Contract2Plan: Verified Contract-Grounded Retrieval-Augmented Optimization for BOM-Aware Procurement and Multi-Echelon Inventory Planning

• URL: http://arxiv.org/abs/2601.06164v1
• Date: Wed, 07 Jan 2026 01:38:05 GMT
• Title: Contract2Plan: Verified Contract-Grounded Retrieval-Augmented Optimization for BOM-Aware Procurement and Multi-Echelon Inventory Planning
• Abstract summary: We introduce Contract2Plan, a verified GenAI-to-optimizer pipeline that inserts a solver-based compliance gate before plans are emitted.<n>The system retrieves clause evidence with provenance, extracts a typed constraint schema with evidence spans, compiles constraints into a BOM-aware MILP, and verifies grounding, eligibility, consistency, and feasibility.
• Score: 0.0
• License: http://arxiv.org/licenses/nonexclusive-distrib/1.0/
• Abstract: Procurement and inventory planning is governed not only by demand forecasts and bills of materials (BOMs), but also by operational terms in contracts and supplier documents (e.g., MOQs, lead times, price tiers, allocation caps, substitution approvals). LLM-based extraction can speed up structuring these terms, but extraction-only or LLM-only decision pipelines are brittle: missed clauses, unit errors, and unresolved conflicts can yield infeasible plans or silent contract violations, amplified by BOM coupling. We introduce Contract2Plan, a verified GenAI-to-optimizer pipeline that inserts a solver-based compliance gate before plans are emitted. The system retrieves clause evidence with provenance, extracts a typed constraint schema with evidence spans, compiles constraints into a BOM-aware MILP, and verifies grounding, eligibility, consistency, and feasibility using solver diagnostics, triggering targeted repair or abstention when automation is unsafe. We formalize which clause classes admit conservative repair with contract-safe feasibility guarantees and which require human confirmation. A self-contained synthetic micro-benchmark (500 instances; T=5) computed by exact enumeration under an execution model with MOQ uplift and emergency purchases shows heavy-tailed regret and nontrivial MOQ-violation incidence for extraction-only planning, motivating verification as a first-class component of contract-grounded planning systems.

Related papers

• Bonded Recourse for Smart-Contract Settlement of Compensable Agent Side Effects [0.6564588068252765]
  authorization and local compensation cover pre-action admission and in-runtime rollback, but neither settles the residual harm left after a permitted action fails.<n>We design Recourse, a smart-contract settlement protocol for compensable agent side effects that binds each admitted action to scope, recovery, evidence, payout, and collateral.
  arXiv  Detail & Related papers  (2026-09-01T23:05:43Z)
• One Gate Is Not Enough: Composing Stateful Pre-Action Controls for Agentic AI [0.0]
  We show that a remediation applied by one control can change the action, evidence, or context another control evaluates, invalidating that control's earlier judgment.<n>We formalize this coupling and give a remediate-and-regate protocol that restores per-action soundness in the current bounded, idempotent setting.<n>This is a mechanism demonstration on open payload data with a synthetic metadata layer, not a claim about production prevalence.
  arXiv  Detail & Related papers  (2026-08-18T22:24:44Z)
• Agentic Security: A Systematization of Tools, Failure Modes, and Design Laws for LLM-Driven Penetration Testing [0.21990652930491858]
  Agentic security uses large-language-model (LLM) agents to plan, dispatch, and interpret security tools.<n>We systematize these failures through a hands-on evaluation of ten widely used static, dynamic, cloud, orchestration, and AI red-teaming tools.
  arXiv  Detail & Related papers  (2026-08-16T07:18:04Z)
• TAPO: Tool-Aware Policy Optimization via Credit Transfer for Multimodal Search Agents [54.08846865906602]
  We characterize credit misassignment as a systematic failure mode of GRPO in tool-augmented multimodal search agents.<n>We propose Tool-Aware Policy Optimization (TAPO), which exploits the parameter-determinism property of information-acquisition tools.
  arXiv  Detail & Related papers  (2026-06-04T07:15:43Z)
• VASO: Formally Verifiable Self-Evolving Skills for Physical AI Agents [57.240036084348354]
  We introduce VASO, a framework for verification-guided self-evolution of robot skill contracts.<n>VASO verifies logically inconsistent skill contracts, then verifies plans induced by the skill against global and local temporal specifications.<n>On Clearpath Jackal and PX4 quadcopter tasks, VASO reaches 97.2% formal-specification compliance using fewer than 100 optimization samples.
  arXiv  Detail & Related papers  (2026-06-03T20:02:35Z)
• PRISM: : Planning and Reasoning with Intent in Simulated Embodied Environments [59.07829883257003]
  Built on five multi-room apartments, PRISM structures 300 human-verified tasks into three capability tiers.<n>PRISM exposes an agent-agnostic executable action API that allows arbitrary agents to be evaluated end-to-end.
  arXiv  Detail & Related papers  (2026-05-12T04:59:47Z)
• Alignment Contracts for Agentic Security Systems [7.008213336755055]
  We introduce alignment contracts, a framework for specifying and enforcing behavioral constraints over observable effect traces.<n>A contract defines scope, allowed and forbidden effects, resource budgets, and disclosure policies.<n>A Lean 4 artifact checks the formal core theorems used by the paper.
  arXiv  Detail & Related papers  (2026-04-30T14:38:05Z)
• IMPACT-CYCLE: A Contract-Based Multi-Agent System for Claim-Level Supervisory Correction of Long-Video Semantic Memory [73.22944697933603]
  Existing pipelines produce opaque, end-to-end outputs that expose no intermediate state for inspection.<n>We present IMPACT-Cycle, a supervisory multi-agent system that reformulates long-video understanding as a multimodal iterative claim-level maintenance.
  arXiv  Detail & Related papers  (2026-04-22T03:03:33Z)
• LLM Readiness Harness: Evaluation, Observability, and CI Gates for LLM/RAG Applications [51.56484100374058]
  We present a readiness harness for LLM and RAG applications that turns evaluation into a deployment decision workflow.<n>The system combines automated benchmarks, OpenTelemetry observability, and CI quality gates under a minimal API contract.<n>We evaluate the harness on ticket-routing and BEIR tasks with full Azure matrix coverage.
  arXiv  Detail & Related papers  (2026-03-28T18:03:32Z)
• Incentive Aware AI Regulations: A Credal Characterisation [14.228416693145649]
  High-stakes ML applications demand strict regulations, but strategic ML providers often evade them to lower development costs.<n>We introduce regulation mechanisms: a framework that maps empirical evidence from models to a license for some market share.<n>We prove that a mechanism has perfect market outcome if and only if the set of non-compliant distributions forms a credal set of probability measures.
  arXiv  Detail & Related papers  (2026-03-05T13:42:19Z)
• Teaching LLMs to Ask: Self-Querying Category-Theoretic Planning for Under-Specified Reasoning [1.8055130471307603]
  Inference-time planning with large language models frequently breaks under partial observability.<n>We introduce textbfSelf-Querying Bidirectional Categorical Planning (SQ-BCP), which explicitly represents precondition status.<n>We prove that when the verifier succeeds and hard constraints pass deterministic checks, accepted plans are compatible with goal requirements.
  arXiv  Detail & Related papers  (2026-01-27T19:41:10Z)
• CreditAudit: 2$^\ ext{nd}$ Dimension for LLM Evaluation and Selection [44.251742023911135]
  CreditAudit is a deployment oriented credit audit framework that evaluates models under a family of semantically aligned and non adversarial system prompt templates.<n>We show that models with similar mean ability can exhibit substantially different fluctuation, and stability risk can overturn prioritization decisions in agentic or high failure cost regimes.
  arXiv  Detail & Related papers  (2026-01-23T07:53:25Z)
• ORCHID: Orchestrated Retrieval-Augmented Classification with Human-in-the-Loop Intelligent Decision-Making for High-Risk Property [6.643427585499247]
  ORCHID is a modular agentic system for HRP classification.<n>It pairs retrieval-augmented generation (RAG) with human oversight to produce policy-based outputs that can be audited.<n>The demonstration shows single item submission, grounded citations, SME feedback capture, and exportable audit artifacts.
  arXiv  Detail & Related papers  (2025-11-07T03:48:05Z)
• 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)
• Automatic Building Code Review: A Case Study [6.530899637501737]
  Building officials face labor-intensive, error-prone, and costly manual reviews of design documents as projects increase in size and complexity.<n>This study introduces a novel agent-driven framework that integrates BIM-based data extraction with automated verification.
  arXiv  Detail & Related papers  (2025-10-03T00:30:14Z)
• Unsupervised Conformal Inference: Bootstrapping and Alignment to Control LLM Uncertainty [49.19257648205146]
  We propose an unsupervised conformal inference framework for generation.<n>Our gates achieve close-to-nominal coverage and provide tighter, more stable thresholds than split UCP.<n>The result is a label-free, API-compatible gate for test-time filtering.
  arXiv  Detail & Related papers  (2025-09-26T23:40:47Z)
• COIN: Uncertainty-Guarding Selective Question Answering for Foundation Models with Provable Risk Guarantees [51.5976496056012]
  COIN is an uncertainty-guarding selection framework that calibrates statistically valid thresholds to filter a single generated answer per question.<n>COIN estimates the empirical error rate on a calibration set and applies confidence interval methods to establish a high-probability upper bound on the true error rate.<n>We demonstrate COIN's robustness in risk control, strong test-time power in retaining admissible answers, and predictive efficiency under limited calibration data.
  arXiv  Detail & Related papers  (2025-06-25T07:04:49Z)
• Supervised Optimism Correction: Be Confident When LLMs Are Sure [91.7459076316849]
  We establish a novel theoretical connection between supervised fine-tuning and offline reinforcement learning.<n>We show that the widely used beam search method suffers from unacceptable over-optimism.<n>We propose Supervised Optimism Correction, which introduces a simple yet effective auxiliary loss for token-level $Q$-value estimations.
  arXiv  Detail & Related papers  (2025-04-10T07:50:03Z)
• Are You Getting What You Pay For? Auditing Model Substitution in LLM APIs [71.7892165868749]
  Commercial Large Language Model (LLM) APIs create a fundamental trust problem.<n>Users pay for specific models but have no guarantee that providers deliver them faithfully.<n>We formalize this model substitution problem and evaluate detection methods under realistic adversarial conditions.<n>We propose and evaluate the use of Trusted Execution Environments (TEEs) as one practical and robust solution.
  arXiv  Detail & Related papers  (2025-04-07T03:57:41Z)
• Attribute Controlled Fine-tuning for Large Language Models: A Case Study on Detoxification [76.14641982122696]
  We propose a constraint learning schema for fine-tuning Large Language Models (LLMs) with attribute control. We show that our approach leads to an LLM that produces fewer inappropriate responses while achieving competitive performance on benchmarks and a toxicity detection task.
  arXiv  Detail & Related papers  (2024-10-07T23:38:58Z)
• Language Models with Conformal Factuality Guarantees [44.767328168194815]
  Conformal factuality is a framework that can ensure high probability correctness guarantees for language model (LM) outputs. We show that conformal prediction in language models corresponds to a back-off algorithm that provides high probability correctness guarantees.
  arXiv  Detail & Related papers  (2024-02-15T18:31:53Z)

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.