D-MOD: Diffusion mode estimation for accelerated MRI
Diffusion models are powerful priors for accelerated MRI reconstruction, but most current methods are posterior samplers, which return a random draw from the posterior. A reproducible and…
nlp
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技術文書・実験ノート検索
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5 / 25Coupled Guidance for Flow Matching
Guidance methods are essential tools for steering the generation process in diffusion models, improving the generalizability of stochastic robotic controllers and enabling high-quality controlled…
nlp
Learning Calibrated Confidence for Multi-Agent Collaboration
Complex information seeking on the Web often requires collecting evidence from multiple documents and reasoning across them, as in multi-hop question answering. Multi-agent systems address such tasks…
reinforcement-learning
TokenRepel: Generating Diverse Image Sets Without Compromising Quality
We present an inference-time approach that enhances the visual diversity of image sets generated by text-to-image models, without compromising the average image quality. Our method leverages the…
nlp
GROME: GROunding Factorized MEmory into Video World Models
Long-horizon consistency is essential for video world models, yet it becomes difficult to maintain once observed content falls outside the context window. Existing memory mechanisms either retrieve…
nlp
Finding Is Not Returning, Returning Is Not Reconstructing: A Multi-Dimensional Evaluation of Agent Social Re-entry
As Large Language Model (LLM) agents increasingly participate in persistent social environments, understanding their long-term social interaction capabilities becomes increasingly important. While…
nlp
Improving the Intrinsic Interpretability of Differentiable Decision Trees through Euclidean Feature-Concentration Regularisation
Differentiable decision trees (DDTs) support end-to-end optimisation and explicit decision paths, but dense oblique splits can obscure the role of individual input features. We propose the…
nlp
TabCUQ: Context-Uncertainty Quantification for Tabular Foundation Models
Tabular foundation models use in-context learning to estimate conditional expected outcomes and average treatment effects for specific groups of individuals. Using these estimates to guide scientific…
nlp
DEG: Diffusion-Enhanced GFlowNet for Solving Vehicle Routing Problems
Traditional neural solvers for solving vehicle routing problems (VRPs) often suffer from limited solution diversity, motivating the development of Generative Flow Network (GFlowNet)-based models.…
nlp
IMRouter: Imbalance-aware Temporal Pattern Routing for LLM-based Time Series Forecasting
Accurate time series forecasting (TSF) is essential for Web applications, such as traffic monitoring and online resource management. Recent advances in leveraging pre-trained large language models…
nlp
Beyond LLM Wrappers: Context Engineering and the Three-Engine Architecture for Reliable Financial AI
Financial AI systems must support decision-bearing questions involving payments, collections, cash flow, and compliance while ensuring numerical accuracy, traceability, and auditability. Large…
nlp
Where Position Matters: Profiling-Guided Layer-Wise RoPE Allocation in Transformers
Rotary Position Embedding (RoPE) typically uses uniform coverage across Transformer layers despite heterogeneous positional requirements. Phase-off interventions reveal non-uniform, model-specific…
deep-learning
Optimization-based lane-changing trajectory planning in mixed traffic with embedded input convex neural networks
Lane-changing (LC) maneuvers are highly challenging vehicle interactions, with substantial implications for traffic safety and efficiency. Developing effective LC strategies for connected and…
deep-learning
ActiveAlign: Aligning Egocentric Manipulation via Active Perception
Real-to-Sim-to-Real (Real2Sim2Real) enables structured world representations for planning, interpretability, and safe execution. Most existing approaches rely on passive perception, which creates…
nlp
Same Luck, Different Choice: Twin-World Credit Assignment for Agentic Reinforcement Learning
Agentic reinforcement learning rewards a complete episode, yet the policy needs credit for every decision inside it. Counterfactual branching estimates this credit by re-running the episode after an…
nlp
Inside-JEPA: From Prediction to Relational Grounding in Latent World Models
Joint-embedding predictive architectures (JEPAs) learn world models in latent space, enabling efficient planning but leaving their states opaque. We introduce Inside-JEPA, which places relational…
deep-learning
Chunk Recomputation under Real Document Revisions: Measuring Update Cost and Retrieval Quality of Fixed, Recursive, and Content-Defined Chunking
Chunking strategies for retrieval-augmented generation are almost always evaluated on a static snapshot, yet the documents underlying a knowledge base continue to change. Because chunks are embedded…
nlp
Evaluating Type-1 Diabetes Digital Twins by Decision Quality
A digital twin (DT) for type 1 diabetes (T1D) can predict how an individual's blood glucose will respond to different insulin treatments without actually trying them. For each treatment, downstream…
nlp
Decision Making and Ride Comfort Evaluation Based on Lane-Change Prediction for Autonomous Vehicles
Predicting surrounding vehicles' lane-change intentions enables autonomous vehicles to anticipate potential hazards and respond proactively rather than relying solely on reactive maneuvers. This…
nlp
Revealing Hidden Failure Modes in Chest X-ray Classification via Spectral Domain Analysis
Deep learning models for chest X-ray anomaly detection remain vulnerable to subtle distributional shifts (e.g., acquisition technique, patient-related factors, and preprocessing). Traditional error…
nlp
Region-Aware Multi-View Inspection Using Deep Learning and Domain-Informed Decision Fusion
Automated optical inspection of precision components with tiny surface defects remains challenging when different regions of the same component exhibit different defect characteristics and,…
nlp
Dynamic Dual-Feedback Conformal Inference for Time Series Forecasting
Quantifying uncertainty in time series forecasting is particularly demanding because sequential data exhibit temporal dependence and are prone to distributional changes. Conformal inference has…
nlp
qdrant — Qdrant - High-performance, massive-scale Vector Database and Vector Search Engine for the next generation of AI. Also available in the cloud https://cloud.qdrant.io/
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AIアプリケーションを製品化するためのプラットフォーム
Tracing the Influence of Retrieved Literature on Scientific Ideation in LLMs
Large language models (LLMs) are increasingly used to generate research ideas from scientific literature. Existing systems combine literature retrieval with search, generation, and refinement, making…
nlp