ToolQA employs an automated three-phase process to evaluate LLMs' external tool usage across 8 domains, showing significant performance gains over internal knowledge-only models.

Yuchen Zhuang, Yue Yu, Kuan Wang et al.

Proposes a black-box confidence estimation framework combining prompting, sampling, and aggregation, improving calibration and failure prediction metrics.

Miao Xiong, Zhiyuan Hu, Xinyang Lu et al.

Proposed PSF-aware Transformer (PART) for minimalist high-quality panoramic imaging, significantly improving image restoration.

Qi Jiang, Shaohua Gao, Yao Gao et al.

This paper establishes fundamental memory-query tradeoffs for randomized convex optimization, proving cutting-plane methods are Pareto-optimal with $Ω(d^{2-δ})$ memory and $Ω(d^{1+δ/6-o(1)})$ queries.

Xi Chen, Binghui Peng

LMFlow is a lightweight toolkit for efficient fine-tuning and inference of large foundation models.

Shizhe Diao, Rui Pan, Hanze Dong et al.

Introduced OBELICS, a web-scale open dataset of interleaved image-text documents, trained 80B-parameter IDEFICS, achieving state-of-the-art results.

Hugo Laurençon, Lucile Saulnier, Léo Tronchon et al.

Proposes a DIKW-based multimodal fusion framework using deep learning and feature selection, achieving 20% performance gain on public datasets.

Thanveer Shaik, Xiaohui Tao, Lin Li et al.

Wanda uses input activation and weight magnitude product for training-free pruning, outperforming magnitude pruning significantly.

Mingjie Sun, Zhuang Liu, Anna Bair et al.

MotionGPT fine-tunes large language models with 0.4% parameters to generate human motion from multimodal signals like text and poses.

Yaqi Zhang, Di Huang, Bin Liu et al.

RealImpact dataset with 150,000 impact sound recordings of 50 objects, enabling improved audio-visual learning and simulation validation.

Samuel Clarke, Ruohan Gao, Mason Wang et al.

Block-State Transformer combines SSM and Block Transformer, improving language modeling performance and speed.

Mahan Fathi, Jonathan Pilault, Orhan Firat et al.

R2A framework surpasses Flamingo-80B in zero-shot video QA with only 1.3B parameters.

Junting Pan, Ziyi Lin, Yuying Ge et al.

DreamSim leverages synthetic data to develop a holistic perceptual similarity metric, outperforming existing pixel-based metrics and generalizing well to real images.

Stephanie Fu, Netanel Tamir, Shobhita Sundaram et al.

LVLM-eHub evaluates 8 large vision-language models, revealing overfitting and object hallucination issues, proposing a multi-turn reasoning framework.

Peng Xu, Wenqi Shao, Kaipeng Zhang et al.

NAVI is a category-agnostic dataset with high-quality 3D models and near-perfect camera parameters, enabling precise multi-scene 3D reconstruction and correspondence tasks.

Varun Jampani, Kevis-Kokitsi Maninis, Andreas Engelhardt et al.

Introduces a deep TTFS-based SNN with exact ReLU equivalence, achieving state-of-the-art accuracy with less than 0.3 spikes/neuron, enhancing energy efficiency.

Ana Stanojevic, Stanisław Woźniak, Guillaume Bellec et al.

Introduces ARGO1M dataset and CIR method for cross-scenario, cross-location action recognition, outperforming prior approaches.

Chiara Plizzari, Toby Perrett, Barbara Caputo et al.

AssistGPT employs PEIL framework with structured code and tools for autonomous multi-modal reasoning.

Difei Gao, Lei Ji, Luowei Zhou et al.

MiniLLM uses reverse-KL on-policy distillation; LLaMA-7B reaches 73.1 GPT-4 and 23.2 Rouge-L on SelfInst.

Yuxian Gu, Li Dong, Furu Wei et al.

Proposes three frameworks—KG-enhanced LLMs, LLM-augmented KGs, and synergized models—to improve knowledge reasoning.

Shirui Pan, Linhao Luo, Yufei Wang et al.