Retrieval-augmented generation

Retrieval pipelines, vector search, chunking and reranking: how models are grounded in a corpus instead of their weights.

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arXiv Computer Vision

Sep 10

Predicting upcoming visual features during eye movements yields scene representations aligned with human visual cortex

arXiv:2511.12715v2 Announce Type: replace-cross Abstract: Natural scenes are complex arrangements of objects, surfaces, and backgrounds. For the brain's visual system to effectively operate, it needs...

By Sushrut Thorat, Adrien Doerig, Alexander Kroner, Carmen Amme, Tim C. Kietzmann

rag benchmarks

arXiv Computation and Language

Sep 10

From Scores to Evidence: Auditable Decisions Can Improve Speech Deepfake Detection

arXiv:2609.08899v2 Announce Type: replace-cross Abstract: Speech deepfakes can mimic a speaker's voice convincingly enough to deceive listeners and automated systems. This has driven strong progress...

By Mengzhe Geng, Yujia Lu, Patrick Littell, Manuela Kunz, Xie Chen

rag

arXiv Machine Learning

Sep 10

Fine-grained Distributed Backdoor Attacks in Federated Learning

arXiv:2609.07147v1 Announce Type: new Abstract: Federated learning, as a privacy-preserving distributed machine learning paradigm, faces significant threats from backdoor attacks. Compared to central...

By Jian Wang, Hong Shen, Wei Ke, Xue Hua Liu

rag safety

arXiv Computer Vision

Sep 10

Low-Rank Prompt Learning for Vision-Language Models with Fixed-Token Bases

arXiv:2609.09462v1 Announce Type: new Abstract: Prompt learning adapts CLIP to downstream recognition by replacing hand-written templates with learned continuous context vectors, which in Context Opt...

By Tanvir Muntakim Tonoy, Sajjad Ghiasvand, Mahnoosh Alizadeh, Ramtin Pedarsani

llms rag multimodal benchmarks

arXiv Computation and Language

Sep 10

Dynamics of meaning: Towards the Evaluation of Diachronic Semantic Change in Sinhala

arXiv:2609.08609v2 Announce Type: replace Abstract: Tracking semantic change in low-resource languages across extensive historical timelines presents significant challenges due to data scarcity and t...

By Nevidu Jayatilleke, Nisansa de Silva

llms rag fine-tuning efficiency safety

arXiv Machine Learning

Sep 10

Novel hybrid protein scaffold gap filling using weighted machine learning ensemble, beam search, and mass-constrained reranking

arXiv:2609.05436v1 Announce Type: cross Abstract: Protein scaffold gap filling is an important computational task in protein sequence reconstruction, where missing amino acid regions must be inferred...

By Tahmid Enam Shrestha, Md. Manzurul Hasan, Md. Rafiqul Islam

rag benchmarks

arXiv Machine Learning

Sep 10

Srijika: OpenType-Layout-Reusing Font Restyling for Nine Indic Scripts

arXiv:2609.05661v1 Announce Type: cross Abstract: We present Srijika, a system for producing installable OpenType fonts for nine Brahmic scripts: Devanagari, Tamil, Bengali, Telugu, Kannada, Malayala...

By Anil Pai

rag diffusion benchmarks

arXiv Machine Learning

Sep 10

Solving the Elastic Wave Equation with Physics-Informed Neural Networks: A Robust and Critical Assessment

arXiv:2609.07983v1 Announce Type: new Abstract: Physics-Informed Neural Networks (PINNs) have recently emerged as a promising approach for solving Partial Differential Equations (PDEs), offering a me...

By Davide Staub, Ben Moseley

rag safety

arXiv Machine Learning

Sep 10

GraphNOSE: A Graph Transformer in Olfaction

arXiv:2609.05694v1 Announce Type: new Abstract: Predicting olfactory qualities from molecular structure is an open problem in chemoinformatics. Although linear models can link molecular features to o...

By Mrityunjay Sharma, Sarabeshwar Balaji, Valentina Parma, Ritesh Kumar

llms rag benchmarks

arXiv AI

Sep 10

CTC-TTS: LLM-based dual-streaming text-to-speech with CTC alignment

arXiv:2602.19574v2 Announce Type: replace-cross Abstract: Large-language-model (LLM)-based text-to-speech (TTS) systems can generate natural speech, but most are not designed for low-latency dual-str...

By Hanwen Liu, Saierdaer Yusuyin, Hao Huang, Zhijian Ou

llms rag multimodal safety

arXiv AI

Sep 10

Beyond Retrieval: Joint Supervision and Multimodal Document Ranking for Textbook Question Answering

arXiv:2505.13520v2 Announce Type: replace-cross Abstract: Textbook question answering (TQA) is a complex task, requiring the interpretation of complex multimodal context. Although recent advances hav...

By Hessa Alawwad, Usman Naseem, Areej Alhothali, Ali Alkhathlan, Amani Jamal

llms rag nlp multimodal safety

arXiv Computation and Language

Sep 10

The Semantic Bottleneck: Leveraging Semantic Representations for Non-Invasive Speech Decoding

arXiv:2609.10296v1 Announce Type: new Abstract: Non-invasive speech decoding remains constrained by the low signal-to-noise ratio of neural recordings, which makes fine-grained reconstruction of phon...

By Gilad D. Landau, Dulhan Jayalath, Oiwi Parker Jones

rag safety

arXiv AI

Sep 10

Improving Federated Graph Recommendation with Semantic Guidance

arXiv:2606.15277v2 Announce Type: replace-cross Abstract: Graph-based recommendation models effectively capture high-order collaborative signals from user--item interaction graphs. Federated learning...

By Thi Minh Chau Nguyen, Hien Trang Nguyen, Duc Anh Nguyen, Van Ho-Long, Thanh Trung Huynh, Zhao Ren

llms rag benchmarks safety

arXiv Machine Learning

Sep 10

Topological Fraud Detection in Latent Transaction Spaces

arXiv:2609.08445v1 Announce Type: new Abstract: Working entirely on topologically anonymized embeddings, we perform fraud detection using iterative rounds of unsupervised filtering followed by superv...

By Avraham Bourla

rag safety

arXiv AI

Sep 10

Hyperparameter Scaling Laws Across MoE Sparsity

arXiv:2609.08690v1 Announce Type: cross Abstract: Mixture-of-Experts (MoE) models expand model capacity without a proportional increase in training compute, but increasing sparsity makes reliable hyp...

By Changxin Tian, Kunlong Chen, Jia Liu, Ziqi Liu, Zhiqiang Zhang, Jun Zhou

rag efficiency

arXiv AI

Sep 10

Memory in Deep Time-Series Models

arXiv:2609.06006v1 Announce Type: cross Abstract: Deep learning for time series has progressed through successive architectural paradigms, from recurrent networks and transformers to structured state...

By Minh Hoang Nguyen, Huu Hiep Nguyen, Manh Nguyen, Van Dai Do, Dung Nguyen, Hung Le

llms rag agents

arXiv Computation and Language

Sep 10

From Retrieval to Weights: Parametric Individualization of Small Language Models with Individual Text Corpora

The paper explores how individual text corpora (ITCs) can be integrated into small language models (SLMs) using DoRA fine‑tuning. By training a DoRA adapter for each of 150 participants, the authors show that the adapter can encode a participant’s own ITC into the model’s weights, improving fit to that participant’s held‑out text. However, while the adapter improves log‑loss on a generalized knowledge test, it does not enhance accuracy under a bias‑corrected PMI readout, and adding retrieval does not provide further benefit.

By Christoph Wigbels, Ali Abusaleh, Markus T. Jansen, Alexander Mehler, Markus J. Hofmann

llms rag agents nlp fine-tuning safety

arXiv AI

Sep 10

AutoLexSteer: Automatic Contrast Construction for Lexical Activation Steering

arXiv:2609.06879v1 Announce Type: cross Abstract: Steering vectors have rapidly emerged as a popular and effective method for guiding the output of LLMs in very specific ways. But constructing accura...

By Shuhe Wang, Lachlan Cowley, Eduard Hovy, Jey Han Lau

llms rag

arXiv AI

Sep 10

Recovering Temporal and Geographic Signals from Language Model Embeddings

arXiv:2609.05721v1 Announce Type: new Abstract: Understanding whether language-model embeddings encode structured real-world information is important for both representation analysis and information...

By Esteban Feuerstein, Victoria Klimkowski, Juan Manuel Ortiz de Zarate, Federico Hern\'an Suaiter

llms rag safety

arXiv AI

Sep 10

Bridging the Semantic-Utility Gap in Multimodal RAG via Generator-in-the-Loop Alignment

arXiv:2609.08188v1 Announce Type: new Abstract: Vision-language models (VLMs) augmented with retrieval-augmented generation (RAG) benefit from access to external evidence. However, standard retriever...

By Zhan-Lun Chang, Dong-Jun Han, Seyyedali Hosseinalipour, Mung Chiang, Christopher G. Brinton

llms rag reinforcement-learning fine-tuning multimodal safety

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