TimeMM proposes a time-as-operator spectral filtering framework with adaptive mixing and modality routing to model non-stationary multimodal user preferences in recommendation systems.
A sur- vey of reasoning and agentic systems in time series with large language models.arXiv preprint arXiv:2509.11575
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A query-agnostic black-box attack uses zero-shot surrogate LLMs and adversarial learning on learnable queries to create transferable injection tokens that alter LLM retriever rankings.
SAVeR adds self-auditing of internal beliefs in LLM agents via persona-based candidates and constraint-guided repairs, improving faithfulness on six benchmarks without hurting task performance.
citing papers explorer
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TimeMM: Time-as-Operator Spectral Filtering for Dynamic Multimodal Recommendation
TimeMM proposes a time-as-operator spectral filtering framework with adaptive mixing and modality routing to model non-stationary multimodal user preferences in recommendation systems.
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"Someone Hid It": Query-Agnostic Black-Box Attacks on LLM-Based Retrieval
A query-agnostic black-box attack uses zero-shot surrogate LLMs and adversarial learning on learnable queries to create transferable injection tokens that alter LLM retriever rankings.
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Verify Before You Commit: Towards Faithful Reasoning in LLM Agents via Self-Auditing
SAVeR adds self-auditing of internal beliefs in LLM agents via persona-based candidates and constraint-guided repairs, improving faithfulness on six benchmarks without hurting task performance.