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Found 14 papers, 7 papers with code
Position: Standard Benchmarks Fail -- LLM Agents Present Overlooked Risks for Financial Applications
Current financial LLM agent benchmarks are inadequate.
Engaging with AI: How Interface Design Shapes Human-AI Collaboration in High-Stakes Decision-Making
In many existing human + AI systems, decision-making support is typically provided in the form of text explanations (XAI) to help users understand the AI's reasoning.
Everyday AR through AI-in-the-Loop
This workshop brings together experts and practitioners from augmented reality (AR) and artificial intelligence (AI) to shape the future of AI-in-the-loop everyday AR experiences.
LocoVR: Multiuser Indoor Locomotion Dataset in Virtual Reality
Understanding human locomotion is crucial for AI agents such as robots, particularly in complex indoor home environments.
SiCo: A Size-Controllable Virtual Try-On Approach for Informed Decision-Making
Based on our evaluation, we believe our VTO design has the potential to reduce return rates and enhance the online clothes shopping experience.
AID-AppEAL: Automatic Image Dataset and Algorithm for Content Appeal Enhancement and Assessment Labeling
We propose Image Content Appeal Assessment (ICAA), a novel metric that quantifies the level of positive interest an image's content generates for viewers, such as the appeal of food in a photograph.
GraphEval2000: Benchmarking and Improving Large Language Models on Graph Datasets
To address this gap, we introduce GraphEval2000, the first comprehensive graph dataset, comprising 40 graph data structure problems along with 2000 test cases.
Artificial Leviathan: Exploring Social Evolution of LLM Agents Through the Lens of Hobbesian Social Contract Theory
We analyze whether, as the theory postulates, agents seek to escape a brutish "state of nature" by surrendering rights to an absolute sovereign in exchange for order and security.
TiNO-Edit: Timestep and Noise Optimization for Robust Diffusion-Based Image Editing
Previous approaches have focused on either fine-tuning pre-trained T2I models on specific datasets to generate certain kinds of images (e. g., with a specific object or person), or on optimizing the weights, text prompts, and/or learning features for each input image in an attempt to coax the image generator to produce the desired result.
XplainLLM: A Knowledge-Augmented Dataset for Reliable Grounded Explanations in LLMs
Large Language Models (LLMs) have achieved remarkable success in natural language tasks, yet understanding their reasoning processes remains a significant challenge.
LMExplainer: Grounding Knowledge and Explaining Language Models
By comparing the explanations generated by LMExplainer with those of other models, we show that our approach offers more comprehensive and clearer explanations of the reasoning process.
SPOTR: Spatio-temporal Pose Transformers for Human Motion Prediction
Our contributions are threefold: (i) we frame human motion prediction as a sequence-to-sequence problem and propose a non-autoregressive Transformer to forecast a sequence of poses in parallel; (ii) our method is activity agnostic; (iii) we show that despite its simplicity, our approach is able to make accurate predictions, achieving better or comparable results compared to the state-of-the-art on two public datasets, with far fewer parameters and much faster inference.
Self-Supervised Knowledge Assimilation for Expert-Layman Text Style Transfer
This is a particularly notable issue in the medical domain, where layman are often confused by medical text online.
Txt2Vid: Ultra-Low Bitrate Compression of Talking-Head Videos via Text
Video represents the majority of internet traffic today, driving a continual race between the generation of higher quality content, transmission of larger file sizes, and the development of network infrastructure.
