Adaptive, Efficient
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OLIVE: Online Low-Rank Incremental Learning for Efficient Adaptive Exoskeletons
Announce Type: new Abstract: Wearable exoskeleton systems hold promise for restoring mobility in individuals with physical impairments, yet most existing controllers rely on static gait policies that lack the ability to adapt to dynamic real-world environments or individual user characteristics. We present \olive (\underline{O}nline \underline{L}ow-rank \underline{I}ncremental Learning for Efficient Adapti\underline{ve} Exoskeletons), a parameter-efficient online adaptation framework that...
Black-box, Adaptive, Efficient, Transferable, Harmful, Applicable... Attacks Are All You Need to Break LLMs
arXiv:2606.03647v1 Announce Type: new Abstract: Accurately evaluating adversarial robustness is a longstanding challenge. A flawed attack design can inflate robustness estimates, making deployment risk assessment and defense comparison unreliable.
Plants boost carbon uptake through water efficiency, not heat adaptation, global analysis reveals
Plants boost carbon uptake through water efficiency, not heat adaptation, global analysis reveals Gaby Clark Scientific Editor Robert Egan Associate Editor An international team of scientists has discovered that plants are not responding to global warming in the way researchers long assumed. Scientists have expected that ecosystems would keep pace with warming by rising the temperature at which photosynthesis works best. A new study published in One Earth is challenging that theory.
Take a Peek: Efficient Encoder Adaptation for Few-Shot Semantic Segmentation via LoRA
Announce Type: replace Abstract: Few-shot semantic segmentation (FSS) aims to segment novel classes in query images using only a small annotated support set. While prior research has mainly focused on improving decoders, the encoder's limited ability to extract meaningful features for unseen classes remains a key bottleneck. In this work, we introduce \textit{Take a Peek} (TaP), a simple yet effective method that enhances encoder adaptability for both FSS and cross-domain FSS \rev{by...
Take a Peek: Efficient Encoder Adaptation for Few-Shot Semantic Segmentation via LoRA
arXiv:2512.10521v2 Announce Type: replace Abstract: Few-shot semantic segmentation (FSS) aims to segment novel classes in query images using only a small annotated support set. While prior research has mainly focused on improving decoders, the encoder's limited ability to extract meaningful features for unseen classes remains a key bottleneck. In this work, we introduce \textit{Take a Peek} (TaP), a simple yet effective method that enhances encoder adaptability for both FSS and cross-domain...
RefLoRA: Refactored Low-Rank Adaptation for Efficient Fine-Tuning of Large Models
arXiv:2505.18877v4 Announce Type: replace Abstract: Low-Rank Adaptation (LoRA) lowers the computational and memory overhead of fine-tuning large models by updating a low-dimensional subspace of the pre-trained weight matrix. Albeit efficient, LoRA exhibits suboptimal convergence and noticeable performance degradation, due to inconsistent and imbalanced weight updates induced by its nonunique low-rank factorizations. To overcome these limitations, this article identifies the optimal low-rank...
The Distillation Game: Adaptive Attacks & Efficient Defenses
arXiv:2605.22737v3 Announce Type: replace Abstract: Distillation attacks create a deployment trade-off for model providers: the same outputs that make a model more useful can also make it easier to imitate. We study this trade-off through a minimax game between a utility-constrained teacher and an adaptive student. Our framework yields tractable one-sided response rules: an adaptive evaluation rule in which the student reweights high-value examples, and a teacher-side defense template that...
Bandits for Efficient Experimentation: Adapting to Control Group, Preferences, and Context Drifts
arXiv:2606.09802v1 Announce Type: new Abstract: We consider a variant of the linear contextual stochastic multi-armed bandits, where the learner must provide recommendations to a group of users, each having its personalized preference vector, and in the presence of context distributions that are drifting over time. Under practitioner-friendly assumptions, we reduce this setting to linear bandit with stationary mean but heteroskedastic and non-stationary noise. We further study the case when...
SLAT: Segment-Level Adaptive Trimming for Efficient CoT Reasoning
arXiv:2605.30832v1 Announce Type: new Abstract: Recent advances in Large Reasoning Models have significantly improved chain-of-thought (CoT) capabilities via reinforcement learning (RL). However, generated reasoning chains frequently suffer from structural redundancy (i.e., \emph{overthinking}), incurring high computational overhead without improving answer correctness. Existing mitigation strategies typically rely on token-uniform length penalties, which provide coarse, segment-agnostic...
Adaptive Head Budgeting for Efficient Multi-Head Attention
Announce Type: replace Abstract: Multi-head attention enables Transformers to capture diverse representations, but all attention heads are typically activated for every input, regardless of task complexity. For coarse-grained tasks such as text classification, where relevant information is often global, this fixed allocation can introduce unnecessary computation. We propose BudgetFormer, a Transformer architecture that dynamically allocates attention heads on a per-input basis.