GenAIR generates LLM-derived archetype embeddings for items and applies behavioral calibration to close the semantic-behavioral gap, yielding performance gains on three real-world datasets when integrated with existing sequential models.
arXiv preprint arXiv:2301.08164 , year=
3 Pith papers cite this work. Polarity classification is still indexing.
3
Pith papers citing it
years
2026 3verdicts
UNVERDICTED 3representative citing papers
In LVLMs, attention can be replaced by random Gaussian weights with little or no performance loss, indicating that current models get lost in attention rather than efficiently using visual context.
SingGuard introduces a policy-adaptive multimodal LLM guardrail with dynamic reasoning regimes and SingGuard-Bench, reporting SOTA F1 scores across 35 datasets and improved policy-following accuracy under runtime shifts.
citing papers explorer
-
Generative Archetype-Grounded Item Representations for Sequential Recommendation
GenAIR generates LLM-derived archetype embeddings for items and applies behavioral calibration to close the semantic-behavioral gap, yielding performance gains on three real-world datasets when integrated with existing sequential models.
-
Large Vision-Language Models Get Lost in Attention
In LVLMs, attention can be replaced by random Gaussian weights with little or no performance loss, indicating that current models get lost in attention rather than efficiently using visual context.
-
SingGuard: A Policy-Adaptive Multimodal LLM Guardrail with Dynamic Reasoning
SingGuard introduces a policy-adaptive multimodal LLM guardrail with dynamic reasoning regimes and SingGuard-Bench, reporting SOTA F1 scores across 35 datasets and improved policy-following accuracy under runtime shifts.