6234 papers
This paper analyzes the semantics and usage of Wikidata qualifiers to develop a refined taxonomy based on frequency and diversity metrics, aiming to improve knowledge graph querying, inference, and contributor guidance.
This paper introduces the Stability and Safety-Governed Memory (SSGM) framework to address critical risks like memory corruption, semantic drift, and privacy vulnerabilities in evolving LLM agents by decoupling memory evolution from execution through consistency verification, temporal decay modeling, and dynamic access control.
This paper presents NETHIC, an automatic text classification tool that combines scalable neural networks with hierarchical taxonomies and document embeddings to achieve significant improvements in both effectiveness and efficiency across generic and domain-specific corpora.
This paper introduces Deliberative Collective Intelligence (DCI), a structured multi-agent framework that employs typed epistemic acts and a convergent flow algorithm to significantly improve performance on complex, non-routine reasoning tasks through accountable deliberation, despite incurring high computational costs and underperforming on routine decisions.
HELM is a novel framework for hierarchical multi-label image classification in remote sensing that combines hierarchy-specific class tokens, graph-based structural encoding, and self-supervised learning to achieve state-of-the-art performance, particularly in low-label scenarios.
This paper proposes a mechanistic fairness audit framework to localize demographic bias within individual attention heads of CLIP's vision encoder, demonstrating that ablating specific heads can reduce gender bias while improving accuracy, whereas age bias appears more diffusely encoded.
DocSage is an end-to-end agentic framework that addresses the limitations of existing RAG systems in multi-document, multi-entity question answering by integrating dynamic schema discovery, error-aware structured extraction, and schema-aware relational reasoning to significantly improve cross-document evidence aggregation and accuracy.
This paper introduces the SDec-POMDP framework and the exact RS-SDA* algorithm to unify decentralized and multiagent POMDPs under a semi-decentralized approach that models communication uncertainty through time-distributed action and observation histories.
This paper presents a framework for automating the acquisition of specialized procedural agent skills by systematically mining open-source repositories to extract, standardize, and evaluate capabilities like mathematical visualization, demonstrating that such methods can significantly enhance LLM performance in autonomous workflows without requiring model retraining.
RADAR is a fully autonomous, closed-loop robotic data generation framework that leverages a four-module pipeline—combining vision-language semantic planning, graph neural network policies, automated success evaluation, and a causal state-machine reset mechanism—to overcome human-in-the-loop bottlenecks and achieve high success rates in both simulation and real-world complex manipulation tasks.
The paper proposes VisiFold, a novel framework that addresses the computational and dependency challenges of long-term traffic forecasting by introducing a temporal folding graph to consolidate temporal snapshots and a node visibility mechanism to efficiently handle large-scale spatial data, thereby significantly reducing resource consumption while outperforming existing baselines.
This research utilizes various Convolutional Neural Network architectures and Explainable AI techniques on a histopathology dataset to develop and evaluate an InceptionV3 model that achieves 94% accuracy in the automated detection of malignant ovarian lesions, aiming to improve non-invasive diagnostic procedures.
This paper investigates how artificial agents utilizing global signals can foster cooperative coordination in hybrid human-agent energy markets, demonstrating that even partial adoption improves aggregate outcomes while revealing that non-adopters may disproportionately benefit from the induced cooperation.
This paper identifies and quantifies a critical "Trusted Executor Dilemma" in high-privilege LLM agents, demonstrating through the ReadSecBench benchmark that agents systematically fail to distinguish malicious instructions embedded in documentation from legitimate guidance, leading to high rates of data exfiltration that current defenses cannot reliably detect.
This paper introduces CreativeBench, a novel benchmark for objectively evaluating machine creativity in code generation through a unified quality-novelty metric, and proposes EvoRePE, an inference-time strategy that leverages self-evolving patterns to enhance creative performance while revealing key insights into how model scaling affects different creativity types.
This paper proposes a systematic framework for operationalizing social, legal, ethical, empathetic, and cultural (SLEEC) norms into concrete, verifiable requirements for AI agents, while surveying current methods and outlining a research agenda to bridge the gap between abstract normative principles and practical implementation in high-stakes domains.
ELISA is an interpretable hybrid AI agent that bridges natural language and single-cell transcriptomic data by integrating scGPT embeddings with LLM reasoning to enable interactive, expression-grounded biological discovery, outperforming existing tools in cell type retrieval and hypothesis generation across diverse datasets.
AdaFuse is a framework that accelerates dynamic adapter inference in Large Language Models by employing a token-level pre-gating strategy to enable a single global routing decision, which is then executed via a custom fused CUDA kernel to reduce decoding latency by over 2.4x while maintaining accuracy.
The paper introduces "Mirror," a data-curation design pattern that utilizes a strictly curated 32-cell topology and a lightweight linear SVM to achieve superior speed, determinism, and detection accuracy for prompt injection screening compared to large neural models, demonstrating that strict data geometry is more critical than model scale for initial defense layers.
This paper introduces Bielik-Minitron-7B, a compressed 7.35B-parameter Polish language model created by applying structured pruning and knowledge distillation to the Bielik-11B-v3.0 model, which achieves a 33.4% parameter reduction and up to 50% inference speedup while retaining approximately 90% of the original model's performance.