7813 papers
This paper introduces PEPA, a three-layer cognitive architecture that leverages personality traits to enable embodied agents to autonomously generate goals and sustain long-term operation in dynamic environments without relying on external task specifications.
This paper introduces a reproducible, scalable framework for evaluating multimodal models on ECG signals by decomposing reasoning into "Perception" (verified via code generation) and "Deduction" (verified via retrieval against clinical criteria) to address the limitations of existing manual or superficial evaluation methods.
This paper proposes a conformal prediction framework that ensures safe, domain-specific deployment of LLMs for medical entity extraction by adapting calibration thresholds to counteract the distinct underconfidence observed in structured FDA labels and overconfidence in free-text radiology reports, thereby achieving target coverage guarantees with manageable rejection rates across diverse clinical settings.
This technical report extends the empirical validation of the Explainability Solution Space (ESS) framework by demonstrating its domain-independent applicability and systematic adaptability to diverse governance roles and stakeholder configurations through a cross-domain evaluation involving both employee attrition and urban resource allocation systems.
This paper proposes a tractable two-layer framework combining a Hidden Markov Model for inferring rival energy states and a Deep Q-Network for decision-making to optimize 2026 Formula 1 energy strategies under partial observability, specifically addressing the "counter-harvest trap" where opponents deliberately mask their deployment signals.
HarmonyCell is an end-to-end agent framework that automates single-cell perturbation modeling by combining an LLM-driven semantic unifier to resolve metadata incompatibilities and an adaptive Monte Carlo Tree Search engine to synthesize architectures that handle distribution shifts, thereby achieving high execution success and outperforming expert baselines without manual engineering.
This paper proposes a novel LLM-driven closed-loop framework that maps natural language instructions to executable rules and semantically annotates options to enhance the data efficiency, interpretability, and cross-environment transferability of Deep Reinforcement Learning, with experimental validation showing superior performance in constraint compliance and skill reuse.
This paper presents a computationally efficient, detection-gated deep learning pipeline that achieves state-of-the-art robustness and cross-dataset generalization in glottal segmentation from high-speed videoendoscopy, enabling reliable extraction of clinical biomarkers for distinguishing healthy from pathological vocal function.
This paper proposes a robust framework combining the hybrid CoAtNet architecture with model soups ensembling to effectively classify Intangible Cultural Heritage images from the Mekong Delta, achieving state-of-the-art performance on the ICH-17 dataset by reducing variance and enhancing generalization in data-scarce, high-similarity settings.
This paper introduces a diagnostic framework demonstrating that in memory-augmented LLM agents, retrieval quality is the dominant factor influencing performance on the LoCoMo benchmark, often outweighing the impact of sophisticated write strategies and suggesting that raw chunked storage can outperform expensive, lossy alternatives.
This paper introduces an agentified assessment framework that utilizes an assessor agent to ensure reproducible and robust evaluation of logical reasoning systems, demonstrating its effectiveness by benchmarking an auto-formalization agent that achieves 86.70% accuracy on a solver-verified FOLIO dataset, significantly outperforming a chain-of-thought baseline.
This paper introduces GramCol and a motion-feature selection algorithm to generate Interpretable Motion-Attentive Maps (IMAPs) that effectively localize both motion and non-motion concepts in Video Diffusion Transformers without requiring gradient calculations or parameter updates.
This paper provides the first theoretical proof that Adam's second-moment normalization yields significantly sharper high-probability convergence guarantees ( dependence) compared to SGD ( dependence) under the classical bounded variance model, thereby explaining its empirical superiority.
This paper introduces Compositional Probe Decomposition (CPD) to demonstrate that linear disentanglement of geometric and compositional information in atomistic foundation models is primarily driven by task alignment rather than architecture, revealing a significant performance gradient where models trained on specific properties like HOMO-LUMO gaps outperform energy-trained models and exhibit symmetry-dependent information routing.
This paper demonstrates that Contamination Detection via output Distribution (CDD) is largely ineffective for small language models (70M–410M parameters) because it fails to detect verbatim memorization, whereas probability-based methods like perplexity and Min-k% Prob consistently outperform it across various benchmarks.
This paper explores the potential of SPARQL-MCP-powered intelligent agents for federated Knowledge Graph Question Answering by extending an existing benchmark to evaluate agentic capabilities in endpoint discovery, schema exploration, and query formulation across multiple data sources.
This paper introduces an open, sport-agnostic platform that standardizes player tracking data to evaluate usable space and optimal off-ball run timing across Ultimate Frisbee, basketball, and soccer, demonstrating a practical path for consistent spatial analysis in invasion sports.
This paper introduces two shortfall-aware reinforcement learning frameworks, RLOP and QLBS, which outperform traditional parametric models in reducing tail risk and hedging shortfalls for SPY and XOP options, thereby offering a more robust approach to autonomous derivatives risk management.
This paper introduces the Isotonic Layer, a novel differentiable framework that integrates piecewise linear fitting and learnable embeddings into neural architectures to enforce global monotonicity, thereby enabling granular, context-aware debiasing and improved calibration for large-scale recommendation systems.
This paper presents a transformer-based system that significantly advances ARC performance by integrating a compact task encoding, symmetry-based data augmentation, test-time LoRA adaptation, and multi-perspective decoding to enable efficient neural inference and human-level generalization from few examples.