STAGE: Stateful Translation to Agentic Graph Execution with Policy-Scoped Context and Deterministic Control
- URL: http://arxiv.org/abs/2608.22538v2
- Date: Thu, 27 Aug 2026 22:56:45 GMT
- Title: STAGE: Stateful Translation to Agentic Graph Execution with Policy-Scoped Context and Deterministic Control
- Abstract summary: We present STAGE, a framework that confines model judgment to policy-scoped nodes while placing procedural control in deterministic code.<n>We evaluate STAGE on three public policy-following benchmarks and Smart Dispute, a proprietary banking benchmark.
- Score: 0.29360071145551064
- License: http://arxiv.org/licenses/nonexclusive-distrib/1.0/
- Abstract: Policy-governed agents must interpret case evidence while reliably following authorized procedures. We present STAGE, an executable-graph framework that confines model judgment to policy-scoped nodes while placing procedural control in deterministic code. We evaluate STAGE on three public policy-following benchmarks and Smart Dispute, a proprietary banking benchmark. Compared with monolithic full-policy execution, STAGE improves task success and repeated-run reliability, with its largest observed gains on the deeper workflows. On $τ^2$-bench Telecom and Smart Dispute, $\mathrm{Pass}^{3}$ improves by up to 55.0 and 65.7 percentage points, respectively. These results demonstrate the value of combining localized policy reasoning with deterministic procedural control for enterprise use.
Related papers
- OpenLoopEvolve: A Verifiable Self-Evolution Framework for Loop Policies in Long-Horizon Complex Tasks [17.773038952370865]
This paper presents OpenLoopEvolve (OLE), a self-evolution framework centered on the Loop Policy.<n>OLE represents an agent's observation, planning, memory, action, verification, recovery, stopping, and budget control as portable policy assets with versions and lineages.<n>Online mode triggers candidate generation based on feedback from continuous operation, whereas the offline mode searches for candidate policies from archived traces and failure evidence.
arXiv Detail & Related papers (2026-08-10T09:57:26Z) - Co-Evolving LLM Evaluators and Policies via DynamicRubric [67.2962847914162]
Post-training with evaluator feedback on policy-induced samples serves as a major mechanism for improving large language models.<n>We propose DynamicRubric, a response-set-conditioned evaluator--policy co-evolution framework.<n>In experiments with 8B backbones, DynamicRubric improves evaluator performance and provides stronger policy supervision than baselines.
arXiv Detail & Related papers (2026-07-22T12:35:40Z) - EvoPolicyGym: Evaluating Autonomous Policy Evolution in Interactive Environments [64.33452656100393]
We introduce Autonomous Policy Evolution, a controlled evaluation setting in which a harness-model agent repeatedly edits an executable policy system under a fixed interaction budget.<n>We instantiate this setting in EvoPolicyGym, a benchmark built from compact interactive RL environments.<n>On the EvoPolicyGym suite, GPT-5.5 achieves the strongest aggregate rank score and top-two performance on all 16 environments.
arXiv Detail & Related papers (2026-07-02T17:10:13Z) - Beyond Goodhart's Law: A Dynamic Benchmark for Evaluating Compliance in Multi-Agent Systems [45.89982596338141]
We introduce MAC-Bench, a dynamic, adversarial benchmark designed to evaluate the procedural alignment of multi-agent systems under realistic pressure.<n>We introduce novel metrics: the Compliance-Weighted Success Rate (CSR) and the Machiavellian Gap (MG)<n>We conduct a comprehensive evaluation of state-of-the-art frontier models to reveal the pervasive trade-offs between success and compliance.
arXiv Detail & Related papers (2026-06-05T19:33:58Z) - ComplexMCP: Evaluation of LLM Agents in Dynamic, Interdependent, and Large-Scale Tool Sandbox [61.862814740220806]
$textbfComplexMCP$ is a benchmark designed to evaluate agents in rigorous conditions.<n>Built on the Model Context Protocol (MCP), $textbfComplexMCP$ provides over 300 meticulously tested tools.
arXiv Detail & Related papers (2026-05-11T16:20:51Z) - OGPO: Sample Efficient Full-Finetuning of Generative Control Policies [53.42266064673132]
Generative control policies (GCPs) have emerged as effective parameterizations for robot learning.<n>This work introduces Off-policy Generative Policy Optimization (OGPO), a sample-efficient for finetuning GCPs.<n>OGPO achieves state-of-the-art performance on manipulation tasks spanning multi-task settings, high-precision insertion, and dexterous control.
arXiv Detail & Related papers (2026-05-04T18:36:40Z) - PolicyBank: Evolving Policy Understanding for LLM Agents [51.86716874651299]
PolicyBank maintains structured, tool-level policy insights and iteratively refines them.<n>PolicyBank closes up to 82% of the gap toward a human oracle.
arXiv Detail & Related papers (2026-04-16T20:29:30Z) - Near-Miss: Latent Policy Failure Detection in Agentic Workflows [9.719140082591956]
We introduce a novel metric for detecting latent policy failures in agent conversations traces.<n>Our results show that latent failures occur in 8-17% of trajectories involving mutating tool calls.
arXiv Detail & Related papers (2026-03-31T12:26:35Z) - Runtime Governance for AI Agents: Policies on Paths [4.111929395230638]
We argue that the execution path is the central object for effective runtime governance.<n>In our view, runtime evaluation is the general case, and it is necessary for any path-dependent policy.
arXiv Detail & Related papers (2026-03-17T14:35:52Z) - Verification of Robust Properties for Access Control Policies [51.736723807086385]
Existing methods for verifying access control policies require the policy to be complete and fully determined before verification can proceed.<n>We introduce robust property verification: the problem of determining what a policy's structure commits it to regardless of how pending decisions are resolved and regardless of subsequent extension.<n>We show that despite quantifying universally over all possible policy extensions the judgment reduces to proof search in a second-order logic programming language.
arXiv Detail & Related papers (2026-03-13T17:14:38Z) - Verified Critical Step Optimization for LLM Agents [67.05296684575445]
Critical Step Optimization focuses preference learning on verified critical steps.<n>Method starts from failed policy trajectories rather than expert demonstrations.<n>Experiments on GAIA-Text-103 and XBench-DeepSearch show that CSO achieves 37% and 26% relative improvement over the SFT baseline.
arXiv Detail & Related papers (2026-02-03T11:41:02Z) - Safe and Compliant Cross-Market Trade Execution via Constrained RL and Zero-Knowledge Audits [0.5586191108738564]
We present a cross-market algorithmic trading system that balances execution quality with rigorous compliance enforcement.<n>The architecture comprises a high-level planner, a reinforcement learning execution agent, and an independent compliance agent.<n>We report effects at the 95% confidence level using paired t-tests and examine tail risk via CVaR.
arXiv Detail & Related papers (2025-10-06T15:52:12Z) - Simulation-Based Optimistic Policy Iteration For Multi-Agent MDPs with Kullback-Leibler Control Cost [3.9052860539161918]
This paper proposes an agent-based optimistic policy (OPI) scheme for learning stationary optimal policies in Markov Decision Processes (MDPs)
The proposed scheme consists of a greedy policy improvement step followed by an m-step temporal difference (TD) policy evaluation step.
We show that both the synchronous (entire state space evaluation) and asynchronous (a uniformly sampled set of substates) versions of the OPI scheme converge to the optimal value function and an optimal joint policy rollout.
arXiv Detail & Related papers (2024-10-19T17:00:23Z) - Distributed-Training-and-Execution Multi-Agent Reinforcement Learning
for Power Control in HetNet [48.96004919910818]
We propose a multi-agent deep reinforcement learning (MADRL) based power control scheme for the HetNet.
To promote cooperation among agents, we develop a penalty-based Q learning (PQL) algorithm for MADRL systems.
In this way, an agent's policy can be learned by other agents more easily, resulting in a more efficient collaboration process.
arXiv Detail & Related papers (2022-12-15T17:01:56Z) - Efficient Policy Iteration for Robust Markov Decision Processes via
Regularization [49.05403412954533]
Robust decision processes (MDPs) provide a framework to model decision problems where the system dynamics are changing or only partially known.
Recent work established the equivalence between texttts rectangular $L_p$ robust MDPs and regularized MDPs, and derived a regularized policy iteration scheme that enjoys the same level of efficiency as standard MDPs.
In this work, we focus on the policy improvement step and derive concrete forms for the greedy policy and the optimal robust Bellman operators.
arXiv Detail & Related papers (2022-05-28T04:05:20Z) - OffCon$^3$: What is state of the art anyway? [20.59974596074688]
Two popular approaches to model-free continuous control tasks are SAC and TD3.
TD3 is derived from DPG, which uses a deterministic policy to perform policy ascent along the value function.
OffCon$3$ is a code base featuring state-of-the-art versions of both algorithms.
arXiv Detail & Related papers (2021-01-27T11:45:08Z)
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.