Chip-processing method could assist cryptography schemes to keep data secure
By enabling two chips to authenticate each other using a shared fingerprint, this technique can improve privacy and energy efficiency.
Archives AI News
A Unifying Framework for Robust and Efficient Inference with Unstructured Data
arXiv:2505.00282v3 Announce Type: replace-cross Abstract: To analyze unstructured data (text, images, audio, video), economists typically first extract low-dimensional structured features with a neural network. Neural networks do not make generically unbiased predictions, and biases will propagate to estimators that use…
Block-Recurrent Dynamics in Vision Transformers
arXiv:2512.19941v5 Announce Type: replace-cross Abstract: As Vision Transformers (ViTs) become standard vision backbones, a mechanistic account of their computational phenomenology is essential. Despite architectural cues that hint at dynamical structure, there is no settled framework that interprets Transformer depth as…
pi-Flow: Policy-Based Few-Step Generation via Imitation Distillation
arXiv:2510.14974v3 Announce Type: replace Abstract: Few-step diffusion or flow-based generative models typically distill a velocity-predicting teacher into a student that predicts a shortcut towards denoised data. This format mismatch has led to complex distillation procedures that often suffer from a…
Biases in the Blind Spot: Detecting What LLMs Fail to Mention
arXiv:2602.10117v3 Announce Type: replace Abstract: Large Language Models (LLMs) often provide chain-of-thought (CoT) reasoning traces that appear plausible, but may hide internal biases. We call these *unverbalized biases*. Monitoring models via their stated reasoning is therefore unreliable, and existing bias…
AutoNumerics: An Autonomous, PDE-Agnostic Multi-Agent Pipeline for Scientific Computing
arXiv:2602.17607v1 Announce Type: cross Abstract: PDEs are central to scientific and engineering modeling, yet designing accurate numerical solvers typically requires substantial mathematical expertise and manual tuning. Recent neural network-based approaches improve flexibility but often demand high computational cost and suffer…
On the Design of KL-Regularized Policy Gradient Algorithms for LLM Reasoning
arXiv:2505.17508v4 Announce Type: replace Abstract: Policy gradient algorithms have been successfully applied to enhance the reasoning capabilities of large language models (LLMs). KL regularization is ubiquitous, yet the design surface, choice of KL direction (forward vs. reverse), normalization (normalized vs.…
LiveClin: A Live Clinical Benchmark without Leakage
arXiv:2602.16747v1 Announce Type: new Abstract: The reliability of medical LLM evaluation is critically undermined by data contamination and knowledge obsolescence, leading to inflated scores on static benchmarks. To address these challenges, we introduce LiveClin, a live benchmark designed for approximating…
Representation Collapse in Machine Translation Through the Lens of Angular Dispersion
arXiv:2602.17287v1 Announce Type: cross Abstract: Modern neural translation models based on the Transformer architecture are known for their high performance, particularly when trained on high-resource datasets. A standard next-token prediction training strategy, while widely adopted in practice, may lead to…
PETS: A Principled Framework Towards Optimal Trajectory Allocation for Efficient Test-Time Self-Consistency
arXiv:2602.16745v1 Announce Type: new Abstract: Test-time scaling can improve model performance by aggregating stochastic reasoning trajectories. However, achieving sample-efficient test-time self-consistency under a limited budget remains an open challenge. We introduce PETS (Principled and Efficient Test-TimeSelf-Consistency), which initiates a principled…