Deploying deep neural networks on edge devices demands architectures that balance accuracy with stringent latency, memory, and energy constraints. Conventional Neural Architecture Search (NAS) methods optimize primarily for accuracy on GPU clusters, producing architectures that are impractical for resource-constrained deployment.
Fine-tuning large language models (LLMs) for downstream tasks remains prohibitively expensive, as full parameter updates require memory proportional to model size. Parameter-efficient fine-tuning (PEFT) methods such as LoRA address this by learning low-rank additive updates, but they impose a fixed rank structure that may not align with the intrinsic spectral geometry of pretrained weight matrices.
Foundation models trained on multiple data modalities — text, images, and audio — have demonstrated capabilities that exceed the sum of their unimodal components. Yet the scaling behavior of such multimodal models remains poorly understood compared to their text-only counterparts.
Vision Transformers (ViTs) have demonstrated remarkable performance across computer vision tasks, yet their robustness properties against adversarial perturbations remain insufficiently understood. In this work, we present a systematic analysis of how the self-attention mechanism in ViTs provides a natural defense against adversarial attacks.
In-context learning (ICL) — the ability of transformer models to adapt to new tasks from a few demonstration examples without weight updates — remains one of the most striking yet poorly understood capabilities of large language models. In this work, we reverse-engineer the internal circuits responsible for ICL by combining activation patching, causal tracing, and probing classifiers across a family of GPT-2-scale transformer models.
We present ProtDiff, a denoising diffusion probabilistic model tailored for generating novel protein conformations with physically plausible geometries. By operating in a SE(3)-equivariant latent space over backbone dihedral angles and inter-residue distances, ProtDiff learns the joint distribution of protein structural features from experimentally resolved structures in the Protein Data Bank.
Reinforcement Learning from Human Feedback (RLHF) has become the dominant paradigm for aligning large language models with human preferences. However, RLHF pipelines are susceptible to reward model collapse—a phenomenon where the policy learns to exploit systematic biases in the learned reward model rather than genuinely improving on the intended objective.
Chain-of-thought (CoT) prompting has demonstrated remarkable effectiveness in eliciting complex reasoning capabilities from large language models (LLMs). In this work, we systematically investigate the emergent reasoning patterns that arise when LLMs are prompted to generate intermediate reasoning steps.