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arXiv cs.DC

DistOptimize: Automatic Optimization of Distributed Protocols

arXiv:2610.39017v1 Announce Type: new Abstract: Distributed consensus and consistency protocols are difficult to reason about yet foundational to the cloud; as such, they often undergo a rigorous proof and formal verification process before deployment. The same level of rigor is rarely applied to protocol modifications, which occur frequently in production and threaten to compromise safety. In this paper, we eliminate the risk of safety violations from manual modifications by presenting an automatic optimizer for distributed protocols: DistOptimize. DistOptimize adapts throughput optimizations from academic languages to frameworks used in production, verifying the correctness of the rewrites in Rocq and automating their application through a combination of calibration and ILP-based search

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arXiv cs.CL

A Quantitative Approach to Asch Conformity in Frontier Large Language Models

arXiv:2610.36668v1 Announce Type: new Abstract: The Asch conformity experiment has recently gained considerable attention as a way of measuring conformity in large language models (LLMs). While previous studies have focused on demonstrating conformity in LLMs, our work adds a further quantitative layer to this approach. Instead of sampling a frontier LLM on a suite of questions, our approach samples a single question repeatedly, leveraging the probabilistic nature of LLMs to produce a distribution of answers. In doing so, we can compare the distributions of answers before and after the model is presented with the manufactured consensus. Our experiments confirm that frontier LLMs do conform, reinforcing prior literature, and, through our distribution-to-distribution comparisons, provide add

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arXiv cs.DC

MANTA: Unlocking DAG Flexibility in Asynchronous BFT

arXiv:2610.34419v1 Announce Type: new Abstract: DAG-based BFT protocols marked a shift in consensus design by replacing single-leader proposal paths with concurrent block proposals linked through a shared graph, where references encode consensus evidence. However, under heterogeneous wide-area networks (WANs), existing designs suffer from prolonged rounds and degraded performance due to straggler effects. The root cause is rigid round advancement: replicas move in synchronized rounds, waiting for enough references from the previous round before advancing. We propose a flexible DAG growth framework for asynchronous BFT consensus. Our key observation is that while reference quorums are necessary for safety and liveness, they need not block every round advancement. Instead, the connectivity r

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arXiv cs.CL

Multi-Agent Retrieval-Augmented Causality Assessment (MARCA) of Individual Case Safety Reports in Pharmacovigilance

arXiv:2610.33279v1 Announce Type: new Abstract: Introduction: Increasing volumes of individual case safety reports (ICSRs) challenge expert-led causality assessment in pharmacovigilance. We developed MARCA (Multi-Agent Retrieval-Augmented Causality Assessment), a four-agent framework for Naranjo-based assessment, and evaluated agreement with human experts and exploratory differences between non-RAG and RAG-labelled scores. Methods: MARCA combined scientific literature retrieval, drug-label assessment, objective-evidence classification, and clinical causality reasoning using Gemini 2.5 Pro and DeepSeek-V3.2. Agent-generated rationales informed human-assisted scoring. A consensus expert reference standard covered 215 ICSRs and 1,562 drug-event combinations (DECs) from FAERS (Q1-Q2 2025). Agr

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SiliconANGLE AI

Microsoft offers voluntary buyouts to 60,000 employees amid AI investments

Microsoft Corp. today issued voluntary buyout offers to about a quarter of its workforce. The approximately 60,000 workers affected by the move are located in the U.S., Canada, India and the UK. They reportedly all have a score of 70 or below on the company’s internal experience scale. That means the most senior employees impacted […] The post Microsoft offers voluntary buyouts to 60,000 employees amid AI investments appeared first on SiliconANGLE.

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arXiv cs.AI

Understanding Communication Failures in Multi-Agent LLM Systems

arXiv:2610.32060v1 Announce Type: new Abstract: Multi-agent LLM systems aim to improve reasoning through debate, evidence sharing, and mutual correction, but interaction can also introduce failures that final accuracy alone cannot explain. Agents may converge too early, ignore dissent, suppress correct minority answers, or preserve a wrong framing across rounds. We present a corpus-grounded taxonomy of communication failures, built from 3,211 failed multi-round trajectories across medical QA, coding, finance, multi-hop reasoning, ethics, and embodied text tasks. Using this taxonomy, we analyze failure distributions across datasets and system designs, round-level error dynamics, recovery hardness, majority transitions, minority-signal suppression, and self-reported confidence. We find that

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arXiv cs.DC

Quantum Cadence: Post-Quantum Consensus with Multiple Concurrent Proposers

arXiv:2610.32803v1 Announce Type: new Abstract: The consensus protocols of most high-performance blockchains use quorum certificates, which bundle the signatures of a quorum of validators. Signature schemes such as BLS can aggregate the signatures of a whole quorum into a single short signature, which keeps these certificates small. A large-scale quantum computer would break these schemes, and no post-quantum scheme with comparable aggregate size and aggregation time is known. Without aggregation, a certificate holds one large post-quantum signature per member of the quorum, so it becomes infeasible for each validator to broadcast certificates, as modern consensus protocols often require. We present Quantum Cadence, a post-quantum consensus protocol with multiple concurrent proposers that

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