Fix It Where It Breaks: The Case for Local Fault Recovery
Summary
This paper argues for localized fault recovery mechanisms in distributed systems, emphasizing the benefits of fixing failures at the component level rather than relying on global recovery.
Similar Articles
Metastability in Recovery: Cascading Recovery with a Loop
Discusses how cascading recovery and feedback loops can cause metastable failures in distributed systems, preventing proper recovery.
The Architecture of Errors: From Universal Impossibility to Patch-Local LLM Reliability
This paper argues that universal LLM reliability is impossible, but within operationally bounded patches (e.g., legal review, medical RAG), failures are sparse and repetitive, making reliability a local catalogue-discovery problem. It formalizes this with propositions and a corollary, relocating rather than dissolving the difficulty of long-context generation.
Building reliable multi-agent systems: patterns for cascading failure recovery
A discussion on patterns for handling cascading failures in multi-agent AI systems, comparing supervisor-worker and peer-to-peer topologies.
When Agentic Executions Fail: Detecting and Localizing Runtime Faults from Telemetry
The paper presents AgentChaosBench, a benchmark for detecting and localizing runtime faults in LLM-based agentic systems, and evaluates it using zero-shot LLM baselines, revealing significant challenges in fault diagnosis.
Diagnosis Before Recovery: Turning Agent Failures into Selective Self-Correction
This paper introduces DARC, a diagnosis-guided recovery harness that makes agent self-correction selective by profiling failure modes and pruning mismatched interventions before test-time correction, improving performance on ALFWorld, AppWorld, and XBRL Finance.