8790 papers
This paper presents a vision-only autonomous bronchoscopy framework utilizing hierarchical long-short agents and a world-model critic to achieve accurate, sensor-free intraoperative navigation in preclinical models, demonstrating performance comparable to expert human operators.
The paper introduces Ares, a framework that dynamically selects the optimal reasoning effort level for each step of an LLM agent's task using a lightweight router, achieving up to 52.7% reduction in token usage with minimal impact on success rates compared to static high-effort strategies.
This paper introduces Rel-MOSS, a novel relation-centric deep learning framework that addresses the critical issue of class imbalance in relational databases by employing a relation-wise gating controller and a relation-guided minority synthesizer to enhance the representation and over-sampling of minority entities, thereby significantly outperforming existing methods in entity classification tasks.
The paper proposes IMSE, a test-time adaptation method that fine-tunes only the singular values of Vision Transformer linear layers via a spectral mixture of experts and a diversity maximization loss to prevent feature collapse, achieving state-of-the-art performance with significantly fewer trainable parameters.
SWE-Fuse is a novel training framework that enhances software engineering agents by fusing issue-free trajectory learning with entropy-aware RLVR to overcome the limitations of noisy real-world issue descriptions, achieving state-of-the-art performance on the SWE-bench Verified benchmark.
This paper proposes that intelligent, human-supervised AI agents built on deep learning and large language models represent the next evolution of the scientific method, enabling researchers to manage unprecedented data complexity and scale discovery, as demonstrated by the Dr. Sai system in particle physics.
This paper introduces ELLMob, a self-aligned Large Language Model framework that leverages Fuzzy-Trace Theory to reconcile habitual patterns with event constraints, addressing the lack of event-annotated datasets and significantly improving the generation of human mobility trajectories during major societal events like typhoons, pandemics, and the Olympics.
This paper introduces PSTNet, a lightweight, physics-structured neural network with only 552 parameters that embeds atmospheric turbulence scaling laws directly into its architecture to enable accurate, real-time turbulence estimation on resource-constrained aircraft guidance systems where traditional models fail.
This paper introduces EvoStage, a novel evolutionary paradigm that leverages large language models with a stagewise, multi-agent approach and real-time feedback to overcome the limitations of black-box modeling, successfully generating algorithm designs that outperform both human experts and existing methods in complex industrial tasks like chip placement and black-box optimization.
This paper introduces HILA, a Human-In-the-Loop Multi-Agent Collaboration framework that employs Dual-Loop Policy Optimization to train agents with metacognitive policies for dynamically deferring to human experts and continuously improving their reasoning capabilities, thereby overcoming the static knowledge limitations of purely autonomous systems.
VORL-EXPLORE is a hybrid learning and planning framework for multi-robot exploration in dynamic environments that couples task allocation with motion execution via a shared navigability fidelity signal, enabling adaptive arbitration between global and reactive policies to prevent bottlenecks and ensure robust, collision-free coverage.
The paper introduces OSExpert, a computer-use agent that leverages a GUI-based depth-first search exploration algorithm to discover action primitives and self-construct a skill curriculum, thereby significantly improving performance and efficiency on complex tasks to approach human expert levels.
This paper challenges the view that true intelligence requires efficient, unified representations by arguing that human expertise relies on vast collections of specialized patterns, thereby suggesting that Artificial General Intelligence should be reconceptualized as an "archipelago of experts" composed of isolated, domain-specific modules rather than a system driven by emergent, generalizable principles.
The paper introduces \$OneMillion-Bench, a novel benchmark comprising 400 expert-curated tasks across five professional domains designed to rigorously evaluate the reliability, reasoning depth, and practical readiness of language agents in complex, real-world scenarios that existing benchmarks fail to address.
The paper proposes CMMR-VLN, a vision-and-language navigation framework that enhances large language model agents with structured multimodal memory retrieval and reflection-based updates to selectively leverage prior experiences, significantly improving performance in long-horizon and unfamiliar scenarios compared to existing methods.
This paper introduces Drag-Aware Aerodynamic Manipulability (DAAM), a geometric framework for control allocation in redundant multirotors that utilizes a Riemannian metric to explicitly account for motor torque limits and aerodynamic drag, thereby generating a state-dependent manipulability volume that serves as a natural barrier function to optimize redundancy resolution while characterizing the resulting smooth manifolds and global jump discontinuities.
This paper proposes the ViSA-enhanced framework, a triple-phase collaborative architecture that leverages structured visual prompting to enable Vision-Language Models to perform direct spatial reasoning on image planes, achieving a 70.3% improvement in success rate over state-of-the-art aerial Vision-Language Navigation methods on the CityNav benchmark.
This paper introduces PIRA-Bench, a novel benchmark and the PIRF baseline framework designed to advance GUI agents from reactive instruction-following to proactive intent recommendation by evaluating their ability to anticipate user needs from noisy, continuous visual inputs.
FedMomentum is a novel federated fine-tuning framework that preserves LoRA training momentum and ensures mathematically correct aggregation by using singular value decomposition (SVD) to extract dominant update directions while retaining residual components, thereby achieving faster convergence and higher accuracy than existing methods.
This paper proposes a unified dynamic framework using "task" and "intent" lenses to reconceptualize human-AI collaboration, arguing that alignment, process structure, and outcome quality are non-linearly related and require a structural analysis beyond simple outcome metrics.