Stay updated with the latest research and technology news
arXiv:2601.22534v1 Announce Type: new Abstract: Interactive computational environments can help students explore algorithmic concepts through collaborative hands-on experimentation. However, static and instructor controlled demos in...
arXiv:2601.22535v1 Announce Type: new Abstract: We construct the first (locally computable, approximately) locally list decodable codes with rate, efficiency, and error tolerance approaching the information...
arXiv:2601.22536v1 Announce Type: new Abstract: Sampling-based decoding underlies complex reasoning in large language models (LLMs), where decoding strategies critically shape model behavior. Temperature- and truncation-based...
arXiv:2601.22538v1 Announce Type: new Abstract: We study Learning to Defer for non-stationary time series with partial feedback and time-varying expert availability. At each time step,...
arXiv:2601.22539v1 Announce Type: new Abstract: Humans learn efficiently from their environment by engaging multiple interacting neural systems that support distinct yet complementary forms of control,...
arXiv:2601.22541v1 Announce Type: new Abstract: Deep learning has been proposed as an efficient alternative for the numerical approximation of PDE solutions, offering fast, iterative simulation...
arXiv:2601.22542v1 Announce Type: new Abstract: Dynamic Optimization Problems (DOPs) are challenging to address due to their complex nature, i.e., dynamic environment variation. Evolutionary Computation methods...
arXiv:2601.22543v1 Announce Type: new Abstract: Cloud-device collaborative recommendation partitions computation across the cloud and user devices: the cloud provides semantic user modeling, while the device...
arXiv:2601.22545v1 Announce Type: new Abstract: Real-time path planning in constrained environments remains a fundamental challenge for autonomous systems. Traditional classical planners, while effective under perfect...
arXiv:2601.22546v1 Announce Type: new Abstract: The recent advancements in Large Language Models (LLMs) have attracted interest in exploring their in-context learning abilities and chain-of-thought capabilities....
arXiv:2601.22547v1 Announce Type: new Abstract: Short-video platforms rely on personalized recommendation, raising concerns about filter bubbles that narrow content exposure. Auditing such phenomena at scale...
arXiv:2601.22548v1 Announce Type: new Abstract: Recent research has shown that large language models (LLM) favor own outputs when acting as judges, undermining the integrity of...
arXiv:2601.22550v1 Announce Type: new Abstract: Exoskeletons show great promise for enhancing mobility, but providing appropriate assistance remains challenging due to the complexity of human adaptation...
arXiv:2601.22551v1 Announce Type: new Abstract: We present a hybrid cross-device localization pipeline developed for the CroCoDL 2025 Challenge. Our approach integrates a shared retrieval encoder...
arXiv:2601.22554v1 Announce Type: new Abstract: Large-scale formalization projects in Lean rely on blueprints: structured dependency graphs linking informal mathematical exposition to formal declarations. While blueprints...
arXiv:2601.22556v1 Announce Type: new Abstract: Generated speech achieves human-level naturalness but escalates security risks of misuse. However, existing watermarking methods fail to reconcile fidelity with...
arXiv:2601.22557v1 Announce Type: new Abstract: Mutexes (i.e., locks) are well understood in separation logic, and can be specified in terms of either protecting an invariant...
arXiv:2601.22560v1 Announce Type: new Abstract: We study an inverse random obstacle scattering problems in $\mathbb{R}^2$ where the scatterer is formulated by a Gaussian process defined...
arXiv:2601.22561v1 Announce Type: new Abstract: This paper considers the bandit quickest change detection problem in which one stream contains a change-point that shifts its distribution...
arXiv:2601.22563v1 Announce Type: new Abstract: Efficient neural networks are essential for scaling machine learning models to real-time applications and resource-constrained environments. Fully-connected feedforward layers (FFLs)...
arXiv:2601.22567v1 Announce Type: new Abstract: Quantum $(r,\delta)$-locally recoverable codes ($(r,\delta)$-LRCs) are the quantum version of classical $(r,\delta)$-LRCs designed to recover multiple failures in large-scale distributed...
arXiv:2601.22569v1 Announce Type: new Abstract: Large language model (LLM) based agents are increasingly used to automate financial transactions, yet their reliance on contextual reasoning exposes...
arXiv:2601.22570v1 Announce Type: new Abstract: Selective prediction aims to endow predictors with a reject option, to avoid low confidence predictions. However, existing literature has primarily...
arXiv:2601.22571v1 Announce Type: new Abstract: The advancement of Large Language Model (LLM)-powered agents has enabled automated task processing through reasoning and tool invocation capabilities. However,...