Ailiya Borjigin

Ailiya Borjigin, Igor Stadnyk, Ben Bilski et al.

Financial AI agents often fail for a simple reason: they make users carry the complexity. A user must repeatedly restate goals, risk preferences, portfolio context, past judgments, and shifting market assumptions, while the agent answers, retrieves, acts, and forgets. In finance, this is not just inconvenient. In tasks such as market analysis, copy-trading review, and trade preparation, forgotten context and stale memory can create latency, repeated errors, weak auditability, and unsafe decisions. We propose the interaction-native knowledge harness (InKH), an architecture for financial LLM agents that absorbs complexity into the system. InKH converts user, market, portfolio, and tool events into structured operational knowledge. It uses passive knowledge injection to assemble a bounded working context buffer before the main model step, temporal graph memory for low-latency retrieval, a wiki audit surface for human-readable governance, and background extraction with maturity, decay, and write-time invalidation. We evaluate InKH on a reproducible controlled synthetic benchmark with 24 random seeds, 4 rounds, 80 episodes per round, and 6 baselines, producing 46,080 baseline-conditioned evaluations. InKH achieves mean task quality of 0.815 at 900 ms latency. Compared with agent-driven wiki-walk memory, it reduces latency by 82.95 percent, token cost by 82.29 percent, and stale-knowledge usage by 96.58 percent, while improving quality by 0.108 and traceability by 0.461. Compared with a temporal-graph system without invalidation, it improves quality by 0.050 and reduces stale-memory usage by 96.58 percent with comparable serving cost. The results support a design thesis for financial AI: adoption happens when complexity is absorbed by the system rather than transferred to the user. The benchmark validates architecture-level behavior, not live trading performance.

Ailiya Borjigin, Igor Stadnyk, Ben Bilski et al.

OpenClaw-style agent stacks turn language into privileged execution: LLM intents flow through tool interception, policy gates, and a local executor. In parallel, skill marketplaces such as skills.sh make capability acquisition as easy as installing skills and CLIs, creating a growing capability supply chain. Together, these trends shift the dominant safety failure mode from "wrong answers" to execution-induced loss, where untrusted prompts, compromised skills, or narrative manipulation can trigger real trades and irreversible side effects. We propose Survivability-Aware Execution (SAE), an execution-layer survivability standard for OpenClaw-style systems and skill-enabled agents. SAE sits as middleware between a strategy engine (LLM or non-LLM) and the exchange executor. It defines an explicit execution contract (ExecutionRequest, ExecutionContext, ExecutionDecision) and enforces non-bypassable last-mile invariants: projection-based exposure budgets, cooldown and order-rate limits, slippage bounds, staged execution, and tool/venue allowlists. To make delegated execution testable under supply-chain risk, we operationalize the Delegation Gap (DG) via a logged Intended Policy Spec that enables deterministic out-of-scope labeling and reproducible DG metrics. On an offline replay using official Binance USD-M BTCUSDT/ETHUSDT perpetual data (15m; 2025-09-01--2025-12-01, incl. funding), SAE improves survivability: MDD drops from 0.4643 to 0.0319 (Full; 93.1%), |CVaR_0.99| shrinks from 4.025e-3 to ~1.02e-4 (~97.5%), and DG loss proxy falls from 0.647 to 0.019 (~97.0%). AttackSuccess decreases from 1.00 to 0.728 with zero FalseBlock in this run. Block bootstrap, paired Wilcoxon, and two-proportion tests confirm the shifts. SAE reframes agentic trading safety for the OpenClaw+skills era: treat upstream intent and skills as untrusted, and enforce survivability where actions become side effects.

Ailiya Borjigin, Wei Zhou, Cong He

Alternative Assets tokenization is transforming non-traditional financial instruments are represented and traded on the web. However, ensuring trustworthiness in web-based tokenized ecosystems poses significant challenges, from verifying off-chain asset data to enforcing regulatory compliance. This paper proposes an AI-governed agent architecture that integrates intelligent agents with blockchain to achieve web-trustworthy tokenization of alternative assets. In the proposed architecture, autonomous agents orchestrate the tokenization process (asset verification, valuation, compliance checking, and lifecycle management), while an AI-driven governance layer monitors agent behavior and enforces trust through adaptive policies and cryptoeconomic incentives. We demonstrate that this approach enhances transparency, security, and compliance in asset tokenization, addressing key concerns around data authenticity and fraud. A case study on tokenizing real estate assets illustrates how the architecture mitigates risks (e.g., fraudulent listings and money laundering) through real-time AI anomaly detection and on-chain enforcement. Our evaluation and analysis suggest that combining AI governance with multi-agent systems and blockchain can significantly bolster trust in tokenized asset ecosystems. This work offers a novel framework for trustworthy asset tokenization on the web and provides insights for practitioners aiming to deploy secure, compliant tokenization platforms.

Wei Zhou, Ailiya Borjigin, Cong He

Modern digital ecosystems feature complex, dynamic interactions among autonomous entities across diverse domains. Traditional models often separate agents and objects, lacking a unified foundation to capture their interactive behaviors. This paper introduces the Behavioral Universe Network (BUN), a theoretical framework grounded in the Agent-Interaction-Behavior (AIB) formalism. BUN treats subjects (active agents), objects (resources), and behaviors (operations) as first-class entities, all governed by a shared Behavioral Information Base (BIB). We detail the AIB core concepts and demonstrate how BUN leverages information-driven triggers, semantic enrichment, and adaptive rules to coordinate multi-agent systems. We highlight key benefits: enhanced behavior analysis, strong adaptability, and cross-domain interoperability. We conclude by positioning BUN as a promising foundation for next-generation digital governance and intelligent applications.

Ailiya Borjigin, Cong He

We present a cross-market algorithmic trading system that balances execution quality with rigorous compliance enforcement. The architecture comprises a high-level planner, a reinforcement learning execution agent, and an independent compliance agent. We formulate trade execution as a constrained Markov decision process with hard constraints on participation limits, price bands, and self-trading avoidance. The execution agent is trained with proximal policy optimization, while a runtime action-shield projects any unsafe action into a feasible set. To support auditability without exposing proprietary signals, we add a zero-knowledge compliance audit layer that produces cryptographic proofs that all actions satisfied the constraints. We evaluate in a multi-venue, ABIDES-based simulator and compare against standard baselines (e.g., TWAP, VWAP). The learned policy reduces implementation shortfall and variance while exhibiting no observed constraint violations across stress scenarios including elevated latency, partial fills, compliance module toggling, and varying constraint limits. We report effects at the 95% confidence level using paired t-tests and examine tail risk via CVaR. We situate the work at the intersection of optimal execution, safe reinforcement learning, regulatory technology, and verifiable AI, and discuss ethical considerations, limitations (e.g., modeling assumptions and computational overhead), and paths to real-world deployment.

Ailiya Borjigin, Cong He, Charles CC Lee et al.

Decentralized trading of real-world alternative assets (e.g., gold) requires bridging physical asset custody with blockchain systems while meeting strict requirements for compliance, liquidity, and risk management. We present GoldMine OS, a research oriented architecture that employs multiple specialized AI agents to automate and secure the tokenization and exchange of physical gold into a blockchain based stablecoin ("OZ"). Our approach combines on chain smart contracts for critical risk controls with off chain AI agents for decision making, blending the transparency and reliability of blockchains with the flexibility of AI driven automation. We describe four cooperative agents (Compliance, Token Issuance, Market Making, and Risk Control) and a coordinating core, and evaluate the system through simulation and a controlled pilot deployment. In experiments the prototype delivers on demand token issuance in under 1.2 s, more than 100 times faster than manual workflows. The Market Making agent maintains tight liquidity with spreads often below 0.5 percent even under volatile conditions. Fault injection tests show resilience: an oracle price spoofing attack is detected and mitigated within 10 s, and a simulated vault mis reporting halts issuance immediately with minimal user impact. The architecture scales to 5000 transactions per second with 10000 concurrent users in benchmarks. These results indicate that an AI agent based decentralized exchange for alternative assets can satisfy rigorous performance and safety requirements. We discuss broader implications for democratizing access to traditionally illiquid assets and explain how our governance model -- multi signature agent updates and on chain community voting on risk parameters -- provides ongoing transparency, adaptability, and formal assurance of system integrity.