2385 papers
This paper proposes and validates "Telogenesis," an endogenous attention mechanism that generates adaptive priorities from internal epistemic gaps (ignorance, surprise, and staleness) without external rewards, demonstrating superior coverage and detection latency compared to fixed strategies while spontaneously recovering latent environmental volatility structures.
GenePlan is a novel framework that leverages large language models to evolve domain-dependent generalized planners in Python, achieving state-of-the-art performance on PDDL benchmarks while significantly outperforming other LLM-based approaches in both accuracy and efficiency.
This paper argues that human-AI interactions generate a distinct "Third Entity" characterized by "vibe-creation" and "asymmetric emergence," fundamentally transforming our understanding of cognition, tacit knowledge, and education beyond traditional metaphors of tool use or collaboration.
This paper introduces temporal-conditioned normalizing flows (tcNF), a novel autoregressive framework that enhances multivariate time series anomaly detection by effectively modeling temporal dependencies and uncertainty to identify low-probability events with improved accuracy and robustness.
The paper proposes EvoPrompt, a novel framework that achieves stable, knowledge-preserving adaptation of vision-language models in few-shot scenarios by employing a modality-shared projector and an evolutionary training strategy that decouples prompt updates into directional and magnitude components to prevent catastrophic forgetting.
This paper introduces Efficient Draft Adaptation (EDA), a parameter- and data-efficient framework that restores speculative decoding performance on fine-tuned target models through a decoupled architecture, data regeneration strategy, and sample selection mechanism, achieving superior acceptance lengths with significantly reduced training costs compared to full retraining.
This study proposes and evaluates a novel methodology for enhancing fake news debunking by using Large Language Models to generate personalized messages tailored to Big Five personality traits, demonstrating that such targeted approaches generally increase persuasiveness while highlighting both the potential and ethical implications of automated, personality-driven disinformation correction.
This paper demonstrates that Mamba-2's state space duality can be implemented entirely using standard XLA primitives without custom kernels, achieving portable, host-synchronization-free autoregressive caching with high performance across CPU, NVIDIA GPU, and Google Cloud TPU hardware.
The paper introduces Routing without Forgetting (RwF), a transformer architecture that addresses Online Continual Learning by replacing iterative gradient-based specialization with dynamic, single-step associative retrieval of input-conditioned prompts via energy-based layers, thereby achieving superior performance on class-incremental benchmarks without explicit task identifiers.
This paper proposes a biologically plausible, local learning framework for time-continuous neuronal networks that approximates backpropagation through time by deriving real-time error dynamics from a prospective energy function, thereby unifying and extending the Generalized Latent Equilibrium model to enable spatiotemporal credit assignment consistent with brain circuitry.
This paper proposes "Context Engineering" as a foundational discipline that, alongside Intent and Specification Engineering, forms a maturity model for scaling autonomous multi-agent systems by shifting focus from individual prompts to the comprehensive management of an agent's informational environment, goals, and policy constraints.
This paper proposes a vision-language grounded framework for interpretable synthetic data generation and evaluation in remote sensing, introducing the ARAS400k dataset which demonstrates that augmenting real data with synthetic images consistently outperforms real-data-only baselines in semantic segmentation and image captioning tasks.
PRECEPT is a unified test-time adaptation framework that enhances LLM agent resilience by integrating deterministic exact-match rule retrieval, conflict-aware memory with Bayesian reliability, and the Pareto-guided COMPASS prompt-evolution loop to achieve superior compositional generalization, continuous learning, and robustness against knowledge drift and adversarial inputs.
This paper introduces MM-tau-p, a novel benchmark featuring 12 metrics to holistically evaluate the robustness and efficiency of multi-modal LLM agents in dual-control settings, specifically assessing their performance with and without user persona adaptation across domains like telecom and retail.
This paper introduces MiniAppBench, a comprehensive benchmark derived from real-world data to evaluate LLMs' ability to generate principle-driven interactive HTML applications, alongside MiniAppEval, an agentic framework that uses browser automation to assess these applications across intention, static, and dynamic dimensions.
KV-Lock is a training-free framework for DiT-based video diffusion models that dynamically adjusts background key-value locking and classifier-free guidance scales based on hallucination detection to simultaneously enhance foreground quality and maintain background consistency.
This paper introduces an open-source framework for Graph Neural Network-based Time Series Anomaly Detection to enable reproducible experimentation and critical evaluation, demonstrating that GNNs enhance both detection performance and interpretability while highlighting the need for standardized metrics and thresholding strategies.
This paper introduces the Omni Parsing framework and the Logics-Parsing-Omni model, which unify document, image, and audio-visual parsing through a three-level hierarchical paradigm of holistic detection, fine-grained recognition, and multi-level interpreting to transform unstructured multimodal signals into traceable, evidence-based structured knowledge.
The paper introduces EsoLang-Bench, a novel benchmark utilizing esoteric programming languages to expose the limitations of large language models' genuine reasoning capabilities by revealing a dramatic performance gap between their high scores on standard benchmarks and near-zero accuracy on tasks requiring the acquisition of new languages through documentation and experimentation rather than memorization.
This study demonstrates that a custom Transformer architecture outperforms both traditional machine learning models and zero-shot generative LLMs in automatically classifying cardiac risk from large-context, unstructured Dutch electronic health records, offering a robust alternative to manual administrative coding for geriatric cardiovascular risk management.