arXiv:2604.02343v1 Announce Type: new
Abstract: We study the compression of LLM-generated text across lossless and lossy regimes, characterizing a compression-compute frontier where more compression is possible at the cost of more compute. For lossles…
arXiv:2601.08845v2 Announce Type: replace-cross
Abstract: In direct response to requests for a logico-mathematical test of the conjecture, we formally disprove a recently conjectured artificial intelligence trade-off between epistemic certainty and sc…
arXiv:2601.22002v2 Announce Type: replace
Abstract: Transformers achieve superior performance on many tasks, but impose heavy compute and memory requirements during inference. This inference can be made more efficient by partitioning the process acros…
arXiv:2312.06608v4 Announce Type: replace-cross
Abstract: The dynamics of many-body systems can often be captured in terms of only a few relevant variables. Mathematical and numerical approaches exist to identify these variables by exploiting a separa…
arXiv:2603.00474v2 Announce Type: replace-cross
Abstract: This paper investigates the power control problem in wireless networks by repurposing pre-trained large language models (LLMs) as relational reasoning backbones. In hyper-connected interference…
arXiv:2604.02196v1 Announce Type: cross
Abstract: Many communication and control problems are cast as multi-objective Markov decision processes (MOMDPs). The complete solution to an MOMDP is the Pareto front. Much of the literature approximates this f…
arXiv:2604.02255v1 Announce Type: cross
Abstract: In this paper, we consider a multi-armed bandit (MAB) instance and study how to identify the best arm when arm commands are conveyed from a central learner to a distributed agent over a discrete memory…
arXiv:2601.21424v2 Announce Type: replace-cross
Abstract: Many computer vision tasks share substantial overlapping information, yet conventional codecs tend to ignore this, leading to redundant and inefficient representations. The Gray-Wyner network, …
arXiv:2604.00060v1 Announce Type: new
Abstract: The low-rank matrix recovery problem seeks to reconstruct an unknown $n_1 \times n_2$ rank-$r$ matrix from $m$ linear measurements, where $m\ll n_1n_2$. This problem has been extensively studied over the…
arXiv:2506.07805v3 Announce Type: replace
Abstract: In many science and industry settings, a central challenge is designing experiments under time and budget constraints. Bayesian Optimal Experimental Design (BOED) is a paradigm to pick maximally info…