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Research#llm📝 BlogAnalyzed: Dec 29, 2025 08:02

How to Build Contract-First Agentic Decision Systems with PydanticAI for Risk-Aware, Policy-Compliant Enterprise AI

Published:Dec 29, 2025 06:04
1 min read
MarkTechPost

Analysis

This article introduces a methodology for building agentic decision systems using PydanticAI, emphasizing a "contract-first" approach. This means defining strict output schemas that act as governance contracts, ensuring policy compliance and risk assessment are integral to the agent's decision-making process. The focus on structured schemas as non-negotiable contracts is a key differentiator, moving beyond optional output formats. This approach promotes more reliable and auditable AI systems, particularly valuable in enterprise settings where compliance and risk mitigation are paramount. The article's practical demonstration of encoding policy, risk, and confidence directly into the output schema provides a valuable blueprint for developers.
Reference

“treating structured schemas as non-negotiable governance contracts rather than optional output formats”

PermalinkMarkTechPost
Paper#llm🔬 ResearchAnalyzed: Jan 3, 2026 16:16

Audited Skill-Graph Self-Improvement for Agentic LLMs

Published:Dec 28, 2025 19:39
1 min read
ArXiv

Analysis

This paper addresses critical security and governance challenges in self-improving agentic LLMs. It proposes a framework, ASG-SI, that focuses on creating auditable and verifiable improvements. The core idea is to treat self-improvement as a process of compiling an agent into a growing skill graph, ensuring that each improvement is extracted from successful trajectories, normalized into a skill with a clear interface, and validated through verifier-backed checks. This approach aims to mitigate issues like reward hacking and behavioral drift, making the self-improvement process more transparent and manageable. The integration of experience synthesis and continual memory control further enhances the framework's scalability and long-horizon performance.
Reference

“ASG-SI reframes agentic self-improvement as accumulation of verifiable, reusable capabilities, offering a practical path toward reproducible evaluation and operational governance of self-improving AI agents.”

PermalinkArXiv
Research#AI Accessibility📝 BlogAnalyzed: Dec 28, 2025 21:58

Sharing My First AI Project to Solve Real-World Problem

Published:Dec 28, 2025 18:18
1 min read
r/learnmachinelearning

Analysis

This article describes an open-source project, DART (Digital Accessibility Remediation Tool), aimed at converting inaccessible documents (PDFs, scans, etc.) into accessible HTML. The project addresses the impending removal of non-accessible content by large institutions. The core challenges involve deterministic and auditable outputs, prioritizing semantic structure over surface text, avoiding hallucination, and leveraging rule-based + ML hybrids. The author seeks feedback on architectural boundaries, model choices for structure extraction, and potential failure modes. The project offers a valuable learning experience for those interested in ML with real-world implications.
Reference

“The real constraint that drives the design: By Spring 2026, large institutions are preparing to archive or remove non-accessible content rather than remediate it at scale.”

Permalinkr/learnmachinelearning
Research Paper#Cryptocurrency Trading, Algorithmic Trading, Backtesting🔬 ResearchAnalyzed: Jan 3, 2026 20:00

AutoQuant: Auditable Framework for Crypto Futures Strategy Tuning

Published:Dec 27, 2025 05:46
1 min read
ArXiv

Analysis

This paper addresses the fragility of backtests in cryptocurrency perpetual futures trading, highlighting the impact of microstructure frictions (delay, funding, fees, slippage) on reported performance. It introduces AutoQuant, a framework designed for auditable strategy configuration selection, emphasizing realistic execution costs and rigorous validation through double-screening and rolling windows. The focus is on providing a robust validation and governance infrastructure rather than claiming persistent alpha.
Reference

“AutoQuant encodes strict T+1 execution semantics and no-look-ahead funding alignment, runs Bayesian optimization under realistic costs, and applies a two-stage double-screening protocol.”

PermalinkArXiv
Research#llm🔬 ResearchAnalyzed: Jan 4, 2026 08:30

VET Your Agent: Towards Host-Independent Autonomy via Verifiable Execution Traces

Published:Dec 17, 2025 19:05
1 min read
ArXiv

Analysis

This research paper, published on ArXiv, focuses on enhancing the autonomy of AI agents by enabling verifiable execution traces. The core idea is to make the agent's actions transparent and auditable, allowing for host-independent operation. This is a significant step towards building more reliable and trustworthy AI systems. The paper likely explores the technical details of how these verifiable traces are generated and verified, and the benefits they provide in terms of security, robustness, and explainability.
Reference

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PermalinkArXiv
Research#llm🔬 ResearchAnalyzed: Jan 4, 2026 07:51

HalluGraph: Auditable Hallucination Detection for Legal RAG Systems via Knowledge Graph Alignment

Published:Dec 1, 2025 13:31
1 min read
ArXiv

Analysis

This article introduces HalluGraph, a method for detecting hallucinations in legal Retrieval-Augmented Generation (RAG) systems. The approach uses knowledge graph alignment to improve the auditability of the detection process. The focus on legal applications suggests a practical and potentially impactful area of research, given the high stakes involved in legal information retrieval and generation. The use of knowledge graphs is a promising technique for improving the reliability of LLMs in this domain.
Reference

“The article's focus on legal applications and the use of knowledge graphs suggests a practical and potentially impactful area of research.”

PermalinkArXiv