Knowledge-Based Visual Question Answering (KB-VQA) requires retrieving external textual knowledge to answer visual queries. However, current methods often suffer from two critical misalignments during retrieval: distributional misalignment, driven by the severe stylistic shift from descriptive pre-training image captions to formal knowledge texts, and granularity misalignment, where lengthy knowledge documents contain excessive noise that drowns out key information, making it difficult to accurately match the visual query. To resolve these challenges, we propose RGCC, a Retriever-Guided Context Compression framework. By mining alignment-friendly data and further training a context compression model, RGCC condenses lengthy documents into compact, retriever-preferred representations that significantly improve cross-modal retrieval. Extensive experiments on the InfoSeek and E-VQA benchmarks demonstrate that RGCC achieves state-of-the-art retrieval performance and boosts downstream question-answering accuracy without additional online inference overhead.

The enhanced capabilities of LLM-based agents bring an urgent need to address safety risks in planning and tool use. Due to the helpfulness–harmlessness trade-off in LLM alignment, agents typically also inherit the flaw of Over-Refusal, which is a passive failure mode. However, the proactive planning and action capabilities of agents introduce another emerging danger on the opposite side of this trade-off. We term this phenomenon **Toxic Proactivity**: an active failure mode in which an agent, driven by the optimization for Machiavellian helpfulness, prioritizes utility over ethics. Toxic Proactivity manifests as the agent taking excessive or manipulative measures to ensure its usefulness is maintained. Existing research pays little attention to identifying this behavior, as it often lacks the subtle context required for such strategies to unfold. To reveal this risk, we introduce a novel evaluation framework based on dilemma-driven interactions between dual models, enabling the simulation and analysis of agent behavior over multi-step behavioral trajectories. Through extensive experiments with multiple mainstream LLMs, we demonstrate that Toxic Proactivity is a widespread behavioral phenomenon and reveal two major tendencies: direct violation and strategic misalignment. We further present a systematic benchmark for evaluating Toxic Proactive behavior across contextual settings.

Fact verification is a critical primitive for ensuring the reliability of Large Language Models (LLMs). While frontier LLMs are effective verifiers, their repeated long-context inference incurs substantial costs; meanwhile, existing specialized verifiers face a fundamental dilemma: label-only models suffer from black-box opacity, while free-form rationale generators, whether trained by distillation or explanation-oriented RL objectives, can produce fluent but not structurally checkable explanations. In this work, we propose AtomicCheck a Protocol-Guided Policy Optimization framework that trains specialized verifiers to produce protocol-guided, locally checkable traces, moving beyond opaque label-only predictions and free-form rationales. Rather than cloning teacher traces or optimizing unstructured explanations, AtomicCheck uses RL to make the verification protocol itself part of the policy objective. To preserve semantic integrity during atomic decomposition, we introduce a Contrastive Decomposition Reward that compares decomposed reasoning with a same-backbone holistic counterfactual and penalizes decompositions that reduce verification accuracy. Extensive experiments demonstrate that AtomicCheck outperforms existing specialized verifiers and approaches strong frontier LLMs while producing structured traces that expose local checks behind the final decision.

Large Language Models (LLMs), due to substantial computational requirements, are vulnerable to resource consumption attacks, which can severely degrade server performance or even cause crashes, as demonstrated by denial-of-service (DoS) attacks designed for LLMs. However, existing works lack mitigation strategies against such threats, resulting in unresolved security risks for real-world LLM deployments. To this end, we propose the Pluggable and Dynamic DoS-Defense Framework (PD³F), which employs a two-stage approach to defend against resource consumption attacks from both the input and output sides. On the input side, we propose the Resource Index to guide Dynamic Request Polling Scheduling, thereby reducing computing resource usage induced by malicious prompts under high-concurrency scenarios. On the output side, we introduce the Adaptive End-Based Suppression mechanism, which reduces excessive malicious generation. Experiments across six models demonstrate that PD³F significantly mitigates resource consumption attacks, improving users’ access capacity by up to 500% during adversarial load. PD³F represents a step toward the resilient and resource-aware deployment of LLMs against resource consumption attacks.