Good to Go: The LOOP Skill Engine That Hits 99% Success and Slashes Token Usage by 99% via One-Shot Recording and Deterministic Replay

• URL: http://arxiv.org/abs/2605.14237v1
• Date: Thu, 14 May 2026 01:05:35 GMT
• Title: Good to Go: The LOOP Skill Engine That Hits 99% Success and Slashes Token Usage by 99% via One-Shot Recording and Deterministic Replay
• Abstract summary: We present the LOOP SKILL ENGINE, a system that achieves a combined 99% success rate and 99% token reduction for periodic agent tasks.<n>A deterministic execution plan captures the task's functional intent while parameterizing time-dependent and result-dependent variables.<n>Across a benchmark of periodic agent tasks spanning intervals from 5 minutes to 24 hours, the Loop Skill Engine reduces monthly token consumption by 93.3%--99.98% and cuts execution latency by 8.7x while eliminating output non-determinism.
• Score: 14.181844060152367
• License: http://arxiv.org/licenses/nonexclusive-distrib/1.0/
• Abstract: Deploying AI agents for repetitive periodic tasks exposes a critical tension: Large Language Models (LLMs) offer unmatched flexibility in tool orchestration, yet their inherent stochasticity causes unpredictable failures, and repeated invocations incur prohibitive token costs. We present the LOOP SKILL ENGINE, a system that achieves a combined 99% success rate and 99% token reduction for periodic agent tasks through a one-shot recording, deterministic replay paradigm. On its first run, the agent executes the task with full LLM reasoning while the system transparently intercepts and records the complete tool-call trajectory. A greedy length-descending template extraction algorithm then converts this recording into a parameterized, branch-free Loop Skill -- a deterministic execution plan that captures the task's functional intent while parameterizing time-dependent and result-dependent variables. All subsequent executions bypass the LLM entirely: the engine resolves template variables against real-time values and replays the tool sequence deterministically. We prove two theorems: (1) Replay Determinism -- the step sequence of a validated Loop Skill is invariant across all future executions; (2) Write Safety -- concurrent access to persistent configuration is serialized through reentrant locks and atomic file replacement. Across a benchmark of periodic agent tasks spanning intervals from 5 minutes to 24 hours, the Loop Skill Engine reduces monthly token consumption by 93.3%--99.98% and cuts execution latency by 8.7x while eliminating output non-determinism. A multi-layer degradation strategy guarantees that tasks never stall. We release the engine as part of the buddyMe open-source agent framework.

Related papers

• Act More, Decide Less: Skill-Guided Adaptive Action Chunking for Long-Horizon LLM Agents [50.85250898062218]
  Large language model (LLM) agents for long-horizon interactive tasks typically follow a ReAct-style protocol.<n>A natural alternative is to let the agent emit variable-length action chunks.<n>But naively training such policies with standard reinforcement learning fails.
  arXiv  Detail & Related papers  (2026-09-02T03:17:11Z)
• OneDayAgent: Towards a Long-Horizon Harness for Autonomous Agents [52.15259607762389]
  We present OneDayAgent, a long-horizon harness for autonomous agents.<n>OneDayAgent turns an open-ended request into a managed execution process.<n>We evaluate OneDayAgent on AgentIF-OneDay across 104 tasks.
  arXiv  Detail & Related papers  (2026-08-04T17:55:41Z)
• A Self-Triggered Agentic Push Recommendation System [77.13502692161426]
  Push notification is a critical recommendation scenario on large-scale platforms.<n>We propose a proactive, Self-Triggered End-to-end Agentic Push Recommendation System.<n>STEPS is already fully deployed at Douyin with over 1 billion users.
  arXiv  Detail & Related papers  (2026-08-03T09:20:53Z)
• Where and When to Commit: Candidate-Aware Decoding for Diffusion Language Models [29.849017661114342]
  Diffusion language models expose a provisional prediction at every denoising step.<n>Generation-time early exit stops decoding before the schedule is exhausted.<n>We introduce a training-free, candidate-aware early-exit framework that keeps the two axes separate and matches each decision to evidence of its own scope.
  arXiv  Detail & Related papers  (2026-07-30T13:04:47Z)
• AgentAbstain: Do LLM Agents Know When Not to Act? [19.223843408018997]
  Existing evaluations mostly focus on task success rather than whether agents know when to abstain.<n>We present the first systematic evaluation framework for agentic abstention: the calibrated ability of tool-using LLM agents to recognize when not to act.<n>AgentAbstain is a paired-task benchmark built on an agent-native taxonomy of 8 abstention scenarios across pre-execution reasoning and runtime discovery.
  arXiv  Detail & Related papers  (2026-07-11T00:57:36Z)
• TempAct: Advancing Temporal Plausibility in Autoregressive Video Generation via Planner-Executor RL [72.7712729541565]
  Autoregressive (AR) video diffusion models enable low-latency streaming generation by synthesizing videos chunk by chunk with cached visual context.<n>A single global prompt does not specify which sub-event should be realized in each chunk, while naively switching to step-wise prompts often leads to delayed reactions, blended step semantics, and error propagation across prompt transitions.<n>We address these challenges with TempAct, a planner--executor reinforcement learning framework that jointly optimize temporal decomposition and step-conditioned execution for temporally plausible AR video generation.
  arXiv  Detail & Related papers  (2026-06-26T12:19:28Z)
• Autonomous Topology Mutation: Safe Runtime Restructuring for Multi-Agent LLM Systems with Capability, State, and Shadow Invariants [0.11470070927586014]
  We introduce Autonomous Topology Mutation (ATM), a runtime team-mutation mechanism for multi-agent LLM frameworks.<n>ATM combines telemetry-driven overload detection with three safety invariants that gate each structural change.<n>On 720 DeepSeek-V3-driven task runs with deterministic tool stubs across four ablation conditions and three workloads, the ATM factoriser split lifts code-task success from 3.3% to 61.7%.
  arXiv  Detail & Related papers  (2026-06-04T11:38:06Z)
• StepFinder: A Temporal Semantic Framework for Failure Attribution in Multi-Agent Systems [11.500948775496218]
  Failure attribution is a task that aims to automatically identify the root cause step responsible for a failure.<n>Existing failure attribution methods mainly rely on LLMs to reason over original execution trajectories.<n>We propose StepFinder, a lightweight failure attribution framework.
  arXiv  Detail & Related papers  (2026-06-02T10:45:49Z)
• OTora: A Unified Red Teaming Framework for Reasoning-Level Denial-of-Service in LLM Agents [21.836018755837085]
  OTora is a framework for instantiating Reasoning-Level Denial-of-Service attacks.<n> OTora generates agent-aware reasoning payloads via an ICL-guided genetic search.<n>It achieves up to 10 times increases in reasoning tokens and order-of-magnitude latency slowdowns.
  arXiv  Detail & Related papers  (2026-05-09T10:55:11Z)
• SAGA: Workflow-Atomic Scheduling for AI Agent Inference on GPU Clusters [13.891522069967507]
  We propose a shift to program-level scheduling for compound AI workloads.<n>We present SAGA, a distributed scheduler that implements this abstraction through three mechanisms.
  arXiv  Detail & Related papers  (2026-05-01T09:05:28Z)
• Scaling Test-Time Compute for Agentic Coding [126.72747643609274]
  We propose a test-time scaling framework for agentic coding based on compact representations of rollout trajectories.<n>Our framework converts each rollout into a structured summary that preserves its salient hypotheses, progress, and failure modes.<n>Our method consistently improves the performance of frontier coding agents across SWE-Bench Verified and Terminal-Bench v2.0.
  arXiv  Detail & Related papers  (2026-04-16T17:39:33Z)
• From Agent Loops to Structured Graphs:A Scheduler-Theoretic Framework for LLM Agent Execution [1.8222732878503212]
  The dominant paradigm for building LLM based agents is the Agent Loop, an iterative cycle where a single language model decides what to do next by reading an ever growing context window.<n>This perspective places Agent Loops and graph based execution engines on a single semantic continuum.<n>We propose SGH, which lifts control flow from implicit context into an explicit static DAG.
  arXiv  Detail & Related papers  (2026-04-13T12:16:45Z)
• KAIJU: An Executive Kernel for Intent-Gated Execution of LLM Agents [5.931389072124715]
  Tool-calling autonomous agents based on large language models using ReAct exhibit three limitations.<n>Recent work moves towards separating planning from execution but in each case model remains coupled to the execution mechanics.<n>We introduce a system-level abstraction for LLM agents which decouples the execution agent from the reasoning layer.
  arXiv  Detail & Related papers  (2026-03-31T21:38:28Z)
• SpecEyes: Accelerating Agentic Multimodal LLMs via Speculative Perception and Planning [104.01865949020304]
  Agentic multimodal large language models (MLLMs) achieve remarkable reasoning capabilities through iterative visual tool invocation.<n>However, the cascaded perception, reasoning, and tool-calling loops introduce significant sequential overhead.<n>This overhead, termed agentic depth, incurs prohibitive latency and seriously limits system-level throughput.<n>We propose SpecEyes, an agentic-level speculative acceleration framework that breaks this sequential bottleneck.
  arXiv  Detail & Related papers  (2026-03-24T17:45:47Z)
• StreamVLA: Breaking the Reason-Act Cycle via Completion-State Gating [8.031311182554981]
  Long-horizon robotic manipulation requires bridging the gap between high-level planning and low-level control.<n>Current Vision-Language-Action models often entangle these processes, performing redundant multimodal reasoning at every timestep.<n>We present StreamVLA, a dual-system architecture that unifies textual task decomposition, visual goal imagination, and continuous action generation within a single parameter-efficient backbone.
  arXiv  Detail & Related papers  (2026-02-01T08:51:17Z)
• DoVer: Intervention-Driven Auto Debugging for LLM Multi-Agent Systems [48.971606069204825]
  DoVer is an intervention-driven debug framework for large language model (LLM)-based multi-agent systems.<n>It augments hypothesis generation with active verification through targeted interventions.<n>DoVer flips 18-28% of failed trials into successes, achieves up to 16% milestone progress, and validates or refutes 30-60% of failure hypotheses.
  arXiv  Detail & Related papers  (2025-12-07T09:23:48Z)
• SABER: Small Actions, Big Errors -- Safeguarding Mutating Steps in LLM Agents [52.20768003832476]
  We analyze execution traces on $$-Bench (Airline/Retail) and SWE-Bench Verified.<n>We formalize emphdecisive deviations, earliest action, level divergences that flip success to failure.<n>We introduce cm, a model-agnostic, gradient-free, test-time safeguard.
  arXiv  Detail & Related papers  (2025-11-26T01:28:22Z)
• Localizing Task Information for Improved Model Merging and Compression [61.16012721460561]
  We show that the information required to solve each task is still preserved after merging as different tasks mostly use non-overlapping sets of weights. We propose Consensus Merging, an algorithm that eliminates such weights and improves the general performance of existing model merging approaches.
  arXiv  Detail & Related papers  (2024-05-13T14:54:37Z)

This list is automatically generated from the titles and abstracts of the papers in this site.

This site does not guarantee the quality of this site (including all information) and is not responsible for any consequences.