3839 papers
This paper introduces Adaptive Activation Cancellation (AAC), a real-time, training-free inference framework that mitigates hallucinations in large language models by identifying and suppressing hallucination-associated neural activations as structured interference, thereby improving factual accuracy across multiple model scales without degrading general capabilities or fluency.
Delta-K is a training-free, plug-and-play inference framework that resolves concept omission in multi-instance image generation by extracting and injecting differential keys into the shared cross-attention Key space to establish coherent semantic representations without requiring architectural modifications or additional training.
This research proposes a novel multilingual password strength meter that leverages AI-generated datasets (specifically ChatGPT) and Jaro similarity-based matching to outperform traditional models like PassGAN, demonstrating that incorporating non-English training data significantly enhances detection accuracy for language-specific vulnerabilities, particularly in the Indian context.
This paper establishes that a continuous-time diffusion approximation of policy gradient for stochastic bandits achieves logarithmic regret with a learning rate of , while demonstrating that a significantly smaller learning rate of is necessary to avoid linear regret in specific instances.
This paper proposes a real-time, deformation-aware framework that integrates robotic ultrasound with a lightweight USCORUNet network to dynamically update static intraoperative CBCT scans, thereby enabling continuous soft-tissue monitoring and navigation refinement without repeated radiation exposure.
This paper extends the harmonic loss framework by systematically evaluating various non-Euclidean distance metrics across vision and language models, demonstrating that cosine-based variants offer superior trade-offs in accuracy, interpretability, and sustainability compared to traditional cross-entropy and Euclidean approaches.
This paper proposes a variational quantum sensing framework that utilizes a quantum circuit-optimized probe to learn environmental features from radio-frequency signals, demonstrating through ray-tracing simulations that this approach enables robust localization with no deployment channel measurements and superior sensitivity to weak or obstructed signals compared to classical baselines.
This paper demonstrates that a natively spiking neuromorphic algorithm for solving partial differential equations possesses intrinsic fault tolerance, maintaining accuracy even when up to 32% of neurons and 90% of spikes are dropped, with this robustness being tunable via structural hyperparameters.
This paper introduces DUCTILE, an agentic LLM orchestration framework that separates adaptive decision-making from deterministic tool execution to automate engineering analysis in product development, successfully handling input deviations in an aerospace case study while highlighting the emerging tension between task automation and the creation of exhausting supervisory roles.
This paper proposes a unified cross-view contrastive learning framework that aligns global imaging and local ROI-graph representations to enhance brain disorder classification performance and reveal their complementary discriminative patterns, as validated on the ADHD-200 and ABIDE datasets.
This paper introduces ADMM-PnP with a novel AC-DC denoiser that resolves manifold mismatch and establishes convergence guarantees for score-based generative models within the ADMM framework, thereby improving solution quality across various inverse problems.
This paper presents a human-centred system design that leverages conversational AI and function-calling capabilities to enable natural language querying and visual-spatial exploration of nearly 1.7 million digitised natural history specimen records at the Australian Museum, overcoming the limitations of traditional keyword-based search tools.
This study demonstrates that quantum entanglement serves as a functional resource in competitive reinforcement learning, enabling hybrid quantum-classical agents trained on the game Pong to consistently outperform separable quantum circuits and match or exceed classical baselines by learning structurally distinct features that better model dynamic agent interactions.
This paper introduces HyMEM, a hybrid self-evolving structured memory system that combines discrete symbolic nodes with continuous embeddings in a graph format to significantly enhance the performance of open-source GUI agents, enabling them to match or surpass strong closed-source models on complex, long-horizon tasks.
This paper introduces Simulation-in-the-Reasoning (SiR), a conceptual framework that enhances Large Language Models for autonomous transportation by embedding domain-specific simulators directly into the reasoning loop to transform hypothetical narratives into falsifiable, empirically grounded workflows.
This paper introduces RINoBench, the first comprehensive benchmark for evaluating automated research idea novelty judgments, which reveals that while large language models can generate reasoning similar to human experts, they still struggle to produce accurate novelty assessments that align with human gold standards.
NasoVoce is a nose-mounted speech interface that fuses acoustic and vibration signals to enable robust, discreet, and always-available voice interaction for AI, effectively overcoming the limitations of existing silent and whispered speech recognition methods.
PC-Diffuser is a safety augmentation framework that integrates a certifiable, path-consistent capsule barrier function directly into the denoising loop of diffusion-based trajectory planners to ensure forward invariance and dynamically feasible motion while preserving the learned path geometry.
This paper presents the first systematic comparison of fairness between reasoning and non-reasoning rerankers, finding that reasoning models neither improve nor harm fairness compared to traditional approaches, as they largely preserve the fairness characteristics of their input rankings.
This paper introduces Concept-Gated Visual Distillation (CGVD), a training-free, model-agnostic inference framework that overcomes the "Precision-Reasoning Gap" in Vision-Language-Action models by parsing instructions to identify distractors and using Fourier-based inpainting to generate clean observations, thereby significantly improving robotic manipulation success rates in highly cluttered environments.