Knowledge catalogue
arXiv:2605.06623v1 Announce Type: cross Abstract: Large language model (LLM)-based Multi-agent systems (MAS) have shown promise in tackling complex collaborative tasks, where agents are typically orch
arXiv:2602.20974v2 Announce Type: replace Abstract: In engineering design and scientific computing, computational cost and predictive accuracy are intrinsically coupled. High-fidelity simulations prov
arXiv:2605.07600v1 Announce Type: cross Abstract: Recent methods for improving LLM mathematical reasoning, whether through MCTS-based test-time search or causal graph-guided knowledge injection, canno
arXiv:2605.07841v1 Announce Type: cross Abstract: Decentralized machine learning often relies on outsourcing computations, such as gradient evaluations, to untrusted worker nodes. Existing robust aggr
arXiv:2605.07646v1 Announce Type: cross Abstract: While explicit reasoning trajectories enhance model interpretability, existing paradigms often rely on monolithic chains that lack intermediate verifi
arXiv:2509.03738v4 Announce Type: replace-cross Abstract: We introduce sparse autoencoder neural operators (SAE-NOs), a new class of sparse autoencoders that operate in function spaces rather than fix
arXiv:2605.07877v1 Announce Type: new Abstract: Robot swarms promise scalable assistance in complex and hazardous environments. Task planning lies at the core of human-swarm collaboration, translating
arXiv:2511.02805v2 Announce Type: replace-cross Abstract: LLM-based search agents often concatenate the full interaction history into the context, producing long and noisy inputs, and increasing compu
arXiv:2603.09652v3 Announce Type: replace Abstract: With the rapid advancement of Large Language Models (LLMs) in code generation, human-AI interaction is evolving from static text responses to dynami
arXiv:2605.06897v1 Announce Type: cross Abstract: The rise of Internet of Things (IoT) devices in the physical world necessitates voice-based interfaces capable of handling complex user experiences. W
arXiv:2603.24946v2 Announce Type: replace-cross Abstract: Large language models (LLMs) have shown strong performance on automated software engineering tasks, yet existing benchmarks focus primarily on
arXiv:2605.08050v1 Announce Type: new Abstract: Talking-head generation requires joint modeling of identity, head pose, facial expression, and mouth dynamics. Existing methods typically address only a
arXiv:2602.07026v2 Announce Type: replace-cross Abstract: Despite the success of multimodal contrastive learning in aligning visual and linguistic representations, a persistent geometric anomaly, the
arXiv:2605.07520v1 Announce Type: new Abstract: Differentiable planning enables gradient-based optimization of decision-making problems by leveraging differentiable models of system dynamics. However,
arXiv:2605.07075v1 Announce Type: new Abstract: The open-source model ecosystem now contains hundreds of thousands of pretrained models, yet picking the best model for a new dataset is increasingly in
arXiv:2605.08024v1 Announce Type: new Abstract: Learning-to-defer (L2D) can make glaucoma screening safer by routing difficult/uncertain cases to humans, yet standard formulations overlook expert avai
arXiv:2605.06951v1 Announce Type: new Abstract: Constraint inference is widely considered essential to align reinforcement learning agents with safety boundaries and operational guidelines by observin
arXiv:2411.16748v5 Announce Type: replace Abstract: Long-duration talking video synthesis faces enduring challenges in achieving high video quality, portrait consistency, temporal coherence, and compu
arXiv:2602.05359v2 Announce Type: replace Abstract: The advancement of multimodal large language models (MLLMs) has enabled impressive perception capabilities. However, their reasoning process often r
arXiv:2605.06317v2 Announce Type: replace-cross Abstract: Existing Vision-Language Navigation (VLN) methods typically adopt an egocentric, step-by-step paradigm, which struggles with error accumulatio
arXiv:2605.07792v1 Announce Type: cross Abstract: Neural operators (NOs) are designed to learn maps between infinite-dimensional function spaces. We propose a novel reframing of their use. By introduc
New episode of the @chain_ofthought Podcast with @jerryjliu0 of @llama_index coming soon! https://chainofthought.show/ We just wrapped recording with @jerryjliu0 of @llama_index 👀 His thesis: the AI f
arXiv:2605.08078v1 Announce Type: new Abstract: Diffusion-based models decompose sampling into many small Gaussian denoising steps -- an assumption that breaks down when generation is compressed to a
arXiv:2605.07476v1 Announce Type: new Abstract: Multivariate time series forecasting remains a challenge due to the complexity of local temporal dynamics and global dependencies across multiple variab
arXiv:2508.01248v4 Announce Type: replace Abstract: The rapid progress of generative models, such as GANs and diffusion models, has facilitated the creation of highly realistic images, raising growing
arXiv:2604.03675v2 Announce Type: replace Abstract: Agentic search enables language models to solve knowledge-intensive tasks by adaptively acquiring external evidence over multiple steps. Reinforceme
arXiv:2605.08031v1 Announce Type: new Abstract: Vision-language models (VLMs) raise growing concerns about privacy, copyright, and bias, motivating machine unlearning to remove sensitive knowledge. Ho
arXiv:2605.07741v1 Announce Type: new Abstract: 3D global relocalization is one of the key capabilities for mobile robots in practical applications. However, in large scale spaces, existing methods of
arXiv:2602.08786v4 Announce Type: replace-cross Abstract: There is an extensive literature that studies how to find optimal policies in resource allocation problems, taking the underlying design param
arXiv:2605.06873v1 Announce Type: cross Abstract: Probabilistic conditioning is concerned with the identification of a distribution of a random variable X given a random variable Y. It is a cornerston
arXiv:2605.07023v1 Announce Type: new Abstract: In many practical 6D object pose estimation scenarios, we often have access to only a single real-world RGB-D reference view per object, typically witho
arXiv:2605.07572v1 Announce Type: new Abstract: When applying Bayesian optimization (BO) to scientific workflow, a major yet often overlooked source of uncertainty is the task itself -- namely, what t
arXiv:2605.07695v1 Announce Type: new Abstract: High-fidelity surgical video generation can greatly improve medical training and the development of AI, adapting these generative models for precise vid
arXiv:2605.06993v1 Announce Type: new Abstract: Causal queries are often only partially identifiable from observational data, and experiments that could tighten the resulting bounds are typically cost
arXiv:2511.22316v2 Announce Type: replace Abstract: Large Language Models (LLMs) quantization facilitates deploying LLMs in resource-limited settings, but existing methods that combine incompatible gr
arXiv:2605.06820v1 Announce Type: cross Abstract: Deep learning-based organs/structures-at-risk(OARs) auto-contouring models can improve radiotherapy workflows, but models trained on adult data often
arXiv:2605.07055v1 Announce Type: cross Abstract: Foundation models (FMs) have shown great promise in medical imaging, but most FMs are trained on unimodal data within isolated domains, such as brain
arXiv:2605.08006v1 Announce Type: cross Abstract: We study a class of bilevel optimization problems in which both the upper- and lower-level problems have minimax structures. This setting captures a b
arXiv:2605.07252v1 Announce Type: cross Abstract: Co-speech gesture generation aims to synthesize realistic body movements that are semantically coherent with speech and faithful to a user-specified g
arXiv:2605.08030v1 Announce Type: new Abstract: Positron Emission Tomography (PET) image reconstruction is inherently challenged by Poisson noise and physical degradation factors, which are further ex
arXiv:2605.06929v1 Announce Type: cross Abstract: Designing photonic integrated circuits requires accurate electromagnetic field simulations, which remain computationally expensive even for simple dev
arXiv:2605.07738v1 Announce Type: cross Abstract: Physics-informed operator learning is an attractive candidate for surrogate modeling of microstructures, especially in multiscale finite-element simul
arXiv:2605.07775v1 Announce Type: cross Abstract: Balancing exploration and exploitation is a core challenge in sequential decision-making and black-box optimization. We introduce POETS (extbf{Po}licy
arXiv:2605.07574v1 Announce Type: new Abstract: Mainstream vision-language models (VLMs) fundamentally struggle with severe optical ambiguities, such as reflections and transparent objects, due to the
arXiv:2605.07796v1 Announce Type: new Abstract: SQL dialects vary in syntax, types, and functions across database engines. Text-to-SQL benchmarks, however, predominantly support only SQLite. This crea
arXiv:2503.17656v4 Announce Type: replace-cross Abstract: Natural products, as metabolites from microorganisms, animals, or plants, exhibit diverse biological activities, making them crucial for drug
arXiv:2603.00223v2 Announce Type: replace Abstract: We investigate a quantum-inspired approach to supervised multi-class classification based on the Pretty Good Measurement (PGM), viewed as an operato
arXiv:2605.06223v2 Announce Type: replace Abstract: Natural-language instance navigation becomes challenging when the initial user request does not uniquely specify the target instance. A practical ag
arXiv:2605.07549v1 Announce Type: new Abstract: Conformal Prediction (CP) is a distribution-free method for constructing prediction sets with marginal finite-sample coverage guarantees, making it a su
arXiv:2605.06830v1 Announce Type: cross Abstract: Protein language models (pLMs) produce per-residue representations that capture evolutionary and structural information, yet their mean-pooled sequenc
arXiv:2605.07804v1 Announce Type: cross Abstract: On-policy distillation (OPD) leverages dense teacher rewards to enhance reasoning models. However, scaling OPD to long-horizon tasks exposes a critica
arXiv:2605.07141v1 Announce Type: cross Abstract: Open-world referring segmentation requires grounding unconstrained language expressions to precise pixel-level regions. Existing multimodal large lang
arXiv:2605.06758v1 Announce Type: cross Abstract: Relative spatial relations provide a compact representation of spatial structure and are fundamental to relative spatial reasoning in 3D layout genera
arXiv:2605.07785v1 Announce Type: new Abstract: Concept Bottleneck Models (CBMs) in medical imaging aim to improve model interpretability by predicting intermediate clinical concepts before final diag
arXiv:2605.07334v1 Announce Type: new Abstract: Video Reasoning Segmentation (VRS) aims to segment target objects in videos based on implicit instructions that convey human intent and temporal logic.
arXiv:2605.06483v2 Announce Type: replace Abstract: Signal Temporal Logic (STL) is an expressive formal language for specifying spatio-temporal requirements over real-valued, real-time signals. It has
arXiv:2503.06223v5 Announce Type: replace Abstract: Large Vision-Language Models (VLMs) are increasingly deployed in open-ended environments, where ensuring reliable safety under multimodal inputs is
arXiv:2605.06901v1 Announce Type: new Abstract: Large Language Models (LLMs) are increasingly shaping the private and professional lives of users, with numerous applications in business, education, fi
arXiv:2605.07134v1 Announce Type: cross Abstract: Web agents perceive web pages through an observation space, yet its granularity has remained an underexamined design choice. Existing work treats obse
arXiv:2605.07174v1 Announce Type: new Abstract: We study the problem of deceptive path planning (DPP), where an agent aims to conceal its true destination from external observers. While existing work