Knowledge catalogue
arXiv:2610.19114v1 Announce Type: new Abstract: Activation steering controls large language models at inference time, but its effectiveness depends on the choice of reference data. We introduce genesi
arXiv:2603.14426v2 Announce Type: replace Abstract: Existing text-to-video retrieval benchmarks often permit matching from static appearance, making it difficult to determine whether models capture st
arXiv:2610.20055v1 Announce Type: new Abstract: The rapid advancement of large vision language models (LVLMs) has made geolocation inference from images highly accurate and accessible. This poses seri
arXiv:2610.20592v1 Announce Type: new Abstract: Existing geometry-aware Multimodal Large Language Models (MLLMs) increasingly rely on external 3D encoders to provide structural priors, but executing t
arXiv:2610.19043v1 Announce Type: cross Abstract: Because its binary optimization landscape contains exponentially many local optima, MAX-CUT is highly sensitive to initialization. We study Particle P
arXiv:2610.20502v1 Announce Type: new Abstract: Longitudinal electronic health records (EHRs) can reveal disease-progression structure, while useful forecasting requires leakage-controlled discovery,
arXiv:2610.19184v1 Announce Type: new Abstract: Harness optimization improves LLMs by refining reusable prompts and scaffolding, yet requires further adaptation for program generation tasks such as te
arXiv:2610.18230v1 Announce Type: cross Abstract: While AI agents are useful for many tasks, they regularly misbehave in destructive ways, such as deleting important data. While prior work has focused
arXiv:2610.19502v1 Announce Type: new Abstract: Spatial intelligence requires scalable 3D environments that are both photorealistic and semantically grounded, yet existing indoor datasets are dominate
arXiv:2610.19064v1 Announce Type: new Abstract: Training large language models (LLMs) across geographically distributed and poorly interconnected compute pools or data centers has become a topic of si
arXiv:2610.19742v1 Announce Type: new Abstract: Autonomous navigation of planetary rovers is challenged by visually repetitive, featureless terrain. SLAM systems based on 3D Gaussian Splatting (3DGS)
arXiv:2610.19129v1 Announce Type: new Abstract: Training from offline data has allowed for substantial progress in domains such as robotics, leading to general-purpose policies that can be easily appl
arXiv:2610.20432v1 Announce Type: new Abstract: Infant fingerprint recognition is hindered by a combination of physiological characteristics, such as small ridge width and spacing, and acquisition cha
arXiv:2610.18547v1 Announce Type: new Abstract: People often tackle difficult problems by thinking more deeply or exploring more possibilities. Similarly, recursive reasoners, which reason by repeated
arXiv:2610.17757v1 Announce Type: new Abstract: Large language model agents can follow complex human instructions to solve real-world tasks. In practice, however, initial instructions are often incomp
arXiv:2610.19347v1 Announce Type: cross Abstract: Novelty detection is crucial in various technological and scientific domains. Its importance spans from ensuring the reliability of sensor networks to
arXiv:2610.19201v1 Announce Type: new Abstract: We present LidarVerse, a real-time unified LiDAR world model for sensor simulation across tasks and sensors. Existing LiDAR simulators largely follow on
arXiv:2610.19159v1 Announce Type: cross Abstract: Can AI agents carry out research on open scientific problems? We introduce Into the Unknown, a benchmark that provides an agent with a science paper a
Introducing ExtractBenchPro: the most challenging benchmark for real-world document extraction. We see a massive long tail of document complexity in terms of use cases brought on from our customers, t
Hey r/LocalLlama - Today we’re excited to release Muse Spark Open, the most capable US open-weight model to date. Built for coding and agentic work, it excels at long-horizon tasks, complex software e
arXiv:2610.18773v1 Announce Type: new Abstract: System one models enable direct, low-latency decision-making. Yet recent practice of predicting an answer from serialized text does not, by itself, resp
arXiv:2610.19822v1 Announce Type: cross Abstract: LLM agents are evolving from a single model calling tools to planners that orchestrate multiple expert models whose capabilities live in distinct weig
arXiv:2610.19290v1 Announce Type: new Abstract: We present Kandinsky S2V 1.0, an open family of speech-to-video models for controllable audio-driven human animation, comprising Kandinsky S2V 1.0 Lite
arXiv:2610.15081v1 Announce Type: new Abstract: Adapting to a new world and reasoning correctly about which outcomes are possible under rules one has not met before is a basic capacity underlying gene
arXiv:2608.01718v2 Announce Type: replace Abstract: Semantic caching, reusing responses to answer semantically similar queries, has seen growing adoption in LLM serving, offering faster responses and
arXiv:2610.19038v1 Announce Type: new Abstract: We study online task assignment with reusable resources. This problem arises in peer reviewer assignment, skill-based call routing, rideshare matching,
arXiv:2610.15501v1 Announce Type: new Abstract: Large language models make decisions through parallel prefill and sequential decoding. Although effective, this approach incurs decoding costs and does
arXiv:2610.16140v1 Announce Type: new Abstract: Transportation planning has traditionally required substantial expertise in network modeling, traffic assignment, and optimization. As a result, strateg
arXiv:2610.16625v1 Announce Type: new Abstract: Post-route FPGA timing closure remains expert-dependent and computationally expensive. We present LATCH (LLM-Assisted Timing Closure Harness), a two-lay
arXiv:2610.20585v1 Announce Type: cross Abstract: Current approaches for steady partial differential equations surrogate models on unstructured meshes apply a single mixing mechanism to the whole solu
arXiv:2610.20462v1 Announce Type: new Abstract: Foundation models have shown strong generalization across scientific domains, but their use in power systems remains largely task-specific. We investiga
arXiv:2610.19295v1 Announce Type: new Abstract: We present Keypoint Constraint Learning (KCL), a framework for learning task-relevant motion constraints from visual demonstrations and for planning saf
arXiv:2610.15158v2 Announce Type: replace Abstract: We study neuro-symbolic natural language inference (NLI) with independently translated propositional representations of the premise and hypothesis.
arXiv:2610.19559v1 Announce Type: new Abstract: Lipreading remains challenging due to the inherent ambiguity of visual speech cues and their sensitivity to head pose and video resolution. In this work
arXiv:2610.20350v1 Announce Type: cross Abstract: Preference optimization ranks final answers and trajectory RL maximizes aggregate returns, yet neither credits the local change that turns a flawed re
arXiv:2610.19767v1 Announce Type: new Abstract: AI-text detection generally asks whether a machine wrote a text. As AI becomes an increasingly integral and accepted part of writing, the more relevant
arXiv:2507.19465v2 Announce Type: replace-cross Abstract: We develop efficient algorithms for optimizing piecewise smooth (PWS) functions when their smooth regions are unknown to the algorithm. For co
arXiv:2610.14705v1 Announce Type: cross Abstract: Large language models have advanced medical reasoning, but clinically grounded reasoning in traditional Chinese medicine (TCM) remains challenging bec
arXiv:2610.16790v1 Announce Type: cross Abstract: Language model APIs can expose token-specific biases while withholding logits and token probabilities. Existing bias-based extraction recovers relativ
arXiv:2610.20256v1 Announce Type: new Abstract: Long-horizon agents must coordinate reasoning, tool use, and environment feedback over extended interactions. Learning from sparse outcome rewards requi
arXiv:2610.18877v1 Announce Type: cross Abstract: Time-series language models typically align numerical signals with language using labels, captions, or question-answer pairs. Although such supervisio
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:2610.16975v1 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:2610.16552v1 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:2610.19679v1 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:2610.14389v1 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: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:2610.19267v1 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:2610.19457v1 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: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:2610.20218v1 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:2610.16601v1 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:2610.19642v1 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:2610.20459v1 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:2610.19587v1 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:2610.19443v1 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