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Yi-Sen Gao

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Preprint Oct 2026

MOSAIC: An Omni-Modal Benchmark for Long-Term Memory and Cross-Modal Reasoning in Conversational Agents

Omni-modal assistants must remember not only what users say, but also what they show and convey. Existing long-term memory benchmarks are mostly text-and-image or report a single end-to-end score, so they cannot locate whether evidence is lost when it is represented, retrieved, delivered, or read. We introduce MOSAIC,...

Hong Ting Tsang, Bo-Yi Zhang, Haoyu Huang et al. · 0 citations
#artificial intelligence Preprint Oct 2026

Safe, Persistent, and Evolving Agent Harness for Understanding Partially Observable Worlds

Large language model agents can invoke tools fluently, but enterprise workflows demand more than selecting the right tools: actions must strictly comply with organizational policies, tool feedback often conceals hidden side effects under partial observability, and long-horizon tasks require persistent state tracking ac...

Yi-Sen Gao, Yue (Sophie) Guo, Qing Zong et al. · 0 citations
#artificial intelligence Preprint Sep 2026

AgentIdeaBench: Benchmarking Scientific Ideation in the Agent Era

AgentIdeaBench is introduced, a multidisciplinary benchmark that evaluates scientific ideation under two matched settings, static observation and active exploration, and Scientific World Modeling is explored, a generation-time loop that refines a draft hypothesis through structured thought experiments.

Yunxiang Mo, Tianshi ZHENG, Yi-Sen Gao et al. · 1 citation
Preprint Aug 2026

Does More Retrieved Evidence Help Visual Retrieval-Augmented Generation with Diffusion Language Models?

It is shown that broader retrieval benefits visual DLM-RAG through selective evidence admission rather than unconditional evidence expansion, and the Entropy-Based Candidate Filter (ECF), a training-free evidence-admission framework is proposed, shows that broader retrieval benefits visual DLM-RAG through selective evi...

Jiankun Wang, Yi-Sen Gao, Zi-Wei Zhang et al. · 0 citations
Preprint Aug 2026

MAFIA: Query-Only Memory Attacks via Probing and Factual Injection against Audited LLM Agents

This work proposes MAFIA, a query-only Memory Attack framework via probing and Factual Injection against Audit, tailored to this extended threat model, and introduces a placement strategy that ensures retrieval-competitive injection via memory probing, budget allocation, and scheduling.

Jiamin Chen, Yi-Sen Gao, Yan-Ping Li et al. · 0 citations

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