Retrieval pipelines, vector search, chunking and reranking: how models are grounded in a corpus instead of their weights.
arXiv:2609.39623v1 Announce Type: new Abstract: The proliferation of high-fidelity generative editing models has made it possible to inject violent or sexual content into otherwise ordinary images wh...
arXiv:2609.39145v1 Announce Type: cross Abstract: Unreliable visual inputs can harm task performance and cause potential physical safety risks for vision-language-action (VLA) models. We analyze how...
arXiv:2609.39599v1 Announce Type: cross Abstract: 3D visuomotor policies provide a strong foundation for spatially precise manipulation, yet current text-to-3D policies struggle to follow unseen fine...
arXiv:2609.38705v1 Announce Type: new Abstract: Foundation models for computational pathology are usually evaluated using AUC and accuracy, while calibration is often left untested. This matters beca...
arXiv:2609.39339v1 Announce Type: cross Abstract: Matching problems are ubiquitous in data science as they enable the alignment of structured objects and distributions. While existing solvers are oft...
arXiv:2507.23559v2 Announce Type: replace-cross Abstract: Certain data are naturally modeled by networks or weighted graphs, be they biological networks or mobility networks. When there is no canonic...
arXiv:2609.38383v1 Announce Type: cross Abstract: Random exploration reveals how an environment can be traversed before a goal is specified. Can this experience support long-range planning without po...
The paper introduces OptTips, a knowledge base of 50 expert optimization modeling techniques, and OptDachshund, a multi‑agent framework that generates mathematical models and solver code from natural‑language problem descriptions. Using these tools, the authors create the EfficientOpt benchmark, comprising 561 expert‑reviewed tasks with paired reference implementations, to evaluate large language models (LLMs) on both correctness and computational efficiency. Their experiments with 11 LLMs show a consistent efficiency gap: even when LLMs produce correct solutions, the resulting programs often take longer to solve than expert‑crafted counterparts, highlighting the need to assess both accuracy and runtime performance in LLM‑based optimization modeling.
The paper introduces MEQ, a mutual feedback architecture that iteratively refines two multimodal inputs into coupled embeddings, each embedding incorporating information from the other. By continuously exchanging information between the modalities, the model converges to a fixed point that improves representation quality. Experiments on classification and visual grounding tasks show that MEQ achieves competitive or superior performance compared to concatenation-based baselines, and qualitatively enhances visual grounding when paired with complementary modalities.
Doc2LoRA introduces a method to represent scientific papers as LoRA adapters generated by a hypernetwork, enabling each point in the resulting vector space—whether a real paper or a mixture—to be interpreted by a large language model (LLM). The approach allows both search and generation: it achieves competitive search performance with models like SPECTER2 and EmbeddingGemma, while also enabling the LLM to label subfields and generate abstracts that smoothly transition between papers. The invertible transform ensures every point maps back to an LLM, making the embeddings usable for both querying and creative idea generation.
arXiv:2609.38834v1 Announce Type: cross Abstract: Contrastive learning is a successful paradigm for learning $d$-dimensional geometric representations from a collection of ``anchor--positive--negativ...
arXiv:2609.38887v1 Announce Type: cross Abstract: Real-time voice conversion (VC) systems commonly rely on pretrained speaker embeddings from automatic speaker verification (ASV) models. While effect...
The paper investigates transferring specialized task-oriented behavior to a general language model without training or distillation. It applies two training‑free heterogeneous merging techniques—Intersection‑Merge (IM) and Activate‑Prune‑Merge (APM)—to project a specialist donor into the recipient’s parameter space and interpolate backbone weights. Experiments across embedding, reranking, reward modeling, and MoE code‑specialist tasks show that both methods improve the general model, demonstrating that simple parameter‑level merging can transfer capabilities across diverse specialist roles.
arXiv:2609.39836v1 Announce Type: new Abstract: Contrastive vision-language models map visual and textual representations into a shared normalized embedding space, making cosine similarity the natura...
arXiv:2603.02561v2 Announce Type: replace-cross Abstract: Attention mechanism remains the defining operator in Transformers since it provides expressive global credit assignment, yet its quadratic co...
arXiv:2605.25765v2 Announce Type: replace-cross Abstract: Existing closed-form methods for concept unlearning in text-to-image diffusion models typically derive editing directions from fixed text emb...
The study introduces an automated system for estimating the Verbal Fluency Index (VFI) in individuals with Motor Neuron Disease (MND) by combining ASR (WhisperX) and VAD (Silero) with precise timestamping. Using a unique MND dataset, the approach outperformed traditional acoustic and self‑supervised embedding methods, achieving high predictive accuracy (R² up to 0.9 for P‑words and 0.8 for S‑words). Clinically inspired features were consistently superior, demonstrating the feasibility of automated VFI estimation for monitoring cognitive impairment in MND.
arXiv:2609.39111v1 Announce Type: new Abstract: Human intelligence relies heavily on learned intuition: recognising patterns and judging situations without explicitly unfolding every intermediate ste...
arXiv:2609.39786v1 Announce Type: new Abstract: Large language models (LLMs) are increasingly combined with knowledge graphs (KGs) to ground reasoning in structured evidence. However, most LLM-based...
arXiv:2609.38722v1 Announce Type: cross Abstract: LLM watermarking has become an effective approach to distinguishing AI-generated text from human-written text by embedding detectable patterns during...