Knowledge catalogue
arXiv:2610.14777v1 Announce Type: new Abstract: LLM-powered data analytics aims to translate natural language instructions into executable analytical programs over underlying data. However, existing a
arXiv:2610.19138v1 Announce Type: cross Abstract: How well can a language model turn a large, incomplete and partly contradictory body of documents into a plan when no single correct answer exists? We
arXiv:2610.16975v1 Announce Type: new Abstract: Drug discovery remains a resource-intensive and time-consuming process, involving billions of dollars and a decade of development to advance a drug to m
arXiv:2610.20238v1 Announce Type: new Abstract: Graph Neural Networks (GNNs) excel on graph-structured data but suffer catastrophic forgetting when classes arrive sequentially. In class-incremental gr
arXiv:2610.19623v1 Announce Type: new Abstract: Single-image 3D reconstruction has advanced rapidly, yet standalone garments remain challenging due to thin-shell geometry, open boundaries, and layered
arXiv:2610.16552v1 Announce Type: new Abstract: An ordered visual code lets a short prefix stand in for an image, and the training loss decides what that prefix contains. Under pixel reconstruction th
arXiv:2610.19735v1 Announce Type: new Abstract: Scenario-based validation is essential for automated road vehicles but cannot cover the full variability of openworld traffic. We present MakSafe, a for
arXiv:2610.20570v1 Announce Type: new Abstract: Brazilian Portuguese remains underserved by open-source language models, and the few that are safety-aligned rarely document what did not work or valida
arXiv:2610.17544v1 Announce Type: cross Abstract: Thought templates, skill libraries, agent memories and caches turn many reasoning steps into one. We call this reasoning contraction. A contracted mem
arXiv:2610.19827v1 Announce Type: cross Abstract: LLMs absorb vast knowledge from training data, yet their inherently probabilistic nature offers no guarantee that this knowledge will surface in their
arXiv:2610.19647v1 Announce Type: new Abstract: Accurately delineating Arctic coastal polynyas from sea ice concentration data remains challenging because of complex coastal geometry and spatially het
arXiv:2603.00720v2 Announce Type: replace Abstract: Fine-tuning Multimodal Large Language Models (MLLMs) with parameter-efficient methods like Low-Rank Adaptation (LoRA) is crucial for task adaptation
arXiv:2610.17613v1 Announce Type: new Abstract: Large language model (LLM)-based multi-agent systems (MAS) can solve complex problems beyond the reach of a single agent by decomposing tasks and coordi
arXiv:2601.22050v2 Announce Type: replace Abstract: In recent years, multilingual Large Language Models (LLMs) have become an inseparable part of daily life, making it crucial for them to master the r
arXiv:2603.18965v2 Announce Type: replace Abstract: Maximum entropy reinforcement learning motivates agents to explore states and actions to maximize the entropy of some distribution, typically by pro
arXiv:2610.19679v1 Announce Type: cross Abstract: Large Language Model (LLM)-based agents have demonstrated remarkable capabilities in completing complex tasks through multi-step reasoning and tool in
arXiv:2610.14956v1 Announce Type: new Abstract: The Model Context Protocol provides a shared interaction substrate for the agentic web, allowing heterogeneous services to expose capabilities through a
arXiv:2610.19659v1 Announce Type: new Abstract: People delegate multi-step work to LLM agents and read only the reply that the agent hands back. Current evaluation scores task completion and the faith
arXiv:2509.09658v3 Announce Type: replace Abstract: Hallucinations in multimodal large language models (MLLMs), where a model generates content inconsistent with the input image, pose significant risk
arXiv:2610.14389v1 Announce Type: new Abstract: Benchmark scores tell us what an LLM can do, but not how much energy is required to deliver that capability. We directly measure this trade-off for 86 o
arXiv:2601.02978v3 Announce Type: replace-cross Abstract: Recent work in Mechanistic Interpretability (MI) has enabled the identification and intervention of internal features in Large Language Models
arXiv:2610.15714v1 Announce Type: new Abstract: Healthcare agents must transform longitudinal clinical evidence into reliable actions through heterogeneous tools and stateful workflows, conditions und
arXiv:2610.19928v1 Announce Type: new Abstract: Reasoning over infinitely long documents is becoming a reality with recurrent memory agents. However, increasing context length exacerbates the trade-of
arXiv:2610.15463v1 Announce Type: new Abstract: With the rapid proliferation of social media, harmful memes have become a growing threat to online ecosystems. Their harmfulness is often implicitly con
arXiv:2610.19267v1 Announce Type: cross Abstract: Online memes are often carriers of unsafe content such as disinformation and propaganda. Their rapid evolution in response to news events and strong d
arXiv:2610.17763v1 Announce Type: new Abstract: Long-term memory supports agents across sessions. Repeated retrieval and write-back can let one historical claim occupy multiple retrieval positions and
arXiv:2610.16298v1 Announce Type: new Abstract: Training neural networks in reduced precision can yield significant efficiency gains, yet all existing methods that scale to standard computer vision be
arXiv:2610.19844v1 Announce Type: new Abstract: Memory-efficient subspace optimization has emerged as a practical approach to reducing the memory cost of LLM training while preserving optimization per
arXiv:2610.14727v1 Announce Type: new Abstract: Autoregressive world models have emerged as a powerful paradigm for sample efficient decision making, yet their open loop rollouts often exhibit increas
arXiv:2610.19681v1 Announce Type: new Abstract: Accurate 3D reconstruction in real-world environments remains a significant challenge due to the coexistence of reflective and non-reflective surfaces,
arXiv:2610.19262v1 Announce Type: new Abstract: Robots following persistent language instructions must decide which cached room judgments remain valid as their environment changes. We present MIND (Ma
arXiv:2607.28103v2 Announce Type: replace Abstract: Memory-augmented LLM-based agents are vulnerable to memory injection attacks: Agents may retrieve poisoned memory from attackers, which diverts thei
arXiv:2610.20007v1 Announce Type: cross Abstract: We study the minimax estimation of the pseudo-spectral gap gamma_{ps} of a Markov chain from the observation of a single trajectory. Extending earlier
arXiv:2610.19457v1 Announce Type: new Abstract: Expected-reward maximization in reinforcement learning concentrates probability mass on a few outcomes, even in tasks that require many distinct, high-q
arXiv:2610.12568v2 Announce Type: replace Abstract: Feed-forward 3D priors enable high-quality reconstruction from image streams but often lack reliable metric scale and suffer from long-term trajecto
arXiv:2606.16710v2 Announce Type: replace-cross Abstract: Multi-agent systems (MAS) are credited with solving complex problems, yet their agents routinely act on context that may be false. Prior work
arXiv:2610.18141v1 Announce Type: new Abstract: Omnimodal large language models (OLLMs) are susceptible to cross-modal hallucinations under audio-visual interference. We find that, for a substantial f
arXiv:2610.19728v1 Announce Type: cross Abstract: Video large language models (Video LLMs) often generate responses unsupported by the input video. Contrastive decoding (CD) can mitigate these halluci
arXiv:2610.20218v1 Announce Type: cross Abstract: Diffusion models are a powerful framework for learning complex probability distributions. While widely used diffusion models for continuous data rely
arXiv:2610.19759v1 Announce Type: new Abstract: Technologies, such as co-simulation, in particular, digital twins (DTs) are used for comprehensive and reliable prototyping, verification and validation
arXiv:2610.16601v1 Announce Type: cross Abstract: Processing multilingual text requires both identifying a broad range of languages and distinguishing the language of each token in inputs containing m
arXiv:2610.19341v1 Announce Type: new Abstract: Multimodal knowledge graphs (MMKGs) organize information across different modalities to support multimodal Graph Retrieval-Augmented Generation (RAG), w
arXiv:2610.20294v1 Announce Type: new Abstract: An agent can improve its behavior within an unresolved problem by changing how it acts and which experience it retains. We propose MMLA-RSI, a recursive
arXiv:2604.01567v2 Announce Type: replace Abstract: A central challenge in mobile manipulation is preserving multiple plausible action modes while remaining reactive during execution. For example, gra
arXiv:2610.19817v1 Announce Type: new Abstract: Human values shape how we expect language models to behave. Dominant alignment approaches treat values as global objectives, although the appropriate va
arXiv:2610.19642v1 Announce Type: cross Abstract: Reinforcement learning provides a direct mechanism for optimizing task objectives in large language model-driven agents, but task rewards are typicall
arXiv:2610.20606v1 Announce Type: cross Abstract: Recent advances in robot learning have driven rapid progress in humanoid control, enabling increasingly agile behaviors in the real world. However, hu
arXiv:2610.19920v1 Announce Type: cross Abstract: Molecular design is often formulated as an optimization problem over existing molecular spaces. However, molecular optimization and the discovery of s
arXiv:2610.20459v1 Announce Type: new Abstract: Chemical chain-of-thought can contain hallucinated structural and property claims. We thereby introduce MolLean, a process-verifiable language that grou
arXiv:2610.15235v1 Announce Type: new Abstract: Despite remarkable progress in language understanding and generation, decoder language models continue to exhibit striking failures on numerical data. N
arXiv:2610.18127v1 Announce Type: new Abstract: Looped language models (LMs) reuse one block of layers several times per token generation, promising more latent computation at no additional storage co
arXiv:2610.19071v1 Announce Type: new Abstract: Monocular 3D object detection methods increasingly rely on depth-guided transformers, but they operate entirely in the perspective image plane where spa
arXiv:2610.14507v1 Announce Type: new Abstract: Generative modeling offers a promising solution to the scarcity of labeled clinical electroencephalography (EEG) data under strict privacy constraints.
arXiv:2610.19587v1 Announce Type: new Abstract: Social psychology has long described how people can be persuaded to interpret a harmful act as moral. Recent research suggests that Large Language Model
arXiv:2610.19789v1 Announce Type: new Abstract: Self-distillation in agentic reinforcement learning has produced a growing number of methods. However, existing evaluations often reuse published baseli
arXiv:2610.19443v1 Announce Type: new Abstract: Native multimodal pre-training trains a single model from scratch on text and multimodal data, enabling it to understand both language and visual inputs
arXiv:2610.20019v1 Announce Type: new Abstract: Progress toward general intelligence in the physical world requires scaling robot learning across diverse environments and interactions. However, existi
arXiv:2610.19942v1 Announce Type: new Abstract: We introduce MorphingNet, a network for symbolic regression (SR) whose units change their function continuously. Instead of searching over expressions b
arXiv:2610.06170v2 Announce Type: replace Abstract: Biomedical large language model (LLM) evaluation requires auditable assessment of narrow, evolving, source-grounded subspecialty knowledge. Multiple
arXiv:2610.19913v1 Announce Type: new Abstract: Learning and executing long-horizon robot manipulation as complete task trajectories raises data-collection costs, lim?its capability reuse, and causes