Decentralized learning eliminates single-point-of-failure risks inherent in parameter-server architectures, but its practical deployment is bottlenecked by the communication cost of exchanging full-precision gradients over peer-to-peer links. Existing compression techniques—random quantization, sparsification, and fixe...
Ze-Hua Cheng, Wei Dai, Jia-Hao Sun· Proceedings of the Internati...· 0 citations
Decentralized agent economies increasingly build a single task from protocols that were designed and secured separately. This creates a simple problem: a workflow can look correct at each step and still produce the wrong outcome. For example, a correct escrow may release payment on an authorized approval that provides...
Rui Sun, Xi-Han Xiong, Qin Wang et al.· 0 citations
Software delegates more of its branches to models every year: which queue a ticket enters, whether a command is safe to run, whether a claim clears without a person. What the program needs back is not prose. It is one of n declared options and a number it can threshold. Today that costs a round trip to a frontier model...
Euston is presented, an 8B mathematical claim-verification model trained to resist the all-false composition of the official evaluation sets and the low precision implied at realistic error prevalence, which is principally the all-false composition of the official evaluation sets.
Constraint-Anchored Reasoning Traces (CART) is proposed, a neuro-symbolic framework that trains MLLMs to interleave natural language reasoning steps with symbolic constraint assertions: lightweight, machine-checkable statements about visual content.
