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 AI

Jun 8

ThinkBooster: A Unified Framework for Seamless Test-Time Scaling of LLM Reasoning

arXiv:2606. 06915v1 Announce Type: cross Abstract: Test-time compute (TTC) scaling has emerged as a powerful paradigm for improving large language model (LLM) reasoning by allocating additional compute during inference, e.

By Vladislav Smirnov (MBZUAI), Chieu Nguyen (MBZUAI), Sergey Senichev (Independent Researcher), Minh Ngoc Ta (MBZUAI), Ekaterina Fadeeva (ETH Z\"urich), Artem Vazhentsev (MBZUAI), Daria Galimzianova (MBZUAI), Nikolai Rozanov (MBZUAI, Imperial College London), Viktor Mazanov (Innopolis University), Jingwei Ni (ETH Z\"urich), Tianyi Wu (NUS), Igor Kiselev (Accenture), Mrinmaya Sachan (ETH Z\"urich), Iryna Gurevych (MBZUAI), Preslav Nakov (MBZUAI), Timothy Baldwin (MBZUAI), Artem Shelmanov (MBZUAI)

llms rag benchmarks

arXiv Machine Learning

Jun 8

SHAP-Guided Kernel Actor-Critic for Explainable Reinforcement Learning

arXiv:2512. 05291v3 Announce Type: replace Abstract: Actor-critic (AC) methods are a cornerstone of reinforcement learning (RL) but offer limited interpretability.

By Na Li, Hangguan Shan, Wei Ni, Wenjie Zhang, Xinyu Li

rag reinforcement-learning safety

arXiv Machine Learning

Jun 8

Controllable Molecular Generative Foundation Models

arXiv:2605. 15354v2 Announce Type: replace Abstract: Despite the success of foundation models in language and vision, molecular graph generation still lacks a unified framework for heterogeneous design tasks with reliable controllability.

By Yihan Zhu, Yuhan Liu, Weijiang Li, Tengfei Luo, Meng Jiang

rag diffusion reinforcement-learning benchmarks

arXiv AI

Jun 8

VALUEFLOW: Toward Pluralistic and Steerable Value-based Alignment in Large Language Models

arXiv:2602. 03160v2 Announce Type: replace Abstract: Aligning Large Language Models (LLMs) with the diverse spectrum of human values remains a central challenge: preference-based methods often fail to capture deeper motivational principles.

By Woojin Kim, Sieun Hyeon, Jusang Oh, Jaeyoung Do

llms rag safety

arXiv AI

Jun 8

A Temporal Spatial Minimax Rate for Smoothly-Varying Distributions in Wasserstein Space

arXiv:2606. 07325v1 Announce Type: cross Abstract: We study the minimax rate of estimating a future value $\mu_{t_n+h}$ of a curve $t\mapsto\mu_t$ in the $2$-Wasserstein space $\mathcal{P}_2(\mathbb{R}^d)$ from finitely many noisy snapshots of its past, under an adiabatic bound $\|\nabla_t^k v\|\le\varepsilon$ on the $k$-th covariant derivative of the velocity field.

By Munsik Kim

rag safety

arXiv Machine Learning

Jun 8

LRMIL: Efficient Low-Resolution Multiple Instance Learning via High-Resolution Knowledge Distillation for Whole Slide Image Classification

arXiv:2606. 06864v1 Announce Type: cross Abstract: Multiple instance learning (MIL) has become a standard paradigm for whole slide image (WSI) analysis in digital pathology, as it enables slide-level prediction without dense annotations.

By Yonghan Shin, Won-Ki Jeong

rag computer-vision efficiency benchmarks

arXiv AI

Jun 8

MCERF: Advancing Multimodal LLM Evaluation of Engineering Documentation with Enhanced Retrieval

arXiv:2604. 09552v2 Announce Type: replace-cross Abstract: Engineering rulebooks and technical standards contain multimodal information like dense text, tables, and illustrations that are challenging for retrieval augmented generation (RAG) systems.

By Kiarash Naghavi Khanghah, Hoang Anh Nguyen, Anna C. Doris, Amir Mohammad Vahedi, Daniele Grandi, Faez Ahmed, Hongyi Xu

llms rag agents nlp multimodal benchmarks

arXiv AI

Jun 8

$\mathrm{ECI}_{\mathrm{sem}}$: Semantic Residual Effective Contrastive Information for Evaluating Hard Negatives

arXiv:2603. 20990v3 Announce Type: replace-cross Abstract: Hard-negative source selection for dense retrieval is usually decided only after fine-tuning and downstream evaluation.

By Aarush Sinha, Rahul Seetharaman, Aman Bansal

llms rag nlp fine-tuning safety

arXiv AI

Jun 8

Understanding Generative Recommendation with Semantic IDs from a Model-scaling View

arXiv:2509. 25522v3 Announce Type: replace Abstract: Recent advancements in generative models have allowed the emergence of a promising paradigm for recommender systems (RS), known as Generative Recommendation (GR), which tries to unify rich item semantics and collaborative filtering signals.

By Jingzhe Liu, Liam Collins, Jiliang Tang, Tong Zhao, Neil Shah, Clark Mingxuan Ju

llms rag nlp efficiency

arXiv AI

Jun 8

CHDP: Cooperative Hybrid Diffusion Policies for Reinforcement Learning in Parameterized Action Space

arXiv:2601. 05675v2 Announce Type: replace Abstract: Hybrid action space, which combines discrete choices and continuous parameters, is prevalent in domains such as robot control and game AI.

By Bingyi Liu, Jinbo He, Haiyong Shi, Enshu Wang, Weizhen Han, Jingxiang Hao, Peixi Wang, Zhuangzhuang Zhang

rag agents diffusion reinforcement-learning robotics benchmarks

arXiv AI

Jun 8

Didact: A Cross-Domain Capability Discovery System for Defence

arXiv:2606. 06942v1 Announce Type: cross Abstract: Policymakers in defence and defence-aligned sectors must monitor rapidly evolving research alongside sector priorities relevant to operational and strategic needs.

By Aarya Bodhankar, Aditya Joshi, Bao Gia Doan, Thomas Marchant, Oscar Leslie, Flora Salim

rag

arXiv Machine Learning

Jun 8

Advanced Flood Prediction with Physics-Guided Deep Learning: Combining UNet, FNO, and SAR/Optical Imagery

arXiv:2606. 06524v1 Announce Type: cross Abstract: Accurate and scalable flood mapping remains challenging due to limited ground observations, heterogeneous terrain conditions, and the difficulty of enforcing hydrodynamic consistency within data-driven models.

By Tewodros Syum Gebre, Jagrati Talreja, Leila Hashemi-Beni

rag multimodal benchmarks

arXiv AI

Jun 8

TEVI: Text-Conditioned Editing of Visual Representations via Sparse Autoencoders for Improved Vision-Language Alignment

arXiv:2606. 07451v1 Announce Type: cross Abstract: Vision-language models such as CLIP are highly useful for diverse tasks due to their shared image-text embedding space.

By Sweta Mahajan, Sukrut Rao, Jiahao Xie, Alexander Koller, Bernt Schiele

llms rag multimodal benchmarks safety

arXiv Machine Learning

Jun 8

ADAGE: Active Defenses Against GNN Extraction

arXiv:2503. 00065v4 Announce Type: replace-cross Abstract: Graph Neural Networks (GNNs) achieve high performance in various real-world applications, such as drug discovery, traffic states prediction, and recommendation systems.

By Jing Xu, Franziska Boenisch, Adam Dziedzic

rag benchmarks

arXiv AI

Jun 8

MHA-RAG: Improving Efficiency, Accuracy, and Consistency by Encoding Exemplars as Soft Prompts

arXiv:2510. 05363v2 Announce Type: replace Abstract: Adapting Foundation Models to new domains with limited training data is challenging and computationally expensive.

By Abhinav Jain, Xinyu Yao, Thomas Reps, Christopher Jermaine

rag benchmarks

arXiv Machine Learning

Jun 8

A Dynamic Self-Evolving Extraction System

arXiv:2603. 06915v2 Announce Type: replace-cross Abstract: The extraction of structured information from raw text is a fundamental component of many NLP applications, including document retrieval, ranking, and relevance estimation.

By Moin Amin-Naseri, Hannah Kim, Estevam Hruschka

llms rag nlp fine-tuning

arXiv AI

Jun 8

Multilingual Multi-Speaker Unit Vocoders: A Systematic Analysis of Discrete Speech Representations

arXiv:2606. 06740v1 Announce Type: cross Abstract: Discrete speech units obtained via k-means clustering of self supervised embeddings entangle phonetic, speaker, and language information, causing speaker mixing and cross-lingual interference in multilingual multi-speaker speech generation.

By Naman Kothari, Arjun Gangwar, Adarsh Arigala, S Umesh

llms rag

arXiv Machine Learning

Jun 8

Chunking the Critic: A Transformer-based Soft Actor-Critic with N-Step Returns

arXiv:2503. 03660v4 Announce Type: replace Abstract: We introduce a sequence-conditioned critic for Soft Actor-Critic (SAC) that models trajectory context with a lightweight Transformer and trains on aggregated $N$-step targets.

By Dong Tian, Onur Celik, Gerhard Neumann

llms rag reinforcement-learning benchmarks

arXiv Machine Learning

Jun 8

PolarQuant: Leveraging Polar Transformation for Efficient Key Cache Quantization and Decoding Acceleration

arXiv:2502. 00527v2 Announce Type: replace Abstract: The KV cache in large language models is a dominant factor in memory usage, limiting their broader applicability.

By Songhao Wu, Ang Lv, Xiao Feng, Yufei Zhang, Xun Zhang, Guojun Yin, Wei Lin, Rui Yan

llms rag efficiency

arXiv AI

Jun 8

Hierarchical Certified Semantic Commitment for Byzantine-Resilient LLM-Agent Collaboration

arXiv:2606. 07316v1 Announce Type: cross Abstract: Byzantine collaboration among large-language-model agents requires a finality-control primitive: given delivered stochastic, structured natural-language proposals, the protocol must decide whether the round supports a commit, what kind of commit, or a typed safe abort.

By Haoran Xu, Lei Zhang, Iadh Ounis, Xianbin Wang

llms rag agents benchmarks safety

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