Beyond End-to-End: Dynamic Chain Optimization for Private LLM Adaptation on the Edge (2026 · 2 citations)
Federated fine-tuning lets LLMs be adapted to private data that never leaves user devices. However, even adapter-based approaches still require the whole model in memory, which excludes phones and other edge hardware with roughly 4–12GB. The authors show that frozen base weights, not activations or adapters, dominate…
Spotlight and Shadow: Attention-Guided Dual-Anchor Introspective Decoding for MLLM Hallucination Mitigation (2026)
Multimodal LLMs often hallucinate, producing text that conflicts with the image. Training-free contrastive decoding methods such as VCD and ICD reduce this, but they need an extra forward pass for a perturbed "negative" input, and that perturbation can add noise of its own. This paper proposes Dual-Anchor…

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Updated 2 November 2026