GaussLock embeds traps targeting position, scale, rotation, opacity, and color in 3D Gaussian models to degrade unauthorized fine-tunes while preserving authorized performance.
arXiv preprint arXiv:2508.01782 , year=
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AdapShot adaptively optimizes shot counts via probe entropy and semantic KV cache reuse with decoupling, reporting ~10% gain and 4.64x speedup over DBSA.
DASH-KV accelerates long-context LLM inference to linear complexity via asymmetric KV cache hashing and mixed-precision retention, matching full attention performance on LongBench.
Tri-RAG turns external knowledge into Condition-Proof-Conclusion triplets and retrieves via the Condition anchor to improve efficiency and quality in LLM RAG.
Agent-GWO uses collaborative grey-wolf-inspired agents to jointly optimize LLM prompts and decoding settings, yielding higher accuracy and stability than prior single-agent prompt optimization methods on math and hybrid reasoning benchmarks.
citing papers explorer
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Immunizing 3D Gaussian Generative Models Against Unauthorized Fine-Tuning via Attribute-Space Traps
GaussLock embeds traps targeting position, scale, rotation, opacity, and color in 3D Gaussian models to degrade unauthorized fine-tunes while preserving authorized performance.
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AdapShot: Adaptive Many-Shot In-Context Learning with Semantic-Aware KV Cache Reuse
AdapShot adaptively optimizes shot counts via probe entropy and semantic KV cache reuse with decoupling, reporting ~10% gain and 4.64x speedup over DBSA.
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DASH-KV: Accelerating Long-Context LLM Inference via Asymmetric KV Cache Hashing
DASH-KV accelerates long-context LLM inference to linear complexity via asymmetric KV cache hashing and mixed-precision retention, matching full attention performance on LongBench.
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Transforming External Knowledge into Triplets for Enhanced Retrieval in RAG of LLMs
Tri-RAG turns external knowledge into Condition-Proof-Conclusion triplets and retrieves via the Condition anchor to improve efficiency and quality in LLM RAG.
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Agent-GWO: Collaborative Agents for Dynamic Prompt Optimization in Large Language Models
Agent-GWO uses collaborative grey-wolf-inspired agents to jointly optimize LLM prompts and decoding settings, yielding higher accuracy and stability than prior single-agent prompt optimization methods on math and hybrid reasoning benchmarks.