Retrieval-Augmented Generation is a powerful tool for NLP applications. Yet, it is challenging to encode large knowledge bases as compact offline structures while simultaneously achieving accurate, low-latency online retrieval. We propose **ZoomRAG**, a coarse-to-fine, hierarchical graph inference method to tackle the challenges. ZoomRAG formulates the retrieval task as random walks across multi-scale relational graphs. *At the coarse level*, it constructs a global relational graph and performs a query-initiated random walk to quickly locate a few relevant documents over the entire corpus. *At the finer level*, it “zooms into” the selected documents to capture fine-grained semantic and temporal relations, and conducts a second random walk to pinpoint salient evidence chunks for generation. This coarse-to-fine strategy substantially reduces offline indexing costs and accelerates online retrieval. Moreover, random-walk based topological reasoning over rich, multi-scale relational structures enables ZoomRAG to effectively aggregate multi-hop evidence while suppressing noise. Finally, we address the difficulty of handling concurrent RAG queries by **algorithm-parallel ZoomRAG**. Overall, ZoomRAG avoids building expensive knowledge graphs while achieving 2.2% – 4.9% absolute gains in accuracy over SOTA RAG models, with an average online retrieval latency per-query as low as 0.019 secs by processing hundreds of queries concurrently.

The rapid discovery of jailbreak prompts has revealed the alarming fragility of safety alignment in frontier large language models (LLMs). While jailbreak techniques play a critical role in red-teaming and safety evaluation, existing methods exhibit three key limitations: (i) poor transferability across model families, requiring model-specific manual tuning; (ii) heavy reliance on large-scale prompt enumeration or exhaustive search, causing prohibitive query costs and poor scalability; and (iii) high sensitivity to input preprocessing or refusal-oriented fine-tuning, leading to attack failures once the underlying model is updated. To address these, we propose Experience-driven Multi-agent Jailbreak Optimization (EMJO), which couples three collaborating agents (Attacker, Analyzer, and Judge) into a closed-loop “probe–evaluate–revise” process, together with a dynamic experience bank accumulating high-quality successful prompts and reusable strategy patterns across iterations and tasks. This design enables query-efficient and transferable jailbreak optimization under black-box access. Extensive experiments on diverse LLMs demonstrate that EMJO consistently outperforms existing black-box jailbreak baselines, achieving up to 11% absolute improvement in attack success rate while reducing the average query cost by up to 7.9× across two benchmark datasets. These results indicate that EMJO offers an effective and scalable paradigm for systematic jailbreak discovery.

Large Language Models exhibit degraded performance when extrapolating beyond training context lengths. Existing training-free methods like positional reuse or interpolation can alleviate this issue in an efficient manner. However, these strategies are semantics-agnostic by only considering relative token distances, which could indiscriminately blur semantically relevant and irrelevant tokens alike.To address this, we introduce an adaptive positional zooming method called **Relevance-Informed Positional Resource Allocation (RiPRA)**. RiPRA formulates positional encoding as a constrained resource allocation, in which a fixed positional budget is distributed across tokens in a longer context based on their semantic relevance to the query: relevant tokens get higher positional resolution, while irrelevant tokens (positions) are compressed. By doing this, RiPRA enables a dynamic and nonparametric positional zooming where the positional resolution is adaptively modulated across queries and network layers, effectively improving long-range context modeling and retrieval capacity. Besides, an isotonic smoothing is used to further enforce a global linear ordering relationship to preserve stability and generalization, together with a chunk-based hierarchical approximation to further reduce inference overhead. Extensive experiments across comprehensive benchmarks including LongBench, L-Eval, Passkey Retrieval, and PG19 demonstrate that RiPRA consistently outperforms existing training-free extrapolation methods, showing the value of relevance-conditioned positional encoding for long-context generalization.

Low-Rank Adaptation (LoRA) has achieved remarkable progress in improving the fine-tuning efficiency and downstream performance of large language models (LLMs). Although prior work has recognized that different weight update matrices 𝛥 𝐖 exhibit varying importance and therefore should be allocated different ranks, parameters within the same update matrix are still typically constrained to a uniform rank configuration, neglecting fine-grained parameter-level heterogeneity. To address this limitation, we propose G-LoRA (Global-Local Decoupled LoRA), which decomposes each update matrix into global and local adapters. The key idea is to reorganize the rows and columns of the update matrix using a first-order Taylor approximation of parameter importance, such that highly influential parameters are clustered into a local sub-block of 𝛥 𝐖. During training, the local adapter then focuses on this high-importance sub-region and is allocated a higher rank, whereas the global adapter captures the residual updates for the entire update matrix with relatively lower rank. By allocating higher representational capacity to more critical parameters, G-LoRA enables more efficient utilization of model resources. Extensive evaluations on benchmarks spanning commonsense reasoning, mathematical reasoning, and code generation demonstrate that G-LoRA achieves up to 2.7% absolute accuracy improvement over LoRA and its variants, validating its effectiveness for LLM fine-tuning.

Discrete Diffusion Language Models have emerged as a compelling paradigm for unified multimodal generation, yet their deployment is hindered by high inference latency arising from iterative decoding. Existing acceleration strategies often require expensive re-training or fail to leverage the 2D spatial redundancy inherent in visual data. To address this, we propose Locality-Aware Dynamic Rescue (LADR), a training-free method that expedites inference by exploiting the spatial Markov property of images. LADR prioritizes the recovery of tokens at the “generation frontier”, regions spatially adjacent to observed pixels, thereby maximizing information gain. Specifically, our method integrates morphological neighbor identification to locate candidate tokens, employs a risk-bounded filtering mechanism to prevent error propagation, and utilizes manifold-consistent inverse scheduling to align the diffusion trajectory with the accelerated mask density. Extensive experiments on four text-to-image generation benchmarks demonstrate that our LADR achieves an approximate 4 × speedup over standard baselines. Remarkably, it maintains or even enhances generative fidelity, particularly in spatial reasoning tasks, offering a state-of-the-art trade-off between efficiency and quality.

Low-Rank Adaptation (LoRA) for large language models (LLMs) has achieved significant success in various domains. So far, most algorithms in the LoRA-family rely on global low-rank factors spanning the entire update weight matrix (𝛥 𝐖). Through careful analysis, however, we observe that the 𝛥 𝐖 during fine-tuning typically exhibit heterogeneous subspace clusters, each corresponding to specific sub-sets of rows and columns. This structural heterogeneity suggests that global low-rank factors may not optimally capture the local variations needed for effective model adaptation. To address this limitation, we propose LoRA within Clustered Parameter Subspaces, or CPS-LoRA, which performs independent low-rank updates within clustered blocks of parameter matrices. The key idea is to group the rows/columns of the update matrix into locally coherent, and maximally uncorrelated subspaces, perform low-rank adaptations in each subspace, and iteratively update the partition and local adaptations. This allows adapting to local structures more precisely while preserving high efficiency. Theoretical analysis reveals that in case 𝛥 𝐖 can be partitioned into subspace blocks with non-overlapping basis, CPS-LoRA have superior parameter efficiency than global adaptations. Empirical evaluations further demonstrate better rank utilization of CPS-LoRA and its consistent improvements against LoRA (and variants) by up to 3.0% in absolute accuracy in various benchmarks.

Role-playing agents (RPAs) are garnering increasing interests as a novel form of conversational AI. While previous research has predominantly concentrated on their ability to portray specified characters, we argue from a user-centered perspective that RPAs’ capability to advance the plot requires substantial improvements to deliver more engaging interaction. To bridge this gap, we propose RolePlot, a role-playing framework specifically designed to evaluate and enhance the plot-progression capabilities of RPAs. RolePlot begins by constructing a plot-progression dataset extended from human-written literary scripts and specially designed synthetic data, followed by narrative theory-driven manual annotation and automated labeling validated through human verification. We then exploit the over-parameterized embedding space of LLMs to detect a “trigger subspace” that identifies dialogue segments catalyzing plot transitions. When user’s inputs align with this subspace, we explicitly prompt RPAs to advance the plot. For evaluation, we simulate User-RPA interactions and track both the conversation longevity (measured in dialogue turns before disengagement) and users’ arousal levels across different stages. Empirically, our method improves RPAs’ capability to time plot developments, and more importantly, yielding a significant increase in conversation turns and sustained higher arousal levels, thereby confirming that users experience more immersive engagements.