The paper compares a non‑generative verifier (Jev) with a frontier LLM (Astra) on 495 human‑labelled claims from LLM‑AggreFact. Both models achieve statistically indistinguishable accuracy, yet Jev is 187‑fold cheaper and admits more unsupported claims. A confidence‑gated cascade that escalates uncertain claims improves cost‑efficiency, though the benefit comes mainly from the escalation share rather than the probability estimates.
By Alden Do Rosario, Kirollos Atef
The paper investigates how few-shot on-device personalization of virtual assistants, specifically using LoRA fine‑tuning, affects the system’s ability to reject out‑of‑scope (OOS) inputs. It finds that naive personalization consistently lowers OOS‑rejection AUROC across five intent‑classification benchmarks while preserving in‑scope accuracy, partly due to miscalibration of the confidence scorer and partly due to a genuine shrinkage of embedding margins. The authors propose a low‑cost fix—scoring rejection with Mahalanobis distance instead of softmax confidence—that restores most of the lost OOS performance on most datasets without harming accuracy, and they release all code for reproducibility.
By Vaibhav Prajapati, Pushp Garg
The paper introduces AURA, a framework that supervises intermediate reasoning states to better separate relevant documents from hard distractors and trains a lightweight utility estimator to adapt reasoning depth during inference. On the BRIGHT benchmark, AURA-4B achieves an average nDCG@10 of 34.7 while using only 2.8 reasoning steps and 96 generated tokens per query, and adaptive stopping reduces latency from 4.6 to 0.76 seconds compared to full-trajectory reasoning. Experiments on MS MARCO indicate that this specialization does not significantly harm conventional in-domain retrieval performance.
By Yingtao Luo, Kangyi Zhao, Shiyi Du, Yubo Li
The paper introduces Convex Gate (C‑Gate), a bridge that maps each speech frame to a convex combination of frozen token embeddings, enabling a single LLM decoder to process both speech and text. Using a Whisper‑large‑v3 encoder and a partially adapted Qwen2.5‑7B‑Instruct decoder, the authors jointly train on speech recognition, emotion recognition, and spoken reasoning, achieving accurate transcription and emotion detection while maintaining strong performance on text‑only benchmarks. Analyses reveal that the fixed text embeddings can support both semantic and acoustic readouts through continuous, speech‑conditioned mixtures.
By Ming-Hao Hsu, Yuxuan Hu, Shujie Liu, Jinyu Li, Yan Lu, Zhizheng Wu
The paper introduces SaraCoder, a retrieval‑augmented framework that improves knowledge density for repository‑level code completion by pruning redundant context and selectively injecting cross‑file dependency definitions within a fixed token budget. It employs a Hierarchical Feature Optimization module to reduce an over‑complete candidate pool in four stages, freeing tokens for an External‑Aware Identifier Disambiguator. Experiments on CrossCodeEval and RepoEval‑Updated demonstrate that SaraCoder outperforms strong RAG baselines such as GraphCoder and Repocoder across multiple code LLMs without increasing token usage, and achieves synergistic gains when combined with other cross‑file methods.
By Xiaohan Chen, Xiang Chen, Yu Tian, Piji Li, Sheng-Jun Huang, Songcan Chen
CoDeFont is a new one-shot font generation framework that learns to synthesize target glyphs by transferring style from a single reference glyph while preserving the target’s structure. It achieves this through multi-level style‑content factorization and progressive style‑content reconciliation, creating factor‑specific representations and using a contrastive objective to separate style and content embeddings. The method is evaluated on Bengali and Latin scripts, showing consistent improvements over existing state‑of‑the‑art approaches for both seen and unseen font styles.
By Rejoy Chakraborty, Prasun Roy, Saumik Bhattacharya, Umapada Pal
CertEx introduces a GPU‑based exact k‑nearest‑neighbor search that relies on certified exclusion, a technique that conservatively eliminates vectors using low‑precision bounds before computing exact distances. By storing a half‑precision lower‑bounding projection and embedding a rounding certificate into the tensor‑core dot product, CertEx only evaluates distances for a small subset of candidates, guaranteeing exact results. Experiments on sixteen datasets with up to 100 million vectors show that CertEx achieves 7.1× to 18.0× the throughput of the fastest cuVS search on an H100 and outperforms quantized and graph indexes when exactness is required.
By Dongfang Zhao
The paper introduces a large corpus of about 160,000 Brazilian Portuguese financial news articles to support Natural Language Processing research in this domain. It presents initial benchmarks for a weakly supervised multi‑class classification task using semantic similarity, evaluating cosine similarity, K‑Nearest Neighbors (KNN), and FAISS‑based nearest‑neighbor search with multilingual embeddings. Results show that KNN performs best on manually labeled data (47.3% accuracy, 31.9% F1), while LLM‑annotated data yields similar accuracy but much lower F1 scores due to label imbalance and noise.
By Leomax Bandeira, Gabriel Aguiar, Dalton C\'ezane Gomes Valadares, Luiz Pereira, Helder Fernando de Ara\'ujo Oliveira, Gustavo Xavier, Andson M. Balieiro
The paper introduces SPRA (Structured Paper Representation for Agents), a comprehensive representation of AI research papers that organizes key research elements—Problems, Contributions, Components, ExperimentSetups, Measures, and Findings—into typed relations for easier querying, traversal, and reuse. SPRA includes paper-level citation relations that capture dependencies, component usage, and comparisons, and it has been instantiated across more than one million AI papers, yielding millions of research units and internal relations. Empirical evaluations show that SPRA improves source-conditioned scientific QA and directed cross-paper reasoning over passage retrieval while significantly reducing input and output token counts, demonstrating its practicality for scientific workflows.
By Yutong Leng, Haojiong Chen, Fukai Shang, Xiaoyang Wang, Mindong Liu, Runyuan Ma, Jiantao Qiu
The paper identifies a theoretical incompatibility between 3D point cloud masked autoencoding and SE(3)-equivariant networks, showing that standard masking causes a topological collapse that forces models to hallucinate orientation. To address this, the authors introduce SIEVE, an SE(3)-equivariant self-supervised framework that compresses point cloud geometry into invariant scalar embeddings using a spherical projection mask that preserves global pose while obfuscating local semantics. Experiments demonstrate that SIEVE avoids the predicted collapse where constant and noise masks fail, and its embeddings outperform coordinate-based baselines and hand-crafted geometric descriptors on downstream point cloud tasks, especially in generalization to unseen shape categories.
By Jongann Lee, Sun Woo Park, Yun Young Choi
The paper introduces query‑guided grounding methods for video understanding, covering object‑level, relationship‑level, and moment‑level tasks. For object grounding, QDETRv and TOViT enable one‑shot and few‑shot localization of unseen objects using query‑context fusion, memory modules, and language‑aligned vision encoders. Relationship grounding is addressed with a few‑shot referring framework that optimizes a T‑partite random field, while moment grounding is tackled by the Moment Alignment Transformer (MATR), which employs dynamic time warping and self‑supervised pretraining for video‑to‑video and text‑to‑video retrieval.
By Yogesh Kumar
The paper introduces NOEMA, a temporal convolutional decoder that aligns consecutive fMRI windows with multimodal embeddings (text, audio, and speech) of naturalistic speech stimuli. By leveraging these complementary representations, NOEMA can retrieve the correct speech segment from a pool of 2,000 candidates in the top 10 for over 42% of held‑out queries, outperforming baseline decoders even when the candidate set is expanded with thousands of unseen distractors. Ablation studies confirm that both temporal modeling and multimodal alignment are key to its performance.
By Corentin Caris, Louis Jalouzot, Nona Rajabi, G\'eraud Ilinca, Ahmed Hassayoune, Asilbek Muminov, Thadd\'ee Delebarre, Clara Fonteneau, Charles Maussion, Bertrand Thirion, Alexis Thual, Antoine Collas
Intern Lumina U2 is a diffusion large language model that uses multi-codebook visual tokens in a fully discrete space, allowing each visual position to be represented by a group of complementary codes. The model concatenates embeddings at each position and employs a lightweight autoregressive head to predict codes sequentially while the diffusion backbone processes all positions in parallel. Built on the unified 4D representation of AToken, it supports image, video, and 3D inputs, achieves leading performance in visual understanding, image generation, and editing, and includes a cross‑platform training, inference, and evaluation system with a stability‑aware sampling strategy that speeds up image generation by 4.45×.
By Qian Yu, Zongyang Qiu, Juncheng Yan, Hongliang Li, Qi Qin, Keqi Wang, Siqi Luo, Junkui Ju, Yapeng Mi, Tiantian Lu, Tian Liu, Pengyu Xie, Chenhao Ji, Minghui Zhang, Yiang Zhao, Siyu Yan, Haiying Xu, Yanbo Wang, Jianghan Shen, Yuandong Pu, Jiayi Lei, Borui Zhang, Jinxin Shi, Bo Zhang, Yuhang Zang, Shenglong Ye, Lixin Gu, Hongjie Zhang, Linghan Liu, Xinyuan Chen, Yuewen Cao, Lihe Zhang, Xiaohong Liu, Junjun He, Qiangwei Liu, Qi Zhang, Yu Cheng, Tianfan Xue, Hui Xiong, Chang Xu, Yu Qiao, Yihao Liu, Bin Fu
The paper introduces a dual‑view approach for time‑series language models, combining line plots that preserve absolute measurements with normalized delay embeddings that highlight temporal structure. By fusing these complementary visual representations through dedicated encoders and multi‑level feature fusion, the model achieves significant performance gains on several benchmarks, outperforming single‑view baselines and existing TSLMs. The results demonstrate that preserving both measurement frame and temporal structure is an effective design principle for general‑purpose time‑series reasoning.
By Hedi Zisling, Liran Nochumsohn, Ilan Naiman, Mihir Parmar, Omri Azencot
The paper introduces RAG4Tickets, an AI system that uses Retrieval-Augmented Generation to improve ticket resolution on JIRA and GitHub. It embeds historical tickets, comments, and pull request data with Sentence-Transformers, indexes them with FAISS, and then uses a Large Language Model to generate grounded, explainable resolution suggestions based on retrieved evidence. Experiments show higher precision, recall, faster resolution times, and better developer acceptance compared to existing methods.
By Mohammad Baqar
The paper introduces a multimodal contrastive learning framework that aligns EEG recordings of imagined speech with language representations from a frozen text encoder. By training an EEG encoder with a composite objective that balances semantic reweighting and class discrimination, the method preserves separability among 39 intent categories while embedding EEG signals into a language embedding space. Experiments on the Chisco dataset show comparable decoding performance to a task‑optimized classifier, and additional metrics such as MaxCosine and semantic axes demonstrate intrinsic reliability and broader semantic structure beyond fixed categories.
By Shahryar G. Namdari, Francesco Iacomi, Riccardo Barbieri, Luca Mainardi
The paper introduces Conditional Program (CP), a compressed representation for large language model memory tables that reconstructs only the rows requested by an input. CP encodes each row with a short two‑bit code and a local scale, using a shared reconstruction program based on a Gaussian trellis. Experiments on three n‑gram memory families show CP reduces output KL divergence by 41–63% compared to 2‑bit round‑to‑nearest, and for DeepSeek it cuts table storage from 203 GB to 51 GB, enabling larger workloads on GPU.
By Donghoon Han, Minseop Jung, Dong Woo Kwak, SeungJae Lee, Junhoo Lee
arXiv:2610. 22221v1 Announce Type: cross Abstract: Physical understanding is crucial for decision making, yet vision models struggle to infer properties such as damping that govern how materials behave.
By Zainab Afolabi, Vadim Selyutin, Zhao Gao, Babs Khalidson, Mohamed Ghoneim
The paper introduces IBIS, a framework for designing canaries that are efficient for one‑run privacy auditing. IBIS optimizes each canary’s membership inference attack score while minimizing interference among canaries by encouraging orthogonality in embedding space. The method uses influence‑based greedy initialization and a bilevel optimization algorithm that updates a single model incrementally, achieving better performance‑cost trade‑offs and reducing computational cost by up to two orders of magnitude compared to existing approaches.
By Mathieu Dagr\'eou, Valentin Dorseuil, Mohammad Yaghini, Nicolas Papernot, Aur\'elien Bellet
A new distribution-to-distribution (D2D) neural probabilistic forecasting framework is introduced, using kernel mean embeddings to encode input distributions and mixture density networks to parameterise output distributions. The framework allows recursive multi-step forecasting directly in distributional form, avoiding the need for explicit ensemble simulations. Demonstrated on the Lorenz63 system, the D2D model captures complex distributional evolution and achieves skillful probabilistic forecasts comparable to a perfect-model benchmark.
By Tianlin Yang, Hailiang Du, Louis Aslett