The paper introduces an observable evidence standard to assess whether adding a planning capability in industrial AI systems delivers operational value. By separating opportunity, plan change, consequence, equivalence, uncertainty, and scope, the authors evaluate diverse planning additions on real retail datasets, revealing that composite policies can mask negligible contributions, execution changes may not always yield benefits, and indirect evidence alone cannot fully attribute value. The study demonstrates how this evidence record can guide decisions on validation, deployment, monitoring, redesign, and retirement of planning capabilities.
By Yihua Xu, Yan Lyu, Jize Li, Jiani He, You Lin, Yidan Hu, Rubing Qiang
The paper introduces an evidence‑guided recovery framework for AI agents that encounter ambiguous tool outcomes and partial execution, such as timeouts or failures. It defines a shared admissibility check for key expiry, authorization timing, dependencies, and residual effects, and a terminal certificate to distinguish actual goal satisfaction from publicly supported safe completion. In a finite study of 37 assignments, the approach achieved 28 certified completions, and in a larger comparison of 58 paired units, evidence‑guided advice certified 30 completions versus 9 for evidence prompting, demonstrating a significant improvement in task completion rates.
By Genliang Zhu, Xiaobo Chen, Ziyuan Wang, Chu Wang
The paper investigates how agent harnesses—specifically planning guidance, execution organization, and completion verification—affect performance in retail and airline pilot tasks. By comparing fixed, task‑specific plans to shuffled policy text of equal length, the study finds that fixed plans improve success rates by about 7 percentage points, especially on complex tasks. A read‑only verifier rejects a majority of invalid episodes while incurring minimal cost, and its impact varies with the penalty for erroneous acceptance, often matching the full planning‑plus‑verification benefit at a lower cost.
By Yukun Zhang, Kemu Xu, Yishen Chen
The paper investigates how the distribution of capacity and task information across stages of AI production affects the economic value of inference. Using controlled workflow experiments on software‑engineering tasks, it finds that direct execution achieves a 59.6% success rate at token ceilings of 12,000 and 24,000, while information‑constrained planning improves from 36.2% to 51.2%. The study also shows that giving planners access to task issues boosts success, and that scaling token limits changes the balance between planning and execution workloads.
By Yukun Zhang, Kemu Xu, Yishen Chen
arXiv:2606. 29878v1 Announce Type: new Abstract: Predictive models are increasingly embedded in operational decision-making, yet standard explanation methods typically explain forecasts rather than the decisions those forecasts induce.
By Konstantinos Ziliaskopoulos, Alexander Vinel, Alice E. Smith
Predictive models are increasingly embedded in operational decision-making, yet standard explanation methods typically explain forecasts rather than the decisions those forecasts induce. This distinction is important in predict-then-optimize systems: large forecast changes may leave the optimizer's action unchanged, while small changes can alter the selected decision and its realized value.
The paper introduces a compute‑aware three‑action controller for evaluator‑guided reasoning, deciding whether to trust the current answer, fallback, or expand inference. It scores terminal actions by predicted success and uses a rollout‑based surrogate for expansion, explicitly pricing compute cost. Experiments on width expansion and revision tasks show the controller improves utility over fixed policies while reducing online compute by about 30% and 16%, respectively, and demonstrates that evaluator‑guided reasoning is best viewed as resource allocation under uncertainty.
By Weida Liang, Kenji Kawaguchi
arXiv:2607. 21268v1 Announce Type: cross Abstract: In many social-science research tasks, such as economics, LLM-based agents must produce outputs for which no cheap, task-complete, machine-readable correctness signal exists.
By Chen Zhu, Xiaolu Wang, Weilong Zhang
arXiv:2608.29560v1 Announce Type: new
Abstract: A company with a fixed artificial intelligence (AI) budget must decide which large language model (LLM) handles each recurring workload. What it lacks...
By Hamed Khosravi, Xiaoming Huo
arXiv:2607. 26159v1 Announce Type: cross Abstract: An AI benchmark result rarely reaches a consequential claim in one step.
By Brett Reynolds
arXiv:2608. 07474v1 Announce Type: new Abstract: Prior work showed that human-in-the-loop oversight becomes structurally untenable in high-loss domains when AI output velocity V exceeds human cognitive capacity C_max.
By Hiroki Naito
arXiv:2609.37125v1 Announce Type: new
Abstract: Prospective memory allows an agent to retain an intention tied to a future condition, but the stored intention does not reveal whether that condition c...
By Zhengkun Di, Bin Shi, Kai Sun, Yiming Xu, Bo Dong