Knowledge catalogue
arXiv:2606.09348v1 Announce Type: new Abstract: Long-horizon agentic tasks pose a fundamental credit assignment challenge for outcome-base reinforcement learning: trajectory-level rewards verify final
arXiv:2510.20182v2 Announce Type: replace Abstract: Pedestrian simulation traditionally relies on expert-tuned, hand-crafted models that limit scalability and generalization. Meanwhile, large-scale vi
arXiv:2606.08059v1 Announce Type: new Abstract: Humanoid behavior foundation models aim to acquire reusable whole-body control policies from broad human motion priors, enabling a single controller to
arXiv:2606.07661v1 Announce Type: new Abstract: Parsing historical documents with complex, non-standard layouts remains a fundamental bottleneck in large-scale archival digitization. Unlike modern typ
arXiv:2602.04402v3 Announce Type: replace-cross Abstract: Performative predictions influence the very outcomes they aim to forecast. We study performative predictions that affect a sample (e.g., only
arXiv:2606.09038v1 Announce Type: new Abstract: Large Language Models (LLMs) have enabled increasingly personalized interactions by adapting to users' preferences, contexts, and long-term histories. H
arXiv:2606.08458v1 Announce Type: new Abstract: Proactive robot assistance in household environments requires accurate prediction of human activities and object usage under dynamic and noisy condition
arXiv:2606.09756v1 Announce Type: new Abstract: Responses to perturbations are key to understanding physical systems. The ability to contrast such responses by comparing how a system reacts under slig
arXiv:2508.00724v2 Announce Type: replace-cross Abstract: The increasing demand for flexible automation has accelerated the adoption of heterogeneous automated guided vehicles (AGVs). This work invest
arXiv:2606.07559v1 Announce Type: cross Abstract: Fine-tuning a language model on contexts whose correct completion has a near-synonym competitor often fails silently. The cross-entropy loss decreases
arXiv:2606.07698v1 Announce Type: cross Abstract: Graph neural networks (GNNs) applied to drug-drug interaction (DDI) prediction rely exclusively on molecular structure encoded as SMILES-derived graph
arXiv:2606.08132v1 Announce Type: new Abstract: Vision Transformers operate on fixed patch grids, which can introduce phase-dependent instability for dense prediction: changing the patch partition can
arXiv:2406.05335v3 Announce Type: replace-cross Abstract: Generation of text and speech in natural languages can be modeled as a stochastic process. This idea dates back to the seminal work of Markov
arXiv:2503.05169v2 Announce Type: replace Abstract: Applying machine learning to increasingly high-dimensional problems with sparse or biased training data increases the risk that a model is used on i
arXiv:2606.08688v1 Announce Type: cross Abstract: Achieving fully automated, physically plausible 3D motion synthesis is a core objective in graphics and generative AI. However, configuring complex en
arXiv:2606.08655v1 Announce Type: cross Abstract: To perform a wide range of daily tasks, robots need to construct a 3D representation that is semantically rich, physically grounded, and structured en
arXiv:2606.08473v1 Announce Type: new Abstract: False data injection attacks (FDIAs) introducing small measurement perturbations can still cause large deviations in power system state estimation when
arXiv:2606.09640v1 Announce Type: new Abstract: Accurate dynamics models are essential for model-based robotic control, yet nominal Euler--Lagrange models often become inaccurate in the presence of pa
arXiv:2606.08563v1 Announce Type: new Abstract: While global data-driven models excel at predicting continuous atmospheric variables, three-dimensional hydrometeor forecasting remains challenging due
arXiv:2606.09266v1 Announce Type: cross Abstract: Acoustic metamaterial (AMM) inverse design is particularly challenging for broadband target responses due to acoustic dispersion: a structure that mat
arXiv:2606.09368v1 Announce Type: cross Abstract: Scene Graphs (SGs) provide structured representations of visual scenes by modeling objects and their pairwise relationships. Despite recent progress,
arXiv:2603.11917v2 Announce Type: replace Abstract: Real-time, on-device segmentation is critical for latency-sensitive and privacy-aware applications such as smart glasses and Internet-of-Things devi
arXiv:2606.08481v1 Announce Type: cross Abstract: Enterprise property graphs vary widely in schema structure, internal terminology, domain assumptions, governance constraints, and user interaction pat
arXiv:2510.17947v3 Announce Type: replace-cross Abstract: Large Language Models (LLMs) are improving at an exceptional rate. With the advent of agentic workflows, multi-turn dialogue has become the de
arXiv:2603.05500v2 Announce Type: replace-cross Abstract: Efficient and stable training of large language models (LLMs) remains a core challenge in modern machine learning systems. To address this cha
arXiv:2606.07931v1 Announce Type: cross Abstract: We prove a variance-aware pointwise majorizing-measure theorem for centered Gaussian processes. Classical generic chaining characterizes the scalar qu
arXiv:2606.07943v1 Announce Type: cross Abstract: Agent skills provide a lightweight mechanism for extending general-purpose agents, but their open format exposes them to skill-poisoning attacks. A pr
arXiv:2602.17337v2 Announce Type: replace Abstract: In this work we present Polaffini, a robust and versatile framework for anatomically grounded registration. Medical image registration is dominated
arXiv:2606.08920v1 Announce Type: cross Abstract: Extracting building polygon contours from high-resolution remote sensing images is a fundamental task for various mapping applications. However, the p
arXiv:2606.07612v1 Announce Type: cross Abstract: We argue that many Anthropomorphic Misalignment Research (AMR) studies need stronger evidence to ensure that they can provide a robust foundation for
arXiv:2606.07607v1 Announce Type: new Abstract: Advances in machine learning and computational power have unlocked the predictive potential of the human genome, yet biologists now demand that these mo
arXiv:2606.08267v1 Announce Type: cross Abstract: The classical Second Welfare Theorem decentralizes any Pareto efficient allocation through prices and transfers under convexity and regularity. In pos
arXiv:2606.08228v1 Announce Type: cross Abstract: Algorithmic trading systems on decentralised exchanges (DEXs) reject most candidate tokens they evaluate. The counterfactual outcome of rejected candi
arXiv:2606.07527v1 Announce Type: cross Abstract: The prevailing paradigm for training LLMs has evolved to rely on a massive post-training phase consisting of SFT and RL. In this position paper, we ar
arXiv:2606.09788v1 Announce Type: new Abstract: Large-scale document processing requires contextually aware table extraction (TE) that is both accurate and efficient. Yet current approaches require bi
arXiv:2606.09617v1 Announce Type: cross Abstract: The rapid expansion of AI globally has led to the proliferation of energy-intensive hyperscale data centres (DCs), making them as a structurally chall
Google DeepMind is launching a three-month accelerator program for early-stage robotics startups across Europe, designed to support the next generation of physical AI. Selected startups receive hands-
arXiv:2606.08365v1 Announce Type: cross Abstract: Sparse autoencoder (SAE) features are increasingly used to steer language models, but feature steering is rarely clean: the same intervention can beha
arXiv:2606.08374v1 Announce Type: cross Abstract: We recast predictive coding as continuous-time proximal gradient descent applied to a regularized maximum-a-posteriori (MAP) objective. We study first
arXiv:2602.15327v2 Announce Type: replace-cross Abstract: Machine learning model performance improvements tend to arise from competition and application. For deployment, we consider prescriptive scali
arXiv:2606.09762v1 Announce Type: cross Abstract: Continual training of deep neural networks under non-stationarity often leads to a progressive loss of plasticity, eventually limiting further learnin
arXiv:2606.09189v1 Announce Type: cross Abstract: EEG foundation-model releases are usually audited one endpoint at a time: raw-reconstruction, membership inference, identity linkage, or DP-SGD on the
arXiv:2606.09396v1 Announce Type: cross Abstract: Supervised fine-tuning (SFT) is an efficient approach for downstream task adaptation and often serves as the initialization stage for reinforcement le
arXiv:2606.07532v1 Announce Type: cross Abstract: RLHF-trained models are systematically biased toward agreement over accuracy, a structural property of the training process. We present Principled Age
arXiv:2606.07555v1 Announce Type: cross Abstract: Glossaries, technical specifications, and system prompts routinely ask language models to use familiar words in unfamiliar ways. When this works, the
arXiv:2606.07974v1 Announce Type: cross Abstract: A learned world model provides a powerful physical intuition for evaluating future states. But its effectiveness in continuous control also depends cr
arXiv:2606.09563v1 Announce Type: new Abstract: As LLMs are deployed as agents, reliable monitoring requires knowing not only what they output, but which instructions are steering their behavior. This
arXiv:2606.09301v1 Announce Type: new Abstract: Multimodal federated graph learning (MM-FGL) aims to collaboratively learn from decentralized graphs with text and images. However, real-world clients m
arXiv:2606.09507v1 Announce Type: new Abstract: Video world models have made rapid progress in generating controllable visual experiences, but most of them still simulate the world from a single obser
arXiv:2606.08926v1 Announce Type: new Abstract: Knowledge graph completion (KGC) models are commonly evaluated using rank-based metrics such as MRR and Hits@K, despite different users often requiring
arXiv:2606.09740v1 Announce Type: new Abstract: Vision-Language-Action (VLA) models demonstrate strong perfor-1 mance on language-conditioned robotic manipulation within their training dis-2 tribution
arXiv:2512.12320v2 Announce Type: replace Abstract: Conventional soft pneumatic actuators, typically based on hollow elastomeric chambers, often suffer from small structural support and require costly
arXiv:2606.08370v1 Announce Type: cross Abstract: Standard dynamic vision sensors approximate retinal processing by detecting temporal contrast changes, offering high speed and high dynamic range. In
arXiv:2606.08500v1 Announce Type: cross Abstract: Software engineering agents (SWE agents) increasingly work through tool-mediated trajectories in real repositories, yet their behavior remains difficu
arXiv:2510.04127v2 Announce Type: replace-cross Abstract: Approximate nearest neighbour (ANN) search underpins large-scale retrieval, increasingly within the retrieval-augmented generation pipelines t
arXiv:2606.08186v1 Announce Type: new Abstract: Monopedal hopping robots are conceptually simple but highly dynamic and inherently unstable. Achieving robust 3D hopping is still difficult because grou
arXiv:2606.08150v1 Announce Type: new Abstract: Designing 3D metamaterial microstructures that meet the intended functions remains a major challenge, as it typically requires domain expertise, iterati
arXiv:2606.09245v1 Announce Type: cross Abstract: Few-shot object detection has gained widely attention in recent years. Some excellent algorithms have been proposed to handle this task. However, most
arXiv:2606.08410v1 Announce Type: cross Abstract: Personalized decision-making in multi-objective bandits requires learning user-specific trade-offs among competing objectives. Since arm utility depen
arXiv:2606.09711v1 Announce Type: new Abstract: Reward hacking is usually studied after it becomes visible, once a model earns high proxy reward while failing the intended task. We instead study what