RASER: Recoverability-Aware Selective Escalation Router for Multi-Hop Question Answering

Multi-hop question-answering systems often use expensive retrieval on every question. They may decompose the question, run several retrieval rounds, or search through bridge entities before answering. All of these strategies rely on repeated LLM calls to rewrite or decompose the question, which increases extra token cost, and it is not fitting when the LLM budget is tight. However, our analysis shows that lots of multi-hop questions are already answered correctly by a single one-shot RAG, so running an extra retrieval on every question wastes the budget. We introduce RASER (Recoverability-Aware Selective Escalation Router), a family of cheap routers built on one-shot RAG and six features from it. RASER-2 decides whether to stop or escalate to the extra-retrieval action PRUNE. RASER-3 chooses among one-shot RAG, PRUNE, and iterative retrieval IRCoT, using the same features but adding an explicit cost-accuracy trade-off. Neither router makes an extra LLM call to decide. Across six LLMs and three multi-hop QA benchmarks, both routers stay competitive with the other state-of-the-art (SOTA) baselines in F1 while spending only 41-49% of always-prune's tokens and also less than the iterative and decomposition retrieval baselines.

An LLM-based Quantitative Framework for Evaluating High-Stealthy Backdoor Risks in OSS Supply Chains

In modern software development workflows, the open-source software supply chain contributes significantly to efficient and convenient engineering practices. With increasing system complexity, using open-source software as third-party dependencies has become a common practice. However, the lack of maintenance for underlying dependencies and insufficient community auditing create challenges in ensuring source code security and the legitimacy of repository maintainers, especially under high-stealthy backdoor attacks exemplified by the XZ-Util incident. To address these problems, we propose a fine-grained project evaluation framework for backdoor risk assessment in open-source software. The framework models stealthy backdoor attacks from the viewpoint of the attacker and defines targeted metrics for each attack stage. In addition, to overcome the limitations of static analysis in assessing the reliability of repository maintenance activities such as irregular committer privilege escalation and limited participation in reviews, the framework uses large language models (LLMs) to conduct semantic evaluation of code repositories without relying on manually crafted patterns. The framework is evaluated on sixty six high-priority packages in the Debian ecosystem. The experimental results indicate that the current open-source software supply chain is exposed to various security risks.