MINT (Multimodal Imaging-to-Speech Knowledge Transfer) is a three-stage framework that transfers MRI-derived biomarkers to speech representations for early Alzheimer’s screening. An MRI teacher creates a compact embedding space for CN‑versus‑MCI classification, and a residual projection head aligns speech features to this space using a geometric loss, allowing imaging‑free inference. Experiments on ADNI‑4 show that aligned speech matches speech baselines, while multimodal fusion outperforms MRI alone, and ablations highlight dropout regularization and self‑supervised pretraining as key design choices.
By Vrushank Ahire, Yogesh Kumar, Anouck Girard, M. A. Ganaie
The paper compares Knowledge-Graph Based Augmentation (Graph-RAG) with Retrieval-Augmented Generation (RAG) for answering culturally specific questions. Using the LatamQA dataset, Graph-RAG, built automatically from Wikipedia via KGGen, matches RAG performance and reduces the base LLM’s error by 72% with a standard KG and 78% with a benchmark-aware variant. The approach also transfers zero‑shot to Portuguese, showing multilingual applicability.
By Pablo Poulenard, Yannis Karmim, Valentin Barri\`ere
The paper investigates which historical examples are most useful for time‑series forecasting by defining predictive relevance as the expected future utility conditioned on inference‑time information. It introduces a two‑stage approach: a normalized‑pattern retriever generates a coarse candidate set, and a lightweight MLP reranks these candidates using future‑supervised relevance while keeping inference strictly past‑only. Experiments on six benchmarks show that this reranker improves pattern retrieval and outperforms a matched‑protocol baseline, revealing that historical relevance is structured, domain‑dependent, and not governed by a single universal retrieval rule.
By Yong-Hoon Choi, Kwang-Hyun Park, Youngjin Cho
NObSP (Nonlinear Oblique Subspace Projections) is a framework that decomposes neural network predictions into explicit per‑feature contribution functions and an interaction residual, leveraging the linear final layer and oblique projections to avoid double counting when feature subspaces overlap. It connects to functional ANOVA and the Kolmogorov‑Arnold representation theorem, and introduces an efficient partial regression algorithm for out‑of‑sample evaluation. For convolutional networks, NObSP‑CAM generates class activation maps without backward passes after a single calibration, and experiments on tabular and vision datasets show faithfulness comparable to established attribution methods, with high function reproduction scores and improved class purity on TinyImageNet.
By Alexander Caicedo, V\'ictor De La Hoz, Santiago Alf\'erez
arXiv:2601.11262v2 Announce Type: replace-cross
Abstract: Music Cover Retrieval, also known as Version Identification, aims to recognize distinct renditions of the same underlying musical work, a tas...
By Joanne Affolter, Benjamin Martin, Elena V. Epure, Gabriel Meseguer-Brocal, Fr\'ed\'eric Kaplan
The paper investigates when compressed vector representations can provide exact linear or affine readouts for a finite lexicon’s truth conditions, establishing a necessary and sufficient row‑space condition. It shows that the augmented truth matrix’s rank determines the minimal dimension needed for exact linear (rank r) and affine (rank r − 1) readouts, and that exact readouts preserve Boolean connectives. Experiments on GloVe and word2vec embeddings reveal that while many predicates are linearly separable, none achieves exact affine recovery from pretrained embeddings, yet supervised transductive training can attain exact affine recovery at dimensions meeting the theoretical bound, preserving most of the original variance.
"whyItMatters":"The results provide a precise mathematical criterion for when vector embeddings can faithfully encode logical truth conditions, informing both theoretical understanding and practical training of language models."
By Daniel Quigley
The paper introduces LGM, a neuro‑symbolic framework that disentangles long‑term memory by mapping historical interactions into a continuous latent graph. Instead of static memory graphs, LGM uses a sparse autoencoder to create query‑aware latent nodes and edges, then applies a graph encoder conditioned on the query to perform non‑linear message passing. Experiments on long‑term personalization benchmarks show that LGM outperforms existing methods in capturing both explicit and implicit user preferences and generating personalized responses.
By Cai Ke, Xinghao Chen, Xiaoyu Shen, Keyu Chen, Siyu An, Junnan Dong, Ruifeng Xu, Ruizhi Qiao, Xing Sun
MiRAGE is a new evaluation framework for retrieval‑augmented generation (RAG) that handles multimodal sources such as audiovisual media. It uses a claim‑centric approach with two metrics: InfoF1, which measures factuality and information coverage, and CiteF1, which measures citation support and completeness. Human evaluation shows MiRAGE aligns well with extrinsic quality judgments, and an automatic implementation outperforms three text‑centric RAG metrics (ALCE, ARGUE, RAGAS) on text while uniquely generalizing to multimodal inputs.
By Alexander Martin, William Walden, Reno Kriz, Dengjia Zhang, Kate Sanders, Eugene Yang, Chihsheng Jin, Benjamin Van Durme
arXiv:2609.18898v1 Announce Type: new
Abstract: Training-free weighted aggregation is widely used to lift 2D semantic features onto 3D Gaussians for open-vocabulary scene understanding, yet its theor...
By Yihan Zang, Da Li, Dominik Engel, Shinkyu Park, Ivan Viola
The paper investigates how to efficiently repair stale key-value (KV) caches in retrieval‑augmented generation systems after document edits. It proposes a budgeted in‑place recomputation approach and evaluates training‑free position‑selection policies on a factual RAG benchmark. Across three model families, a contiguous edit‑local window consistently recovers most of the post‑edit answer quality while being 13–21 times faster than a full re‑prefill, though its effectiveness diminishes when answer‑bearing text moves downstream.
By Mingyang Mao, Wyatt Mackey, Xiaomin Lin
TalkMatrix is a method for generating character dialogue that balances consistency and diversity across multiple prompts. It produces several candidate lines for each character-situation pair and then jointly selects a complete matrix of lines by optimizing embedding-based consistency and diversity objectives. The approach uses a two‑level minimax formulation and multi‑start coordinate ascent to approximate the discrete optimization, outperforming random, independent, and other baseline selection strategies in experiments on synthetic and board‑game role‑playing scenarios.
By Ayuto Tsutsumi, Yuu Jinnai
The paper introduces WFM, a Wiki Foundation Model designed to support complex agentic reasoning by combining dense document contexts with markdown-based topological linkages. It formalizes a Wiki Graph schema that preserves explicit topologies while embedding continuous semantics, and employs a query‑conditioned attentive aggregation for efficient message passing. The authors also propose an NCCL‑based protocol to reduce distributed system overhead, achieving a 10.5× training speedup and strong performance on long‑term memory and multi‑hop reasoning benchmarks.
By Junnan Dong, Linhao Luo, Senlei Zhang, Gong Chen, Taian Guo, Yifei Yu, Rong Tao, Tao Guo, Qian-Wen Zhang, Siyu An, Ruizhi Qiao, Xing Sun
The paper introduces REASONS, a benchmark comprising 12,723 sentence-level citation instances across 12 arXiv subject categories, to evaluate scientific citation attribution by large language models. It proposes a dual-metric framework—Abstention Rate (AR) and Hallucination Rate (HR)—to assess the trade-off between reliability and responsiveness. Experiments on proprietary and open-source LLMs under various prompting and retrieval settings show that advanced Retrieval-Augmented Generation (RAG) reduces hallucinations but may increase abstention, while retrieval-augmented variants often maintain near-zero abstention. Human evaluation reveals a high ratio of factual hallucinations to acceptable paraphrases, underscoring the need for systems that can appropriately abstain under uncertainty.
By Deepa Tilwani, Yash Saxena, Seyedali Mohammadi, Ankur Padia, Edward Raff, Amit Sheth, Srinivasan Parthasarathy, Manas Gaur
NeuroECG is a deep learning framework that repurposes a pretrained ECG foundation model to predict neurological outcomes after cardiac arrest without using electroencephalography (EEG). The model fine‑tunes the backbone with a gradual unfreezing strategy on single‑channel bedside ECG, aggregates multiple ECG segments via quantile pooling and PCA, and achieves an AUROC of 0.7333 using ECG alone. When combined with static clinical covariates, NeuroECG improves performance to an AUROC of 0.8077 and an AUPRC of 0.8970, demonstrating that bedside ECG can serve as a low‑cost, auxiliary prognostic tool in an EEG‑free setting.
By Jiaju Gao, Yi Zhao, Chenyang Xu, Yuxi Zhou, Hao Wang
The paper re‑examines the impact of removing the encoder from Action Chunking Transformers (ACT), a model used for robot manipulation learning. Contrary to the original claim that encoder removal drops success rates from 35% to 2%, the authors find no such dramatic decline in their re‑runs, though minor variations remain uncertain. They attribute the discrepancy to factors like training length and checkpoint selection, and note that the encoder’s latent variable offers little reconstruction benefit on the tested benchmark, while its removal speeds up training.
By Bo Kang
arXiv:2605.19309v4 Announce Type: replace
Abstract: Document Layout Analysis (DLA) pipelines provide structured page representations for retrieval-augmented generation, long-document question answeri...
By Yue Chen, Yihao Wang, Ziyi Tang, Yongsen Zheng, Keze Wang
The paper introduces Multiple Embedding Replay Selection (MERS), a graph‑based method that combines supervised and self‑supervised embeddings to improve sample selection for replay buffers in continual learning. MERS replaces traditional buffer selection modules and demonstrates consistent performance gains over state‑of‑the‑art strategies, especially in low‑memory settings. Experiments on CIFAR‑100 and TinyImageNet show that MERS outperforms single‑embedding baselines without adding model parameters or increasing replay volume, making it a practical, drop‑in enhancement for replay‑based continual learning.
By Danit Yanowsky, Daphna Weinshall
The paper introduces a semantic communication approach for CSI feedback in FDD massive MIMO, where the UE sends a learned embedding optimized for beam selection rather than reconstructing the full channel. Experiments show that an 8‑dimensional embedding derived from just 43 NR CSI‑RS pilots in the angular‑delay domain achieves the best beam prediction accuracy, surpassing methods that use the entire 512‑subcarrier channel. This demonstrates that beam‑relevant information is inherently low‑dimensional, allowing the semantic encoder to discard irrelevant details and transmit only the intent needed for beam selection.
By Cristian J. Vaca-Rubio, Konstantinos Vandikas, Aneta Vulgarakis Feljan
The paper introduces EffiRAG, a graph-based retrieval‑augmented generation system that reduces the cost of building and querying a graph by using it only to locate relevant passages and generating answers from the original text. On the UltraDomain benchmark, EffiRAG outperforms LightRAG‑hybrid in 93 of 120 questions while cutting total system cost by 57 % (from USD 0.952 to USD 0.408). The study shows that graph‑based RAG can be both more accurate and cheaper, especially as the corpus grows, and recommends evaluating such systems on both answer quality and cost.
By Yuzhong Zhang, Haoyang Ma, Chao Peng, Lionel Briand, Boxi Yu, Jialun Cao
The paper presents modular deep learning augmentations for next‑day wildfire spread prediction, including wind‑ and slope‑conditioned attention biases, physics‑feature retrieval‑augmented output correction, and fire‑conditioned dual‑stream gating. These modules are evaluated on five backbone models using the Next Day Wildfire Spread benchmark, with staged ablations, directional audits, retrieval perturbations, calibration measures, and computational comparisons. The best augmented SwinUNETR model achieves an F1 score of 0.4216 and an AUC‑PR of 0.3673, while a mixed ensemble reaches 0.4292 and 0.3790, demonstrating that predictive performance, operational trustworthiness, and computational practicality can be simultaneously improved.
By Miguel Esparza, Aydin Ayanzadeh Ahmad Mousavi, Ali Mostafavi