Knowledge catalogue
arXiv:2606.10241v1 Announce Type: new Abstract: Autonomous improvement loops are hard to trust because the improvement process is usually external scaffolding bolted onto the agent: failures go unlogg
arXiv:2606.10528v1 Announce Type: cross Abstract: Current reinforcement learning from human feedback (RLHF) methods primarily rely on scalar rewards from a trained reward model (RM). While effective,
arXiv:2606.09891v1 Announce Type: cross Abstract: Ranking in digital marketplaces is a dynamic exposure-allocation mechanism: displayed items shape discovery trajectories and success events logged by
arXiv:2606.10971v1 Announce Type: new Abstract: Precise state estimation for navigation of autonomous agricultural robots is often compromised by sensor outages (GNSS/LiDAR/Visual) and high-frequency
arXiv:2606.10348v1 Announce Type: new Abstract: Object navigation requires an agent to locate a target in an unknown environment through visual observations. Existing methods typically rely on open-vo
arXiv:2602.01179v2 Announce Type: replace Abstract: Gradual domain adaptation (GDA) aims to mitigate domain shift by progressively adapting models from the source domain to the target domain via inter
arXiv:2510.07061v2 Announce Type: replace Abstract: While automatic metrics drive progress in Machine Translation (MT) and Text Summarization (TS), existing metrics have been developed and validated a
arXiv:2606.10284v1 Announce Type: new Abstract: Graph Contrastive Learning (GCL), which trains graph encoders by maximizing similarity between positive samples and minimizing it between negative ones,
arXiv:2509.17251v2 Announce Type: replace-cross Abstract: Existing theory suggests that for linear regression problems categorized by capacity and source conditions, gradient descent (GD) is always mi
arXiv:2606.09937v1 Announce Type: cross Abstract: We introduce RKSC (Reasoning-Aware KV Cache Sharing), a training-free inference framework that eliminates two structural redundancies in multi-branch
arXiv:2510.14828v3 Announce Type: replace Abstract: Improving the reasoning capabilities of embodied agents is crucial for robots to complete complex human instructions in long-view manipulation tasks
arXiv:2606.11092v1 Announce Type: cross Abstract: Elite humanoid soccer shooting requires whole-body stability, high-impulse whole-body interactions, and accuracy to targets. Motion tracking-driven re
arXiv:2605.06234v2 Announce Type: replace Abstract: Embodied AI is a prominent research topic in both academia and industry. Current research centers on completing tasks based on explicit user instruc
arXiv:2606.10039v1 Announce Type: new Abstract: We consider the nonprehensile object transportation task known as the waiter's problem, in which a robot must move an object balanced on a tray from one
arXiv:2606.10125v1 Announce Type: cross Abstract: Few-shot example retrieval is the dominant paradigm for grounding large language models (LLMs) in domain-specific text-to-SQL systems. However, the qu
arXiv:2403.00420v3 Announce Type: replace-cross Abstract: Deep Reinforcement Learning (DRL) is a subfield of machine learning for training autonomous agents that take sequential actions across complex
arXiv:2606.11130v1 Announce Type: new Abstract: We study the task of agnostically learning general (as opposed to homogeneous) ReLUs under the Gaussian distribution with respect to the squared loss. I
arXiv:2407.05886v3 Announce Type: replace Abstract: Continuum and soft robots can transform automation tasks requiring compliant interaction in constrained or unstructured environments, including heal
arXiv:2606.10917v1 Announce Type: new Abstract: Although Large Language Model (LLM) agents have demonstrated strong performance on complex tasks, their learning is often limited by inefficient interac
arXiv:2606.10746v1 Announce Type: new Abstract: Robot Operating System 2 (ROS 2), the de facto standard middleware framework for robots, runs each robot as a graph of nodes communicating over the Data
arXiv:2606.09873v1 Announce Type: cross Abstract: Reasoning models achieve strong performance on challenging tasks by generating explicit intermediate reasoning traces before producing a final answer.
arXiv:2606.10338v1 Announce Type: cross Abstract: Machine unlearning is increasingly important for large language models, yet unlearning in Mixture-of-Experts (MoE) architectures remains underexplored
arXiv:2604.01993v2 Announce Type: replace-cross Abstract: Multi-hop QA benchmarks often reward Large Language Models (LLMs) for spurious correctness, where models reach correct answers through invalid
arXiv:2606.09926v1 Announce Type: cross Abstract: Sampling from the sequence-level power distribution p^alpha elicits RL-level reasoning from base language models without any parameter updates, but th
arXiv:2605.27770v2 Announce Type: cross Abstract: We introduce `dualGNN', an autoregressive message-passing GNN for sampling fine, regular triangulations (FRTs) of convex polytopes. dualGNN operates o
arXiv:2606.10305v1 Announce Type: new Abstract: Fine-tuning vision-language-action (VLA) policies for long-horizon manipulation still relies heavily on behavior cloning, which requires costly high-qua
arXiv:2606.10804v1 Announce Type: new Abstract: Controlled character animation requires transferring motion from a driving sequence to a reference character. Prior works heavily rely on intermediate r
arXiv:2603.07238v2 Announce Type: replace Abstract: Similarities between language representations derived from Self-Supervised Speech Models (S3Ms) have been observed to primarily reflect geographic p
arXiv:2606.10874v1 Announce Type: new Abstract: In quantum image processing, a fundamental step is encoding classical image data into quantum states. This can be achieved using methods such as Flexibl
arXiv:2512.17629v4 Announce Type: replace-cross Abstract: Prescriptive Process Monitoring (PresPM) recommends interventions during running business processes to optimize key performance indicators (KP
arXiv:2606.09871v1 Announce Type: cross Abstract: Group Relative Policy Optimization (GRPO) and its variants, originally developed for Large Language Models (LLMs), have recently been applied to Multi
arXiv:2602.19086v2 Announce Type: replace Abstract: Kuzushiji was one of the most widely used cursive writing systems in pre-modern Japan. Due to its highly cursive forms and extensive glyph variation
arXiv:2606.10780v1 Announce Type: cross Abstract: Secure aggregation is a vital component for mitigating gradient leakage in federated learning, but its communication cost conventionally scales with t
arXiv:2606.10594v1 Announce Type: new Abstract: 3D vision-language segmentation aims to segment target objects in 3D scenarios according to the linguistic instructions and visual observations. Prior a
arXiv:2601.04776v2 Announce Type: replace Abstract: Monocular shape-from-polarization (SfP) leverages the intrinsic relationship between light polarization properties and surface geometry to recover s
arXiv:2606.10398v1 Announce Type: cross Abstract: Does personalizing what a reader sees pay off, and where does it stop? Using a social web highlighter and a co-readership identity control (the same d
arXiv:2511.19706v2 Announce Type: replace-cross Abstract: Rotation invariance is a fundamental requirement across many computer vision tasks. Historically, this inductive bias has been encoded through
arXiv:2606.10334v1 Announce Type: new Abstract: Code-generating large language models (LLMs) increasingly produce visual artifacts such as charts, web pages, and slides by writing programs that are ex
arXiv:2606.09837v1 Announce Type: cross Abstract: Emotional interaction is increasingly crucial for conversational AI, yet current systems lack a self-emotion determination mechanism to drive the stre
arXiv:2606.10688v1 Announce Type: new Abstract: Autonomous driving relies on computationally intensive perception pipelines to continuously detect and track objects in the surrounding environment. Whi
arXiv:2606.09886v1 Announce Type: cross Abstract: Sparse Mixture-of-Experts (MoE) large language models achieve strong quality with low per-token compute, yet their deployment is often limited by the
arXiv:2606.10228v1 Announce Type: cross Abstract: Safe exploration is a prerequisite for deploying reinforcement learning (RL) agents in safety-critical domains. In this paper, we approach safe explor
arXiv:2606.09924v1 Announce Type: cross Abstract: Deploying deep neural networks on memory-constrained edge accelerators is bottlenecked by per-inference off-chip weight transfer rather than computati
arXiv:2606.10286v1 Announce Type: new Abstract: Open-pit mine scheduling is a critical process for maximizing economic return under complex geotechnical and operational constraints. While Mixed-Intege
arXiv:2606.09888v1 Announce Type: new Abstract: Linear attention provides an efficient backbone for long-sequence recommendation by avoiding the quadratic cost of standard Transformers, but its compre
arXiv:2606.09849v1 Announce Type: cross Abstract: Building-integrated photobioreactors (PBRs) offer a pathway for carbon-neutral architecture, yet deployment is hindered by configuration complexity an
arXiv:2606.10388v1 Announce Type: cross Abstract: Agent skill libraries are becoming routable software assets: a retrieved skill can contribute instructions, scripts, resource bindings, and execution
arXiv:2606.10889v1 Announce Type: cross Abstract: Early detection of neurodegeneration remains a critical clinical challenge. This study investigates whether sleep EEG signal criticality, quantified v
arXiv:2606.10610v1 Announce Type: new Abstract: Parameter-Efficient Fine-Tuning (PEFT) has become essential for adapting foundation models to downstream NLP tasks. However, current PEFT methods often
arXiv:2606.09887v1 Announce Type: cross Abstract: Reinforcement learning (RL) for large language models usually supervises reasoning with scalar outcome rewards, such as binary correctness. Such rewar
arXiv:2606.10091v1 Announce Type: cross Abstract: Machine learning (ML) models are susceptible to various security, privacy, and fairness risks. Adversaries with different characteristics (i.e., objec
arXiv:2606.10413v1 Announce Type: new Abstract: Breakthroughs in large language models and multimodal generation technologies have propelled the digital reconstruction of human mental traits, emotiona
arXiv:2606.09868v1 Announce Type: cross Abstract: As Multimodal Large Language Models (MLLMs) face growing privacy risks and regulatory constraints, machine unlearning (MU) has emerged as a crucial so
arXiv:2606.10734v1 Announce Type: new Abstract: Conformal Prediction (CP) provides robust uncertainty guarantees for predictive models, but is typically applied post hoc, which misaligns model trainin
arXiv:2606.09946v1 Announce Type: cross Abstract: Edge-AI systems increasingly require real-time CNN inference under strict energy, performance, security, and privacy constraints. Approximate computin
arXiv:2606.10738v1 Announce Type: cross Abstract: Recent multimodal large language models mainly process audio as monaural signals, thereby discarding the spatial cues contained in spatial audio for s
arXiv:2606.10775v1 Announce Type: new Abstract: Unsupervised building change detection aims to learn building-change masks from unlabeled bi-temporal remote sensing images. Existing label-free methods
arXiv:2603.10676v2 Announce Type: replace Abstract: Industrial Control Systems (ICS) underpin critical infrastructure and face growing cyber-physical threats due to the convergence of operational tech
arXiv:2606.10227v1 Announce Type: new Abstract: Metal additive manufacturing enables the fabrication of complex parts, but achieving consistent build quality remains challenging due to interactions in
arXiv:2606.10069v1 Announce Type: new Abstract: In this paper we build upon a previous study in which we demonstrated, using XGBoost and earthquake catalogue data from Japan and Chile, that a set of 6