Retrieval-augmented generation (RAG) remains challenging for multi-hop question answering when supporting evidence is distributed across heterogeneous sources, including text, tables, and knowledge graphs (KGs). Existing systems often rely on modality-specific retrieval pipelines or uncontrolled iterative search, making it difficult to preserve source structure while maintaining efficient evidence acquisition. We present **RELOOP**, a structure-aware retrieval framework built on **Hierarchical Sequence (HSEQ)**, a reversible representation that maps heterogeneous sources into provenance-preserving typed segments with lightweight structural links. Operating on this unified evidence interface, a lightweight iterator selects and expands candidate evidence through structure-respecting actions, while an explicit sufficiency signal determines when the accumulated evidence is adequate for answering. A head module then synthesizes the final answer from a compact, canonicalized evidence package, with an optional refinement step when inconsistent evidence is detected. Experiments on HotpotQA, HybridQA, TAT-QA, and MetaQA show that RELOOP consistently improves answer quality over strong single-pass and iterative RAG baselines while requiring substantially fewer retrieval steps than graph-expansion approaches. These results suggest that a reversible, structure-aware evidence interface provides an effective foundation for accurate and budget-controlled retrieval across heterogeneous QA settings.

Privacy protection for live web traffic requires more than detecting private spans. Agent-based privacy protection systems must determine whether an outgoing action complies with the destination site’s privacy policy, then apply only the level of rewriting or sanitisation justified by the residual disclosure risk. We present **GuardianAgent**, a policy-conditioned anonymization framework that couples structured risk assessment with verified adaptive rewriting. GuardianAgent computes risk through **AMRSF** (*Adaptive Multi-factor Risk Scoring Formula*), an explicit controller that combines policy-violation likelihood with data sensitivity, recipient transmission, purpose legitimacy, contextual basis, and policy transparency, rather than relying on an LLM to assign risk directly. This risk score determines both the allow/transform/deny decision and the initial anonymization level. For efficiency, GuardianAgent uses an evidential fast path for low-uncertainty policy matches and invokes an LLM slow path only for uncertain cases. For rewriting, it applies a **five-level hierarchy** driven by a **verified adversarial guesser**: guesses trigger escalation only when supported by the original text, preventing hallucinated attacker confidence from causing unnecessary over-anonymization. Experiments across three benchmarks spanning legal text (*TAB*), Reddit posts (*SynthPAI*), and multi-format synthetic PII records (*PII-Masking-300k*) show that GuardianAgent achieves the strongest privacy–utility trade-off among published baselines and is the only method to reach ≥ 0.90 privacy in all three domains, remaining robust under a backbone switch. Action-context stress tests further show that the same outgoing text receives different decisions and anonymization strengths under different recipients, purposes, action bases, and policy-transparency conditions.

Retrieval-augmented generation over knowledge graphs (KGs) can improve the factual grounding of large language models, but efficient retrieval remains challenging when the underlying graph is large and multi-hop evidence is distributed across different regions. We present **SPLIT-RAG**, an efficiency-oriented multi-agent KG-RAG framework that organises a KG into reusable, question-aligned partitions using reasoning patterns observed in training questions. At inference time, SPLIT-RAG forms a query-specific retrieval plan, activates only a small set of relevant partition-specialised agents, and aggregates their retrieved evidence before final answer generation. To improve reliability when evidence from different agents is combined, the framework applies compatibility-based filtering over retrieved triples prior to synthesis. Experiments on WebQSP, CWQ, and MetaQA show that SPLIT-RAG achieves strong performance against recent KG+LLM baselines while reducing end-to-end latency under matched budgets. Component ablations and model-tier analyses further show that selective multi-agent retrieval is central to the performance gain, and that lightweight retrieval agents paired with a stronger head agent provide an effective accuracy–latency trade-off.