downstream fine-tuning, where reused checkpoints are
arXiv:2605.19418v1 Announce Type: new Abstract: LLM-based multi-agent systems (MAS) have demonstrated strong reasoning and decision-making capabilities that consistently surpass those of single LLM ag
arXiv:2602.09259v2 Announce Type: replace Abstract: In robot-assisted minimally invasive surgery (RMIS), reduced haptic feedback and depth cues increase reliance on expert visual perception, motivatin
arXiv:2509.26464v2 Announce Type: replace Abstract: Self-preference is a fundamental feature of biological organisms. Since large language models (LLMs) lack sentience, they might be expected to avoid
arXiv:2605.19739v1 Announce Type: new Abstract: Recent advances in flow matching models have significantly improved text-to-image generation quality, but also introduce growing safety risks due to the