Knowledge catalogue
arXiv:2606.27305v1 Announce Type: new Abstract: Reinforcement learning from human feedback (RLHF) for 3D generation is now established across a number of works, but most existing pipelines optimise ex
arXiv:2606.27307v1 Announce Type: new Abstract: While modern multimodal models integrate vision with language, audio, or touch, olfaction remains largely unexplored due to the lack of paired visuo-olf
arXiv:2606.26194v1 Announce Type: new Abstract: Urban tree biomass remains less spatially explicitly quantified than biomass in managed forests because many estimates rely on inventories or coarse pro
arXiv:2606.27009v1 Announce Type: new Abstract: Multi-agent large language model (LLM) loops, for example a Writer that drafts and a Critic that revises, are almost always terminated by a fixed iterat
arXiv:2606.27027v1 Announce Type: cross Abstract: With the rapid evolution of LLM-driven agents, Model Context Protocol (MCP), an open protocol bridging LLMs with external tools, has quickly become fo
arXiv:2606.26587v1 Announce Type: cross Abstract: Low-bit floating-point formats and semi-structured sparsity are increasingly supported by modern accelerators, yet combining them for LLM activation c
arXiv:2606.18430v2 Announce Type: replace Abstract: Statistical watermarks help organizations attribute large language model (LLM) outputs, yet existing detectors often struggle when watermark signals
arXiv:2603.10446v4 Announce Type: replace Abstract: Sign Language Production (SLP) faces a fundamental trade-off: direct text-to-pose models suffer from regression-to-the-mean effects, while dictionar
arXiv:2606.27286v1 Announce Type: new Abstract: Mechanistic epidemiological models are widely used to support infectious disease forecasting and public-health decision making. Bayesian calibration of
arXiv:2606.26617v1 Announce Type: new Abstract: Scaling laws describe how learning performance varies with model size, data size, and compute. While recent theoretical work has established scaling law
arXiv:2606.26669v1 Announce Type: new Abstract: Agents often repeatedly solve similar task instances from scratch, leading to unnecessary reasoning cost and long execution traces. Prior work has explo
arXiv:2606.26654v1 Announce Type: new Abstract: Personalized language-model assistants are often evaluated through a memory lens: can a model recall preferences users have explicitly stated in dialogu
arXiv:2606.26722v1 Announce Type: new Abstract: The automation of scientific discovery has reached an inflection point. While AI systems now operate instruments, optimize parameters and generate hypot
arXiv:2606.26466v1 Announce Type: new Abstract: Multilingual large language models often produce inconsistent reasoning and answers for semantically equivalent prompts in different languages. Prior wo
arXiv:2606.26383v1 Announce Type: cross Abstract: How fast could a deep-learning model run on target hardware, and how far is today's implementation from that limit? These questions are central to sof
arXiv:2508.03247v2 Announce Type: replace Abstract: Prior clinical psychology research shows that Western individuals with depression tend to report psychological symptoms, while Eastern individuals r
arXiv:2606.26559v1 Announce Type: cross Abstract: Earth observation satellite networks generate massive volumes of high-resolution imagery, whereas inter-satellite and downlink resources remain limite
arXiv:2606.17936v2 Announce Type: replace Abstract: In autonomous racing, fast detection of other participants' movements is required to plan safe, collision-free trajectories with non-cooperative opp
arXiv:2606.26872v1 Announce Type: new Abstract: Recent online reinforcement learning has substantially improved image editing quality. However, existing Flow-GRPO-style methods usually rely on a singl
arXiv:2606.26487v1 Announce Type: cross Abstract: Large language models (LLMs) are attractive for context-aware time series forecasting because they can integrate heterogeneous textual signals, yet th
arXiv:2601.11061v2 Announce Type: replace-cross Abstract: Reinforcement Learning with Verifiable Rewards (RLVR) is highly effective for enhancing LLM reasoning, yet recent evidence shows models like Q
arXiv:2606.26800v1 Announce Type: new Abstract: Real-world robotic manipulation demands spatial grounding, task-aware reasoning, and precise control. Learning such capabilities becomes particularly ch
arXiv:2606.26290v1 Announce Type: cross Abstract: While parameter-efficient fine-tuning (PEFT) typically targets attention projectors, its efficacy for tasks requiring sequential state accumulation re
arXiv:2606.27032v1 Announce Type: cross Abstract: Energy trading decisions depend not only on current market prices, but also on expected future market conditions, and operational constraints. This ma
arXiv:2606.26723v1 Announce Type: cross Abstract: Respiratory activity is a direct and interpretable physiological channel for wearable stress and affective-state recognition, yet many studies emphasi
arXiv:2606.26200v1 Announce Type: cross Abstract: Modern machine learning systems have outgrown their origins as isolated predictive constructs, evolving into complex socio-technical architectures tha
arXiv:2606.26387v1 Announce Type: cross Abstract: Multimodal large language models (MLLMs) extend large language models (LLMs) with visual perception, enabling joint reasoning over images and text. De
arXiv:2606.27171v1 Announce Type: new Abstract: This work addresses the problem of variance in stochastic gradient estimation for machine learning optimization. Deep learning relies on mini-batch meth
arXiv:2601.20381v3 Announce Type: replace Abstract: Visual foundation models provide strong perceptual features for robotics, but their dense representations lack explicit object-level structure, limi
arXiv:2606.26749v1 Announce Type: cross Abstract: Neural Collapse predicts that balanced one-hot classification pushes model representations to be equally far from each other; a symmetric configuratio
arXiv:2606.27088v1 Announce Type: new Abstract: Mesh subdivision is a fundamental operation for converting coarse, editable meshes into high-resolution surfaces, with broad applications in digital ass
arXiv:2606.27029v1 Announce Type: new Abstract: Hamiltonian Neural Networks (HNNs) integrate physical priors into neural models by learning a system's Hamiltonian, improving generalization and sample
arXiv:2606.12808v3 Announce Type: replace-cross Abstract: Adaptive Hamiltonian learning is central to calibrating and characterizing quantum devices. In an adaptive controller, choosing the next exper
arXiv:2606.27206v1 Announce Type: new Abstract: Garden path sentences present a processing difficulty for humans -- the sentence prefix leads the listener towards one interpretation, until the listene
arXiv:2606.26663v1 Announce Type: new Abstract: World Action Models (WAMs) generate actions together with predicted futures, offering a powerful interface for robot decision making. In contact-rich ma
arXiv:2606.26312v1 Announce Type: cross Abstract: Autoencoders transformed classical machine learning by solving the curse of dimensionality, enabling principled weight initialization and learning com
arXiv:2606.26657v1 Announce Type: new Abstract: Identifying high-utility candidates from massive discrete spaces under expensive evaluations is a recurring challenge across the sciences, with structur
arXiv:2606.26215v1 Announce Type: cross Abstract: How do we learn to hit a tennis backhand? Not from a thousand hours of tennis tournaments on TV - we work with a coach and practice. We argue this is
arXiv:2606.26615v1 Announce Type: new Abstract: While traditional image restoration focuses on perceptual quality, Task-Driven Image Restoration (TDIR) aims to maximize the performance of downstream h
arXiv:2606.26874v1 Announce Type: new Abstract: Transcatheter Aortic Valve Replacement (TAVR) planning requires meticulous multimodal reasoning. However, adapting Multimodal Large Language Models (MLL
arXiv:2606.26285v1 Announce Type: cross Abstract: Noise-based backdoor attacks on diffusion models typically rely on input-time trigger injection, untargeted activation, and out-of-distribution target
arXiv:2606.26511v1 Announce Type: cross Abstract: Retrieval-augmented generation (RAG) gives agents access to accumulated knowledge, but has no model of time. When a fact changes (e.g., a function is
arXiv:2606.26634v1 Announce Type: new Abstract: Effective multi-task learning for surgical scene understanding is fundamentally hindered by annotation granularity mismatch; temporal workflow tasks suc
arXiv:2606.26963v1 Announce Type: new Abstract: Organizing knowledge from diverse text sources into interpretable hierarchies is crucial for tasks such as policy analysis, innovation monitoring, and e
arXiv:2503.15283v2 Announce Type: replace Abstract: Text-and-Image-To-Image (TI2I), an extension of Text-To-Image (T2I), integrates image inputs with textual instructions to enhance image generation.
arXiv:2606.26664v1 Announce Type: cross Abstract: Existing homomorphic encryption (HE)-based GNN systems adopt a graph-centric paradigm that couples per-query cost to global graph size, limiting evalu
arXiv:2606.26836v1 Announce Type: new Abstract: Existing benchmarks typically report accuracy for a single model on a single run. This systematically understates real-world LLM capabilities, particula
arXiv:2606.27242v1 Announce Type: cross Abstract: Training-free source selection for LLM families with shared vocabularies arises in scientific string domains such as SMILES, protein, and genomic sequ
arXiv:2606.26117v1 Announce Type: cross Abstract: This paper introduces the Governance Inversion Hypothesis (GIH) to explain a growing paradox in artificial intelligence (AI) governance: under conditi
arXiv:2606.26529v1 Announce Type: cross Abstract: AI safety is evaluated by how reliably a model detects the hazards it is told to find, yet accidents often arise from the hazard no one specified. We
arXiv:2606.26118v1 Announce Type: cross Abstract: We work towards measuring both AI adoption and the capability of AI to perform discrete labor tasks across various occupations. To measure adoption, w
arXiv:2606.26294v1 Announce Type: cross Abstract: Self-improving agents are state-of-the-art (SOTA) on agentic coding benchmarks and have recently been extended to general domains. However, their sear
arXiv:2606.27103v1 Announce Type: new Abstract: Humans flexibly adapt their reasoning strategies to the requirements of a given problem. Large language models (LLMs) have performed well on many cognit
arXiv:2606.26207v1 Announce Type: cross Abstract: Several theoretical works have tried to explain the adversarial vulnerability of deep neural networks through properties of high-dimensional geometry.
arXiv:2606.27045v1 Announce Type: cross Abstract: AI coding agents dramatically accelerate implementation speed but introduce two structural failure modes that existing spec-driven approaches do not f
arXiv:2606.26300v1 Announce Type: new Abstract: A classical intuition holds that verifying a solution is easier than producing one. For today's coding agents, this intuition is being inverted: as foun
arXiv:1906.09235v3 Announce Type: replace Abstract: Along with fruitful applications of Deep Neural Networks (DNNs) to realistic problems, recently, some empirical studies of DNNs reported a universal
arXiv:2606.26525v1 Announce Type: new Abstract: Auto-formalization is critical for scalable formal verification, but existing progress largely focuses on isolated statements, while theory-scale auto-f
arXiv:2606.26130v1 Announce Type: cross Abstract: Large Language Models (LLMs) are increasingly used to guide research methodology, yet their default methodological tendencies under minimal prompting
arXiv:2508.16159v2 Announce Type: replace-cross Abstract: Meta-learning aims to uniformly sample homogeneous support-query pairs, characterized by the same categories and similar attributes, and extra