Knowledge catalogue
arXiv:2610.17831v1 Announce Type: cross Abstract: Diffusion large language models (dLLMs) are appealing for code generation because they can update many tokens in parallel, yet many are optimized for
arXiv:2610.19091v1 Announce Type: cross Abstract: Contact-rich inverse problems require efficient gradients and reliable enforcement of non-penetration. Existing differentiable methods provide fast si
arXiv:2610.19490v1 Announce Type: cross Abstract: Digital-domain self-interference cancellation is essential in in-band full-duplex radios after passive suppression and analog cancellation. It is part
arXiv:2610.19279v1 Announce Type: new Abstract: Modern machine learning models increasingly operate on variable-size structured data, such as sets and graphs. A central requirement is size generalizat
arXiv:2610.20187v1 Announce Type: new Abstract: Tracking changing comparators is a central problem in online learning. For exp-concave losses, previous work established the T^{1/3}P_T^{2/3} dependence
arXiv:2610.18990v1 Announce Type: cross Abstract: Low-Rank Adaptation (LoRA) has made diffusion model personalization affordable and widely shareable, but it also creates moderation risks when harmful
arXiv:2610.19020v1 Announce Type: cross Abstract: Discrete diffusion models have emerged as a leading alternative to autoregressive models for generating text and other categorical data, yet their the
arXiv:2610.19743v1 Announce Type: new Abstract: Discriminative models are trained with task-specific objectives such as classification, detection, or representation learning, yet their practical utili
arXiv:2610.18904v1 Announce Type: cross Abstract: A reading departs from what a monitoring system expects for one of three reasons: the physical process changed, the instrument is failing, or the valu
arXiv:2610.19157v1 Announce Type: cross Abstract: Code generation models are actively being used in professional and educational settings. However, the competence of large language models (LLMs) to ge
arXiv:2610.19614v1 Announce Type: cross Abstract: In this work, we reframe agentic trajectories as paths through a shared embedding space of actions and environmental observations. We show that succes
arXiv:2610.20360v1 Announce Type: new Abstract: Gossip, defined as evaluative talk about absent third parties, is widely seen as a uniquely human social practice. This study examines whether comparabl
arXiv:2610.18415v1 Announce Type: cross Abstract: Large Language Models (LLMs) are aligned to human preferences, after pre-training and supervised fine-tuning steps. However, standard RL alignment ste
arXiv:2610.15692v2 Announce Type: replace Abstract: Large language models can produce fluent tu- toring dialogue, but whether they actually help students learn remains unclear. We present a process-ce
arXiv:2610.18880v1 Announce Type: new Abstract: High-throughput perturbation screens increasingly use machine-learning models to predict the effects of gene combinations that were never measured. A di
arXiv:2610.19231v1 Announce Type: new Abstract: Transformer language models develop attention heads that specialize in syntactic relations, for example, attending from verbs to their subjects. Do thes
arXiv:2610.17331v1 Announce Type: new Abstract: General-purpose coding agents use standard shell and file interactions, yet formal theorem-proving agents often rely on specialized, ad-hoc tooling. Thi
arXiv:2610.18879v1 Announce Type: new Abstract: Protein function annotation operates over an expanding label space, making class-incremental learning (CIL) a natural framework for incorporating newly
arXiv:2610.14173v1 Announce Type: cross Abstract: Digital libraries increasingly host scientific publications whose figures encode primary research findings, yet making this visual content searchable,
arXiv:2610.17256v1 Announce Type: new Abstract: Conversational analytics lets people ask questions of records spread across many systems and get answers in plain language; it is useful only if each an
arXiv:2610.16655v1 Announce Type: new Abstract: Decision APIs that return a probability for each option are routinely used as if those probabilities were properties of the options themselves. That rea
arXiv:2610.19945v1 Announce Type: new Abstract: Within the most recent two decades, machine translation (MT) has been advanced from a research area to a mature, deployed technology for prose text and
arXiv:2610.16534v1 Announce Type: new Abstract: Deployed models can fail silently as the world they describe evolves. Attribution drift has served as an early warning in tabular data; we ask whether,
arXiv:2610.19741v1 Announce Type: new Abstract: Visible reasoning traces of Chinese reasoning-oriented LLMs are typically written in modern vernacular Chinese, which may be verbose under limited decod
arXiv:2510.08464v2 Announce Type: replace-cross Abstract: Vision-Language-Action (VLA) models are a promising paradigm for field robotics, but their deployment is bottlenecked by strict edge SWaP-C co
arXiv:2610.19120v1 Announce Type: cross Abstract: Purpose: Free-breathing abdominal MRI is limited by respiratory motion, which introduces image artifacts and k-space inconsistencies. Existing reconst
arXiv:2610.16593v1 Announce Type: new Abstract: Reinforcement learning with verifiable rewards (RLVR) implemented with Group Relative Policy Optimization (GRPO) has driven substantial progress in trai
arXiv:2603.00578v2 Announce Type: replace-cross Abstract: Long chain-of-thought~(CoT) reasoning improves the problem-solving capability of large reasoning models~(LRMs), but often incurs substantial i
arXiv:2610.20124v1 Announce Type: new Abstract: Human mesh recovery (HMR) provides a geometric foundation for understanding human motion, but accurately reconstructing both body motion and fine hand a
arXiv:2606.15251v2 Announce Type: replace-cross Abstract: Accurate and interpretable motion prediction for heterogeneous traffic spaces, including pedestrians, bicycles, cars, and trucks, is essential
arXiv:2610.14700v1 Announce Type: new Abstract: Most text-to-SQL benchmarks provide the target database. In a large data catalog, however, choosing the sources is part of the problem: an agent must fi
arXiv:2610.19302v1 Announce Type: new Abstract: On-policy distillation (OPD) provides dense token-level supervision on student rollouts, but this supervision can become unreliable when the student and
arXiv:2610.18635v1 Announce Type: cross Abstract: The development of a digital twin brings together many stakeholders and technical components. We have previously investigated the reporting of the con
arXiv:2610.19527v1 Announce Type: new Abstract: We devise a dual-arm cooperative planning framework for wiping table surface in the presence of scattered objects. Unlike conventional surface-cleaning
arXiv:2610.19206v1 Announce Type: new Abstract: While work on LLM-based regression has largely focused on scalar prediction, multi-output text regression remains less explored. We present DualView, a
arXiv:2610.19523v1 Announce Type: new Abstract: Robotic exploration, coverage, and multi-agent deployment algorithms require reproducible environments with controllable geometry and visibility. This p
arXiv:2610.17738v1 Announce Type: new Abstract: End-to-end (E2E) full-duplex spoken language models (FD-SLMs) enable simutanous listening and speaking continously, requiring models to take appropriate
arXiv:2610.15658v1 Announce Type: cross Abstract: Distributed Constraint Satisfaction Problems (DCSPs) model distributed decision-making in multi-agent systems, where agents assign values to local var
arXiv:2610.19364v1 Announce Type: new Abstract: Dynamic graph learning models how entities and their relations evolve from historical observations to predict future graph states. Existing methods do n
arXiv:2610.20598v1 Announce Type: new Abstract: Achieving general spatial intelligence requires a unified understanding of both static 3D and dynamic 4D. However, we find that existing models struggle
arXiv:2610.19627v1 Announce Type: new Abstract: Differentiable reinforcement learning shows strong promise for robot policy learning, but its application to legged locomotion has remained limited to b
arXiv:2610.20531v1 Announce Type: new Abstract: Human listeners can often identify the intended object several words before an instruction ends. Robots, however, are typically designed to wait until t
arXiv:2610.17869v1 Announce Type: new Abstract: Multi-agent Large Language Model (LLM) systems typically commit to a deliberation protocol before inference. A fixed protocol cannot allocate computatio
arXiv:2603.13535v5 Announce Type: cross Abstract: Evaluating Ollivier-Ricci (OR) curvature on large-scale graphs is computationally prohibitive due to the necessity of solving an optimal transport pro
arXiv:2610.19804v1 Announce Type: new Abstract: We study finite stochastic partial monitoring (PM) games with side information, a paradigm of sequential learning with partial feedback. On each round t
arXiv:2610.18304v1 Announce Type: new Abstract: Guardrail approaches that rely on reasoning-based large language models (LLM) improve safety decisions, but come with substantial inference overhead due
arXiv:2610.20318v1 Announce Type: cross Abstract: Reinforcement-learning policies for robot locomotion can fail at deployment when operating conditions shift (e.g., rough or slippery terrain, payload
arXiv:2610.17767v1 Announce Type: cross Abstract: Large reasoning models (LRMs) achieve strong performance by externalizing explicit reasoning traces before producing the answer, yet suffer from overt
arXiv:2610.19836v1 Announce Type: new Abstract: Enterprise group chats record project progress, decisions, task ownership, and other organizational information valuable to enterprise agents. Reliable
arXiv:2509.21193v2 Announce Type: replace Abstract: Large language models (LLMs) have recently shown strong progress on scientific reasoning, yet two major bottlenecks remain. First, explicit retrieva
arXiv:2610.19388v1 Announce Type: new Abstract: Massively multilingual speech modeling has made remarkable progress in recent years, yet existing training resources and benchmarks still underrepresent
arXiv:2610.19198v1 Announce Type: cross Abstract: With the growing number of LLMs available, selecting which one to use for a prompt has become a key question. A large part of the routing literature r
arXiv:2610.15976v1 Announce Type: cross Abstract: This paper investigates a new role of randomness in stochastic policies--as a medium for embedding and communicating information. Specifically, we con
arXiv:2501.06597v1 Announce Type: cross Abstract: The widespread adoption of generative AI has generated diverse opinions, with individuals expressing both support and criticism of its applications. T
arXiv:2610.19997v1 Announce Type: new Abstract: Efficient sequence modeling remains a fundamental challenge in machine learning. Despite the success of Transformers, recurrent neural networks (RNNs) h
arXiv:2610.17816v1 Announce Type: new Abstract: Edge-cloud collaboration combines the lower inference overhead and greater data locality of edge-side execution with the stronger capabilities of cloud-
arXiv:2610.20150v1 Announce Type: new Abstract: Robotic vacuum cleaners must extend their functionality within tight limits on size, cost, and durability. Deployable linkages with few actuators meet t
arXiv:2506.04713v4 Announce Type: replace Abstract: A key challenge in Few-Shot Recognition (FSR) is the scarcity of labeled task-specific data for training. Contemporary methods mitigate this challen
arXiv:2610.17672v1 Announce Type: new Abstract: Large language models (LLMs) have shown strong potential for human mobility generation by modeling individual routines, activities, and mobility prefere
arXiv:2610.18262v1 Announce Type: cross Abstract: Third-party skills can expose LLM agents to malicious instructions hidden by encoding. We introduce Ciphered Skill Injection (CSI), which embeds such