4arXiv cs.AI (Artificial Intelligence)·
Adaptive Digital Twin framework uses LoRA and drift detection for continual surrogate model validation
A new arXiv preprint presents a framework for self-adaptive Digital Twins that combines Fisher score-based drift detection, Low-Rank Adaptation (LoRA) for parameter-efficient continual learning, and Mann-Whitney U tests for online statistical validation of surrogate model updates. The system triggers fine-tuning of fewer than 1% of model parameters upon drift detection and certifies predictive improvement before deploying updated surrogates. Case studies in stochastic linear systems and directed energy deposition additive manufacturing demonstrate successful detection of distributional shifts with short delays. The work addresses the open challenge of maintaining surrogate model fidelity under concept drift in real-time physical system mirroring.
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Using LoRA for Efficient Stable Diffusion Fine-Tuning
This Hugging Face blog post explains how Low-Rank Adaptation (LoRA) can be applied to fine-tune Stable Diffusion models efficiently. LoRA reduces the number of trainable parameters by decomposing weight updates into low-rank matrices, enabling fine-tuning on consumer hardware with significantly less memory. The post covers practical implementation details using the diffusers library.
DRIFT: Decoupled Rollouts and Importance-Weighted Fine-Tuning for Efficient Multi-Turn Optimization
DRIFT is a training framework that bridges online RL and offline SFT for multi-turn LLM optimization by exploiting the theoretical equivalence between KL-regularized RL and importance-weighted supervised learning. It decouples rollout generation from policy optimization: trajectories are sampled from a fixed reference policy offline, weighted by return-based importance scores, and used for weighted SFT. Empirically, DRIFT matches or exceeds multi-turn RL baselines while retaining the efficiency and simplicity of standard supervised fine-tuning. Code is publicly released.
Language-based digital twins using LLMs proposed for elderly cognitive health monitoring
Researchers propose a framework that uses large language models to construct digital twins of elderly individuals by mimicking their conversational patterns and stylometric cues, enabling continuous, non-invasive monitoring for Mild Cognitive Impairment. A multi-head conditional variational autoencoder (cVAE) is introduced to evaluate fidelity and predict cognitive scores (MoCA). Experiments on the I-CONECT dataset show the approach preserves individual identity characteristics and outperforms baseline GPT-generated responses on reconstruction and cognitive score prediction. The work positions language-based digital twins as a scalable alternative to clinical cognitive assessment.
ADAS: Attention-Discounted Adaptive Sampler improves parallel decoding for masked diffusion language models
Researchers propose ADAS, a training-free reranking rule for masked diffusion language model decoding that addresses token interaction failures in parallel token commitment. The method greedily penalizes candidates that attend strongly to already-selected uncertain positions, using attention weights as soft marginal penalties rather than hard constraints. Evaluated on LLaDA-8B-Base and Dream-7B-Base across GSM8K, MATH500, HumanEval, and MBPP, ADAS improves low-NFE performance by 9–10 percentage points on average when plugged into existing samplers with only 3.1% runtime overhead.
Self-evolving agent harness framework uses quality-diversity archive to generalize harness improvements across model families
A new arXiv preprint introduces a framework for automatically improving LLM agent harnesses (prompts, injected knowledge, runtime control, configuration) without modifying model weights. The system separates change proposal (done by an LLM diagnosing failures) from credit assignment (done by deterministic statistical testing), populating a gated quality-diversity archive keyed on failure pathology rather than task identity to prevent overfitting. Evaluated across seven domains with a frozen open-weight model, the framework achieves +9 to +15.5 percentage point gains on sealed test sets, retaining 86–147% of training gains, with the key finding that pathology-to-patch mappings transfer across model families.
LoRA-based cascaded multimodal fusion framework for action recognition in medical training environments
A new arXiv preprint proposes a cascaded Low-Rank Adaptation (LoRA) framework for multimodal fusion applied to action and activity recognition in healthcare training settings. The architecture integrates modalities sequentially without retraining prior components, enabling scalable adaptation across datasets with different modality sets. Evaluation on two healthcare datasets (NurViD and Nurse Training) shows competitive performance against dataset-specific baselines. The work is a narrow application of parameter-efficient fine-tuning techniques to a specialized domain.
DADiff: Diffusion-based framework for cross-domain policy adaptation in reinforcement learning
DADiff is a new diffusion-based framework for online dynamics adaptation in reinforcement learning, addressing the domain transfer problem by leveraging discrepancies between source and target domain generative trajectories during next-state prediction. The authors develop both reward modification and data selection variants, and provide theoretical bounds on policy performance differences in terms of generative trajectory deviation. Experiments across environments with various domain shifts show superior performance over existing approaches. Code is publicly released.
Kolmogorov Regression lifts diffusion policies to Cameron-Martin space for robust long-horizon control
Researchers introduce a backward Kolmogorov equation framework that reformulates diffusion policy training as a deterministic boundary-value PDE problem in Cameron-Martin space, replacing stochastic score matching. The approach uses a precision-weighted Cameron-Martin loss and a Kolmogorov residual as an inference-time failure detector, yielding convergence guarantees tied to kernel effective rank rather than action dimension. Validation on the PushT manipulation benchmark shows 17% improvement in episode reward and 67.6% reduction in inter-step drift; a 6-station manufacturing scheduling task shows 28.4% lower RMSE than LSTM baselines and 96% reduction in deadlock events via Hamilton-Jacobi reachability certification.