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Kai-Wei Chang

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#artificial intelligence Preprint Oct 2026

Continual Graph Memory for Mathematical Research Agents

Using frontier agent harnesses to tackle mathematical research problems has emerged as an effective means of advancing mathematics. However, solving frontier problems in mathematics may require a massive number of agents working in parallel for extended periods to construct proofs, thereby generating an enormous volume...

Jun-Yi Zhang, Jin-Xi Yu, E. Jiang et al. · 0 citations
#artificial intelligence Preprint Sep 2026

Privacy-Preserving Topology-Guided Safety for LLM-Based Multi-Agent Systems via Federated Graph Learning

Topology-guided safeguards for LLM-based multi-agent systems (MAS) train a GNN over the inter-agent communication graph to localize risky agents and intervene on the topology---but they assume one operator can pool all labeled traces. Across organizations that assumption breaks: episodes contain private prompts, tool o...

Jin-Xi Yu, Eric Jiang, Levina Li et al. · 0 citations
#artificial intelligence Preprint Sep 2026

Codebook Agent: Amortized Topology Design for LLM Multi-Agent Systems

Codebook Agent is the most accurate method on all six benchmarks the authors compare, and an MLP proxy that reads the flattened adjacency, regressed on measured utility and per-task normalized token cost, reranks the top decoded candidates in a single batched forward pass.

Jin-Xi Yu, Yubei Li, E. Jiang et al. · 1 citation

Memory as a Controlled Process: Learned Adaptive Memory Management for LLM Agents

This work presents Memory as a Controlled Process (MemCon), a framework that models memory operations as a Markov Decision Process and learns an online policy that adaptively decides when, what, and how much to retrieve, when to inject a distilled plan, and when to consolidate or forget.

Eric Jiang, Zhi Zhang, Yuchen Wu et al. · 1 citation
Preprint Aug 2026

Harness the Memory: A Holistic Evaluation of Memory Substrates in Memory Agents

A controlled harness evaluation of memory substrates for memory-augmented agents, covering dense and sparse indices, text records, structural stores, hierarchical stores, refinement-based memories, parametric updates, and activation-compatible context mechanisms, shows that no single substrate consistently dominates.

Wei-Chieh Huang, Wei-Zhi Zhang, Yu-Chen Wu et al. · 2 citations
Review Jul 2026

From Solvers to Research: Large Language Model-Driven Formal Mathematics at the Research Frontier

It is argued that the next leap in AI4Math systems requires a decisive shift from predefined problem-solvers to research agents that can address frontier mathematical challenges with rigorous formal mathematical reasoning, highlighting core limitations of existing systems in serving as mathematical research agents.

E. Jiang, Xiao Liang, Yikai Zhang et al. · 1 citation

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