MSR

D - MSR Tuscany Woods Fund, LP (0002136742) (Filer)

Filed: 2026-06-02 AccNo: 0002136742-26-000001 Size: 6 KB

Teach Multimodal Recommendation Model to See via Personalized Visual Extraction and Adaptive Learning

Announce Type: new Abstract: Multimodal sequential recommendation (MSR) incorporates textual and visual information to improve recommendation quality. However, recent studies and our empirical analysis show that visual features are often underutilized, thereby contributing far less than textual signals. We attribute this issue to two factors: insufficient visual representation learning (pretrained encoders fail to capture preference-relevant cues) and unbalanced visual-text optimization...

Code Lifespan Survival Analysis (CLSA): Predicting the Survival of Source Code Lines Using AST-Aware Mining

arXiv:2606.04993v1 Announce Type: new Abstract: Context: Predicting which source lines will be deleted - and when - matters for maintenance, technical debt, and review prioritization. Existing MSR approaches work at file or method granularity, masking individual-statement risk. Objective: We introduce Code Lifespan Survival Analysis (CLSA), the first framework to model code survival at individual-line granularity.

PEEK: Picking Essential frames via Efficient Knowledge distillation

arXiv:2605.31029v1 Announce Type: new Abstract: Video-language models can process only a limited number of frames, making frame selection a key bottleneck for efficient video captioning. Most captioning pipelines still rely on uniform sampling, which is computationally cheap but agnostic to visual content. Adaptive frame sampling has recently emerged as a promising approach for selecting the most informative frames from a video; however, existing methods remain computationally expensive.

Robust Multi-Mutant Protein Stability Prediction from a Fine-Tuned Evolutionary Scale Model

Recently, high-throughput experimental techniques have propelled improvements in deep learning-based prediction of mutation effects on protein stability. However, leading stability predictors still struggle to predict the combined effect of multiple mutations and prefer mutations that negatively impact other properties, including expressibility. To mitigate these limitations, we apply Low-Rank Adaptation (LoRA) to specialize ESM3 for stability prediction by fine-tuning on the Megascale...

Whatever the mirror test tells us, beluga whales pass it

Whatever the mirror test tells us, beluga whales pass it

In hours of underwater video footage from a New York aquarium, a beluga whale named Natasha stretches her neck, pirouettes, nods, and shakes her head in front of a two-way mirror. Her daughter Maris does much the same. According to a new study published in PLOS One, both animals show the behavioral hallmarks of mirror self-recognition—a cognitive ability long considered a marker of self-awareness, and one that had never before been documented in beluga whales.

MAVIS: Multi-Agent Video Retrieval via Structured Video Understanding

arXiv:2606.09641v1 Announce Type: new Abstract: The dominant paradigm in video retrieval relies on embedding-based full-corpus scanning, which suffers from inherent computational inefficiency and the semantic asymmetry between information-dense videos and sparse textual queries. To bridge this gap, we introduce \textbf{MAVIS}, a novel multi-agent framework that rethinks retrieval as cooperative reasoning rather than brute-force search. MAVIS first bridges the granularity mismatch by parsing...

SRENet: Spectral Re-Entry Network for Point Cloud Action Recognition

arXiv:2606.03160v1 Announce Type: new Abstract: Recognizing human actions from point cloud sequences is critical for 3D perception driven applications such as autonomous driving and human-computer interaction. However, the irregular structure and temporal inconsistency of point clouds pose unique challenges for spatio-temporal representation learning, especially in capturing both global motion context and fine-grained temporal dynamics. We propose SRENet, a spectral-aware framework designed...