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 Machine Learning

Jun 19

Evolutionary Two-Stage Hyperparameter Optimization Strategies for Physics-Informed Neural Networks

arXiv:2606. 20442v1 Announce Type: new Abstract: Physics-Informed Neural Networks (PINNs) solve Partial Differential Equations (PDEs) by embedding physical laws into neural network training.

By Fedor Buzaev (HSE University), Dmitry Efremenko (HSE University), Egor Bugaev (HSE University), Andrei Ermakov (HSE University, AXXX), Denis Derkach (HSE University), Daria Pugacheva (HSE University, AXXX), Fedor Ratnikov (HSE University)

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arXiv Machine Learning

Jun 19

Spectral DPPs via NEPv: A Scalable Continuous Relaxation of Determinantal MAP for Diversity-Aware Data Selection

arXiv:2606. 19411v1 Announce Type: new Abstract: Selecting a small, diverse, high-quality subset from a massive pool of candidates is a recurring primitive in modern machine learning -- data curation and coreset selection for training and fine-tuning large models, active-learning batch acquisition, prompt and exemplar selection for in-context learning, retrieval diversification, and experimental design.

By Richard Yi Da Xu

llms rag fine-tuning benchmarks

arXiv AI

Jun 19

VCG: A Multimodal Retrieval Framework for E-Commerce Video Feeds under Extreme Cold-Start Conditions

arXiv:2606. 19627v1 Announce Type: cross Abstract: The digital commerce landscape is shifting from static, search-driven catalogs to dynamic, immersive video feeds.

By Katya Mirylenka, Egor Malykh, Mahdyar Ravanbakhsh, Michael Gygli, Marco-Andrea Buchmann, Andrew Dzhoha, Svitlana Borzenko, Francesca Catino, Mohamed Gaafar, Maarten Versteegh, Thomas Kober, Dario d'Andrea, Ellie Langhans

llms rag multimodal

arXiv AI

Jun 19

FlowEdit: Associative Memory for Lifelong Pronunciation Adaptation in Flow-Matching TTS

arXiv:2606. 20518v1 Announce Type: new Abstract: Flow-matching text-to-speech systems achieve remarkable zero-shot quality but remain static after deployment: pronunciation errors on out-of-vocabulary proper nouns persist unless the model is retrained.

By Harshit Singh, Ayush Pratap Singh, Nityanand Mathur

rag multimodal benchmarks

arXiv AI

Jun 19

Implicit Semantic-Aware Communication Based on Hypergraph Reasoning

arXiv:2606. 20162v1 Announce Type: new Abstract: Semantic-aware communication has emerged as a transformative paradigm for next-generation communication systems, shifting the fundamental goal from transmitting bit-level symbols to reliably recovering and understanding the semantic meaning of information.

By Yiwei Liao, Shurui Tu, Yong Xiao, Yingyu Li, Guangming Shi

rag benchmarks

arXiv AI

Jun 19

Emyx: Fast and efficient all-atom protein generation

arXiv:2606. 19377v1 Announce Type: cross Abstract: Computational enzyme design requires generating proteins that scaffold catalytic residues and ligands, a task that demands both geometric accuracy and structural diversity from the underlying generative model.

By Nicholas J. Williams, Ward Haddadin, Matteo P. Ferla, Constantin Schneider, Nicholas B. Woodall, Ruby Sedgwick, Christian D. Madsen, Andrew L. Hopkins, Edward O. Pyzer-Knapp

llms rag diffusion benchmarks

arXiv Machine Learning

Jun 19

Multi-Modal Contrastive Learning for Implicit Earth Embeddings via Location Tying

arXiv:2606. 20167v1 Announce Type: new Abstract: Spatial prediction tasks are often limited by a lack of high-quality labelled ground-truth observations.

By Jonathan Hecht, Lukas Arzoumanidis, Ziyue Li, Youness Dehbi

rag multimodal

Towards Data Science

Jun 18

Dispatching the Parsed RAG Question: Chunk Strategy, Model Tier, Activations, Audit

Enterprise Document Intelligence [Vol. 1 #6c] - The decisions the parser makes on top of the user string, using the document’s profile: dispatch, activations, full schema, three approaches to deciding what fires, the audit _meta block, and a broker-corpus walkthrough The post Dispatching the Parsed RAG Question: Chunk Strategy, Model Tier, Activations, Audit appeared first on Towards Data Science .

By angela shi

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arXiv AI

Jun 18

G-IdiomAlign: A Gloss-Pivoted Benchmark for Cross-Lingual Idiom Alignment

arXiv:2606. 18989v1 Announce Type: cross Abstract: Idioms are difficult to transfer across languages due to their non-compositionality and weak surface-form grounding, making literal mappings unreliable.

By Fengying Ye, Yanming Sun, Runzhe Zhan, Zheqi Zhang, Lidia S. Chao, Derek F. Wong

llms rag benchmarks safety

arXiv AI

Jun 18

Structured Representation Learning with Locally Linear Embeddings and Adaptive Feature Fusion

arXiv:2606. 18469v1 Announce Type: cross Abstract: Neuroscientific research has revealed that the brain encodes complex behaviors by leveraging structured, low-dimensional manifolds and dynamically fusing multiple sources of information through adaptive gating mechanisms.

By Somjit Nath, Jackson J Cone, Derek Nowrouzezahrai, Samira Ebrahimi Kahou

rag reinforcement-learning benchmarks

arXiv AI

Jun 18

CaVe-VLM-CoT: An Interpretable Vision-Language Model Framework

arXiv:2606. 18385v1 Announce Type: new Abstract: Vision-Language Models (VLMs) remain prone to hallucinations, producing fluent but visually unfaithful outputs.

By Sneha Rao, Shaina Raza, Dhanesh Ramachandram

llms rag agents multimodal safety

arXiv Machine Learning

Jun 18

FORGE: Foundational Optimization Representations from Graph Embeddings

arXiv:2508. 20330v5 Announce Type: replace Abstract: Combinatorial optimization problems are ubiquitous in science and engineering.

By Zohair Shafi, Serdar Kadioglu

rag fine-tuning efficiency benchmarks

arXiv AI

Jun 18

RippleBench: Capturing Ripple Effects Using Existing Knowledge Repositories

arXiv:2512. 04144v2 Announce Type: replace Abstract: Targeted interventions on language models, such as unlearning or model editing, aim to modify specific information, but their effects often propagate to related, unintended areas (e.

By Roy Rinberg, Usha Bhalla, Igor Shilov, Flavio P. Calmon, Rohit Gandikota

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arXiv Machine Learning

Jun 18

TIGER: Inverting Transformer Gradients via Embedding-Subspace Distance Optimization

arXiv:2606. 18312v1 Announce Type: cross Abstract: Federated learning allows multiple clients to jointly train a shared model by sending gradient updates to a central server while keeping raw inputs local.

By William Kalikman, Ivo Petrov, Dimitar I. Dimitrov, Martin Vechev

llms rag efficiency safety

arXiv Machine Learning

Jun 18

UST-GNN: A Unified Spatial--Topological Graph Neural Network Framework for Urban Analytics--Demonstrated through a Case Study on Urban Health Prediction

arXiv:2504. 04739v3 Announce Type: replace Abstract: Understanding how social, demographic, environmental, and spatial factors jointly shape urban outcomes is essential for sustainable urban development and evidence-based policy.

By Minwei Zhao, Sanja Scepanovic, Stephen Law, Ivica Obadic, Cai Wu, Daniele Quercia

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arXiv AI

Jun 18

The More the Merrier: Combining Properties for ABox Abduction under Repair Semantics for ELbot

arXiv:2606. 19197v1 Announce Type: cross Abstract: Abduction is a central approach to explain missing entailments from a knowledge base by providing a hypothesis, that would, if added to the knowledge base, make the missing entailment become true.

By Anselm Haak, Patrick Koopmann, Yasir Mahmood, Anni-Yasmin Turhan

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arXiv Machine Learning

Jun 18

Graph Instance Landscapes: When Structural Similarity Does (Not) Reflect Shortest-Path Performance

arXiv:2606. 18267v1 Announce Type: cross Abstract: Benchmarking shortest-path algorithms is commonly based on aggregate performance over heterogeneous graph sets, which limits insight into how different search paradigms react to instance structure.

By Maryam Gholami Shiri, Ivana Krminac, Marko Djukanovi\'c, Sa\v{s}o D\v{z}eroski, Eva Tuba, Tome Eftimov

rag benchmarks

arXiv Machine Learning

Jun 18

Quantifying and Auditing LLM Evaluation via Positive--Unlabeled Learning

arXiv:2606. 19057v1 Announce Type: cross Abstract: Large Language Models (LLMs) are increasingly used as judges for scalable evaluation, yet such LLM--as--a--Judge systems exhibit systematic biases that are decoupled from semantic quality, most notably verbosity bias.

By Zilong Zhang, Yi-Ting Hung, Lei Ding, Chi-Kuang Yeh

llms rag safety

arXiv Machine Learning

Jun 18

Signature filtering: a lightweight enhancement for statistical watermark detection in large language models

arXiv:2606. 18430v1 Announce Type: new Abstract: Statistical watermarks help organizations attribute large language model (LLM) outputs, yet existing detectors often struggle when watermark signals are weak, texts are repetitive, or watermarks are edited.

By Chih-Duo Hong, Yen-Pang Chen, Fang Yu

llms rag benchmarks

arXiv Machine Learning

Jun 18

A finite-element-inspired bipartite graph learned simulator for manufacturability assessment in large-deformation sheet forming

arXiv:2605. 22845v2 Announce Type: replace-cross Abstract: Explicit dynamic finite element (FE) simulations are widely used for large deformation engineering analysis, but repeated simulations remain costly during design space exploration and optimisation.

By Yingxue Zhao, Haoran Li, Haosu Zhou, Tobias Pfaff, Nan Li

rag benchmarks

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