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arXiv:2604.02652v1 Announce Type: new Abstract: The safety of large language models (LLMs) relies on alignment techniques such as reinforcement learning from human feedback (RLHF). However, …
arXiv:2604.03199v1 Announce Type: new Abstract: All prior membership inference attacks for fine-tuned language models use hand-crafted heuristics (e.g., loss thresholding, Min-K\%, reference calibration), each bounded …
arXiv:2604.02504v1 Announce Type: new Abstract: Electric utilities must make massive capital investments in the coming years to respond to explosive growth in demand, aging assets …
arXiv:2604.02659v1 Announce Type: new Abstract: The massive scale of pretrained models has made efficient compression essential for practical deployment. Low-rank decomposition based on the singular …
arXiv:2302.08150v2 Announce Type: cross Abstract: We use both Bayesian and neural models to dissect a data set of Chinese learners' pre- and post-interventional responses to …
arXiv:2604.02472v1 Announce Type: new Abstract: B2B sales organizations must identify "persuadable" accounts within zero-inflated revenue distributions to optimize expensive human resource allocation. Standard uplift frameworks …
arXiv:2604.03157v1 Announce Type: new Abstract: The recent advancements in Vision Language Models (VLMs) have demonstrated progress toward true intelligence requiring robust reasoning capabilities. Beyond pattern …
arXiv:2604.02438v1 Announce Type: new Abstract: The deployment of reinforcement learning (RL)-based controllers on physical systems is often limited by poor generalization to real-world scenarios, known …
arXiv:2604.02638v1 Announce Type: new Abstract: We propose AXELRAM, a smart SRAM macro architecture that computes attention scores directly from quantized KV cache indices without dequantization. …
arXiv:2604.02863v1 Announce Type: new Abstract: Majority voting is the standard for aggregating multi-agent responses into a final decision. However, traditional methods typically require all agents …
arXiv:2604.02772v1 Announce Type: new Abstract: Multilingual Pre-trained Language Models (MPLMs) have become essential tools for natural language processing. However, they often exhibit biases related to …
arXiv:2604.02343v1 Announce Type: cross Abstract: We study the compression of LLM-generated text across lossless and lossy regimes, characterizing a compression-compute frontier where more compression is …