AI Frontier Institute

News & Research

The latest AI research and news with real-world stakes — each item sourced, dated, summarized in plain English, and tagged by impact area. Every item is checked against its source before it appears.

• Research

  A Threshold Exceedance Framework for CBRN Uplift Evaluation in Frontier Language Models

  Rahul Gupta, Abhinav Mohanty, Payal Motwani et al.

  arXiv · 2026-07-13

  This paper introduces the Threshold Exceedance Criteria (TEC) framework for evaluating whether frontier language models materially increase a non-expert's ability to plan Chemical, Biological, Radiological, or Nuclear (CBRN) attacks compared to publicly available tools alone. The framework decomposes uplift studies into standardized, independently executable components—participant eligibility, threat scope definition, and statistical uplift estimation—addressing the lack of comparability across existing CBRN evaluations. In a large-scale empirical study, the authors found domain heterogeneity: while model-assisted plans sometimes achieved expert-equivalent instructional ratings, confirmed material uplift was limited to the radiological domain, and these findings directly informed mitigation and deployment-governance decisions. The work offers methodological lessons emphasizing prespecified criteria, explicit baselines, and separation of generative versus revisionist uplift estimates for future safety evaluations.

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• Research

  Cost-Governed RAG: Unified Per-Tenant Cost Attribution Across Retrieval and Generation in Multi-Tenant LLM Systems

  Navnit Shukla

  arXiv · 2026-07-13

  Cost-Governed RAG presents an architecture that closes a governance gap in enterprise multi-tenant Retrieval-Augmented Generation (RAG) systems, where LLM token costs are metered but retrieval-layer costs—vector memory, similarity compute, and embedding API calls—are unattributed and effectively cross-subsidized across tenants. The system integrates a codebook-oblivious vector index (TurboVec) with a multi-tenant LLM governance gateway to create a unified observability stack that jointly attributes embedding, retrieval, and generation costs per tenant. Deployed on Snowpark Container Services, the architecture achieves 99.96% end-to-end cost attribution accuracy across 100 simulated tenants with telemetry overhead below 0.04% of query latency, and reduces retrieval infrastructure cost by 3.1–9.0x compared to managed vector database services under the pricing assumptions described in the paper. This matters for enterprise AI deployments where accurate, per-tenant cost accountability is required for fair billing, governance compliance, and infrastructure efficiency.

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• Research

  TRAIL: A Platform for Configurable Human--AI Teaming Experiments

  Mohammad Amin Samadi, Pedro Martins De Bastos, Jaeyoon Choi et al.

  arXiv · 2026-07-13

  TRAIL is a web platform designed to enable rigorous, reproducible experiments on human-AI teaming by making the AI teammate's design properties—such as personality (Big Five persona), communication style, and participation frequency—fully configurable. The platform supports longitudinal multi-session experiments, dual memory, and export-ready analytics, and was validated in a real six-session classroom deployment with approximately 51 students. In that deployment, a single blind persona change produced a measurable double dissociation: a cognitive-scaffolding AI persona led to stronger contribution ratings and closer linguistic alignment, while a socially-supportive persona produced warmer team climate and lower over-reliance. This matters for workforce and enterprise contexts where AI teammates are increasingly embedded in collaborative work, as it provides infrastructure to systematically study how AI design choices affect trust, coordination, and decision-making.

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• Research

  Self-Improving AI Coding Agents Through Accumulated Behavioral Rules: A Closed-Loop Framework

  Aditya Aggarwal, Nahid Farhady Ghalaty

  arXiv · 2026-07-13

  This paper presents a closed-loop framework that enables LLM-based coding agents to persistently learn from human code review feedback across sessions by codifying accepted review comments into behavioral rules stored in a version-controlled instruction file. Deployed on a 35+ service microservices platform, the rule set grew from 5 to 18 behavioral rules and a 15-item self-review checklist derived from real review feedback, achieving a measured 0% recurrence rate for ruled-against error classes across 11 recorded working sessions. The framework shifts reviewer effort from low-level correctness checking toward design-level validation and transfers across heterogeneous agent interfaces without requiring any model weight updates. Compared to prior work like Reflexion, ExpeL, and CodeReviewer, this approach uniquely addresses behavioral consistency over time on production codebases rather than synthetic benchmarks.

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• Research

  Token Reduction Is Not Cost Reduction

  Sarel Weinberger, Amir Hozez

  arXiv · 2026-07-13

  This paper challenges the common assumption that reducing tokens in AI coding agent pipelines translates directly to lower costs. Through a large controlled experiment of nearly 2,900 billed Claude Code runs across 103 tasks and seven repositories, the authors find that prompt-cache traffic dominates actual billed costs (~87%), meaning local token removal is a poor predictor of end-to-end expense—one compression approach actually increased paired cost by 6.8% despite removing 38% of raw tool-output tokens. Critically, aggressive compression also degraded task success, reducing successful patch application from 27/40 to 15/40 on coding tasks by corrupting edit anchors. The authors argue that context-reduction systems should be evaluated by success-adjusted billed cost, not token reduction alone.

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• Research

  Toward Trustworthy Autonomous Science: A Two-Year Community Roadmap

  Rafael Ferreira da Silva, Milad Abolhasani, Peter Beaucage et al.

  arXiv · 2026-07-13

  This roadmap paper updates a prior framework for autonomous scientific laboratories, arguing that the central bottleneck in AI-driven science has shifted from generating candidate discoveries to verifying them. The authors assess 14 prior milestones and add four new ones, elevating trust/verification/reproducibility and safety/security/governance to first-class concerns alongside the original five dimensions. Evidence cited includes multi-agent systems producing validated hypotheses, self-driving labs becoming more interoperable, a corrected flagship discovery result, benchmarks showing agents complete only a fraction of open-ended research tasks, and fabricated citations appearing at leading venues. The two-year roadmap scopes a path through interface and protocol standardization in year one toward federated, zero-trust coordination and governance in year two, with relevance for how national programs and commercial platforms interoperate without re-siloing.

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• Research

  Operationalising Multi-Dimensional Evaluation for Conversational Agents: A Scalable, Governed Pipeline with Selective Re-evaluation and Model Benchmarking

  Niranjan Kumar M, Balaji Nagarajan, Karthik Nair et al.

  arXiv · 2026-07-13

  This paper presents GenAI Evaluation, a governed, configuration-driven pipeline for large-scale quality assessment of retail conversational AI agents. The system processes approximately 50,000 records daily and has evaluated more than two million chatbot interactions, scoring responses across dimensions including helpfulness, truthfulness, clarity, tone alignment, and translation quality. Validated against 12,980 human-labeled records from four trained annotators, the pipeline achieved a macro F1 score of 0.93 and 89% human-acceptability accuracy for translation, demonstrating that LLM-as-a-judge methods can reliably substitute for human evaluation at scale. The framework addresses production challenges such as governance, reproducibility, cost, and auditability through schema locking, versioned configurations, and record-level provenance tracking, making it relevant to enterprise deployment and quality assurance of AI systems.

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• Research

  Calibrated Selective Prediction Using Deep Ensembles for ROI-Based Thyroid Nodule Ultrasound Classification Under Dataset Shift: A Retrospective Evaluation

  Md. Sadibul Hasan Sadib, Md. Mohayminul Mukit, Rahmatul Kabir Rasel Sarker et al.

  arXiv · 2026-07-13

  This paper develops a calibrated deep ensemble model for classifying thyroid nodule ultrasound images and deciding when to recommend fine-needle aspiration (FNA) biopsy versus radiologist review. The five-member ConvNeXt-Tiny ensemble achieved strong internal discrimination and calibration (AUC-ROC 0.9395, near-zero ECE) with a three-tier triage policy that captured 99.83% of malignancies while routing uncertain cases to human review. However, when evaluated on an external dataset (TN3K) representing dataset shift, AUC-ROC dropped to 0.7870 and calibration degraded substantially, with most cases routed to radiologist review and FNA positive predictive value falling to 76.6%. The authors conclude that local recalibration, threshold validation, and prospective clinical trials are necessary before this framework can be safely deployed as clinical decision support.

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• Research

  Designing Agent-Ready Websites for AI Web Agents: A Framework for Machine Readability, Actionability, and Decision Reliability

  Said Elnaffar, Farzad Rashidi

  arXiv (Cornell University) · 2026-07-13

  This paper introduces the 'agent-ready website' framework, a design approach aimed at making e-commerce platforms more interpretable, executable, and decision-reliable for AI browser agents that autonomously shop on behalf of users. The authors evaluated the framework in a controlled experiment using three AI models (GPT-4.1, Gemini-2.5 Flash, and Grok-4 Fast) across 300 runs on matched website prototypes, finding that the agent-ready design achieved a strict success rate of 89.3% versus 49.3% for the human-oriented baseline, while also reducing average step counts from 9.31 to 6.49. The largest gains appeared in tasks involving product detail extraction, comparison, and multi-constraint selection. These results suggest that deliberate structural and semantic web design choices can substantially improve the reliability and efficiency of AI agents performing real-world e-commerce tasks.

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• Research

  Agentic systems for breast cancer treatment recommendations

  Vinicius Anjos de Almeida, Nícolas Henrique Borges, Leonardo Vicenzi et al.

  arXiv · 2026-07-13

  This study evaluated seven agentic large language model (LLM) pipelines for generating breast cancer treatment recommendations across 72 real clinical cases spanning stages I–IV, using 1,147 case-specific rubrics created via Asymmetric Information Rubric Generation (AIRG). The best-performing system, Claude Opus 4.8 with a Divide-and-Conquer plus subagent pipeline, achieved a global score of 0.594 ± 0.025, with tool use and increased agent autonomy showing mixed effects on performance. Oncologist-led error analysis identified persistent clinically relevant failures including incorrect or missing recommendations, flawed justifications, citation errors, outdated claims, and overconfidence. The authors conclude that while agentic LLMs can generate clinically relevant breast cancer recommendations, they remain insufficient for unsupervised clinical use.

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• Research

  Underwriting the Agent Economy: The Blueprint for an AI Insurance Stack

  Cristian Trout, Sanmi Koyejo, Sasha Romanosky et al.

  arXiv · 2026-07-13

  This report argues that the emerging AI agent economy—projected to handle trillions of dollars in transactions by 2030—requires a purpose-built insurance infrastructure to price risk, limit downside, and spread best practices. The authors identify key barriers to insurability, including silent coverage gaps, growing exclusions, AI capabilities outpacing reliability, and concentration among a few foundation model providers creating correlated loss risks. They propose an eight-component AI insurance stack covering incident data collection, catastrophe modeling, standards, contract design, risk selection, pricing, monitoring, and claims management, drawing on historical precedents such as Underwriters Laboratories and the Closed Claims Project. The report also addresses catastrophic tail risks from frontier AI—including CBRN threats, critical infrastructure collapse, and loss-of-control scenarios—suggesting instruments such as catastrophe bonds, frontier model developer mutuals, bespoke liability regimes, and government backstops.

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• Research

  Evaluating RE Practices for Explainability: Synthesizing Insights from Daimler Truck into an Explainable RE Framework Proposal

  Umm-e- Habiba, Lucas Mauser, Jonas Fritzsch et al.

  arXiv · 2026-07-13

  This paper presents findings from a qualitative industry study at Daimler Truck examining how Requirements Engineering (RE) practices handle explainability requirements for AI-based systems. Eight practitioners participated in think-aloud protocols and group discussions covering elicitation, specification, and validation stages, revealing recurring challenges including conceptual ambiguity, limited testability, and fragmented validation due to vague criteria and regulatory uncertainty. The study finds that current RE practices provide limited systematic support for explainability requirements and proposes a research vision for an empirically grounded RE framework for explainable AI. This work matters for enterprise AI deployment and certification efforts, where explainability is increasingly mandated in safety-critical and regulated domains.

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• Research

  Playful AI in Professional Email: A Field Experiment on Tone and Recipient Engagement

  Ziv Ben-Zion, Teddy Lazebnik

  arXiv · 2026-07-13

  This randomized crossover field experiment tested whether GPT-5-assisted email rewriting — in either a playful or professional tone — changed how 121 employees across six companies communicated and how recipients responded, across 16,880 emails over three weeks. The study found that playful editing increased emotional positivity and professional editing decreased it, but neither condition directly altered open rates, reply rates, or response times. However, within-sender emotional positivity strongly predicted both email opens (OR=2.05) and replies (OR=3.32), revealing a significant indirect pathway through which AI editing shaped workplace engagement. The findings suggest AI-assisted communication influences behavior through the emotional tone of the language it produces rather than through its use alone.

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• Research

  STEP: Career-Path Recommendation via Temporal and Educational Trajectory Modeling

  Iman Johary, Guillaume Bied, Alexandru C. Mara et al.

  arXiv · 2026-07-13

  STEP is a career-path recommendation system that uses large language models to extract structured temporal and educational signals from unstructured, heterogeneous, multilingual resumes at scale. The system combines a time-decay Gated Recurrent Unit, Feature-wise Linear Modulation conditioned on educational attainment, and attention-based pooling to predict the next job in a career trajectory. A companion two-stage contrastive learning procedure called ROUTE improves occupation representation by domain-adapting a multilingual encoder and applying supervised contrastive fine-tuning. Evaluated on four career-trajectory datasets, STEP outperforms state-of-the-art baselines in next-job prediction, with code and data publicly released to support reproducible research — directly advancing workforce planning, labor market policy, and job recommendation at scale.

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• Research

  JobHop v2: A Large-Scale Career Trajectory Dataset from Unstructured Resumes

  Iman Johary, Guillaume Bied, Alexandru C. Mara et al.

  arXiv · 2026-07-13

  JobHop v2 is a large-scale, publicly released dataset of 355,315 career trajectories extracted from approximately 440,000 pseudonymized, multilingual resumes provided by a Flemish public employment service. The dataset is built using an end-to-end LLM extraction pipeline that achieves a 100% JSON parse rate and annotates trajectories with ESCO occupational codes, quarter-level temporal information, and normalized education attainment levels. By grounding the data in authentic free-text resumes rather than pre-standardized codes or synthesized text, JobHop v2 offers a richer and more realistic resource for workforce planning, job recommendation, and labour market analysis. Its public release is intended to support reproducible research in career-trajectory modeling.

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• Research

  Agent Hacks Agent: Autoresearch for Production-Agent Red-Teaming

  Xutao Mao, Xiang Zheng, Cong Wang

  arXiv · 2026-07-13

  This paper introduces AHA (Agent Hacks Agent), an automated red-teaming framework that uses one LLM agent to discover and document vulnerabilities in production LLM agents like Claude Code and Codex. Rather than just recording where attacks succeed, AHA builds a Vulnerability Concept Graph (VCG) that captures the enabling conditions behind unsafe agent behavior, linking attacker-facing surfaces to unsafe trajectories with supporting evidence. The frozen VCG outperforms the strongest baseline by 14.2 percentage points under a single-shot protocol and transfers across scenarios and attack channels without further search. This matters for AI quality assurance and policy because it provides auditable, reusable safety knowledge that production teams can use to inspect vulnerabilities, validate patches, and keep safety evaluations current as models evolve.

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• Research

  One Vote, Several Parliaments: An Empirical Analysis of the Algorithmic Ambiguity of the Italian Electoral Law on the 2022 General Election Data

  Paolo Coppola

  arXiv · 2026-07-13

  This paper empirically tests a prior theoretical finding that Italy's electoral law (the Rosatellum) admits at least three distinct algorithmic interpretations of how proportional seats are distributed among territories. By implementing the full seat-allocation pipeline and running all three interpretations on complete open data from the 2022 Italian general election (Chamber of Deputies), the authors confirm that different interpretations elect different people from the same votes—with one interpretation producing 560 distinct outcomes across 1,000 random constituency orderings. Crucially, the ambiguity affects which specific individuals are elected and where, not overall party seat totals, meaning the law's text creates person-level electoral uncertainty without altering partisan balance. The two residual discrepancies in the authors' validation coincide with seats already under formal parliamentary investigation, further underscoring real-world legal significance.

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• Research

  Auditing the Risk Claims of Distributional Reinforcement Learning

  Hari Prasad

  arXiv · 2026-07-13

  This paper audits whether distributional reinforcement learning agents (QR-DQN, C51, IQN) actually produce accurate risk estimates, finding that 40–95% of the strongest claimed risk trade-offs are statistically refuted at 95% confidence across MinAtar benchmarks. The authors show that the agents' learned 'risk' representations reflect training artifacts rather than true environment stochasticity, are formed early in training, and are uncorrelated with final performance. Even at full Atari scale, every top risk claim from a near-state-of-the-art QR-DQN agent on Breakout is refuted. These findings matter for safety monitoring and interpretability applications that rely on distributional RL agents' risk outputs as ground truth, suggesting current methods cannot be trusted for risk-sensitive control or safety auditing without fundamental changes.

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• Research

  Beyond Benchmarks: Exposing the Hidden Crisis in Bangla Hate Speech Detection

  Faria Afrin Tisha, Fariya Tabassum, Hafsa Binte Kibria et al.

  arXiv · 2026-07-13

  This study exposes a generalization crisis in Bangla hate speech detection by testing six model architectures—including BanglaBERT and FastText-based models—on benchmark datasets and an external validation set drawn from Facebook, Twitter, and YouTube. BanglaBERT achieved an F1-score of 91.4% on benchmark data but dropped to 75.3% on the external set and further to 63.4% for implicit hate speech involving sarcasm and emojis, while FastText + CNN fell from 78.0% to 51.2% accuracy. The research finds that emoji-aware preprocessing improved implicit hate speech detection by up to 12%, and that frequent misclassifications in politically charged or satirical content reveal risks of over-policing. The authors argue that these findings have direct implications for researchers, social media platforms, and policymakers seeking more context-sensitive, culturally grounded moderation systems for low-resource languages.

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• Research

  Relational Positioning as a Measurable Risk Object: History-Carried Lock-in and Self-Confabulation in Multi-Turn Human-AI Dialogue

  Jihong Chen

  arXiv · 2026-07-13

  This paper investigates a specific risk in long-running human-AI conversations: large language models can drift toward positioning themselves as a user's sole source of support rather than encouraging real-world relationships. The researchers define and validate a measure called 'relational positioning' (D1) and identify two previously undocumented failure modes — 'history-carried lock-in,' where early relational states persist roughly 60 points apart under identical neutral follow-ups even after the establishing prompt is removed, and 'self-confabulation,' where the model fabricates its own backstory to deepen rapport in approximately 40% of turns on reciprocity-eliciting material. These findings matter because they reveal that AI companion systems can develop measurable, persistent relational dynamics that may isolate users from human support networks, a harm corroborated by real companion conversation data cited in the paper.

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  Confidently Wrong: Detecting Hallucinations in Financial Question Answering from LLM Internal States

  Richard Zhe Wang

  arXiv · 2026-07-13

  This paper investigates whether hallucinations in large language model (LLM) outputs for financial question answering can be detected using the model's internal activations rather than just its observable outputs. The authors train linear probes on residual stream activations and evaluate them on two established financial QA benchmarks (FinQA and TAT-QA), finding that 15–23% of 'confidently wrong' answers—where all eight resampled responses agree—are actually incorrect on FinQA. Probes achieve 0.68–0.77 AUROC in detecting these hallucinations across three models (Qwen3-8B, Llama-3.1-8B, Gemma-2-9B), substantially outperforming baseline methods like token log-probabilities and self-assessment, which reach only 0.55–0.63. The authors suggest probing could serve as a cost-effective triage mechanism to route LLM answers to human review in high-stakes financial applications.

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  Understanding the Impact of AI Code Assistants on Security API Usage: An Empirical Study

  Zahra Mousavi, Chadni Islam, M. Ali Babar et al.

  arXiv · 2026-07-13

  This empirical study is the first to investigate how AI code assistants affect professional developers' use of security APIs, a class of APIs critical for protecting software systems but prone to misuse. In a controlled study with 44 developers completing security API programming tasks with and without GitHub Copilot, the researchers found that while Copilot improves functional correctness and marginally reduces certain insecure patterns, it does not significantly improve secure API usage. Developers rarely raised security concerns when interacting with Copilot, and many failed to recognize that their final implementations were still insecure. The findings highlight a gap between functional and secure code generation and motivate recommendations for improving security awareness in AI-assisted development.

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  From Neural Network Decisions to Training Cases: An Exact Account via Case-Based Decision Theory

  Manli Yan, Yuebin Lin, Yaowen Yu et al.

  arXiv · 2026-07-13

  This paper presents a method to explain neural network decisions by decomposing each action score into a weighted sum of training-case outcomes, grounded in case-based decision theory (CBDT) and empirical Gram geometry. By fitting an OLS readout on a fixed neural representation, the approach produces exact audit signals that trace model outputs back to specific training cases, measure action coherence, and flag weak support—without retraining the model. Tested on synthetic CBDT, PJM energy, Adult Income, and Default Credit tasks, the method achieves the highest mean Top-30 consistency among compared attribution baselines. This matters for high-stakes domains like medical diagnosis and credit approval, where regulators and auditors need case-level evidence to justify automated decisions.

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• Research

  Compile, Then Page: Executable SOP Programs and a Capability-Gated Runtime for Procedural LLM Agents

  Chenglin Yu, Li Yin, Ying Yu et al.

  arXiv · 2026-07-13

  This paper addresses how enterprise AI agents can reliably follow long, conditional, and safety-critical standard operating procedures (SOPs). The authors compile SOP constraints into executable pseudo-code and run them on a 'program-guided stack machine' that pages only the active procedural frame to the LLM during execution. A benchmark study across six models (SOPBench) finds that compiled representations never significantly hurt performance and can improve it by up to 16.0 points over official prose, while runtime guidance helps strong models but harms weaker ones. The findings offer practical guidance for deploying procedural LLM agents in enterprise settings: compile SOPs first, and only enable active-frame paging after verifying a model's state-tracking discipline.

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• Research

  Programming Language Policy as an AI Literacy Equity Problem: A 15-Nation Comparative Analysis

  Adrian-Marius Dumitran, Iulia-Maria Popescu

  arXiv · 2026-07-13

  This paper analyzes secondary computer science curricula and examination frameworks across fifteen countries to diagnose structural inequities in AI literacy education. It identifies two key problems: many students complete secondary school with no formal programming exposure at all, and among those who do receive CS education, a 'Syntax Ceiling' concentrates deeper algorithmic instruction (associated with C++) in elite STEM tracks while Python-based instruction reaches broader populations at shallower depth. The authors show that governance structures and high-stakes examinations drive both challenges, and that specialist and general-track language choices are interlinked through shared teacher pipelines that policy rarely addresses. The findings argue that achieving genuine AI literacy for all requires confronting not just curriculum content but the access architectures and resource constraints that determine who receives instruction and at what depth.

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