4988 papers
World2Mind is a training-free toolkit that enhances foundation models' allocentric spatial reasoning by constructing structured cognitive maps and an Allocentric-Spatial Tree, enabling significant performance gains and even allowing text-only models to achieve complex 3D spatial reasoning comparable to advanced multimodal systems.
This paper demonstrates that simulation-based inference (SBI) is a viable and potentially superior alternative to traditional empirical tuning for determining neutrino interaction model parameters, as it successfully reproduces and slightly improves upon the MicroBooNE collaboration's tuned GENIE configuration while also approximating the NuWro simulation.
This paper introduces the concept of "opaque serial depth" to formally quantify the limits of internal reasoning in language models without externalized chain-of-thought, providing computational bounds for various architectures and an open-source tool to analyze the potential for non-externalized reasoning.
This paper proposes and validates a hybrid quantum-classical framework that integrates a Long Short-Term Memory (LSTM) network with a Quantum Circuit Born Machine (QCBM) to significantly improve financial volatility forecasting accuracy on high-frequency stock market data compared to traditional classical models.
This paper proposes ACP-SL, an adaptive channel pruning scheme for Split Learning that utilizes a label-aware channel importance scoring module to compress smashed data, thereby significantly reducing communication overhead while improving test accuracy and training efficiency.
The paper introduces MITRA, an on-premise Retrieval-Augmented Generation (RAG) system designed to enhance knowledge retrieval in large-scale physics collaborations like CMS by employing a novel automated pipeline for document processing and a two-tiered vector database architecture to accurately answer context-aware questions while ensuring data privacy.
This paper introduces the smoothing pseudo-projector, a lightweight, multigrid-inspired module that corrects hidden representations in transformer-based models to suppress noise from label-irrelevant inputs, thereby improving training dynamics and robustness without altering the core architecture.
This paper introduces MA-EgoQA, a novel benchmark and dataset featuring 1,700 questions across five categories designed to evaluate the ability of AI models to understand and reason over multiple long-horizon egocentric videos from embodied agents, alongside a proposed baseline model named EgoMAS that highlights current limitations in system-level multi-agent understanding.
This paper introduces SCENEBench, a comprehensive benchmark suite designed to evaluate Large Audio Language Models on critical non-speech and cross-lingual audio understanding tasks relevant to assistive and industrial applications, revealing significant performance gaps and latency challenges in current state-of-the-art models.
This paper introduces HR-GAT, a hierarchical resolution graph attention network that leverages geospatial data to predict spectrum demand with 21% higher accuracy than baseline models, effectively addressing spatial autocorrelation challenges to enable more efficient spectrum sharing and policy-making.
This paper introduces the Dynamics-Aware Policy Learning (DAPL) framework, which leverages explicit world modeling to learn contact-induced dynamics, enabling robots to achieve robust extrinsic dexterity in cluttered environments without hand-crafted heuristics and significantly outperforming existing manipulation methods in both simulation and real-world deployments.
This paper proposes Local Classifier Alignment (LCA), a novel loss function that resolves the mismatch between task-specific classifiers and adapted backbones in continual learning, thereby enhancing generalization and robustness while achieving state-of-the-art performance on standard benchmarks.
This paper proposes a lightweight, training-free framework that parameterizes prompts as actions to dynamically influence LLM multi-agent dialogue behaviors, demonstrating through experiments that this policy-based approach effectively controls conversational dynamics across various indicators.
The paper proposes MSSR, a memory-aware adaptive replay framework that estimates sample-level memory strength to dynamically schedule rehearsal intervals, effectively mitigating catastrophic forgetting while maintaining fast adaptation in continual LLM fine-tuning.
MedMASLab is a unified framework and benchmarking platform that addresses architectural fragmentation in medical multi-agent systems by introducing a standardized communication protocol, an automated zero-shot clinical reasoning evaluator, and an extensive multimodal benchmark spanning 473 diseases to reveal critical performance gaps in cross-specialty transitions.
This paper presents an AI-driven, data-driven framework that utilizes validated proxies from site license and crowdsourced data to accurately forecast spectrum demand across five major Canadian cities, thereby enabling more efficient dynamic spectrum planning and resource allocation for regulators and network operators.
The paper introduces ACADiff, an adaptive clinical-aware latent diffusion framework that synthesizes missing multimodal brain imaging data (sMRI, FDG-PET, and AV45-PET) by integrating imaging observations with GPT-4o-encoded clinical metadata, achieving superior generation quality and robust diagnostic performance even when up to 80% of modalities are missing.
This paper presents a data-driven methodology using geospatial analytics and machine learning to accurately estimate and identify the drivers of spectrum demand variations across urban regions, achieving 70% predictive accuracy to support flexible spectrum access policies for future 6G networks.
PathMem is a memory-centric multimodal framework that enhances pathology large language models by organizing structured domain knowledge into long-term memory and utilizing a Memory Transformer to dynamically activate and ground this knowledge for improved diagnostic reasoning and report generation.
The paper proposes k-MTR, a novel framework that bypasses the traditional image reconstruction step by directly learning multi-task cardiac diagnostic features from undersampled k-space data through a shared semantic manifold, thereby eliminating reconstruction artifacts and achieving competitive performance across regression, classification, and segmentation tasks.