6127 papers
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.
The paper proposes "Think While Watching," a memory-anchored streaming framework that enables efficient multi-turn video reasoning in multimodal large language models by preserving segment-level memory and overlapping perception with generation, thereby significantly improving accuracy on streaming benchmarks while reducing output tokens.
EnTransformer is a deep generative framework that combines the sequence modeling power of Transformers with the stochastic learning paradigm of engression to produce well-calibrated, non-parametric multivariate probabilistic forecasts that outperform existing benchmarks.
This paper systematically evaluates how nine large language models, including the latest versions, often fail to refuse processing user-supplied harmful content even when performing benign tasks, revealing a critical content-level ethical vulnerability that current safety alignment overlooks.
This paper introduces MobileKernelBench, a framework revealing that current LLMs struggle to generate efficient mobile kernels due to compilation failures and hallucinations, and proposes MoKA, a multi-agent system that significantly improves compilation success and kernel performance.
This paper introduces Fair-PaperRec, an MLP-based model that effectively mitigates demographic biases in paper recommendations by penalizing disparities through intersectional criteria and a customized fairness loss, achieving a significant increase in underrepresented group participation while simultaneously improving overall utility without compromising academic rigor.
The paper proposes ProtoSR, a prototype-based framework that leverages an instruction-tuned LLM to extract visual prototypes from free-text radiology reports, thereby injecting unstructured knowledge to significantly improve fine-grained structured report generation and achieve state-of-the-art performance on the Rad-ReStruct benchmark.
This paper introduces Effective Resistance Rewiring (ERR), a parameter-free topological correction method that iteratively optimizes graph connectivity by adding and removing edges based on global effective resistance to alleviate over-squashing in Graph Neural Networks, while demonstrating that combining this approach with normalization techniques effectively balances the trade-off between improved long-range signal propagation and oversmoothing.