Knowledge catalogue
arXiv:2610.02706v1 Announce Type: new Abstract: Visuomotor policies trained via imitation learning often inherit the unnecessarily slow timing of teleoperated demonstrations. Yet uniform speedup is un
arXiv:2610.02869v1 Announce Type: cross Abstract: Autonomous penetration testing agents conduct multi-step attacks by continuously adapting their plans and actions to target responses. As a common def
arXiv:2605.06478v2 Announce Type: replace Abstract: Heterogeneous air-ground robot teams combine complementary sensing modalities, mobility characteristics, and spatial viewpoints that can significant
arXiv:2610.02442v1 Announce Type: new Abstract: The emerging of large language models (LLMs) has posed significant challenges to the thermal management of data center. Intense GPU computation for LLMs
arXiv:2610.03413v1 Announce Type: new Abstract: Sequential learning systems often make decisions from accumulated experience while receiving high-dimensional inputs whose distribution may change over
arXiv:2610.02661v1 Announce Type: new Abstract: Real-time data streams in Web of Things (WoT) and edge computing environments often evolve through latent regime changes. For online representation lear
arXiv:2610.02835v1 Announce Type: new Abstract: During post-training of large language models (LLMs) with Reinforcement Learning with Verifiable Rewards (RLVR), GRPO-style algorithms can exhibit sever
arXiv:2610.02831v1 Announce Type: new Abstract: Vision-language models (VLMs) are powerful listwise rerankers for multimodal retrieval, but high inference costs restrict them to evaluating small local
arXiv:2610.03647v1 Announce Type: cross Abstract: Many machine learning methods aim to approximate the lower-dimensional manifold on which the data lives. A desirable feature of such methods is that t
arXiv:2610.02421v1 Announce Type: new Abstract: Androgenetic alopecia (AGA) is characterized by patterned follicular miniaturization, increased single-hair follicular units, and altered hair-shaft dia
arXiv:2610.03505v1 Announce Type: new Abstract: Post-exposure microscopy is central to qualification of fusion materials. However, manual analysis does not scale to the volume, heterogeneity, and mult
arXiv:2610.03078v1 Announce Type: new Abstract: Quantifying conversational dynamics requires reliable identification of interactional units and their temporal boundaries, but speech activity alone doe
arXiv:2602.09009v2 Announce Type: replace-cross Abstract: Scaling network depth has been a central driver behind the success of modern foundation models, yet recent investigations suggest that deep la
arXiv:2610.02576v1 Announce Type: new Abstract: Systematic reviews condense clinical trials into evidence tables, yet clinicians can interrogate these tables only through database queries, and many qu
arXiv:2610.02472v1 Announce Type: cross Abstract: Personalized LLM assistants must recover sparse evidence from long conversation histories across queries of varying complexity. We introduce APDMem (A
arXiv:2610.02253v1 Announce Type: new Abstract: The universal approximation property of dropout neural networks does not by itself describe the network size required for an accurate random realization
arXiv:2610.02770v1 Announce Type: new Abstract: Document databases such as MongoDB are core infrastructure for modern applications, and natural-language interfaces to them---text-to-MQL---would let no
arXiv:2610.03289v1 Announce Type: new Abstract: Multimodal Large Language Models (MLLMs) achieve strong performance across vision-language tasks, yet the internal mechanisms by which visual and textua
arXiv:2610.02438v1 Announce Type: cross Abstract: Large language models (LLMs) have demonstrated strong performance in code generation, where success depends on both recalling relevant algorithmic kno
arXiv:2610.03483v1 Announce Type: cross Abstract: We introduce AREX, a training-free sampler for pretrained flow matching models that uses the target mean and covariance to capture an analytically tra
arXiv:2610.02728v1 Announce Type: new Abstract: Closed-loop learned locomotion is established on commercial quadrupeds but remains uncommon at the sub-kilogram scale. Continuous-rotation legs give MiN
arXiv:2610.02363v1 Announce Type: new Abstract: Benchmarks for agent-generated data work grade a pipeline by running it once against a fixed snapshot. ArrivalBench instead re-executes the pipeline an
arXiv:2610.03102v1 Announce Type: cross Abstract: An LLM agent can recognize uncertainty yet still choose the wrong next step: asking when action is already justified, or seeking clarification when th
arXiv:2607.18257v2 Announce Type: replace-cross Abstract: When AI agents shift from answering questions to taking actions, users face a new problem: deciding what to delegate, to a system whose action
arXiv:2610.02673v1 Announce Type: cross Abstract: A theory draws many independent observations into one framework with novel hypotheses. A researcher building such a theory must synthesize observation
arXiv:2505.13012v3 Announce Type: replace-cross Abstract: Time-Varying Bayesian Optimization (TVBO) is the go-to framework for optimizing a time-varying black-box objective function that may be noisy
arXiv:2610.03531v1 Announce Type: new Abstract: Authorship Attribution (AA) requires capturing fine-grained stylistic characteristics, making it particularly challenging in zero-shot (ZS) settings whe
arXiv:2609.35013v2 Announce Type: replace Abstract: The rising cost and demand for energy, together with environmental sustainability goals, create major challenges for energy management. Energy Consu
arXiv:2610.02775v1 Announce Type: new Abstract: Mental health stigma has profoundly harmful impacts but its complexity makes it difficult to evaluate. Stigma may involve explicit derogation, but also
arXiv:2406.19892v5 Announce Type: replace Abstract: This article presents multilingual deep learning models for identifying web registers -- text varieties such as news reports and discussion forums -
arXiv:2610.02369v1 Announce Type: cross Abstract: Human-computer interaction (HCI) is in the middle of a transition: large language models can now generate functional user interfaces (UIs) on demand f
arXiv:2610.02428v1 Announce Type: new Abstract: Autonomous mobile robots (AMRs) increasingly perform material transport in production logistics, where their operation is governed by job generation, di
arXiv:2610.03537v1 Announce Type: cross Abstract: Endovascular brain-computer interfaces (BCIs) avoid craniotomy but require precise device delivery through anatomically variable cerebral veins. This
arXiv:2610.02528v1 Announce Type: new Abstract: We introduce an autoregressive differentiable method to solve 0-1 integer programs. We fix an arbitrary order of the binary variables and we train a tra
arXiv:2607.09277v2 Announce Type: replace Abstract: Three-dimensional (3D) molecule generation has been dominated by diffusion models, which achieve strong generation quality but typically require mol
arXiv:2610.03587v1 Announce Type: new Abstract: Joint embedding predictive architectures (JEPAs) predict future latent representations without reconstructing observations, enabling world models to foc
arXiv:2610.03007v1 Announce Type: cross Abstract: Long-output reasoning shifts the KV-cache bottleneck from the fixed prompt to the generated trace. Existing reasoning-cache eviction methods largely t
arXiv:2610.02274v1 Announce Type: new Abstract: Generating useful robot-training data requires more than visually plausiblescenes: objects must support interaction, placements must remain physicallyva
arXiv:2610.02524v1 Announce Type: new Abstract: Unstructured pruning at extreme sparsity often suffers from representational collapse, causing sharp drops in accuracy. To address this, we study Backbo
arXiv:2610.03035v1 Announce Type: new Abstract: Multimodal learning often suffers from modality imbalance, where the joint optimization process is dominated by a single modality. Existing methods typi
arXiv:2610.02440v1 Announce Type: new Abstract: Contextual bandits require balancing nonlinear reward modeling with online efficiency. Tree ensembles and neural methods capture nonlinearities but requ
arXiv:2610.02638v1 Announce Type: new Abstract: Full-duplex voice agents make many small, closed decisions, which current systems answer by slow autoregressive decoding. We propose DuplexJev, which fe
arXiv:2610.03425v1 Announce Type: new Abstract: Suspicion is an important, yet elusive concept in anti-money laundering and counter-terrorist financing (AML/CFT), which allows for intervention below t
arXiv:2610.03051v1 Announce Type: new Abstract: Social bees are important pollinators that support biodiversity and crop pollination globally and serve as important model systems for collective behavi
arXiv:2610.03141v1 Announce Type: new Abstract: Video multimodal large language models (MLLMs) keep climbing video question answering benchmarks, yet shuffling the frames, masking the segment that sup
arXiv:2610.02730v1 Announce Type: new Abstract: This paper proposes a new functional approximation approach to reduce Bellman error in high-dimensional dynamic programming and Reinforcement Learning p
arXiv:2610.03500v1 Announce Type: new Abstract: Deep tabular generative models are benchmarked on datasets with tens of thousands of rows; clinical datasets have hundreds. We preregistered and ran a s
arXiv:2610.03387v1 Announce Type: new Abstract: Typed decision models return choices or distributions over answer options supplied at request time. Accuracy with complete options does not establish wh
arXiv:2610.03130v1 Announce Type: cross Abstract: Biological literature retrieval systems are often developed and evaluated using broad biomedical corpora and general-purpose search tasks. However, ma
arXiv:2610.02739v1 Announce Type: new Abstract: Agentic Text-to-SQL systems can interact with users to clarify underspecified queries before generating SQL. However, a correct execution result does no
arXiv:2610.03400v1 Announce Type: new Abstract: Reinforcement learning with verifiable rewards has substantially advanced multimodal reasoning, yet it remains fundamentally limited by ambiguous token-
arXiv:2610.03014v1 Announce Type: cross Abstract: Large language model (LLM)-based agents increasingly connect model-generated decisions to security-sensitive software capabilities such as command exe
arXiv:2610.03456v1 Announce Type: new Abstract: Drug-target affinity (DTA) prediction is widely used to prioritize candidate compounds before costly experimental screening. DTA models are often compar
arXiv:2610.03196v1 Announce Type: new Abstract: A reinforcement-learning (RL) pipeline for a legged robot is assembled from proxies. A reward stands in for intended behaviour, a curriculum gate stands
arXiv:2610.03099v1 Announce Type: cross Abstract: E-commerce videos are information-dense and frequently compared by consumers evaluating products and merchants assessing marketing strategies. However
arXiv:2610.03526v1 Announce Type: cross Abstract: Explainers for Graph Neural Networks (GNNs) are commonly evaluated by their plausibility, i.e., how well their explanations recover a predefined groun
arXiv:2610.03395v1 Announce Type: new Abstract: Long-horizon tasks with sparse rewards pose an exploration bottleneck for goal-conditioned reinforcement learning: a policy started from the initial sta
arXiv:2511.03756v2 Announce Type: replace-cross Abstract: We present a bifidelity Karhunen--Loeve expansion (KLE) surrogate model for field-valued quantities of interest (QoIs) under uncertain inputs.
arXiv:2506.05766v2 Announce Type: replace Abstract: Retrieval augmented generation (RAG) has shown great power in improving Large Language Models (LLMs). However, most existing RAG-based LLMs are dedi
arXiv:2610.02800v1 Announce Type: new Abstract: Speculative decoding accelerates autoregressive generation by using a lightweight draft to propose multiple tokens for parallel verification. However, e