9642 papers
Dr. Seg challenges the assumption that language-based GRPO training transfers seamlessly to visual perception by introducing a plug-and-play framework with a Look-to-Confirm mechanism and Distribution-Ranked Reward module that significantly enhances performance in complex visual scenarios without requiring architectural modifications.
This paper proposes AlignVAR, a globally consistent visual autoregressive framework for image super-resolution that overcomes locality bias and error accumulation through Spatial Consistency Autoregression and Hierarchical Consistency Constraint, achieving superior structural coherence and perceptual fidelity with significantly faster inference and fewer parameters than diffusion-based methods.
The paper introduces SOLACE, a fully unsupervised post-training framework for text-to-image generation that leverages intrinsic self-confidence signals derived from noise recovery to optimize model performance without external reward models or annotated datasets.
AutoSkill is a model-agnostic, experience-driven lifelong learning framework that enables LLM agents to automatically derive, evolve, and dynamically reuse personalized skills from interaction traces without retraining, thereby transforming ephemeral user experiences into explicit, composable capabilities for personalized digital surrogates.
This paper demonstrates that interpreter persistence is a critical training-time semantic that significantly impacts agent efficiency and stability, revealing that misalignment between training data and deployment runtime causes substantial token waste or error rates despite achieving comparable solution quality.
ToolRLA is a three-stage post-training pipeline that employs a novel multiplicative reward decomposition across four dimensions to significantly enhance the accuracy, compliance, and task completion rates of tool-integrated agents in high-stakes financial advisory scenarios.
FreeAct is a novel quantization framework that improves Large Language Model performance by relaxing rigid one-to-one transformation constraints to dynamically allocate token-specific activation transformations, thereby addressing the distinct distribution patterns in diffusion and multimodal models.
This paper presents the first systematic audit revealing that widely used "shadow APIs," which claim to provide access to restricted frontier LLMs, frequently employ deceptive practices such as model substitution and safety manipulation, thereby compromising the reliability, reproducibility, and validity of downstream applications and academic research.
MatRIS is a novel, computationally efficient invariant machine learning interatomic potential that utilizes a linear-complexity separable attention mechanism for three-body interactions to achieve accuracy comparable to state-of-the-art equivariant models at a significantly lower training cost.
Kiwi-Edit addresses the limitations of instruction-based video editing and the scarcity of reference-guided training data by introducing a scalable data generation pipeline to create the RefVIE dataset and a unified architecture that synergizes learnable queries with latent visual features to achieve state-of-the-art controllable video editing.
The paper introduces IoUCert, a novel formal verification framework that overcomes the challenges of non-linear coordinate transformations and IoU metrics to enable the first robustness verification of realistic, anchor-based object detection models like SSD and YOLO.
AOI is a secure, trainable multi-agent framework that automates Site Reliability Engineering by leveraging Group Relative Policy Optimization and a read-write separated architecture to distill expert knowledge into local models and convert failed trajectories into corrective signals, achieving state-of-the-art performance on the AIOpsLab benchmark while ensuring data privacy and safe execution.
RADAR is a scalable neural framework that enhances vehicle routing problem solvers for asymmetric scenarios by leveraging Singular Value Decomposition to encode static distance asymmetry and Sinkhorn normalization to model dynamic interaction asymmetry, thereby achieving superior generalization and performance on both synthetic and real-world benchmarks.
This paper proposes a mathematical framework based on a set-valued set function within a Template-Based and Modular Cognitive model to formally explain the nonlinear dynamics of grammatical gender shifting and noun-to-noun derivation, using empirical data from Riffian to challenge conventional views on word formation.
This study evaluates eleven general-purpose and education-specific AI tools, finding that they achieve only moderate accuracy (63%) in classifying the cognitive demand of mathematical tasks due to a systematic bias toward middle-level categories and a tendency to prioritize surface textual features over underlying cognitive processes, thereby limiting their immediate reliability for teacher planning without improved prompt engineering or tool development.
This paper proposes a DMD-augmented Unpaired Neural Schrödinger Bridge framework that enhances Ultra-Low Field (64 mT) MRI image quality by leveraging diffusion-guided distribution matching and anatomical structure preservation to achieve superior realism and structural fidelity in translating unpaired 64 mT scans to 3 T quality.
This paper proposes a privacy-preserving, offline Central Bank Digital Currency (CBDC) payment model for resource-constrained IoT devices that integrates Secure Elements, lightweight Zero-Knowledge Proofs, and intermittent synchronization to enable secure, cash-like transactions while preventing double-spending and ensuring AML/CFT compliance without continuous internet connectivity.
This paper proposes a set of empirical metrics to track the extent and consequences of AI R&D automation, aiming to address data gaps regarding its impact on capability acceleration, safety progress, and oversight capabilities to guide better decision-making by companies and governments.
This paper presents Bielik-Q2-Sharp, a systematic evaluation of six 2-bit quantization methods on a Polish 11B language model that identifies QuIP# as a high-performing variant comparable to the IQ2_XXS baseline while revealing a critical dissociation between log-likelihood preservation and autoregressive generation in rotation-based methods.
This paper introduces FinRetrieval, a benchmark evaluating AI agents' ability to retrieve specific financial data from structured databases, revealing that tool availability (particularly structured APIs) is the primary driver of performance while highlighting nuanced impacts of reasoning modes and geographic naming conventions.