adapters

ACE introduces a method to consolidate redundant adapters across experts in MoE large language models for more efficient parameter-efficient fine-tuning, achieving up to 1.48× training speedup without increasing peak memory.

LatentPress introduces a method to compress conversational and document context into continuous memory tokens, enabling frozen decoders to read directly without text reconstruction, achieving higher compression ratios and improved performance on long-context tasks.

The paper describes a submission to the WMT 2026 MIST shared task, using the Tiny Aya Global model with task-specialized QLoRA adapters for multilingual summarization and question answering.

The author built two open-source projects, A2A Adapter and Hybro, to enable interoperability between different AI agents, allowing them to collaborate without replacing each other.

Tencent researchers propose YOLO-PEFT, a structure-aware framework that treats adapter placement as constraint planning, outperforming full fine-tuning on YOLO detectors while using significantly fewer trainable parameters.

AuroSFT introduces a parameter-efficient adapter-based framework for multi-task supervised fine-tuning that rolls back adapter checkpoints at task-wise peaks instead of full-model checkpoints, achieving higher average accuracy than mSFT.

SemiAdapt-Instruct proposes a modular framework that discovers latent instruction domains, trains per-domain LoRA adapters in parallel, and routes among them without extra parameters, enabling extensible instruction tuning where new domains require only single-adapter updates instead of full retraining.

LARA is a method for efficient adaptation that adds low-rank corrections to a frozen model's residual stream instead of modifying weights, matching LoRA's performance while enabling composable behaviors and inference-time steering.

SafeGene proposes a reusable safety-adapter module that decouples safety capabilities from task-specific updates, enabling efficient restoration of safety alignment in open-weight LLMs after downstream fine-tuning through few-shot recalibration.

This paper introduces Code2LoRA, a hypernetwork-based method to generate adapters for code language models, addressing challenges under software evolution.

This paper explores using parameter-efficient fine-tuning (PEFT) as a compact substrate for persistent personal models, studying scaling up, down, and out, and introduces MinT for managing adapters.

The author benchmarks serving 1,000 LoRA adapters on one GPU using vLLM, finding that active adapter count and traffic shape are the real bottlenecks, and provides recommendations for tuning max_loras.