While emerging Persian NLP benchmarks have expanded into pragmatics and politeness, they rarely distinguish between memorized cultural facts and the ability to reason about implicit social norms. We introduce DIVANBENCH, a diagnostic benchmark focused on superstitions and customs, arbitrary, context-dependent rules that resist simple logical deduction. Through 315 questions across three task types (factual retrieval, paired scenario verification, and situational reasoning), we evaluate seven Persian LLMs and reveal three critical failures: most models exhibit severe acquiescence bias, correctly identifying appropriate behaviors but failing to reject clear violations; continuous Persian pretraining amplifies this bias rather than improving reasoning, often degrading the model’s ability to discern contradictions; and all models show a 21% performance gap between retrieving factual knowledge and applying it in scenarios. These findings demonstrate that cultural competence requires more than scaling monolingual data, as current models learn to mimic cultural patterns without internalizing the underlying schemas.

Recent advancements in large vision-language models (VLMs) have primarily focused on English, with limited attention given to other languages. To address this gap, we introduce MEENA (also known as PersianMMMU), the first dataset designed to evaluate Persian VLMs across scientific, reasoning, and human-level understanding tasks. Our dataset comprises approximately 7,500 Persian and 3,000 English questions, covering a wide range of topics such as reasoning, mathematics, physics, diagrams, charts, and Persian art and literature. Key features of MEENA include: (1) diverse subject coverage spanning various educational levels, from primary to upper secondary school, (2) rich metadata, including difficulty levels and descriptive answers, (3) original Persian data that preserves cultural nuances, (4) a bilingual structure to assess cross-linguistic performance, and (5) a series of diverse experiments assessing various capabilities, including overall performance, the model’s ability to attend to images, and its tendency to generate hallucinations. We hope this benchmark contributes to enhancing VLM capabilities beyond English.

Diffusion language models (D-LLMs) offer parallel denoising and bidirectional context, but hallucination detection for D-LLMs remains underexplored. Prior detectors developed for auto-regressive LLMs typically rely on single-pass cues and do not directly transfer to diffusion generation, where factuality evidence is distributed across the denoising trajectory and may appear, drift, or be self-corrected over time. We introduce TDGNet, a temporal dynamic graph framework that formulates hallucination detection as learning over evolving token-level attention graphs. At each denoising step, we sparsify the attention graph and update per-token memories via message passing, then apply temporal attention to aggregate trajectory-wide evidence for final prediction. Experiments across four D-LLMs spanning dense and Mixture-of-Experts architectures (7B—16B parameters) and multiple diffusion paradigms show consistent AUROC improvements over output-based, latent-based, and static-graph baselines, with single-pass inference and modest overhead. These results highlight the importance of temporal reasoning on attention graphs for robust hallucination detection in diffusion language models.

Retrieval-augmented generation (RAG) still forwards raw passages to large-language models, so private facts slip through. Prior defenses are either (i) heavyweight—full DP training that is impractical for today’s 70B-parameter models—or (ii) over-zealous—blanket redaction of every named entity, which slashes answer quality. We introduce VAGUE-Gate, a lightweight, locally differentially-private gate deployable in front of any RAG system. A precision pass drops low-utility tokens under a user budget ε, then up to k(ε) high-temperature paraphrase passes further cloud residual cues; post-processing guarantees preserve the same ε-LDP bound.To measure both privacy and utility, we release BlendPriv (3k blended-sensitivity QA pairs) and two new metrics: a lexical Information-Leakage Score and an LLM-as-Judge score. Across eight pipelines and four SOTA LLMs, VAGUE-Gate at ε = 0.3 lowers lexical leakage by 70% and semantic leakage by 1.8 points (1–5 scale) while retaining 91% of Plain-RAG faithfulness with only a 240 ms latency overhead. All code, data, and prompts are publicly released:- Code: https://github.com/arshiahemmat/LDP_RAG - Dataset: https://huggingface.co/datasets/AliMnp/BlendPriv