arXiv AI
Jul 3
Optimizing RAG Rerankers with LLM Feedback via Reinforcement Learning
arXiv:2604. 02091v2 Announce Type: replace-cross Abstract: Rerankers play a pivotal role in refining retrieval results for Retrieval-Augmented Generation.
Retrieval-augmented generation
Retrieval pipelines, vector search, chunking and reranking: how models are grounded in a corpus instead of their weights.
arXiv AI
Jul 3
XSearch: Explainable Code Search via Concept-to-Code Alignment
arXiv:2605. 16046v2 Announce Type: replace-cross Abstract: Semantic code search has been widely adopted in both academia and industry.
arXiv Machine Learning
Jul 3
Multilayer Q-Matrix-Embedded Neural Network for Cognitive Diagnosis (M-QCDNet): Structure-Aware Deep Learning Architecture for Psychometric Interpretability
arXiv:2607. 01278v1 Announce Type: new Abstract: The research proposes a multilayer Q-matrix-embedded neural network for cognitive diagnosis (M-QCDNet), which integrates the structural interpretability of cognitive diagnostic models (CDMs) with the deep learning neural network (NN).
arXiv AI
Jul 3
Evaluating Chunking Strategies for Retrieval-Augmented Generation on Academic Texts
arXiv:2607. 01852v1 Announce Type: cross Abstract: Retrieval-Augmented Generation (RAG) systems use the question-answering capabilities of Large Language Models (LLMs) to access information outside their parameters.
arXiv AI
Jul 3
CausalSteward: An Agentic Divide-Conquer-Combine Copilot for Causal Discovery
arXiv:2607. 01936v1 Announce Type: cross Abstract: Learning causal models from high-dimensional data is a significant challenge, particularly in real-world settings where violations of core assumptions lead to causal identifiability issues.
arXiv AI
Jul 3
MKGR: Multimodal Knowledge-Graph Representation Learning for Cold-Start Protein-Protein Interaction Prediction
arXiv:2607. 01627v1 Announce Type: cross Abstract: Accurate protein-protein interaction (PPI) prediction is central to functional genomics, disease mechanism discovery, and drug development.
arXiv AI
Jul 3
SelectTSL: Prompt-Guided Selective Target Sound Localization in Complex Scenarios
arXiv:2607. 02343v1 Announce Type: cross Abstract: Humans can selectively attend to a target sound and estimate its direction in complex scenarios, whereas such selective localization remains challenging for current deep learning-based systems.
arXiv AI
Jul 3
Safety Targeted Embedding Exploit via Refinement
arXiv:2607. 01859v1 Announce Type: new Abstract: Safety training for large language models (LLMs) is conducted predominantly in English, leaving uncertain how well safety mechanisms generalize to low-resource languages and mixed-language code-switching.
arXiv AI
Jul 3
SemHash-LLM: A Multi-Granularity Semantic Hashing Framework for Document Deduplication
arXiv:2607. 01601v1 Announce Type: new Abstract: Large scale document deduplication must preserve semantic equivalence while remaining efficient over massive corpora.
arXiv AI
Jul 3
Spatial Support Matters: Geometry-Aware Graph Fusion for Rainfall Field Reconstruction
arXiv:2607. 01621v1 Announce Type: new Abstract: Fine-scale rainfall reconstruction is critical for urban flood modeling, but real rainfall sensing systems observe the field through incompatible spatial supports: gauges measure points, microwave links measure paths, and radar/satellite products measure gridded areas.
arXiv AI
Jul 3
Verifiable Knowledge Expansion through Retrieval-Grounded Formal Concept Analysis
arXiv:2607. 01773v1 Announce Type: new Abstract: Ontology construction requires deciding which objects, attributes, and structural relations should be accepted as valid knowledge.
arXiv Machine Learning
Jul 3
Towards Learning Representations of Policies in Two-Player Zero-Sum Imperfect-Information Games
arXiv:2607. 01498v1 Announce Type: new Abstract: We investigate the problem of learning useful policy representations (embeddings) in two-player zero-sum imperfect-information games.
arXiv Machine Learning
Jul 3
UniWind: Toward Unified Day-Ahead Wind Power Forecasting via Physics-Informed State Routing
arXiv:2607. 01670v1 Announce Type: new Abstract: Day-ahead wind power forecasting is essential for cost-effective power-system operation.
arXiv AI
Jul 3
BLAgent: Agentic RAG for File-Level Bug Localization
arXiv:2605. 17965v2 Announce Type: replace-cross Abstract: Bug localization remains a key bottleneck for large language model (LLM)-based software maintenance, where accurately identifying faulty code is essential for debugging, root cause analysis, triage, and automated program repair (APR).
arXiv AI
Jul 3
PreScience: A Dataset and Benchmark for Scientific Forecasting
arXiv:2602. 20459v2 Announce Type: replace Abstract: Can AI systems trained on the existing scientific record forecast the advances that will follow?
arXiv AI
Jul 3
Neuron-Aware Active Few-Shot Learning for LLMs
arXiv:2607. 02423v1 Announce Type: cross Abstract: Active Few-Shot Learning (AFSL) adapts LLMs to specialized domains by identifying the most valuable unlabeled samples for annotation and use as few-shot demonstrations, effectively reducing human annotation costs while promoting high performance.
arXiv AI
Jul 3
VisionAId: An Offline-First Multimodal Android Assistant for People with Visual Impairment, Featuring Personalized Object Retrieval
arXiv:2607. 02371v1 Announce Type: cross Abstract: Over 285 million people worldwide live with a visual impairment, for whom everyday tasks such as avoiding obstacles, locating personal belongings, recognizing familiar faces, or handling cash remain persistent obstacles to personal autonomy.
arXiv AI
Jul 3
RadiomicNet: A Hybrid Radiomics-Guided Lightweight Architecture for Interpretable Medical Image Segmentation
arXiv:2607. 02185v1 Announce Type: cross Abstract: Deep learning has achieved remarkable performance in medical image segmentation, yet it suffers from critical limitations: mathematical intractability, substantial parameter requirements, and lack of clinical interpretability.
arXiv AI
Jul 3
Assessing VLM Reliability for Medical Image Quality Evaluation Under Corruption and Bias
arXiv:2607. 01973v1 Announce Type: cross Abstract: Vision-Language Models (VLMs) are increasingly applied in medical tasks such as pathology description, report generation, and visual question answering.
arXiv AI
Jul 3
ProCal: Inference-Time Proposal Calibration for Open-Vocabulary Object Detection
arXiv:2607. 01759v1 Announce Type: cross Abstract: Open-vocabulary object detection aims to localize and classify objects beyond the fixed set of categories seen dur ing training.