Posters

AutoPSV automates process annotation for LLMs, improving reasoning by detecting confidence shifts in reasoning steps, thus efficiently enhancing model performance.

Spiking Autonomous Driving (SAD) is the first unified SNN for autonomous driving, achieving competitive performance in perception, prediction, and planning while significantly reducing energy consumpt…

iAgents: a novel multi-agent system leveraging LLMs, overcomes information asymmetry by mirroring human social networks to enable effective collaboration in complex tasks, achieving high accuracy in d…

AutoMix intelligently routes queries to different-sized LLMs based on a smaller model’s self-verification, minimizing cost while maintaining performance.

AI models retraining with model-annotated data incorporating human strategic responses can lead to unexpected outcomes, potentially reducing the proportion of agents with positive labels over time, wh…

This study presents a novel single-step outlier rectification method using optimal transport with a concave cost function, surpassing the limitations of conventional two-stage approaches by jointly op…

AutoDP automates multi-task learning for joint disease prediction on EHRs, significantly improving performance via automated task grouping and architecture search.

Automated Multi-level Preference (AMP) framework significantly improves multimodal large language model (MLLM) performance by using multi-level preferences during training, reducing hallucinations and…

GNNs automate multi-dataset semantic segmentation label unification, improving model training efficiency and performance by resolving conflicts across label spaces.

LLM agents can now autonomously build environmental understanding via interactive learning, generating human-readable instruction manuals that boost task success rates.

AutoGuide: Automated generation of context-aware guidelines significantly improves LLM agent performance in unfamiliar domains.

Boosting AI’s math skills, this paper introduces a novel framework for autoformalizing mathematical statements, improving accuracy by 0.22-1.35x via symbolic equivalence and semantic consistency check…

More sophisticated autobidders surprisingly worsen online auction efficiency; a fine-grained analysis reveals that less powerful, uniform bidders lead to better market outcomes.

Auditing local explanations is surprisingly hard: proving explanation trustworthiness requires far more data than previously thought, especially in high dimensions, challenging current AI explainabil…

AUCSeg tackles pixel-level long-tail semantic segmentation by introducing an AUC-oriented loss function and a Tail-Classes Memory Bank to efficiently manage memory and improve performance on imbalance…

New method maximizes AUC under positive distribution shift using only positive and unlabeled training data, and unlabeled test data; improving imbalanced classification.

Attraos: a novel long-term time series forecasting model leveraging chaos theory, significantly outperforms existing methods by utilizing attractor dynamics for efficient and accurate prediction.

AttnDreamBooth: A novel approach to text-to-image generation that overcomes limitations of prior methods by separating learning processes, resulting in significantly improved identity preservation and…

Boosting Vision Transformer’s transferability in cross-domain few-shot learning is achieved by a simple yet effective method: strategically adjusting attention temperature to remedy ineffective target…

Attention boosted Individualized Regression (AIR) provides a novel individualized modeling framework for matrix data, leveraging sample-specific internal relations without needing extra sample similar…