arXiv AI
Jun 26
Reconstruction Alignment Improves Unified Multimodal Models
arXiv:2509. 07295v4 Announce Type: replace-cross Abstract: Unified multimodal models (UMMs) unify visual understanding and generation within a single architecture.
GALA: Generation-Aware Cross-Modal Alignment for Text-to-Time-Series Synthesis
arXiv:2608. 13741v1 Announce Type: cross Abstract: Synthesizing time series from natural language is emerging as the most expressive form of controllable time series generation.
Related stories
arXiv Machine Learning
Jul 2
MoVA: Learning Asymmetric Dual Projections for Modular Long Video-Text Alignment
arXiv:2607. 00858v1 Announce Type: cross Abstract: Contrastive pre-training has propelled video-text alignment, yet models often inherit the critical limitations of their image-text predecessors like CLIP, resulting in entangled representations.
arXiv AI
Jun 8
Modality Gap-Driven Subspace Alignment Training Paradigm For Multimodal Large Language Models
arXiv:2602. 07026v3 Announce Type: replace-cross Abstract: Despite the success of multimodal contrastive learning in aligning visual and linguistic representations, a persistent geometric anomaly, the Modality Gap, remains: embeddings of distinct modalities expressing identical semantics occupy systematically offset regions.
arXiv AI
Jul 9
Gradient-Based Speech-to-Text Alignment for Any ASR Model: From CTC to Speech LLMs
arXiv:2607. 06831v1 Announce Type: cross Abstract: Speech-to-text alignment means finding the temporal boundaries of each word in the audio.
arXiv AI
Jun 18
Beyond Tokenization: Direct Timestep Embedding and Contrastive Alignment for Time-Series Question Answering
arXiv:2606. 18986v1 Announce Type: cross Abstract: Recent advances in large language models (LLMs) have given rise to time-series question answering (TSQA), which formulates time-series analysis as natural-language question answering.
arXiv AI
Aug 7
MM-ISTS: Cooperating Irregularly Sampled Time Series Forecasting with Multimodal Vision-Text LLMs
arXiv:2603. 05997v2 Announce Type: replace-cross Abstract: Irregularly sampled time series (ISTS) are widespread in real-world scenarios, exhibiting asynchronous observations on uneven time intervals across diverse variables.
arXiv Machine Learning
Jun 8
Making the Most of Limited Data: Score-Aware Training for Text-to-Music Generation
arXiv:2606. 07387v1 Announce Type: new Abstract: State-of-the-art text-to-music generation systems rely on massive proprietary datasets and industrial-scale compute, making it impossible to disentangle architectural contributions from resource advantages.
arXiv AI
Jun 18
InstructTime++: Time Series Classification with Multimodal Language Modeling via Implicit Feature Enhancement
arXiv:2601. 14968v2 Announce Type: replace-cross Abstract: Most existing time series classification methods adopt a discriminative paradigm that maps input sequences directly to one-hot encoded class labels.
arXiv Machine Learning
Jun 2
Reconsidering Positional Supervision in Masked Diffusion Language Model Training
arXiv:2601. 22947v2 Announce Type: replace-cross Abstract: Masked diffusion language models (MDLMs) generate text by unmasking tokens in parallel and have recently emerged as alternatives to autoregressive language models.
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.
arXiv AI
Jul 20
LVSum: A Benchmark for Timestamp-Aware Long Video Summarization
arXiv:2604. 10024v2 Announce Type: replace-cross Abstract: Long video summarization presents significant challenges for multimodal large language models (MLLMs), particularly in maintaining temporal fidelity over extended durations and producing summaries that are both semantically and temporally grounded.
arXiv AI
1d ago
Adapting LLMs to Time Series Forecasting via Temporal Heterogeneity Modeling and Representation Alignment
arXiv:2508. 07195v2 Announce Type: replace-cross Abstract: Recent advances have demonstrated that Large Language Models (LLMs) can be effectively adapted for time series forecasting, revealing strong potential beyond natural language tasks.