A self-improving agent has to decide which new skills to keep, and in practice it keeps whatever earns a high reward, convinces a judge model, or compresses its library, even when the skill is wrong. That is fine until the answers have to be exactly right: over a table of transactions, a total is either right or wrong....
Yun-Shiuan Chuang, Yi-Rou Ge, Yi-Xi Li et al.· 0 citations
Recovering complete physical fields from sparse observations is challenging because the measurements may not uniquely determine the underlying state. Diffusion-based PDE solvers address this problem through iterative sampling whereas neural operators provide deterministic one-pass predictions. We propose SCOPE (Sparse-...
Rui-Chen Xu, Si-Yao Wang, Fang Wan et al.· 0 citations
This workshop aims to bring together researchers and practitioners to discuss recent advances, practical applications, and future directions of AI agents in information retrieval, while encouraging collaboration and knowledge exchange within the community.
Qing-Song Wen, P. Mehrotra, Yongfeng Zhang et al.· Proceedings of the 32nd ACM...· 0 citations
A Cardinality-Decomposed Loss (CDL) is proposed that combines both Cross Entropy (CE) and BPR to enable the model to collectively optimize for relations across cardinalities and is found that CDL consistently improves discriminability in attribute embeddings.
Parul Maheshwari, Amulya Paruchuri, Yiqing Zou et al.· arXiv.org· 0 citations
PACE (Policy-Attested Contract Execution), a transaction-level authorization framework that interposes between an LLM-based agent and on-chain execution, is presented and frame its claims as logic-level safety within a reproducible benchmark rather than deployment-ready DeFi security.
Rabimba Karanjai, Yang Lu, Richard Williamson et al.· 1 citation
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