arXiv AI

Predicting Space Groups of Double Perovskites by LLM with Dynamic Few-Shot Learning

arXiv:2608. 10483v1 Announce Type: new Abstract: Double perovskites (DPs) offer broad compositional tunability, but predicting the space groups (SGs) of stable structures remains difficult because available datasets are often strongly imbalanced toward dominant SG classes.

arXiv AI

Oct 15

LLM Agents for Automated Post-Training via Structured Recipe Optimization

arXiv:2610.18572v1 Announce Type: new Abstract: Automated post-training transforms pre-trained large language models (LLMs) into models capable of handling diverse domains by automatically constructi...

By Ruohong Liu, Bohan Tang, Lekang Jiang, Jack Umenberger, Yiwen Guo

llms agents benchmarks

arXiv AI

Sep 2

DISTAL: Distillation and Self-Supervised Pretraining for Structure-Agnostic Materials Property Prediction

arXiv:2609.00059v1 Announce Type: cross Abstract: Materials property prediction remains difficult in low-data settings, where many target properties are supported by only a limited number of labeled...

By Weiran Wang, Xintong Huo, Yueying Wang, Yusi Fan, Wenyan Wang, Xin Feng, Ruihao Xin, Lan Huang, Kewei Li, Fengfeng Zhou

efficiency multimodal benchmarks

arXiv AI

Sep 4

CORE: Improving Compositional Reasoning in MLLM Embedding via Reranker Distillation

CORE improves compositional reasoning in multimodal language models by distilling a cross‑attentive reranker’s fine‑grained judgments into the embedding model. It generates candidate lists across five compositional matching levels and trains with a Rank‑KL objective to replicate the reranker’s ranking. Experiments on COLA, SUGARCREPE++, and NEGBENCH show CORE‑RERANKER‑8B outperforms Jina‑Reranker by 10.7 points, while CORE‑EMBED‑8B achieves the best overall average among evaluated embeddings, with gains also transferring to the MCMR benchmark without harming COCO or Flickr30K retrieval.

By Tingyu Song, Mingxin Li, Yanzhao Zhang, Dingkun Long, Chu Liu, Pengjun Xie, Yilun Zhao, Shu Wu

rag efficiency benchmarks

Hugging Face Trending Papers

Aug 2

From Patches to Evidence Balls: Class-Conditioned Evidence Retrieval for Few-Shot Whole Slide Image Classification

Whole slide image (WSI) classification is an evidence-driven task, where diagnostic cues are often sparse, spatially organized, and class-dependent. Existing MIL and vision-language methods aggregate a large pool of patch features into a single global slide representation.

computer-vision multimodal safety

arXiv AI

Jul 21

Prismatic Synthesis: Gradient-based Data Diversification Boosts Generalization in LLM Reasoning

arXiv:2505. 20161v2 Announce Type: replace-cross Abstract: Effective generalization in language models depends critically on the diversity of their training data.

By Jaehun Jung, Seungju Han, Ximing Lu, Skyler Hallinan, David Acuna, Shrimai Prabhumoye, Mostafa Patwary, Mohammad Shoeybi, Bryan Catanzaro, Yejin Choi

llms benchmarks

arXiv Machine Learning

Oct 28

TCSP3.0: Template-Based Crystal Structure Prediction with Coefficient-Preserving Element Substitution and Structural Diversity

TCSP3.0 is an improved template‑based crystal structure prediction method that enforces coefficient‑preserving element mappings, substitutes species simultaneously on parsed structures, and validates each candidate’s composition and cell. It adds periodic‑number retrieval, soft oxidation‑state evidence, radius‑compatibility scoring, structural deduplication, and ORB‑v3 machine‑learning‑potential relaxation and energy ranking, achieving Top‑1 and Top‑20 match rates of 72.2 % and 90.6 % on the T180 benchmark and outperforming previous TCSP2.0 and several deep generative models. The method runs in a median of 28 s per target on two CPU cores, roughly ten times faster than CrystalFlow, with most gains attributed to the correctness fixes and structural deduplication.

By Ying Feng, Jeffrey Hu, Lai Wei, Liang Tao, Madeline Miklos, Dipannoy Das Gupta, Hossein Erfanshekooh, Jianjun Hu

rag benchmarks

arXiv AI

Jul 29

From Cellular Responses to Pharmacological Domains: Multimodal Zero-Shot Drug Representation Learning

arXiv:2607. 25322v1 Announce Type: new Abstract: Multimodal drug discovery enables drug representation learning beyond chemical structure by incorporating cellular responses such as gene expression and cell morphology.

By Jintao Huang, Lu Leng, Ziyuan Yang

multimodal safety

arXiv Computer Vision

Sep 14

Retrieved Images as Visual Thought: Training-Free Multimodal In-Context Learning for the Open-vs-Closed Gap

ReVisIT is a train‑free framework that turns retrieved image‑label pairs into units of visual thought, combining structured class definitions, multimodal retrieval, and alternating user/assistant injection before joint decoding. On several benchmarks—including Fast Open MiniImageNet, Bongard‑OpenWorld, and the newly released MAAC‑Bench—ReVisIT achieves performance comparable to or surpassing large, trained models while using far fewer parameters. The approach demonstrates that high‑quality retrieval and a simple turns layer can provide a universal performance boost across diverse multimodal tasks.

By Bingchen Huang, Zhiling Wang, Yifu Chen, Yuanchao Du

llms multimodal benchmarks

arXiv AI

Jun 26

Generative Retrieval via Diffusion Transformer with Metric-Ordered Sequence Training and Hybrid-Policy Preference Optimization

arXiv:2606. 26899v1 Announce Type: new Abstract: Embedding-based retrieval ranks items by their similarity to a query in a shared vector space and usually aims to return the highest-scoring items.

By Chenghao Liu, Yu Zhang, Zhongtao Jiang, Kun Xu, Zhenwei An, Renzhi Wang, Zhao Wang, Jiachen Zhang, Yuxiao Zhang, Kun Xu, Songfang Huang

llms rag diffusion fine-tuning benchmarks

arXiv Computer Vision

Sep 16

HyCal: A Training-Free Prototype Calibration Method for Cross-Discipline Few-Shot Class-Incremental Learning

arXiv:2604.15678v2 Announce Type: replace Abstract: Pretrained Vision-Language Models (VLMs) like CLIP show promise in continual learning, but existing Few-Shot Class-Incremental Learning (FSCIL) met...

By Eunju Lee, MiHyeon Kim, JuneHyoung Kwon, Yoonji Lee, JiHyun Kim, Soojin Jang, YoungBin Kim

llms rag multimodal benchmarks safety

arXiv AI

Aug 3

Adjudicated Captioning: Multi-Agent Alignment Scoring and Consensus-Distilled Beam Arbitration for Strict Zero-Shot Image Captioning

arXiv:2607. 28986v1 Announce Type: cross Abstract: Zero-shot image captioning (ZIC) describes images without paired image-caption supervision during captioner training, relying on text-only corpora and frozen pretrained image-text scorers.

By Duy Tran Thanh, Thien-Phuc Doan, Long Nguyen-Vu, Ngo Tan Vu Khanh

llms rag agents efficiency multimodal safety

Hugging Face Trending Papers

Jul 21

CR-Refiner: An Object-Centric Optimal Transport Reranker for Edit-Conditioned 3D Scene Retrieval

Edit-conditioned 3D scene retrieval pairs a reference 3D room with a natural-language modification and retrieves rooms from a corpus that satisfy the edit. Three lines of prior work each fall short on this task.

llms benchmarks

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