ASB is a new benchmark that tests 10 prompt injection attacks, memory poisoning, a novel Plan-of-Thought backdoor attack, and 11 defenses on LLM agents across 13 models, finding attack success rates up to 84.3% and limited defense effectiveness.
Slmrec: empowering small language models for sequential recommendation
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ASER distills sensory attributes extracted from reviews into item embeddings that improve sequential recommendation metrics by an average of 7.9% HR@10 and 11.2% NDCG@10 across 20 Amazon domain-backbone tests.
MoS applies theme-aware routing to extract multi-scale theme-specific subsequences from noisy long user sequences, achieving state-of-the-art recommendation performance with fewer FLOPs than comparable MoE models.
LongAct uses saliency from high-magnitude activations to guide sparse weight updates in long-context RL, yielding about 8% gains on LongBench v2 across multiple algorithms.
G2Rec unifies holistic graph-based user co-engagement modeling with semantic tokenization for scalable generative recommendation without ground-truth user interests.
citing papers explorer
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Agent Security Bench (ASB): Formalizing and Benchmarking Attacks and Defenses in LLM-based Agents
ASB is a new benchmark that tests 10 prompt injection attacks, memory poisoning, a novel Plan-of-Thought backdoor attack, and 11 defenses on LLM agents across 13 models, finding attack success rates up to 84.3% and limited defense effectiveness.
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Sensory-Aware Sequential Recommendation via Review-Distilled Representations
ASER distills sensory attributes extracted from reviews into item embeddings that improve sequential recommendation metrics by an average of 7.9% HR@10 and 11.2% NDCG@10 across 20 Amazon domain-backbone tests.
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Mixture of Sequence: Theme-Aware Mixture-of-Experts for Long-Sequence Recommendation
MoS applies theme-aware routing to extract multi-scale theme-specific subsequences from noisy long user sequences, achieving state-of-the-art recommendation performance with fewer FLOPs than comparable MoE models.
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LongAct: Harnessing Intrinsic Activation Patterns for Long-Context Reinforcement Learning
LongAct uses saliency from high-magnitude activations to guide sparse weight updates in long-context RL, yielding about 8% gains on LongBench v2 across multiple algorithms.
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Structuring and Tokenizing Distributed User Interest Context for Generative Recommendation
G2Rec unifies holistic graph-based user co-engagement modeling with semantic tokenization for scalable generative recommendation without ground-truth user interests.