6253 papers
This paper proposes Natural Language-Based Societies of Mind (NLSOMs), a modular framework where large multimodal neural networks communicate via natural language to solve complex AI tasks more effectively than single models, while also exploring the emerging social, economic, and structural challenges of scaling these heterogeneous societies to include billions of agents.
This study demonstrates that large language models, utilized through zero-shot prompting or as embedding generators for supervised learning, offer a flexible and data-efficient alternative to traditional numerical models for predicting travel behavior.
This paper proposes an optimal transport-based aggregation framework that efficiently combines locally trained Mixture-of-Experts models into a global estimator with a single communication step, achieving performance comparable to centralized training while significantly reducing computational and communication costs.
This paper presents an empirical study demonstrating that personalizing AI-driven hint explanations in an Intelligent Tutoring System for students with low Need for Cognition and Conscientiousness significantly increases their engagement, understanding, and learning outcomes.
This paper introduces HyWIA, a novel adaptive structured pruning method for Large Language Models that leverages an attention mechanism to hybridize fine-grained and coarse-grained weight importance assessments, thereby significantly outperforming existing approaches in accuracy retention across various benchmarks.
This paper argues that large language models serve as valuable scientific models of public languages as external social entities, defending their utility against claims of lacking linguistic insight and proposing a framework to interpret their internal mechanisms as model construals.
This study utilizes a 28-year dataset and explainable machine learning techniques, specifically a Random Forest model, to identify key toxic phytoplankton species and environmental factors driving diarrhetic shellfish poisoning in the Gulf of Trieste, thereby enhancing early warning systems for sustainable aquaculture.
This paper proposes a novel method that leverages Large Language Models to evolve interpretable control policies represented as standard Python programs, offering a transparent and verifiable alternative to black-box neural network controllers for dynamical systems.
The paper introduces EoRA, a fine-tuning-free method that utilizes eigenspace low-rank approximation and an optimized CUDA kernel to significantly recover the accuracy of compressed LLMs while offering flexible trade-offs between performance and computational overhead.
This paper proposes a generic token cleaning pipeline for supervised fine-tuning of large language models that filters out uninformative tokens based on their influence on model updates, thereby improving downstream performance by prioritizing data quality over quantity at the token level.
This paper proposes DIFU-Ada, a training-free inference time adaptation framework that enables diffusion-based neural combinatorial solvers to achieve zero-shot cross-problem and cross-scale generalization without additional training, as demonstrated by a TSP-trained model successfully solving variants like PCTSP and OP.
This paper proposes a methodology that enables natural language interaction with Piping and Instrumentation Diagrams (P&IDs) by converting DEXPI data into labeled property graphs and integrating them with Large Language Models via graph-based retrieval augmented generation to enhance data retrieval, reduce hallucinations, and support engineering tasks.
This paper introduces SCAM, the largest and most diverse real-world dataset of typographic attack images, to evaluate and demonstrate the significant vulnerability of state-of-the-art multimodal foundation models to such attacks while providing empirical insights into how model architecture and training data influence robustness.
This paper proposes a novel data-driven framework using offline reinforcement learning and survival analysis to estimate optimal pricing and inventory control policies in sequential environments with censored and dependent demand, overcoming challenges like missing profit information and non-stationarity by approximating the problem as a high-order Markov decision process.
The paper proposes SwitchMT, a novel methodology for scalable multi-task learning in resource-constrained autonomous agents that combines a Deep Spiking Q-Network with active dendrites and an adaptive task-switching policy to effectively mitigate task interference and outperform state-of-the-art methods in Atari games.
This systematic review introduces the emerging interdisciplinary field of LLM Psychometrics, which applies psychometric theories and instruments to develop comprehensive evaluation frameworks for measuring human-like psychological constructs in large language models, ultimately guiding the creation of more robust, human-centered AI systems.
This paper introduces REI-Bench, the first benchmark for evaluating robot task planning under vague referring expressions, revealing that such vagueness significantly degrades performance and demonstrating that a task-oriented context cognition approach effectively mitigates this issue to improve accessibility for non-expert users.
This paper proposes a training-time approach that fine-tunes Large Language Models using instruction-tuning data augmented with pseudo-code representations of natural language instructions, resulting in significant improvements in instruction-following reliability and overall reasoning performance across multiple benchmarks.
This paper presents a data-driven survey of 14,648 studies from 2022 to early 2025, revealing that research on the limitations of large language models (LLLMs) has surged to over 30% of all LLM-related work, with reasoning, generalization, and hallucination being the most prominent areas of focus.
This paper proposes a consistency-based abductive reasoning framework that integrates predictions from multiple pre-trained models at test time to mitigate performance degradation in novel environments, achieving significant improvements in accuracy and F1-score over individual models and standard ensembles by selecting a subset of predictions that maximizes coverage while minimizing logical inconsistencies.