arXiv AI
Jun 6
Personal AI Agent for Camera Roll VQA
arXiv:2606. 05275v1 Announce Type: cross Abstract: We study the personal camera roll visual question answering setting.
Natural language processing
Classical and neural NLP: translation, question answering, tokenization and the evaluation of language understanding.
arXiv AI
Jun 6
IDEAL: Leveraging Infinite and Dynamic Characterizations of Large Language Models for Query-focused Summarization
arXiv:2407. 10486v3 Announce Type: replace Abstract: Query-focused summarization (QFS) aims to produce summaries that answer particular questions of interest, enabling greater user control and personalization.
arXiv AI
Jun 6
Noise-Aware Visual Representation Learning for Medical Visual Question Answering
arXiv:2606. 05535v1 Announce Type: cross Abstract: Medical visual question answering (Med-VQA) has strong potential for clinical decision support by enabling AI models to interpret medical images and answer clinically relevant queries.
arXiv AI
Jun 6
A2RAG: Adaptive Agentic Graph Retrieval for Cost-Aware and Reliable Reasoning
arXiv:2601. 21162v2 Announce Type: replace-cross Abstract: Graph Retrieval-Augmented Generation (Graph-RAG) enhances multihop question answering by organizing corpora into knowledge graphs and routing evidence through relational structure.
arXiv AI
Jun 6
MAviS: A Multimodal Conversational Assistant For Avian Species
arXiv:2603. 07294v2 Announce Type: replace-cross Abstract: Fine-grained understanding and species-specific multimodal question answering are vital for advancing biodiversity conservation and ecological monitoring.
arXiv AI
Jun 6
A Systematic Analysis of Biases in Large Language Models
arXiv:2512. 15792v3 Announce Type: replace-cross Abstract: Large language models (LLMs) have rapidly become indispensable tools for acquiring information and supporting human decision-making.
arXiv AI
Jun 6
Synthetic Contrastive Reasoning for Multi-Table Q&A
arXiv:2606. 05382v1 Announce Type: new Abstract: Multi-table question answering requires models to retrieve relevant evidence, link schemas, and perform compositional reasoning across relational tables.
arXiv Machine Learning
Jun 5
Balancing Image Compression and Generation with Bootstrapped Tokenization
arXiv:2606. 05552v1 Announce Type: new Abstract: Despite progress in image tokenization, standard methods encode redundant information by mixing all granularities within each token, thus redundancy persists between tokens.
arXiv Machine Learning
Jun 5
Maximizing Mutual Information Between Prompt and Response Improves LLM Performance With No Additional Data
arXiv:2603. 19294v4 Announce Type: replace Abstract: While post-training has successfully improved large language models (LLMs) across a variety of domains, these gains heavily rely on human-labeled data or external verifiers.
arXiv Machine Learning
Jun 5
LLM Explainability with Counterfactual Chains and Causal Graphs
arXiv:2606. 05972v1 Announce Type: new Abstract: Causal graphs provide a high-level language for making mechanisms transparent.
arXiv Machine Learning
Jun 5
Multilingual Coreference Resolution via Cycle-Consistent Machine Translation
arXiv:2606. 05444v1 Announce Type: cross Abstract: Coreference resolution is a core NLP task, having a broad range of downstream applications, e.
arXiv Machine Learning
Jun 5
MSTN: A Lightweight and Fast Model for General TimeSeries Analysis
arXiv:2511. 20577v5 Announce Type: replace Abstract: Real-world time series often exhibit strong non-stationarity, complex nonlinear dynamics, and behavior expressed across multiple temporal scales, from rapid local fluctuations to slow-evolving long-range trends.
arXiv Machine Learning
Jun 5
A Survey on Diffusion Language Models
arXiv:2508. 10875v3 Announce Type: replace-cross Abstract: Diffusion Language Models (DLMs) are rapidly emerging as a powerful and promising alternative to the dominant autoregressive (AR) paradigm.
arXiv Machine Learning
Jun 5
Is Diversity All You Need for Scalable Robotic Manipulation?
arXiv:2507. 06219v2 Announce Type: replace-cross Abstract: Data scaling has driven remarkable success in foundation models for Natural Language Processing (NLP) and Computer Vision (CV), yet the principles of effective data scaling in robotic manipulation remain insufficiently understood.
arXiv Machine Learning
Jun 5
A Vision-language Framework for Comparative Reasoning in Radiology
arXiv:2606. 06407v1 Announce Type: cross Abstract: Medical imaging artificial intelligence has achieved strong performance in isolated image interpretation, but remains poorly aligned with radiological practice, where diagnosis and follow-up rely on comparison across prior studies and analogous reference cases.
arXiv Machine Learning
Jun 5
ABBEL: Learning Natural-Language Belief States for Memory-Efficient Interaction
arXiv:2512. 20111v2 Announce Type: replace-cross Abstract: As the time horizons of sequential decision-making tasks grow, keeping full interaction histories in model context becomes increasingly costly.
arXiv Machine Learning
Jun 5
GenFT: A Generative Parameter-Efficient Fine-Tuning Method for Pretrained Foundation Models
arXiv:2506. 11042v2 Announce Type: replace Abstract: Parameter-efficient fine-tuning (PEFT) has emerged as a resource-efficient strategy for adapting Pretrained Foundation Models (PFMs) by learning a small number of task-specific updates $\Delta W$.
arXiv Machine Learning
Jun 5
GOTabPFN: From Feature Ordering to Compact Tokenization for Tabular Foundation Models on High-Dimensional Data
arXiv:2606. 05441v1 Announce Type: new Abstract: We investigate how to make small tabular foundation models effective for High-Dimensional, Low-Sample Size (HDLSS) tabular prediction without retraining large backbones.
Hugging Face Trending Papers
Jun 4
MARDoc: A Memory-Aware Refinement Agent Framework for Multimodal Long Document QA
Iterative retrieval-reasoning agents have recently shown promise for multimodal long-document question answering. However, most existing systems maintain a single growing context that mixes retrieval traces, observations, and intermediate reasoning.
arXiv AI
Jun 4
Arithmetic Pedagogy for Language Models
arXiv:2606. 05106v1 Announce Type: cross Abstract: We investigate whether methods of human mathematics pedagogy can guide the training of language models toward arithmetic reasoning.