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The article explains how to use Temporal or Restate in agent-sdk-go to build durable AI agent loops in Go that survive process crashes by replaying steps and avoiding re-execution of LLM calls, demonstrated with an agent-chat application.
This paper empirically investigates how pruning, adversarial training, and hardware-induced weight faults jointly affect the reliability of convolutional neural networks, finding that adversarial training increases sensitivity to stuck-at-zero faults while pruning has little effect on fault sensitivity.
This paper introduces Self-Organising Digital Circuits, using a topology-masked Transformer to configure lookup tables in Boolean gates, enabling circuits to self-assemble and self-repair around hardware faults. It demonstrates near-perfect recovery from soft errors and generalization to larger circuit scales, bridging biological self-organization with digital hardware resilience.
This paper introduces a machine-checked conformance contract for checkpoint, interrupt, and resume semantics in workflow persistence layers, and empirically evaluates several agent workflow frameworks, finding that none fully conforms. It proposes a reference implementation and a repair for cross-process consumption.
Proposes a Center of Gravity guided weight correction method for fault-tolerant deep neural networks, achieving significant fault tolerance improvements on LSTM and CNN models without retraining.
Agora enables collective, permissionless internet-scale pretraining of large language models using heterogeneous, preemptible consumer GPUs connected via internet, demonstrated by the successful Pluralis-8B training run with 330 nodes.
This article argues that achieving exactly-once execution in distributed systems is impossible, and that agent stacks must be designed to deal with this limitation.
AMD and Meta contributors ported PyTorch Monarch to AMD Instinct GPUs with ROCm, enabling fault-tolerant distributed training at scale. The blog details the engineering work and validation on large clusters.
The article describes the porting of PyTorch Monarch, a distributed training runtime, to AMD GPUs with ROCm, enabling single-controller fault-tolerant training at scale and addressing reliability challenges in large-scale LLM training.
DeadPool introduces a fault-tolerance mechanism for LLM training that enables hot-swapping of failed nodes with spare nodes using zero-overhead in-memory checkpointing, achieving fast recovery without interrupting the job.
ProWAFT is a proactive workload-aware fault-tolerance framework for FPGA-based CNN accelerators that uses partial reconfiguration to selectively apply triple modular redundancy (TMR), minimizing a composite objective over latency, energy, and reliability risk.
Microsoft open sourced pg_durable, a PostgreSQL extension that enables durable execution of long-running SQL functions with automatic checkpointing and fault-tolerant resumption.
This article explains how to add fault tolerance to LangGraph agents using RetryPolicy, TimeoutPolicy, and error handlers, covering retries with backoff, timeouts, and compensation logic for production reliability.
DeepMind introduces Decoupled DiLoCo, a new distributed AI training architecture that enables resilient, low-bandwidth training of large models across globally dispersed data centers by isolating hardware failures.