4988 papers
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.
This paper introduces AutoViVQA, a large-scale automatically constructed dataset for Vietnamese Visual Question Answering, and evaluates transformer-based multimodal models alongside various automatic metrics to assess their performance and alignment with human judgment in the Vietnamese context.
The paper proposes ESAinsTOD, a unified end-to-end schema-aware instruction-tuning framework that leverages full-parameter LLM fine-tuning with instruction and schema alignment mechanisms to achieve superior performance, generalization in low-resource settings, and robustness against noise across diverse task-oriented dialog benchmarks.
The paper introduces ActiveUltraFeedback, an efficient active learning pipeline that leverages uncertainty estimates and novel selection strategies like Double Reverse Thompson Sampling to generate high-quality preference data, enabling Large Language Models to achieve superior alignment performance with as little as one-sixth of the annotated data required by static baselines.
Mousse is a novel optimizer that improves upon the Muon algorithm by integrating Shampoo's Kronecker-factored preconditioning to adaptively handle the heavy-tailed curvature of deep neural networks, thereby achieving faster training convergence with negligible computational overhead.
The paper introduces OOD-MMSafe, a benchmark revealing significant causal blindness in current Multimodal Large Language Models regarding hidden consequences, and proposes the Consequence-Aware Safety Policy Optimization (CASPO) framework to effectively mitigate these risks by shifting safety alignment from intent detection to consequence projection.
This paper introduces MUGEN, a comprehensive benchmark revealing that Large Audio-Language Models struggle with multi-audio understanding as input scaling increases, and demonstrates that combining training-free strategies like Audio-Permutational Self-Consistency with Chain-of-Thought can significantly improve performance.
This paper introduces CVS, a training-free data selection method that identifies high-quality vision-language samples by measuring how much a question alters a frozen model's assessment of answer validity, thereby improving multimodal reasoning performance while reducing computational costs.
AutoAgent is a self-evolving multi-agent framework that integrates evolving cognition, on-the-fly contextual decision-making, and elastic memory orchestration to enable autonomous agents to adaptively learn from experience and make reliable, context-aware decisions in dynamic environments without external retraining.
This paper introduces RbtAct, a framework that leverages peer review rebuttals as implicit supervision to train large language models to generate more actionable and specific review feedback through a novel perspective-conditioned task and a new dataset called RMR-75K.
This paper introduces EXPLORE-Bench, a benchmark derived from real first-person videos to evaluate the ability of multimodal large language models to perform long-horizon egocentric scene prediction, revealing significant performance gaps compared to humans and demonstrating that stepwise reasoning offers partial improvements at a computational cost.
The paper proposes "Ego," an efficient personalization method for vision-language models that extracts visual tokens representing target concepts via internal attention mechanisms to serve as memory, enabling strong performance across single-concept, multi-concept, and video personalization tasks without requiring additional training stages or external modules.