# ALAS: Transactional and Dynamic Multi-Agent LLM Planning arXiv: https://arxiv.org/abs/2511.03094 ยท HTML: https://mlsanity.com/paper/2511.03094/ ## Key facts - Cited by 3 papers in the map (data through 14 Oct 2026). - Placed in Software engineering coding agents, one of 16 directions in LLM agents and tool use. - Builds on 29 of its 74 references that are in the map. - First posted on arXiv 5 Nov 2025. ## Placed in - LLM agents and tool use / [Software engineering coding agents](https://mlsanity.com/agents/software-engineering-coding-agents/) ## Builds on (most cited first, 29 of 29) - [Language Models are Few-Shot Learners](https://mlsanity.com/paper/2005.14165/) (2020) - [ReAct: Synergizing Reasoning and Acting in Language Models](https://mlsanity.com/paper/2210.03629/) (2022) - [Efficient Streaming Language Models with Attention Sinks](https://mlsanity.com/paper/2309.17453/) (2023) - [Beyond the Imitation Game: Quantifying and extrapolating the capabilities of language models](https://mlsanity.com/paper/2206.04615/) (2022) - [MetaGPT: Meta Programming for A Multi-Agent Collaborative Framework](https://mlsanity.com/paper/2308.00352/) (2023) - [CAMEL: Communicative Agents for "Mind" Exploration of Large Language Model Society](https://mlsanity.com/paper/2303.17760/) (2023) - [DeepSeek-V3 Technical Report](https://mlsanity.com/paper/2412.19437/) (2024) - [Large Language Models: A Survey](https://mlsanity.com/paper/2402.06196/) (2024) - [AFlow: Automating Agentic Workflow Generation](https://mlsanity.com/paper/2410.10762/) (2024) - [Understanding the planning of LLM agents: A survey](https://mlsanity.com/paper/2402.02716/) (2024) - [Larger language models do in-context learning differently](https://mlsanity.com/paper/2303.03846/) (2023) - [Llemma: An Open Language Model For Mathematics](https://mlsanity.com/paper/2310.10631/) (2023) - [Large Language Model Agent: A Survey on Methodology, Applications and Challenges](https://mlsanity.com/paper/2503.21460/) (2025) - [Navigate through Enigmatic Labyrinth A Survey of Chain of Thought Reasoning: Advances, Frontiers and Future](https://mlsanity.com/paper/2309.15402/) (2023) - [Text and Patterns: For Effective Chain of Thought, It Takes Two to Tango](https://mlsanity.com/paper/2209.07686/) (2022) - [Large Language Models Can Learn Temporal Reasoning](https://mlsanity.com/paper/2401.06853/) (2024) - [Found in the Middle: Calibrating Positional Attention Bias Improves Long Context Utilization](https://mlsanity.com/paper/2406.16008/) (2024) - [Context-faithful Prompting for Large Language Models](https://mlsanity.com/paper/2303.11315/) (2023) - [SagaLLM: Context Management, Validation, and Transaction Guarantees for Multi-Agent LLM Planning](https://mlsanity.com/paper/2503.11951/) (2025) - [A Survey on Large Language Models with some Insights on their Capabilities and Limitations](https://mlsanity.com/paper/2501.04040/) (2025) - [A Closer Look at the Self-Verification Abilities of Large Language Models in Logical Reasoning](https://mlsanity.com/paper/2311.07954/) (2023) - [TimeBench: A Comprehensive Evaluation of Temporal Reasoning Abilities in Large Language Models](https://mlsanity.com/paper/2311.17667/) (2023) - [Multi-LogiEval: Towards Evaluating Multi-Step Logical Reasoning Ability of Large Language Models](https://mlsanity.com/paper/2406.17169/) (2024) - [ACPBench: Reasoning about Action, Change, and Planning](https://mlsanity.com/paper/2410.05669/) (2024) - [Dissecting Chain-of-Thought: Compositionality through In-Context Filtering and Learning](https://mlsanity.com/paper/2305.18869/) (2023) - [REALM-Bench: A Benchmark for Evaluating Multi-Agent Systems on Real-world, Dynamic Planning and Scheduling Tasks](https://mlsanity.com/paper/2502.18836/) (2025) - [Starjob: Dataset for LLM-Driven Job Shop Scheduling](https://mlsanity.com/paper/2503.01877/) (2025) - [LLMs can Schedule](https://mlsanity.com/paper/2408.06993/) (2024) - [PlaSma: Making Small Language Models Better Procedural Knowledge Models for (Counterfactual) Planning](https://mlsanity.com/paper/2305.19472/) (2023) ## Built on by (most cited first, 3 of 3) - [Atomix: Timely, Transactional Tool Use for Reliable Agentic Workflows](https://mlsanity.com/paper/2602.14849/) (2026) - [SKILL.nb: Selective Formalization and Gated Execution for Durable Agent Workflows](https://mlsanity.com/paper/2606.08049/) (2026) - [Graph Engineering in the Era of LLM Agents: From Individual Intelligence to System Intelligence](https://mlsanity.com/paper/2608.21156/) (2026) ## Abstract (arXiv) Large language models enable flexible multi-agent planning but remain fragile in practice: verification is often circular, state changes are not tracked for repair, and small faults trigger costly global recomputation. We present ALAS, a stateful, disruption-aware framework that separates planning from non-circular validation, records a versioned execution log for grounded checks and restore points, and performs localized repair that preserves work in progress. The validator operates independently of the planning LLM with fresh, bounded context, avoiding self-check loops and mid-context attrition. The repair protocol edits only the minimal affected region under explicit policies (retry, catch, timeout, backoff, idempotency keys, compensation, loop guards) defined in a canonical workflow IR that maps to Amazon States Language and Argo Workflows. On job-shop scheduling suites (DMU, TA) across five classical benchmarks, ALAS matches or exceeds strong single-LLM and multi-agent baselines, achieving 83.7% success, reducing token usage by 60%, and running 1.82times faster under comparable settings. A minimal reliability study shows that the validator detects injected structural faults with low overhead, and that localized repair contains runtime perturbations with a bounded edit radius and less makespan degradation than global recompute. Results indicate that the combination of validator isolation, versioned execution logs, and localized repair provides measurable efficiency, feasibility, and scalability for multi-agent LLM planning. Code and seeds will be released.