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

Jul 22

Biological Amnesia in ICU Time-Series Prediction: A Drift-Adaptive Two-Stream Architecture with Temporal Retrieval

arXiv:2607. 19020v1 Announce Type: cross Abstract: Background: Clinical decision support systems degrade silently as treatment protocols evolve, yet standard adaptation methods treat models as monolithic blocks, unable to distinguish stable patient physiology from shifting institutional practice.

By Fatema Ferdous Tamanna, K. M. Merajul Arefin, Md. Abdul Masud

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arXiv AI

Jul 22

Hyperdimensional Probe: Decoding LLM Representations via Vector Symbolic Architectures

arXiv:2509. 25045v3 Announce Type: replace-cross Abstract: Despite their capabilities, Large Language Models (LLMs) remain opaque with limited understanding of their internal representations.

By Marco Bronzini, Carlo Nicolini, Bruno Lepri, Jacopo Staiano, Andrea Passerini

llms rag efficiency safety

arXiv Machine Learning

Jul 22

Decafs: Disentangled Conditional adversarial Flows

arXiv:2607. 18755v1 Announce Type: new Abstract: Flow-based models have established state-of-the-art performance in generative modeling across domains, but are hard to interpret due to their complex latent embeddings.

By Anirudh jain, Sakshi Varshney, Samuel Kaski, Vikas Garg

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arXiv Machine Learning

Jul 22

Hierarchical Physics-Embedded Learning for Partially Known Spatiotemporal Dynamics

arXiv:2510. 25306v3 Announce Type: replace Abstract: Partial physical knowledge--governing structures known, constitutive relations or their combinations not--pervades spatiotemporal systems.

By Xizhe Wang, Xiaobin Song, Hongbo Zhao, Qingshan Jia, Qianchuan Zhao, Hao Sun, Benben Jiang

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arXiv AI

Jul 22

AILQA: Evaluating AI-Driven Legal Question Answering Systems for the Indian Legal System

arXiv:2607. 18825v1 Announce Type: cross Abstract: This comprehensive study introduces an advanced Artificial Intelligence for Indian Legal Question Answering (AILQA) system tailored to the Indian legal context.

By Shubham Kumar Nigam, Shubham Kumar Mishra, Noel Shallum, Kripabandhu Ghosh, Arnab Bhattacharya

llms rag nlp benchmarks safety

arXiv AI

Jul 22

Breaking the MoE LLM Trilemma: Dynamic Expert Clustering with Structured Compression

arXiv:2510. 02345v4 Announce Type: replace-cross Abstract: Mixture-of-Experts (MoE) Large Language Models (LLMs) face a trilemma of load imbalance, parameter redundancy, and communication overhead.

By Peijun Zhu, Ning Yang, Baoliang Tian, Jiayu Wei, Weihao Zhang, Haijun Zhang, Pin Lv

llms rag efficiency

arXiv AI

Jul 22

Attacking Graph Foundation Models Through Their Shared Representation

arXiv:2607. 18567v1 Announce Type: new Abstract: A graph foundation model generalizes across graph domains by mapping every input into one shared representation before any task reasoning.

By Pankaj Kumar, Subhankar Mishra

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arXiv AI

Jul 22

FindStatBench: Evaluating Large Language Models on Combinatorial Code Synthesis

arXiv:2607. 18260v1 Announce Type: new Abstract: We introduce FindStatBench, an execution benchmark for evaluating large language models on combinatorial code synthesis.

By Soham Dan

llms rag agents benchmarks

arXiv AI

Jul 22

GUIDED Network-Agnostic Feature Initialization for Spatial Transferability in GNN-based Models

arXiv:2607. 19270v1 Announce Type: cross Abstract: The Traffic Assignment Problem is a fundamental but computationally expensive component of transportation planning.

By Alessandro Scalese, Santhanakrishnan Narayanan, Constantinos Antoniou

rag fine-tuning multimodal benchmarks

arXiv Machine Learning

Jul 22

Finite-Agent Stochastic Differential Games on Large Graphs: II. Graph-Based Architectures

arXiv:2509. 12484v2 Announce Type: replace Abstract: We propose a novel neural network architecture, called Non-Trainable Modification (NTM), for computing Nash equilibria in stochastic differential games (SDGs) on graphs.

By Ruimeng Hu, Jihao Long, Haosheng Zhou

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arXiv AI

Jul 22

AI Tool Discovery at Scale: All You Need is DNS

arXiv:2607. 18242v1 Announce Type: new Abstract: The coming era of autonomous AI agents demands a discovery mechanism capable of navigating millions of tools, yet existing solutions buckle under O(N) complexity and centralized governance.

By Enhao Chen, Yulin Shao

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arXiv AI

Jul 22

MambaLSTM: A Spatio-Temporal Framework for Enhanced Traffic Accident Risk Prediction

arXiv:2607. 18353v1 Announce Type: cross Abstract: In traffic accident risk prediction, most studies overlook the extra noise that could be incorporated when fusing temporal features into spatial features, and some models struggle to capture global correlations among spatial regions.

By Zhen Yu, Yachao Yuan, Zixiang Peng, Muting Li, Thar Baker

rag benchmarks

arXiv AI

Jul 22

State Compression in Two-Agent LLM Relays: A Closed-World Study of Constraint Preservation

arXiv:2607. 18265v1 Announce Type: new Abstract: Long-running Large Language Model (LLM)-based agents often accumulate large intermediate traces containing audits, eliminations, and numeric calculations.

By Anantha Sharma, Sheeba Elizabeth John, Kaarthik Senthil Kumar, Saratsuhas Vijayababu

llms rag agents nlp efficiency

arXiv AI

Jul 22

Trajectory-Aware Clinical Risk Prediction via Severity-Grounded Knowledge Graphs and Retrieval-Augmented Generation

arXiv:2607. 18270v1 Announce Type: new Abstract: While Electronic Health Records (EHRs) offer a wealth of clinical data, effectively augmenting a patient's records with heterogeneous external knowledge to predict the patient's clinical risk remains a significant challenge.

By Kyunghoon Jeon, Youmin Ko, Woohwan Jung, Hyunjoon Kim

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arXiv AI

Jul 22

LinguistAgent Technical Report: A Reflective Multi-Model Platform for Automated Linguistic Annotation

arXiv:2602. 05493v2 Announce Type: replace-cross Abstract: Data annotation remains a significant bottleneck in the field of humanities and social sciences, particularly for complex linguistic tasks such as metaphor identification.

By Bingru Li

llms rag fine-tuning

arXiv Machine Learning

Jul 22

AHEAD: Advancing Multi-Class Label Aggregation with Interpretable Cross-Annotator Modeling

arXiv:2607. 18465v1 Announce Type: new Abstract: Crowdsourced labeling provides valuable labeled data for domains across natural language processing, computer vision, and video.

By Ju Chen, Sijia Xu, Jun Feng, Zhiqiang Gao, Zhengyi Yang

rag computer-vision nlp

arXiv Machine Learning

Jul 22

A Better Start for Language Models: Domain-Conditional Position Offsets

arXiv:2607. 18302v1 Announce Type: new Abstract: Autoregressive language models are least accurate at the beginning of a sequence, where little context forces reliance on a generic pretraining prior.

By Ye Qiao

llms rag fine-tuning safety

arXiv Machine Learning

Jul 22

On the Diverse Dynamical Behaviors Arising in Deep Linear Transformers

arXiv:2607. 18584v1 Announce Type: new Abstract: We study the inference-time behavior of deep linear encoder-only transformers through the lens of interacting particle systems.

By Sixu Li, Thomas Jacob Maranzatto, Jan Peszek, Trevor Teolis, Semih Akkoc, Konstantin Riedl, Sennur Ulukus, Nicol\'as Garc\'ia Trillos

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arXiv Machine Learning

Jul 22

Beyond Content: Behavioral Policies Reveal Actors in Information Operations

arXiv:2602. 02838v2 Announce Type: replace-cross Abstract: The detection of online influence operations -- coordinated campaigns by malicious actors to spread narratives -- has traditionally depended on content analysis or network features.

By Philipp J. Schneider, Lanqin Yuan, Marian-Andrei Rizoiu

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arXiv Machine Learning

Jul 22

Node-as-Agent: Graph Agentic Network

arXiv:2508. 00429v5 Announce Type: replace-cross Abstract: Graph Neural Networks (GNNs) have achieved remarkable success in graph-based learning by propagating information among neighbor nodes via predefined aggregation mechanisms.

By Minghao Guo, Xi Zhu, Qingyue Jiao, Xiujin Liu, Haochen Xue, Chong Zhang, Shuhang Lin, Jingyuan Huang, Ziyi Ye, Yongfeng Zhang

llms rag agents fine-tuning

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