Paper proposes blockchain-linked LLM-based decision management for telecom fraud control
A new arXiv paper reframes telecom fraud control as blockchain-linked auditable decision management for synthetic telecom/IoT fraud-control requests. The main finding is that a QLoRA-tuned LLM branch becomes much more usable than zero-shot prompting but mainly approaches, rather than outperforms, a lower-cost centralized ensemble.
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Semantic Pareto-DQN: Multi-Objective RL for Financial Anomaly Detection
A new research paper proposes Semantic Pareto-DQN, a multi-objective reinforcement learning framework that uses LLMs to encode transaction features into natural-language narratives for financial anomaly detection. The method aims to overcome class imbalance and 'fraud collapse' without distortive data resampling.
Neuro-Agentic Control Framework Combines LLM Planner with Time-Series Foundation Model for Industrial IoT Security
A new research paper proposes a neuro-agentic control framework that couples an LLM-based planner (e.g., Gemini 2.5 Flash-Lite) with a pre-trained Time-Series Foundation Model to address the safety limitations of using LLMs for closed-loop control in industrial IoT environments. The framework aims to mitigate LLM hallucination risks while leveraging semantic reasoning for security control decisions.
Penn State researchers introduce FARMA attack that poisons LLM agents' reasoning logs
Researchers at Penn State proposed FARMA, a two-phase attack that poisons an LLM agent's own decision logs and rationales rather than external knowledge sources. The attack first injects seed entries that mimic normal reasoning logs, then amplifies them to manipulate future agent behavior. This shifts the threat model for agent security beyond retrieval poisoning.
Paper proposes correlation-aware bandits with surrogate rewards for LLM routing
A new arXiv paper introduces contextual bandit algorithms that leverage surrogate reward signals from machine learning models to improve LLM routing decisions. The approach accounts for inter-arm correlations and noisy auxiliary rewards, addressing limitations of classical bandits that assume conditional independence.
Agora paper proposes auction-based task allocation for LLM agents
A new arXiv paper introduces Agora, a framework that uses an incentive-compatible auction mechanism to dynamically allocate tasks to expert LLMs and tools, aiming to improve reasoning performance while accounting for cost and performance variability. The approach addresses limitations in current orchestration methods that rely on coarse-grained function matching.