arXiv AI August 19, 2026 By Ngoc Luyen Le, Marie-H\'el\`ene Abel, Bertrand Laforge

From Student Risk Prediction to SC2R: Semantics-Constrained Counterfactual Recourse for Educational Decision Support

arXiv:2608. 17618v1 Announce Type: cross Abstract: Learning analytics models can identify students at risk of poor performance, but they do not directly indicate which interventions are feasible, actionable, and compatible with educational constraints.

Summary generated by The Flow from the publisher's feed. The full article lives at arXiv AI.

arXiv AI

Jun 9

Agentic Search for Counterfactual Recourse under Fixed LLM Budgets

arXiv:2606. 08696v1 Announce Type: cross Abstract: Counterfactual recourse aims to provide actionable feature changes that would alter an unfavorable decision made by a predictive model.

By Yasuo Tabei

llms agents efficiency

arXiv AI

Jul 3

PACE: A Neuro-Symbolic Framework for Plausible and Actionable Counterfactual Explanations

arXiv:2607. 01306v1 Announce Type: new Abstract: Counterfactual explanations explain machine learning predictions by identifying minimal input changes that would alter a model's decision.

By Pavel Iakovets, Liyanapathiranage Sudeepika Wajirakumari Samarathunga, Martin Thomas Horsch, Fadi Al Machot

arXiv AI

Jul 1

Qualified Educational Capacity Planning under Heterogeneous Student Support Needs: A Synthetic Benchmark and Decision-Support Framework

arXiv:2606. 30650v1 Announce Type: cross Abstract: Educational support services often face a qualified-capacity problem: staff time is scarce, qualifications decay, new support needs can appear before anyone is prepared for them, and training consumes the same hours needed by current students.

By Carlos Eduardo Sanoja, Oscar Enrique Moreno Mayz

benchmarks safety

arXiv AI

Jun 16

RecourseBench: A Modular Framework for Reproducible Algorithmic Recourse Evaluation

arXiv:2606. 16113v1 Announce Type: new Abstract: Algorithmic recourse methods provide counterfactual explanations that inform individuals of the actions required to overturn an unfavorable model decision.

By Zahra Khotanlou, Hashir Ahmed, Chenghao Tan, Ahmed Abdelaal, Amir-Hossein Karimi

benchmarks

arXiv AI

Jun 30

Deterministic Decisions for High-Stakes AI. A Zero-Egress Pipeline with the Deployability of RAG and the Accuracy of Machine Learning

arXiv:2606. 29280v1 Announce Type: cross Abstract: We identify intervention bias as a previously unquantified failure mode of zero-shot large-language-model (LLM) educational advisory agents: without task-specific training, they recommend action when a hindsight-optimal oracle policy mandates inaction.

By Craig Atkinson

llms rag agents efficiency benchmarks safety data-engineering

arXiv AI

Jun 30

Toward Secure and Reliable PDDL Formalization of Large Language Models with Planner-in-the-Loop Feedback

arXiv:2606. 29700v1 Announce Type: new Abstract: Planning often requires symbolic specifications that are both executable and verifiable.

By Jiamei Jiang, Jiajing Zhang, Feifei Mo, Linjing Li, Daniel Zeng

llms agents fine-tuning benchmarks safety

Related stories

Agentic Search for Counterfactual Recourse under Fixed LLM Budgets

PACE: A Neuro-Symbolic Framework for Plausible and Actionable Counterfactual Explanations

Qualified Educational Capacity Planning under Heterogeneous Student Support Needs: A Synthetic Benchmark and Decision-Support Framework

RecourseBench: A Modular Framework for Reproducible Algorithmic Recourse Evaluation

Deterministic Decisions for High-Stakes AI. A Zero-Egress Pipeline with the Deployability of RAG and the Accuracy of Machine Learning

Toward Secure and Reliable PDDL Formalization of Large Language Models with Planner-in-the-Loop Feedback