Knowledge catalogue
arXiv:2609.38156v1 Announce Type: new Abstract: Distribution matching distillation (DMD) provides a general framework for few-step diffusion generation, but its modern text-to-image instantiations hav
arXiv:2609.37315v1 Announce Type: cross Abstract: Tool-using agents are entering settings where a wrong action carries real cost, and the benchmarks certifying them grade what each simulated tool call
arXiv:2609.37472v1 Announce Type: cross Abstract: Behavioral tests measure how a language model reads evidence. We ask whether those measurements help choose a recommendation interface. We evaluate si
arXiv:2606.18623v2 Announce Type: replace Abstract: Gaussian segmentation is usually posed as transferring object knowledge from 2D foundation models into a 3D representation. This leaves a fundamenta
arXiv:2609.37378v1 Announce Type: cross Abstract: Latent world models are trained to predict what happens next, so nothing in their objective separates what an action caused from what merely co-occurr
arXiv:2609.38108v1 Announce Type: new Abstract: Large language models (LLMs) enable agents to solve long-horizon tasks by generating a plan and then executing it in an environment. However, successful
arXiv:2609.36612v1 Announce Type: new Abstract: Unlearning in large language models (LLMs) is typically evaluated at the output level, where a model appears to suppress sensitive or undesirable conten
arXiv:2609.36754v1 Announce Type: cross Abstract: Explicit prosodic cues may help automatic speech recognition (ASR) of spontaneous speech, but auxiliary representations typically require additional t
arXiv:2609.37938v1 Announce Type: cross Abstract: Embodied systems must make knowledge acquired during one encounter usable in another despite changes in viewpoint, motion, and illumination. Yet aggre
arXiv:2609.36849v1 Announce Type: cross Abstract: Safety-aligned language models are commonly deployed as multi-turn assistants, which lets adversaries spread unsafe intent across several user turns i
arXiv:2609.36842v1 Announce Type: new Abstract: Which layers of a feed-forward geometry model are needed to preserve both camera poses and dense 3D structure? We study layer redundancy in VGGT, pi^3,
Natalie Lung / Bloomberg: DoorDash opens US waitlists for an Apple Messages-integrated AI agent for ordering and a B2B API allowing assistants like Slack bots to execute bulk orders — DoorDash Inc. un
arXiv:2609.38028v1 Announce Type: cross Abstract: Autonomous vehicles interacting with passengers through natural language must reason beyond immediate commands. Passenger intent may span multiple sta
arXiv:2609.36395v1 Announce Type: new Abstract: Teams of unmanned aerial vehicles (UAVs) deployed for search and monitoring missions frequently operate as partially connected networks, forcing each ro
arXiv:2609.36482v1 Announce Type: new Abstract: Quadrotor racing demands aggressive attitude and progress control while passing through every gate, and conventional quadrotor MPCC formulations state t
arXiv:2609.38025v1 Announce Type: cross Abstract: On-policy distillation (OPD) trains a student on its own generated responses using dense, token-level supervision from a stronger teacher. Vanilla OPD
arXiv:2609.36173v1 Announce Type: cross Abstract: Parallel speculative drafting generates multiple candidates in one backbone pass, but independent token selection can produce inconsistent continuatio
arXiv:2609.36544v1 Announce Type: cross Abstract: Generative AI has changed how students produce writing assignments. The final artifact is no longer sufficient to understand the process through which
arXiv:2609.36755v1 Announce Type: new Abstract: Modern image editors excel at semantic manipulation and visual synthesis, yet remain limited in precise spatial control, motivating the development of d
arXiv:2609.36779v1 Announce Type: cross Abstract: Accurate hand-eye calibration is crucial for precision manipulation. Traditional methods rely on markers, with their precision dependent on marker acc
arXiv:2609.37348v1 Announce Type: new Abstract: Learning robust manipulation policies for diverse, long-horizon tasks from limited demonstrations remains a fundamental challenge in robotics. We presen
arXiv:2609.37532v1 Announce Type: cross Abstract: Growing large language model applications demand efficient inference. At high concurrency, block-diffusion speculative decoding suffers from verificat
arXiv:2609.36775v1 Announce Type: new Abstract: Reinforcement Learning has significantly advanced the complex reasoning capabilities of MLLMs. However, prevailing RL algorithms suffer a severe failure
arXiv:2609.36845v1 Announce Type: cross Abstract: Uncrewed aerial vehicle base stations (UAV-BSs) are expected to cover traffic demand that shifts across space and time, yet most repositioning schemes
arXiv:2609.36033v1 Announce Type: cross Abstract: Among distinct optimal acceleration mechanisms for deterministic monotone root-finding problems and fixed-point problems, dual-anchoring has recently
arXiv:2609.36944v1 Announce Type: new Abstract: Group-relative policy optimization relies on reward-derived advantages and sequence-level likelihood weights, both of which can be sensitive to localize
arXiv:2609.36759v1 Announce Type: cross Abstract: Benefiting from transferable visual-textual alignment, CLIP has been widely adopted for class-incremental learning (CIL). However, existing learners e
arXiv:2609.36624v1 Announce Type: new Abstract: Joint speech-gesture synthesis must coordinate two modalities despite limited paired data. Existing approaches often lack bidirectional interaction, hav
arXiv:2609.36116v1 Announce Type: new Abstract: We study how to build more capable general-purpose agents by extending large language models (LLMs) with native typed decision-making. We introduce Dyad
arXiv:2502.04492v3 Announce Type: replace Abstract: The advancement of LLMs and their accessibility have triggered renewed interest in multi-agent reinforcement learning as robust and adaptive framewo
arXiv:2602.08372v2 Announce Type: replace Abstract: We study dynamic regret minimization in non-stationary online learning, with a primary focus on follow-the-regularized-leader (FTRL) methods. FTRL i
arXiv:2609.36454v1 Announce Type: new Abstract: We study hand-object interaction (HOI) reconstruction from monocular RGB videos, where partial observations can produce visually plausible yet mechanica
arXiv:2609.37533v1 Announce Type: new Abstract: Masked diffusion models (MDMs) generate sequences by progressively unmasking several tokens per denoising step, but their reverse process is typically f
arXiv:2609.36081v1 Announce Type: new Abstract: Representations continually change as a network learns new tasks. We ask whether early representational changes naturally form a geometric structure tha
arXiv:2609.36746v1 Announce Type: new Abstract: Agent skills provide a lightweight mechanism for self-evolving agents to accumulate reusable procedural knowledge without updating model parameters. How
arXiv:2609.36802v1 Announce Type: new Abstract: A key strength of Proximal Policy Optimization (PPO) is its learned critic, which uses historical trajectories collected during reinforcement learning t
arXiv:2609.38099v1 Announce Type: cross Abstract: Turning decoder-only large language models (LLMs) into strong dense retrievers typically requires some form of retriever training. In this paper, we a
arXiv:2609.36504v1 Announce Type: new Abstract: Accurate prediction of three-dimensional (3D) microstructure evolution remains computationally demanding because high-fidelity phase-field simulations r
arXiv:2501.18157v2 Announce Type: replace-cross Abstract: Building reliable speech systems often requires combining multiple modalities, like audio and visual cues. While such multimodal solutions fre
arXiv:2609.36740v1 Announce Type: new Abstract: Many recommender systems such as for e-commerce and news platforms aim to provide users with rankings they are likely to interact with. Off-Policy Learn
arXiv:2609.37261v1 Announce Type: new Abstract: Softmax attention is ubiquitous in modern machine learning, but its quadratic scaling with sequence length makes it costly. To reduce this cost, attenti
arXiv:2609.38046v1 Announce Type: new Abstract: Egocentric human demonstrations offer an accessible source of task experience, but differences in body scale and controller response, together with miss
arXiv:2609.37181v1 Announce Type: cross Abstract: Human demonstrations capture diverse scenes and rich whole-body skills without requiring robot teleoperation. Prior work on egocentric transfer has em
arXiv:2609.36803v1 Announce Type: new Abstract: Real-time video understanding requires incrementally maintaining a memory of streaming content, and optimizing this requires dense process signals. On-P
arXiv:2606.16564v2 Announce Type: replace Abstract: Robotic systems routinely encounter conflicting objectives, modeling errors, and degenerate contact conditions that render quadratic programs (QPs)
arXiv:2609.37013v1 Announce Type: cross Abstract: Rapid assessment of building damage after natural disasters is essential to support emergency response. Earth Observation satellites can acquire relev
arXiv:2609.35936v1 Announce Type: cross Abstract: As autonomous systems and embodied intelligence enter the dynamic physical world, multi-agent collaboration calls for a paradigm shift in communicatio
arXiv:2609.36455v1 Announce Type: new Abstract: Large language models are thought to represent features by vectors in a hidden space of dimension given by the model's width. Superposition, in which mo
arXiv:2609.36770v1 Announce Type: new Abstract: Can a population of neural networks develop a useful division of labor without a shared gate or gradients between agents? We study a setting where each
arXiv:2609.36460v1 Announce Type: cross Abstract: Several tonal pitch spaces and computational models have been proposed to analyze tonal structure in Western tonal music, many of them grounded in pri
arXiv:2609.38157v1 Announce Type: cross Abstract: Emotion-conditioned text-to-speech (TTS) models may fail to express the requested emotion reliably, and improving controllability by additional traini
Today, we’re expanding our ecosystem of managed remote MCP servers by introducing the Google Cloud CLI remote MCP server in preview. Powered by the popular gcloud and bq (BigQuery) command-line tools,
arXiv:2609.36157v1 Announce Type: cross Abstract: Most federated learning frameworks for vehicular ad hoc networks assume that all vehicles collaboratively train a single model for a common task. This
arXiv:2609.37359v1 Announce Type: cross Abstract: Coding agents can now write, run, and debug programs with little human help. Robot tasks, however, are usually specified by a sentence that leaves out
arXiv:2609.37551v1 Announce Type: cross Abstract: Emerging optical-network applications increasingly use received data for inference and control rather than exact source reproduction, creating an oppo
arXiv:2609.37162v1 Announce Type: new Abstract: RGB-T object tracking leverages the complementary characteristics of visible and thermal infrared modalities to improve robustness under adverse conditi
arXiv:2609.37874v1 Announce Type: new Abstract: Dynamic endoscopic reconstruction is fundamental to robotic surgery and computer-assisted interventions. While 3D Gaussian Splatting (3DGS) realises rea
arXiv:2609.36215v1 Announce Type: new Abstract: Power-grid digital twins must combine data-driven prediction with physically meaningful state evolution while preserving the provenance of environmental
arXiv:2609.37447v1 Announce Type: cross Abstract: How strong can an AlphaZero-style chess system become under limited training compute when its entire learning loop is engineered for efficiency? We tr
arXiv:2609.36365v1 Announce Type: new Abstract: Can interaction formats and textual scaffolds help large language model (LLM) agents make better decisions, and do better decisions come with better exp