Knowledge catalogue
arXiv:2510.16727v2 Announce Type: replace-cross Abstract: Large language models internalize a structural trade-off between truthfulness and obsequious flattery, emerging from reward optimization that
arXiv:2605.17693v1 Announce Type: cross Abstract: Structure-based drug design has been accelerated by pocket-aware 3D generative models, yet most methods primarily fit the training distribution and ma
arXiv:2605.18475v1 Announce Type: cross Abstract: Mixed-precision quantization improves the budget--accuracy trade-off for large language models (LLMs) by allocating more bits to sensitive modules. Ho
arXiv:2602.02039v2 Announce Type: replace Abstract: The agency expected of Agentic Large Language Models goes beyond answering correctly, requiring autonomy to set goals and decide what to explore. We
arXiv:2605.16457v1 Announce Type: cross Abstract: Transformer-based world models have shown strong performance in visual reinforcement learning, but often suffer from temporal inconsistency in long-ho
arXiv:2510.10528v3 Announce Type: replace Abstract: Large reasoning models (LRMs) have demonstrated remarkable proficiency in tackling complex tasks through step-by-step thinking. However, this length
arXiv:2605.16409v1 Announce Type: cross Abstract: Optical character recognition (OCR) and multilingual text understanding remain major failure modes of multimodal large language models (MLLMs), partic
arXiv:2605.18431v1 Announce Type: new Abstract: Multimodal Large Language Models (MLLMs) have made substantial progress in egocentric video understanding, but their ability to reason cooperatively fro
arXiv:2602.10503v2 Announce Type: replace Abstract: Pretrained on large-scale and diverse datasets, VLA models demonstrate strong generalization and adaptability as general-purpose robotic policies. H
arXiv:2602.07272v2 Announce Type: replace Abstract: Creating photorealistic materials for 3D rendering requires exceptional artistic skill. Generative models for materials could help, but are currentl
arXiv:2605.17026v1 Announce Type: new Abstract: Recent progress in large language models has led to the emergence of reasoning models, which have shown strong performance on complex tasks through spec
arXiv:2605.15635v1 Announce Type: new Abstract: Linguistic ambiguity is critical to the robustness of Large Language Models (LLMs), yet existing research focuses mostly on English, with limited attent
arXiv:2605.15417v1 Announce Type: cross Abstract: In GFlowNets and variational inference, it has been shown that the mean square error between target and model log probabilities is an effective, low v
arXiv:2605.15482v1 Announce Type: new Abstract: Large language models (LLMs) are increasingly being applied to financial analysis, reporting, investment decision support, risk management, compliance,
arXiv:2511.09378v2 Announce Type: replace Abstract: A series of influential studies established that large language models cannot reliably solve even simple planning tasks. We show that the latest gen
arXiv:2605.15487v1 Announce Type: cross Abstract: Generative diffusion models can provide powerful prior probability models for inverse problems in imaging, but existing implementations suffer from tw
arXiv:2605.15589v1 Announce Type: new Abstract: Large language models (LLMs) are increasingly used in the mental health domain, yet it remains unclear how well they capture related biomedical knowledg
arXiv:2605.15424v1 Announce Type: new Abstract: Human trajectory forecasting is crucial for safe navigation in crowded environments, requiring models that balance accuracy with computational efficienc
arXiv:2605.15311v1 Announce Type: new Abstract: The identification and modeling of time-varying systems is a fundamental challenge in signal processing and system identification. To address this chall
arXiv:2605.15333v1 Announce Type: new Abstract: Large language models have recently reached near-parity with classical planners on well-known planning domains, yet this competence relies on world-know
arXiv:2605.15179v1 Announce Type: cross Abstract: Scaling Scientific Machine Learning (SciML) toward universal foundation models is bottlenecked by negative transfer: the simultaneous co-training of d
arXiv:2602.13483v2 Announce Type: replace-cross Abstract: Understanding the internal circuits that language models use to solve tasks remains a central challenge in mechanistic interpretability. A cru
arXiv:2605.14843v1 Announce Type: new Abstract: Text- and image-conditioned video generation models have achieved strong visual fidelity and temporal coherence, but they often fail to generate motion
arXiv:2605.12964v1 Announce Type: new Abstract: Flow-based generation in high-dimensional spaces is difficult because velocity prediction requires modeling high-dimensional noise, even when data has s
arXiv:2605.13773v1 Announce Type: cross Abstract: Large Language Models (LLMs) are being employed widely to automate tasks across the software development life-cycle. It is, however, unclear whether t
arXiv:2602.11618v4 Announce Type: replace Abstract: Chemical Language Models (CLMs) pre-trained on large scale molecular data are widely used for molecular property prediction. However, the common bel
arXiv:2605.13338v1 Announce Type: cross Abstract: Large Reasoning Models (LRMs) are increasingly integrated into systems requiring reliable multi-step inference, yet this growing dependence exposes ne
arXiv:2605.13045v1 Announce Type: new Abstract: The existing methods for evaluating the medical knowledge of Large Language Models (LLMs) are largely based on atemporal examination-style benchmarks, w
arXiv:2505.15616v2 Announce Type: replace Abstract: Multimodal Large Language Models (MLLMs) have achieved significant advances in integrating visual and linguistic information, yet their ability to r
arXiv:2605.13339v1 Announce Type: cross Abstract: Large language models (LLMs) can be said to have preferences: they reliably pick certain tasks and outputs over others, and preferences shaped by post
arXiv:2602.07342v2 Announce Type: replace Abstract: Large language models (LLMs) have shown promise in complex reasoning and tool-based decision making, motivating their application to real-world supp
arXiv:2605.12597v1 Announce Type: cross Abstract: Computational sampling has been central to the sciences since the mid-20th century. While machine-learning-based approaches have recently enabled majo
arXiv:2605.12034v1 Announce Type: cross Abstract: Omni-modal language models are intended to jointly understand audio, visual inputs, and language, but benchmark gains can be inflated when visual evid
arXiv:2605.11723v1 Announce Type: new Abstract: In this paper, we propose Concentrate and Concentrate (CaC), a coarse-to-fine anomaly reward model based on Vision-Language Models. During inference, it
arXiv:2512.24985v4 Announce Type: replace Abstract: Vision Language Models (VLMs) are increasingly adopted as central reasoning modules for embodied agents. Existing benchmarks evaluate their capabili
arXiv:2605.12183v1 Announce Type: new Abstract: Drifting Models have emerged as a new paradigm for one-step generative modeling, achieving strong image quality without iterative inference. The premise
arXiv:2605.11629v1 Announce Type: new Abstract: Recent multimodal large language models (MLLMs) have shown strong chain-of-thought (CoT) reasoning ability on vision-language tasks, but their direct de
arXiv:2605.11459v1 Announce Type: cross Abstract: Vision-Language-Action (VLA) models achieve remarkable flexibility and generalization beyond classical control paradigms. However, most prevailing VLA
arXiv:2605.10953v1 Announce Type: cross Abstract: The demand for high-resolution subsurface imaging and continuous Earth monitoring has driven rapid growth in active and passive seismic data from dens
arXiv:2602.02408v4 Announce Type: replace Abstract: Model editing aims to correct errors in large, pretrained models without altering unrelated behaviors. While some recent works have edited vision-la
arXiv:2605.11563v1 Announce Type: new Abstract: State Space Models (SSMs) have emerged as a compelling alternative to attention models for long-range vision tasks, offering input-dependent recurrence
arXiv:2605.08115v1 Announce Type: cross Abstract: Wepresent Alice v1, a 14-billion parameter open-source video generation model that achieves state-of-the-art quality through consistency distillation
arXiv:2605.08853v1 Announce Type: new Abstract: Mechanistic interpretability assumes that circuit analysis becomes harder as models scale. We challenge this assumption by showing that the attention ar
arXiv:2605.09496v1 Announce Type: new Abstract: Large language models represent the same reasoning in vastly different surface forms -- English prose, Python code, mathematical notation -- yet whether
arXiv:2605.08404v1 Announce Type: cross Abstract: This work investigates the use of large language models (LLMs) for tasks in smart cities. The core idea is to leverage remote sensing imagery to chara
arXiv:2605.10556v1 Announce Type: new Abstract: As large language models span dense, mixture-of-experts, and state-space architectures and are deployed on heterogeneous accelerators under increasingly
arXiv:2605.10410v1 Announce Type: new Abstract: Large language models can score well on named game-theory benchmarks while failing on the same strategic computation once semantic cues are removed. We
arXiv:2605.08685v1 Announce Type: cross Abstract: We propose a new class of waveform foundation models that departs from conventional sequence based representations by modeling physiological time seri
arXiv:2404.18923v5 Announce Type: replace Abstract: We introduce Holmes, a new benchmark designed to assess language models (LMs) linguistic competence - their unconscious understanding of linguistic
arXiv:2605.08254v1 Announce Type: cross Abstract: As foundation models grow in capability, the ability to efficiently and reliably control their behavior becomes critical. Fine-tuning these models can
arXiv:2605.09806v1 Announce Type: cross Abstract: Large reasoning models, such as OpenAI o1 and DeepSeek-R1, tend to become increasingly verbose as their reasoning capabilities improve. These inflated
arXiv:2605.09879v1 Announce Type: new Abstract: While reasoning has become a central capability of large language models (LLMs), the reasoning patterns required for different scenarios are often misal
arXiv:2605.08415v1 Announce Type: new Abstract: Since the advent of Large Language Models (LLMs), a significant area of research has focused on their intrinsic biases, particularly in political discou
arXiv:2605.08800v1 Announce Type: cross Abstract: Multimodal Large Language Models (MLLMs) may memorize sensitive cross-modal information during pretraining. However, existing MLLM unlearning benchmar
arXiv:2504.14697v3 Announce Type: replace Abstract: The evolution of tokens through deep transformer models can be modeled as an interacting particle system that has been shown to exhibit an asymptoti
arXiv:2605.08334v1 Announce Type: new Abstract: We present SalesSim, a framework and testbed for evaluating the ability of Multimodal Large Language Models (MLLMs) to simulate realistic, persona-drive
arXiv:2605.09031v1 Announce Type: new Abstract: Energy-based models (EBMs) are flexible generative architectures inspired by statistical physics, but their learning and generative properties remain po
arXiv:2605.08611v1 Announce Type: new Abstract: Current language model memory systems store what happened but not how it felt. This distinction -- between semantic memory (knowing about a past event)
arXiv:2601.02954v3 Announce Type: replace-cross Abstract: Large audio-language models have made rapid progress in recognizing what is present in an audio clip, but spatial audio-language understanding
arXiv:2605.09904v1 Announce Type: new Abstract: Video large language models (Video-LLMs) have achieved remarkable progress in general video understanding, yet their ability to maintain temporal object