Re-derivability Decides What a Staged Agent Pipeline Recovers After an Upstream Fault
On the map: LLM agents and tool use → Software engineering coding agents also fits: Agent threats, guardrails and governance
Abstract (arXiv)
One variable sets what an upstream fault costs a staged pipeline of language-model agents: re-derivability, how much of what a stage needs it can rebuild from the original problem. Grounding an inspector agent in that problem is worth +0.608 [+0.517, +0.700] to +0.358 over a blind one on four open-weight backbones served with thinking disabled, and on the two Qwen backbones the blind inspector changes no item at all. That head-to-head is exploratory. One deterministic fault enters the first stage, and we re-expose the original problem to k = 0,dots,3 of the downstream stages with agents, items, fault and topology held fixed, on 120 gsmhard items per arm at temperature zero. Accuracy under fault rises on four of four backbones, from +0.233 to +0.392, the largest Holm-adjusted p being 2.1×10-6. A registered kill test rules out tokens. Blanking every word holds the word slots fixed, and retention tracks the visible fraction on four of four, climbing from 0.221 to 0.692 on the primary. Those two families are confirmatory and everything else here is exploratory. The interaction excludes zero on two of four backbones under the registered pipeline, four of four under a three-stage pipeline, and three of four under full message history, the primary at +0.317. On Llama-3.1-8B the fault carries no detectable cost at any dose, so the other three carry every claim about what a fault costs. Re-derivability also sets what the architecture costs, and no decomposition we measured reliably beats one direct call. With no fault injected the registered pipeline loses to that call by -0.267, -0.125 and -0.317, and on Phi-4 reads +0.058 at p = 0.118, which the test fails to separate from zero. The repair that works is cheap and front-loaded: the first re-grounded stage buys +0.394 of matched retention for +59.8 tokens per item on Qwen3-14B, and the stages after it buy nothing.
Key facts. No citing papers in the map yet (data through 7 Oct 2026). Placed in Software engineering coding agents, one of 16 directions in LLM agents and tool use. Builds on 51 of its 69 references that are in the map. First posted on arXiv 26 Sep 2026.
Builds on
51 of 69 references.
Closest ancestors · 10
Same direction, newest first
- 2026Observability and Fault Injection for LLM-Based Multi-Agent Systems in Software Engineering25 Aug
- 2026VerifyMAS: Hypothesis Verification for Failure Attribution in LLM Multi-Agent Systems17 May
- 2026MAS-FIRE: Fault Injection and Reliability Evaluation for LLM-Based Multi-Agent Systems23 Feb
- 2025Towards Engineering Multi-Agent LLMs: A Protocol-Driven Approach14 Oct
- 2025CORRECT: COndensed eRror RECognition via knowledge Transfer in multi-agent systems28 Sep
- 2025AgenTracer: Who Is Inducing Failure in the LLM Agentic Systems?3 Sep
- 2025Why Do Multi-Agent LLM Systems Fail?17 Mar
- 2024AFlow: Automating Agentic Workflow Generation14 Oct
- 2023AutoGen: Enabling Next-Gen LLM Applications via Multi-Agent Conversation16 Aug
- 2023ChatDev: Communicative Agents for Software Development16 Jul
Other references · 41
- 2024The Llama 3 Herd of Modelscited by 14,192
- 2021Training Verifiers to Solve Math Word Problemscited by 9,743
- 2025Qwen3 Technical Reportcited by 8,195
- 2020Measuring Massive Multitask Language Understandingcited by 7,768
- 2023Efficient Memory Management for Large Language Model Serving with PagedAttentioncited by 5,767
- 2022Self-Consistency Improves Chain of Thought Reasoning in Language Modelscited by 5,601
- 2021Measuring Mathematical Problem Solving With the MATH Datasetcited by 5,203
- 2023Lost in the Middle: How Language Models Use Long Contextscited by 3,383
- 2023Self-Refine: Iterative Refinement with Self-Feedbackcited by 3,317
- 2023MetaGPT: Meta Programming for A Multi-Agent Collaborative Frameworkcited by 1,761
- 2023Improving Factuality and Reasoning in Language Models through Multiagent Debatecited by 1,739
- 2022Least-to-Most Prompting Enables Complex Reasoning in Large Language Modelscited by 1,397
29 more
- 2023Large Language Models Cannot Self-Correct Reasoning Yetcited by 965
- 2023Large Language Models Can Be Easily Distracted by Irrelevant Contextcited by 820
- 2024Phi-4 Technical Reportcited by 675
- 2023Measuring Faithfulness in Chain-of-Thought Reasoningcited by 668
- 2023Faith and Fate: Limits of Transformers on Compositionalitycited by 496
- 2023How Language Model Hallucinations Can Snowballcited by 310
- 2025Multi-agent Architecture Search via Agentic Supernetcited by 164
- 2024G-Designer: Architecting Multi-agent Communication Topologies via Graph Neural Networkscited by 110
- 2023Question Decomposition Improves the Faithfulness of Model-Generated Reasoningcited by 107
- 2024On the Resilience of LLM-Based Multi-Agent Collaboration with Faulty Agentscited by 92
- 2025AMAS: Adaptively Determining Communication Topology for LLM-based Multi-Agent Systemcited by 15
- 2025Byzantine-Robust Decentralized Coordination of LLM Agentscited by 13
- 2025Dynamic Generation of Multi-LLM Agents Communication Topologies with Graph Diffusion Modelscited by 11
- 2025Beyond Single-Agent Safety: A Taxonomy of Risks in LLM-to-LLM Interactionscited by 10
- 2026Robust Multi-Agent LLMs under Byzantine Faultscited by 10
- 2025Stochastic Self-Organization in Multi-Agent Systemscited by 9
- 2026CARD: Towards Conditional Design of Multi-agent Topological Structurescited by 7
- 2026On the Reliability Limits of LLM-Based Multi-Agent Planningcited by 7
- 2026Reclaim Evaluation: A Lossy Memory Is Worse Than an Empty Onecited by 7
- 2026Skill-as-Pseudocode: Refactoring Skill Libraries to Pseudocode for LLM Agentscited by 4
- 2026The Hallucination Snowball: Modeling Error Propagation as State Transitions in Multi-Agent LLM Pipelinescited by 4
- 2026RUMAD: Reinforcement-Unifying Multi-Agent Debatecited by 3
- 2026Byzantine Cheap Talk: Adversarial Resilience and Topology Effects in LLM Coordination Gamescited by 3
- 2026On the risk of coding before testing: An empirical study on LLM-based test generation workflowcited by 3
- 2026Beyond the Leaderboard: A Synthesis of Tool-Use, Planning, and Reasoning Failures in Large Language Model Agentscited by 3
- 2025The Social Cost of Intelligence: Emergence, Propagation, and Amplification of Stereotypical Bias in Multi-Agent Systemscited by 2
- 2026SIGMA: Skill-Incidence Graphs for Compositional Multi-Agent Designcited by 2
- 2026Adversarial Attacks in Multi-Agent LLM Pipelines: Unveiling Structural Vulnerabilities in Agentic AI Architecturescited by 1
- 2026Combating Knowledge Corruption in Agent Systems: A Byzantine-Tolerant Secure Collaborative RAG Frameworkcited by 1
Built on by
Not cited yet.
Similar, not cited
Close in wording, no citation either way
- 2026Why Retrying Fails: Context Contamination in LLM Agent Pipelines
- 2026Diagnosis Is Not Prescription: Linguistic Co-Adaptation Explains Patching Hazards in LLM Pipelines
- 2026Recoverability Has a Law: The ERR Measure for Tool-Augmented Agents
- 2026Teach it to stop, not just to click
- 2026Loud Failures, Quiet Failures: Fault Detection and Recovery in Tool-Using Language Model Agents
- 2026RESKILL: Explicit Failure Attribution and Structured Repair for Interactive Language Agents
- 2026When Harnesses Lose the Signal: Causal Evaluation of Recovery in LLM Agents
- 2026When "Must" Becomes "Maybe": Constraint Weakening in LLM Agent Workflows