11817 papers
This paper proposes a cost-effective approach to detecting bias in financial language models by leveraging cross-model patterns to identify bias-revealing inputs early, demonstrating that up to 73% of a model's biased behaviors can be uncovered using only 20% of the input pairs when guided by another model's outputs.
SAIL is a test-time scaling framework that enhances one-shot robot imitation learning by reframing trajectory generation as an iterative refinement process guided by Monte Carlo Tree Search, an automated retrieval archive, and a vision-language model-based scoring mechanism, thereby significantly improving success rates across diverse manipulation tasks.
The paper proposes SCL-GNN, a novel framework that enhances the generalization of Graph Neural Networks on both IID and OOD graphs by utilizing the Hilbert-Schmidt Independence Criterion to identify and mitigate spurious correlations through an efficient bi-level optimization strategy.
This study utilizes a massive 172-billion-token evaluation across diverse models, context lengths, and hardware to reveal that while model selection is the primary determinant of accuracy, hallucination rates in document Q&A rise significantly with context length and vary non-linearly with temperature, highlighting that grounding ability and fabrication resistance are distinct capabilities.
The paper proposes AdaCultureSafe, a framework that addresses the lack of correlation between cultural safety and knowledge in Large Language Models by constructing a novel dataset of culturally grounded queries and introducing a knowledge-integrated method to significantly enhance adaptive cultural safety.
The paper proposes TA-RNN-Medical-Hybrid, a time-aware and interpretable deep learning framework that integrates continuous-time encoding, SNOMED-based disease representations, and a hierarchical dual-level attention mechanism to accurately predict ICU mortality risk while providing clinically meaningful explanations.
This paper evaluates LLM-based grant proposal reviews using structured perturbations on six quality axes, finding that a section-by-section analysis approach outperforms other architectures but that current models still struggle with clarity detection and holistic assessment, suggesting they are best suited as supplementary tools rather than replacements for human reviewers.
This paper presents BladeChain, a blockchain-based system that integrates multi-stakeholder endorsement, automated scheduling, and AI model provenance to provide immutable, auditable traceability for aircraft engine blade inspections across the entire component life cycle.
This paper reveals that Sharpness-Aware Minimization (SAM) exhibits depth-dependent implicit biases in linear diagonal networks, where -SAM's convergence becomes initialization-sensitive and unstable at depth , while -SAM displays "sequential feature amplification" that prioritizes minor features early in training, demonstrating that infinite-time implicit bias analyses fail to capture SAM's critical finite-time dynamics.
This paper systematically reviews recent advancements in Multimodal Mathematical Reasoning by proposing a unified Perception-Alignment-Reasoning paradigm, categorizing existing approaches around four fundamental questions regarding information extraction, representation, reasoning, and evaluation, while outlining future research challenges.
This paper proposes Graph-Instructed Neural Networks (GINNs) as a robust and scalable alternative to classical reduced order methods for efficiently simulating parametric partial differential equations with varying boundary conditions by learning the direct mapping between domain descriptions and PDE solutions.
This paper proposes a retrieval-augmented framework for text-to-CT generation that leverages a 3D vision-language encoder to retrieve semantically related clinical cases and their anatomical annotations as structural proxies, thereby enhancing image fidelity and spatial controllability in a realistic inference setting without requiring ground-truth annotations.
This paper introduces a concept-guided fine-tuning framework that enhances Vision Transformer robustness against distribution shifts by automatically generating and aligning model attention with fine-grained semantic concepts rather than spurious background correlations.
This paper presents a large-scale comparative study using the Epic ReduAct dataset and over 3,000 human participants to demonstrate that while humans rely on sparse, semantically critical cues for egocentric action recognition, state-of-the-art AI models degrade more gradually by depending on contextual and low-level features, revealing fundamental divergences in how humans and machines process spatial and spatiotemporal information.
CORE-Acu is a neuro-symbolic framework for acupuncture clinical decision support that integrates structured reasoning traces, a knowledge graph-based safety verification system, and a specialized loss function to ensure interpretable, hallucination-free, and strictly safe treatment recommendations, outperforming standard LLMs with zero observed safety violations.
This paper presents a neurosymbolic collaboration between an LLM-powered agent, symbolic computation tools, and human researchers that successfully discovered and formally verified a new tight lower bound on the imbalance of Latin squares for the case , demonstrating the potential of AI-human partnerships in pure mathematical discovery.
EndoSERV is a novel vision-based navigation system for endoluminal robots that overcomes challenges like tissue deformation and label scarcity by combining segment-to-structure odometry with real-to-virtual transfer learning to achieve accurate localization without requiring real-world pose labels.
SPD-RAG is a hierarchical multi-agent framework that improves scalability and answer quality for complex cross-document queries by assigning dedicated agents to process individual documents and synthesizing their outputs through a token-bounded coordinator, achieving superior performance on the LOONG benchmark with significantly reduced API costs compared to standard RAG and full-context baselines.
The paper proposes DS-DGA-GCN, an adaptive graph learning model that integrates diversity- and similarity-aware dynamic graph attention with a Network Feature Scoring system to effectively detect organized fake reviewer groups in dynamic networks, achieving state-of-the-art performance on real-world datasets.
This paper demonstrates that while wavelet features excel in binary ECG classification, a transformer-based model utilizing Koopman operator features derived from an optimized Extended Dynamic Mode Decomposition (EDMD) with a radial basis function dictionary achieves superior performance in multi-class ECG classification, outperforming both wavelet-only and hybrid approaches.