Retrieval pipelines, vector search, chunking and reranking: how models are grounded in a corpus instead of their weights.
arXiv:2608.22103v1 Announce Type: new Abstract: As agents grow more capable and autonomous, their tendency to reward hack, satisfying a task's checks while violating its intent, becomes an increasing...
The paper presents a seismic acoustic impedance inversion framework that uses a conditional latent generative diffusion model. By performing inversion in latent space and incorporating a lightweight wavelet-based module, the method reduces training overhead and improves efficiency. Numerical and field experiments show high accuracy, strong generalization, and enhanced geological detail with fewer diffusion steps.
arXiv:2608.22066v1 Announce Type: cross Abstract: Attention-based multiple instance learning (ABMIL) using pathology foundation model embeddings is effective for slide-level tasks, but exhaustive inf...
arXiv:2608.21487v1 Announce Type: cross Abstract: Vision-Language Models (VLMs) exhibit strong zero-shot capabilities, making them an attractive solution for continual learning across diverse tasks....
arXiv:2608.23484v1 Announce Type: new Abstract: We present Team Semiintelligencn's solution for the ACM RecSys 2026 TalkPlayData Challenge, addressing conversational music recommendation through a mu...
arXiv:2608.21464v1 Announce Type: cross Abstract: We demonstrate that structured distortion of training data - which we term complexity induction - can induce compositional generalization in a standa...
arXiv:2601.15380v2 Announce Type: replace-cross Abstract: We generalize the attention mechanism by viewing it through the lens of Entropic Optimal Transport, revealing that standard attention corresp...
arXiv:2608.21367v1 Announce Type: cross Abstract: Protein-peptide interactions are central to cellular regulation and peptide-based drug discovery, yet existing computational methods mainly focus on...
arXiv:2608.22044v1 Announce Type: new Abstract: Modern machine learning (ML) methods are highly effective for prediction tasks, but many commonly used representations reduce complex data to fixed dim...
AI University (AI‑U) is a flexible framework that uses a fine‑tuned large language model (LLM) combined with retrieval‑augmented generation (RAG) and a reasoning synthesis model to produce style‑aligned responses from lecture videos, notes, and textbooks. In a graduate‑level finite‑element‑method (FEM) course, the authors created a pipeline to generate course‑grounded training data, fine‑tuned an open‑source LLM with Low‑Rank Adaptation (LoRA), and applied RAG‑based synthesis. Evaluation through cosine similarity, LLM‑based assessment, expert review, and user studies showed that the expert model outperformed the base model in alignment with course materials, with 86 % of test cases scoring higher and human users preferring the expert model roughly twice as often. whyItMatters":"The study demonstrates a practical method for building course‑specific learning assistants that improve alignment with instructional content, offering a template that can be extended across STEM fields."
W-RAG is a source-aware retrieval framework designed for enterprise document generation from heterogeneous knowledge bases. It uses ontology-guided retrieval, local ranking within each knowledge base, and source-level weighting to balance evidence from diverse sources. A new dataset covering multiple document types and industry domains demonstrates that W-RAG improves document coverage and generation quality compared to standard RAG pipelines.
arXiv:2608.23024v1 Announce Type: new Abstract: Counterfactual medical image generation aims to modify an existing image to reflect a hypothetical scenario in which certain characteristics of the ima...
arXiv:2608.22485v1 Announce Type: new Abstract: Current visual tokenizers in Multimodal Large Language Models (MLLMs) predominantly rely on patch-based partitioning, which causes severe semantic mixt...
The paper introduces Mapping the Concept Landscape (MCL), a framework that replaces high‑dimensional feature embeddings with explicit sample‑level graphs of entities, events, and attributes for image‑caption pairs. By aggregating these graphs into a dataset‑level graph, MCL captures the global distribution of semantic concepts and identifies rare concepts. A greedy algorithm then selects samples to maximize coverage of under‑represented concepts, achieving better pruning efficiency and providing a transparent audit trail.
arXiv:2608.03446v2 Announce Type: replace Abstract: Multilingual large language models (LLMs) have been shown to perform better on non-English classification tasks when the representations of the giv...
SketchFlow is a new generative framework for creating high‑quality vector sketches from text prompts. It uses a Gaussian Mixture Model prior in the CLIP latent space and an Optimal Transport Conditional Flow Matching model to map this prior to sketch features, which are then decoded by a Hybrid Diffusion Decoder combining 1D U‑Net and Transformer architectures. The approach achieves superior visual quality and human‑like drawing styles, and supports zero‑shot synthesis for unseen concepts and smooth semantic interpolation.
The paper explores remaining useful life (RUL) estimation using multimodal large language models (MLLMs) that are grounded through time‑series retrieval. It proposes a framework that retrieves historically similar degradation segments, combines them with the test trajectory into a visual comparison artifact, and processes this via a structured multimodal prompt. Experiments on the FD001 partition of the C‑MAPSS benchmark show that retrieval consistently improves RUL prediction, with greater benefits for larger MLLMs, while also revealing current limitations in practical prognostics and health management (PHM) settings.
arXiv:2511.05575v2 Announce Type: replace Abstract: Diffusion-based approaches have recently achieved strong results in face swapping, offering improved visual quality over traditional GAN-based meth...
arXiv:2608.23086v1 Announce Type: new Abstract: Black-box large language models need confidence scores that can separate likely-correct from likely-incorrect outputs, enabling systems to prioritize h...
ExtrinSplat is a new framework that separates geometry from semantics in 3D Gaussian Splatting scenes. It clusters Gaussians into overlapping 3D object groups and uses a Vision‑Language Model to generate lightweight textual hypotheses, creating an extrinsic index layer that handles complex polysemy. This approach reduces adaptation time from hours to minutes, cuts storage overhead by orders of magnitude, and outperforms existing embedding‑based methods on open‑vocabulary 3D object selection and semantic segmentation benchmarks.