8046 papers
This paper audits the LAION-Aesthetics Predictor to reveal how its algorithmic gaze reinforces Western, male, and imperial biases by disproportionately filtering content and prioritizing specific cultural aesthetics, ultimately urging a shift toward pluralistic evaluation methods in AI development.
This paper introduces "Single-Shot Planning," a secure architecture for Computer Use Agents that generates a complete, trusted execution graph before observing untrusted UI states to effectively mitigate prompt injection and branch steering attacks while maintaining competitive task performance.
This paper introduces BoxMind, a closed-loop AI system that transforms unstructured boxing footage into hierarchical tactical indicators and predictive gradients to generate expert-level strategic recommendations, which were validated during the 2024 Paris Olympics by contributing to the Chinese National Team's historic medal success.
This paper proposes the Multifaceted Scenario-Aware Hypergraph Learning (MSAHG) framework, which addresses the limitations of existing methods in handling mobility variations across distinct contexts by constructing scenario-specific disentangled sub-hypergraphs and employing a parameter-splitting mechanism to resolve inter-scenario conflicts, thereby significantly improving next POI recommendation performance.
DevBench is a realistic, telemetry-driven benchmark comprising 1,800 instances across six languages that evaluates LLMs on code completion tasks with a focus on ecological validity, contamination-free assessment, and detailed diagnostic insights to guide practical model selection and development.
This paper introduces MAS-Orchestra, a training-time framework that optimizes multi-agent system orchestration via function-calling reinforcement learning, alongside the MASBENCH benchmark, to demonstrate that multi-agent benefits are task-dependent and to achieve significant performance gains with over 10x efficiency on complex reasoning tasks.
This paper introduces the Determinism-Faithfulness Assurance Harness (DFAH), a framework and set of financial benchmarks demonstrating that decision determinism and task accuracy in LLM agents are uncorrelated, thereby necessitating independent measurement to ensure reliable regulatory audit replay in financial services.
This paper proposes a novel data-rate-aware continuous-flow architecture for CNN inference on FPGAs that mitigates hardware underutilization caused by data reduction in pooling and strided convolution layers by interleaving signals and sharing resources, thereby enabling the high-throughput implementation of complex models like MobileNet on a single device.
MeanCache is a training-free framework that accelerates Flow Matching inference by replacing instantaneous velocity caching with an average-velocity approach using cached Jacobian-vector products and a trajectory-stability scheduling strategy, achieving significant speedups (up to 4.56X) while maintaining high generation quality across models like FLUX.1 and HunyuanVideo.
This paper introduces BioAgent Bench, a comprehensive evaluation suite and dataset for assessing AI agents in bioinformatics, which reveals that while frontier models can reliably construct multi-step pipelines, they lack robustness against perturbations and may be unsuitable for privacy-sensitive applications compared to open-weight alternatives.
The paper introduces RedSage, an open-source, locally deployable cybersecurity LLM trained on a massive curated dataset and agentic augmentation pipeline, which achieves state-of-the-art performance on specialized cybersecurity benchmarks while also improving general reasoning capabilities.
This paper introduces R2M, a novel lightweight RLHF framework that mitigates reward overoptimization by leveraging real-time policy hidden states to dynamically align the reward model with the policy's evolving distribution, rather than relying solely on static semantic representations.
This paper proposes a Bitcoin price prediction model using Combinatorial Fusion Analysis (CFA) to integrate diverse machine learning models via rank-score characteristics and weighted combinations, achieving a superior Mean Absolute Percentage Error (MAPE) of 0.19% that outperforms individual models and existing prediction methods.
This paper evaluates the impact of LLM-based news sentiment analysis on stock price prediction, demonstrating that DeBERTa outperforms other models and that an ensemble approach achieves 80% accuracy, while sentiment features provide modest improvements to various time-series forecasting architectures.
This paper introduces Adam-Aware In-Run Data Shapley, a novel method that overcomes the limitations of SGD-based attribution in adaptive optimizers by deriving a closed-form approximation and a Linearized Ghost Approximation to achieve near-perfect fidelity in data contribution estimation while maintaining high training efficiency.
This paper investigates whether Schwartz higher-order values improve sentence-level human value detection, finding that while hierarchical gating offers limited benefits, calibration techniques and hybrid ensembles significantly boost performance, suggesting the value hierarchy is more effective as an inductive bias than a rigid routing mechanism.
This paper introduces T2T (Thickening-to-Thinning), a dynamic reward shaping framework inspired by human learning dynamics that enhances LLM reasoning by encouraging longer, exploratory trajectories on incorrect attempts and penalizing length upon success, thereby outperforming standard baselines on mathematical benchmarks.
This paper introduces FSTab, a framework that demonstrates how LLM-generated software exhibits predictable, recurring vulnerabilities by enabling black-box attacks based on frontend features and quantifying the consistency of these flaws across different domains and model variations.
This paper introduces a scalable semantic search system for a corpus of 9.2 million mathematical theorems, demonstrating that representing theorems with natural-language descriptions significantly improves retrieval accuracy for both specific theorems and entire papers compared to existing baselines.
This paper introduces LMMRec, a model-agnostic framework that leverages large language models and chain-of-thought prompting to extract fine-grained user and item motivations from heterogeneous text data, effectively aligning them with interaction signals to significantly improve multimodal recommendation performance.