Knowledge catalogue
arXiv:2605.27643v1 Announce Type: new Abstract: Light-based advanced manufacturing increasingly requires programmable, closed-loop tools that translate human design intent into executable operations a
arXiv:2605.27396v1 Announce Type: cross Abstract: Autonomous AI agents now plan, decide, and act on behalf of users across healthcare, financial services, and workplace contexts, often without step-by
arXiv:2605.27531v1 Announce Type: cross Abstract: Specification synthesis, the task of automatically inferring formal specifications from program implementations and natural language, is important for
arXiv:2605.27621v1 Announce Type: cross Abstract: As multi-agent systems (MAS) become increasingly complex, identifying the contributions of individual agents is critical for system optimization. Howe
arXiv:2605.27575v1 Announce Type: new Abstract: As organizations move toward production deployments of AI agents, which execute non-deterministic workflows, maintain stateful sessions, and often opera
arXiv:2605.28680v1 Announce Type: cross Abstract: The proliferation of Artificial Intelligence (AI) in workplaces is transforming how we work. While existing research on human-AI collaboration at work
arXiv:2605.27905v1 Announce Type: new Abstract: AI research agents can now generate research ideas, design experiments, run code, and draft papers, raising the possibility of large-scale AI-assisted s
arXiv:2605.28255v1 Announce Type: new Abstract: AI systems are fallible, and humans can make mistakes in deciding whether to trust AI over their own judgment. Thus, improving human-AI collaboration re
arXiv:2605.27873v1 Announce Type: new Abstract: AI models underpin data-centric applications from image and text processing to scientific discovery in biology, physics, and chemistry. Yet developing t
arXiv:2602.02898v2 Announce Type: replace Abstract: Language model benchmarks are pervasive and computationally-efficient proxies for real-world performance. However, many recent works find that bench
arXiv:2605.27668v1 Announce Type: cross Abstract: Probabilistic forecasting estimates the likelihood of uncertain future events. To improve LLM forecasting, existing methods typically learn from binar
arXiv:2602.18481v2 Announce Type: replace-cross Abstract: The rapid advancement of Large Language Models (LLMs) has led to a surge of financial benchmarks, evolving from static knowledge evaluation to
arXiv:2605.28730v1 Announce Type: new Abstract: Designing a transit network requires many sequential route extension decisions, but their quality is often visible only after the full network is assemb
arXiv:2601.10714v2 Announce Type: replace Abstract: We introduce Alterbute, a diffusion-based method for editing an object's intrinsic attributes in an image. We allow changing color, texture, materia
arXiv:2601.10334v2 Announce Type: replace Abstract: Supervised convolutional neural networks (CNNs) are widely used to solve imaging inverse problems, achieving state-of-the-art performance in numerou
arXiv:2605.28001v1 Announce Type: new Abstract: We empirically audit the k-NAF budget-accounting mechanism in Anchored Decoding using (i) a fixed, class-stratified workload (approximately 8,500 random
arXiv:2510.15541v2 Announce Type: replace-cross Abstract: Accurate brain tumor segmentation from MRI is vital for diagnosis and treatment planning. Although Monte Carlo (MC) Dropout is widely used to
arXiv:2605.28337v1 Announce Type: new Abstract: A new variant of the classic capacitated facility location problem, which considers incompatibilities between customers, has recently been introduced in
arXiv:2605.27986v1 Announce Type: new Abstract: Messenger RNA (mRNA) sequences as therapeutics require optimized design to ensure efficient translation, structural stability, and minimal immunogenicit
arXiv:2605.28666v1 Announce Type: new Abstract: In modern industry, dynamic environments and the complexity of modular and reconfigurable resources require automated planning of process sequences. Cap
arXiv:2605.28092v1 Announce Type: new Abstract: Signal Temporal Logic (STL), has recently seen extensive development, owing to its rich expressivenes for autonomous planning and control. Nevertheless,
arXiv:2601.12154v2 Announce Type: replace Abstract: This study investigates the use of neural topic modeling and LLMs to uncover meaningful themes from patient storytelling data, to offer insights tha
arXiv:2605.28222v1 Announce Type: new Abstract: We study quality-latency-resource trade-offs in a documentation-grounded retrieval-augmented generation (RAG) system that uses Low-Rank Adaptation (LoRA
arXiv:2605.27761v1 Announce Type: new Abstract: The rapid development of GUI foundation models and mobile GUI agents has spurred numerous evaluation benchmarks, yet most rely on simulated environments
arXiv:2605.28428v1 Announce Type: cross Abstract: Detecting subtle visual anomalies in images remains challenging, particularly when only normal samples are available a priori. Such unsupervised anoma
arXiv:2605.28021v1 Announce Type: new Abstract: Out-of-distribution (OOD) detection is essential for deploying machine learning models in open-world and safety-critical scenarios, where test inputs ma
arXiv:2407.21075v2 Announce Type: replace Abstract: We present foundation language models developed to power Apple Intelligence features, including a ~3 billion parameter model designed to run efficie
arXiv:2605.28659v1 Announce Type: new Abstract: Biological foundation models have shown strong performance in single-cell representation learning by applying transformer architectures directly to gene
arXiv:2605.27469v1 Announce Type: cross Abstract: Continual Learning (CL) is a practical paradigm to utilize power of deep pre-trained neural networks, but which pre-trained model has a better ability
arXiv:2508.11011v2 Announce Type: replace Abstract: Construction safety inspections typically involve a human inspector identifying safety concerns on-site. With the rise of powerful Vision Language M
arXiv:2602.06054v3 Announce Type: replace Abstract: Assessing originality in AI research is arguably the most consequential yet least reliable step in peer review. Reviewer judgments of originality re
arXiv:2605.28809v1 Announce Type: new Abstract: Class-Incremental Learning (CIL) is important in building real-world learning systems. In CLIP-based CIL, the model performs classification by comparing
arXiv:2605.28313v1 Announce Type: new Abstract: Large Language Models (LLMs) have demonstrated remarkable capabilities in tasks related to reasoning and judgment. However, assessing the quality of arg
arXiv:2512.09800v2 Announce Type: replace Abstract: Low-power microcontroller (MCU) hardware is currently evolving from single-core architectures to predominantly multi-core architectures. In parallel
arXiv:2512.01988v2 Announce Type: replace Abstract: Recent reinforcement-learning frameworks for visual perception policy usually incorporate intermediate reasoning chains expressed in natural languag
arXiv:2605.28108v1 Announce Type: new Abstract: A long-lived LLM agent, such as OpenClaw, earns its value by acting on a user's preferences and constraints across sessions, not just the current reques
arXiv:2605.27752v1 Announce Type: new Abstract: LLM confidence calibration is often evaluated by comparing two signals: token-probability scores and verbalized confidence. These signals are sometimes
arXiv:2605.27472v1 Announce Type: cross Abstract: Assertion-based verification (ABV) is a cornerstone of modern hardware design, yet manually translating design intent into formal SystemVerilog Assert
arXiv:2511.05550v2 Announce Type: replace-cross Abstract: Large audio language models (LALMs) leverage multimodal representations to generate open-ended answers to natural language queries about audio
arXiv:2505.19342v2 Announce Type: replace-cross Abstract: Multi-device inference can reduce Transformer latency by parallelizing computation. However, existing methods require high inter-device bandwi
arXiv:2605.27726v1 Announce Type: new Abstract: Frequent cloud cover severely limits the usability of Sentinel-2 (S2) optical time series for Earth surface monitoring. Sentinel-1 (S1) SAR provides all
arXiv:2605.27995v1 Announce Type: new Abstract: Large language model (LLM)-based agents have shown strong capabilities in using external tools to solve complex tasks. However, existing evaluations oft
arXiv:2605.28079v1 Announce Type: new Abstract: Long-context language models now advertise context windows up to millions of tokens, yet evaluations typically report a single length or a narrow task f
arXiv:2605.28287v1 Announce Type: new Abstract: Discovering novel stable molecules without training data remains a grand scientific challenge. Current molecular generative models are trained on large,
arXiv:2512.01970v3 Announce Type: replace Abstract: Does Reinforcement Learning (RL) merely amplify existing skills, or synthesize novel skills? We investigate this question through the lens of Comple
arXiv:2601.08131v4 Announce Type: replace Abstract: Cross-layer reuse of early attention projections can improve optimization and data efficiency, but it creates a structural conflict: the first layer
arXiv:2605.27768v1 Announce Type: new Abstract: Production AI systems often operate with incomplete, conflicting, or insufficient evidence. Forced classifiers collapse such cases into action labels, w
arXiv:2605.27988v1 Announce Type: new Abstract: Bias evaluation for language models has made substantial progress on bounded comparisons, such as overt derogation, stereotype association, or label-sen
arXiv:2605.28640v1 Announce Type: new Abstract: Efficient inference is critical for long-context language models, where attention computation and KV-cache access dominate the cost. Recent work RAT+, i
arXiv:2605.27699v1 Announce Type: new Abstract: Sampling-based motion planners offer a practical and scalable approach to kinodynamic motion planning, notably for high-dimensional, underactuated, or n
arXiv:2605.28011v1 Announce Type: new Abstract: Quantifying impact phenomena in badminton smashes is important for evaluating both athletic performance and equipment; however, conventional measurement
arXiv:2511.15390v2 Announce Type: replace Abstract: Large language models (LLMs) have achieved remarkable performance on a wide range of tasks, hindering real-world deployment due to their massive siz
arXiv:2605.27485v1 Announce Type: cross Abstract: Formal verification offers a path to provably correct software, but writing verified code remains expensive enough that the technique is rarely used i
arXiv:2605.28655v1 Announce Type: new Abstract: Scientific research proceeds through iterative cycles of hypothesis generation, experiment design, execution, and revision. AI agents can automate parts
arXiv:2605.27674v1 Announce Type: cross Abstract: Cyber-Physical Systems (CPS) integrate sensing, communication, computation, and control to support critical infrastructure, including smart grids, ind
arXiv:2605.27476v1 Announce Type: cross Abstract: We characterize the pre-softmax attention matrix mathbf{QK^op} in transformers as an associative memory matrix encoding pairwise associations between
arXiv:2605.28642v1 Announce Type: new Abstract: Multimodal large language models (MLLMs) have demonstrated significant potential for speech-to-text translation (S2TT). However, existing deployment par
arXiv:2605.27720v1 Announce Type: new Abstract: Reinforcement learning and data-driven autonomous controllers are commonly evaluated using cumulative reward and empirical success frequency under finit
arXiv:2605.28441v1 Announce Type: cross Abstract: While Contrastive Learning (CL) has revolutionized self-supervised representation learning, its latent representations remain highly entangled and opa
arXiv:2512.12649v2 Announce Type: replace Abstract: Parameter tuning in real-world experiments is constrained by the limited evaluation budget available on hardware. The path-following controller stud