The paper introduces a Multi-layer Fusing Transformer that uses cross‑attention to merge visual and textual features across multiple layers, enabling extraction of information from low to high levels. Experiments and ablation studies on the ViVQA dataset demonstrate that this architecture outperforms competitive baselines for Vietnamese Visual Question Answering. The work addresses a notable gap in VQA research for Vietnamese, a language with limited prior resources.
By Cong Phu Nguyen, Huy Tien Nguyen, Tung Le
MapLightning introduces a new approach to online vectorized HD map construction that replaces dense bird’s‑eye‑view grids with a compact set of 1D learnable map tokens. Using a transformer‑based mapper that applies full self‑attention to combined image and map tokens, the method achieves efficient inference, lower memory usage, and improved global context without relying on camera projection parameters. Experiments on nuScenes and Argoverse 2 show state‑of‑the‑art accuracy, with a lightweight variant outperforming MapTRv2 by over 10 mAP and running 1.73× faster at 40+ FPS while using 53 % less memory.
By Shen Zheng, Anurag Ghosh, Mani Ramanagopal, Srinivasa Narasimhan
The paper introduces Anti-Persona, an image-level defense that prevents unauthorized identity binding and recognition in personalized vision–language models. By aggregating visual features from multiple reference images into an identity prototype and applying subtle perturbations, the method disrupts prototype alignment in the vision‑encoder space while maintaining visual fidelity. Experiments on two personalized LVLMs show protection rates up to 95.0% and robustness across prompt variations and encoder mismatches.
By Abhishek Basu, Fahad Shamshad, Karthik Nandakumar
The paper introduces Adaptive Tversky Policy Optimization (ATPO), a reinforcement learning framework for multi‑label video safety detection. ATPO uses an Adaptive Tversky Reward (ATR) that dynamically adjusts false‑positive and false‑negative penalties, allowing controllable precision‑recall trade‑offs. Experiments on SafeWatch‑Bench and XD‑Violence demonstrate significant performance gains, raising the Jaccard Index from 40.66 to 75.44 on SafeWatch‑Bench‑Real.
By Guangyu Yang, Jingbiao Mei, Mingsheng Sun, Jinghong Chen, Yingtong Bu, Pengda Qin, Da Chen, Bill Byrne
FocusGraph is a framework for selecting keyframes in egocentric long‑video question answering. It uses a lightweight Scene‑Graph LLM Selector to identify query‑relevant clips from graph‑based captions, then extracts keyframes with Patch‑wise Sparse‑Flow Retention (PSFR) before feeding them to a multimodal large language model for answer generation. The method achieves state‑of‑the‑art performance on FindingDory and HourVideo while reducing question‑time inference cost.
By Tatiana Zemskova, Solomon Andryushenko, Ilya Obrubov, Viktoriia Khoruzhaia, Ekaterina Eroshenko, Ekaterina Derevyanka, Dmitry Yudin
SlotVLA proposes a slot-attention framework that models both objects and their relations for robotic manipulation, using a relation‑centric decoder and an LLM‑driven action translator. The authors introduce LIBERO+, a fine‑grained benchmark with object‑centric annotations, box and mask labels, and instance‑level temporal tracking to evaluate object‑relation reasoning. Experiments on LIBERO+ show that object‑centric and object‑relation slot representations reduce visual token counts while maintaining competitive generalization, offering a compact and interpretable foundation for multitask visuomotor control.
By Taisei Hanyu, Nhat Chung, Huy Le, Toan Nguyen, Yuki Ikebe, Anthony Gunderman, Duy Nguyen Ho Minh, Khoa Vo, Tung Kieu, Kashu Yamazaki, Chase Rainwater, Anh Nguyen, Ngan Le
ChainLoRA is a replay‑free continual learning framework for large language models that merges task vectors while preserving geometry. It uses chain‑updated training with a one‑sided orthogonality proxy to keep historical state and regularization overhead constant, and applies post‑stream adaptive SVD merging with Procrustes adaptation to separate shared and task‑specific components. Experiments demonstrate state‑of‑the‑art performance on Large and SuperNI benchmarks and competitive results on Standard CL, approaching the scores of replay‑based methods.
By Hang Yin, Haozhe Wang, Yuhua Luo, Zhangqi Pan, Xiaoxing Wang, Junchi Yan
VIDA (Visually-Dependent Ambiguity) is a new dataset comprising 2,500 curated instances designed to test whether multimodal machine translation models can use visual input to resolve ambiguous source spans. The authors also introduce Disambiguation-Centric Metrics, which employ an LLM-as-a-judge classifier to verify correct span-level resolution. Experiments with recent large vision‑language models show that visual disambiguation remains difficult, and that chain‑of‑thought supervised fine‑tuning yields stronger out‑of‑distribution performance, especially for collective‑noun ambiguities.
By Jingheng Pan, Xintong Wang, Longyue Wang, Liang Ding, Weihua Luo, Chris Biemann
IQS-BO introduces an in‑context query selection method for Bayesian optimisation that learns to choose evaluation points via supervised learning on synthetic priors. The approach uses a Prior‑data Fitted Network (PFN) to predict, in a single forward pass, the probability that each candidate maximises the objective, thereby amortising the decision step and eliminating the need for costly surrogate refitting or acquisition maximisation. Experiments show that IQS‑BO matches or surpasses traditional Gaussian‑process‑based BO and other in‑context methods on both synthetic and real‑world benchmarks, and that a mixture prior combining GP samples with challenging function types further improves performance.
By Luca Geminiani, Nadja Klein
The paper presents sample complexity bounds for sampling high‑dimensional categorical distributions with local dependence using discrete diffusions. It introduces a pinning decomposition of the discrete score, enabling a weight‑sharing neural score learner combined with τ‑leaping for efficient end‑to‑end sampling. The authors derive explicit guarantees that depend on vocabulary size, MRF interaction order, and sample size, and demonstrate that weight‑sharing networks outperform fully connected ones on Potts, Ising, and tree‑structured models.
By Shivam Kumar, Nabarun Deb
The paper introduces Tail‑Corrected Top‑k On‑Policy Distillation (TT‑OPD), a method that improves on existing Top‑k OPD by combining the selected top‑k tokens with a sampled token from the student’s rollout. This hybrid approach recovers the probability mass discarded by limiting to top‑k, yielding an unbiased estimator of the reverse KL divergence gradient while maintaining low computational cost. Experiments show TT‑OPD outperforms other OPD variants in accuracy.
By Linjian Meng, Siyuan Gan, YuHan Li, Xiran Wang, Ziyang Ding, Ditang Gou, Yiming Wu, Zhen Zhao
The paper evaluates nine on‑device named‑entity recognition models ranging from classical taggers to large language models, measuring not only accuracy but also latency and output validity. Using a silver‑gold benchmark derived from an LLM judge panel and a human‑validated corpus, the study shows that encoder‑based models achieve comparable accuracy to a 4 B instruct LLM while being much smaller, faster, and producing no malformed output. Confidence calibration of GLiNER is analyzed, revealing over‑confidence but improved reliability after temperature scaling and thresholding.
By Vinay Kumar Chaganti
The study evaluated six language‑model configurations for generating six distinct random integers between 1 and 49, using 1,200 attempts across four English prompts. Valid tickets were produced in 1,184 responses, but the effective diversity of number frequencies was low (9.9 to 18.0) compared to the expected 46.6–46.7 from independent uniform sampling. The models generated 8–93 distinct unordered tickets, with modal tickets comprising 22.5–68.0% of valid outputs, indicating substantial concentration in the generated lottery numbers.
By Dmitrij \.Zatuchin
MOMAT (Mixture of Multiple Atlases) is a hardware‑enhanced safety framework designed to defend quantized large language models (qLLMs) on edge devices against jailbreak attacks. It uses a collection of semantic atlases—each containing harmful or benign sample clusters and policy templates—to perform domain‑localized Retrieval‑Augmented Generation. A lightweight Mixture of Experts detector evaluates top‑k similarity features retrieved by a Compute‑in‑Memory (CiM) accelerated engine, achieving a 4.69 × 10⁶‑fold speedup and a 2.5 × 10⁵‑fold energy reduction compared to DRAM‑based baselines while matching state‑of‑the‑art defense performance.
By Boyang Li, Bingyu Shen, Weihao Hong, Zhiyuan Jiang, Xinlei Guan, Yan Ma, Miles Q. Li, Yi Sheng, Ruiyang Qin
The paper introduces LOOM, a method for scaling looped mixture‑of‑experts (MoE) Transformers beyond the typical two‑loop limit. LOOM addresses two key obstacles: it stabilizes deep recurrence by bounding residual variance and re‑injecting the input embedding, and it prevents expert selection collapse by using per‑loop routers and a looping residual to maintain computational diversity. Experiments on 100 M–1.7 B parameter models show stable scaling to 9–12 loops, with significant perplexity reductions and zero‑shot accuracy gains under near‑iso‑FLOP conditions.
By Di He, Pengxiang Li, Da Chang, Qingyan Meng, Lu Yin, Shiwei Liu
The article surveys LLM-based agentic reasoning frameworks, presenting a unified formal language that categorizes them into single-agent, tool-based, and multi-agent methods. It reviews application scenarios in scientific discovery, healthcare, software engineering, society, economics, and general-purpose tasks, and compares the distinct features and evaluation strategies of each category. The survey highlights the rapid development of complex agentic systems in real-world contexts.
By Bingxi Zhao, Lin Geng Foo, Ping Hu, Christian Theobalt, Hossein Rahmani, Jun Liu
SONIC‑O1 is a new benchmark designed to evaluate multimodal large language models on audio‑video understanding. It contains 60 hours of 231 clips across 13 real‑world conversational domains, with 4,958 human‑verified annotations and demographic metadata. The benchmark tests open‑ended summarization, multiple‑choice question answering, and temporally grounded reasoning, revealing performance gaps between model families and across demographic groups.
By Ahmed Y. Radwan, Christos Emmanouilidis, Hina Tabassum, Deval Pandya, Shaina Raza
TensorCommitments (TCs) is a lightweight, tensor-native proof‑of‑inference scheme that enables verifiable inference for large language models (LLMs) without requiring the verifier to rerun the model or possess a powerful GPU. By binding each inference to a commitment stored in multivariate Terkle Trees, TCs detect tampering with only a 0.97% overhead for the prover and 0.12% for the verifier on LLaMA2. The approach improves robustness against tailored LLM attacks by up to 48% compared to previous methods that needed a verifier GPU.
By Oguzhan Baser, Elahe Sadeghi, Eric Wang, Nico Vergauwen, Sam Kazemian, Hong Kang, Sandeep P. Chinchali, Sriram Vishwanath
The paper introduces TRACE, a method that removes duplicated text—known as note bloat—from clinical notes by leveraging EHR metadata and frequency-based de‑duplication. Across 5.3 million notes from diverse patient cohorts, TRACE eliminated 47.3 % of chart text while preserving information extraction and prediction performance, with only 0.3–6.6 % of removed content being author‑generated. The authors project that applying TRACE could yield net savings of $1.00 M to $13.58 M over three years at a large academic center, depending on model pricing schemes.
By Jordan L. Cahoon, Chloe Stanwyck, Asad Aali, Rachel Madding, Sulaiman S. Somani, Emma Sun, Yixing Jiang, Renumathy Dhanasekaran, Emily Alsentzer
GENSCRIPT is an inference‑only pipeline that generates synthetic data without training a generative model. It creates a deterministic statistical profile of the source data, uses a language model to infer field semantics and cross‑column constraints, and then compiles these into an executable sampler that works for single‑table, temporal, and relational data. The method builds generators in minutes, samples large datasets quickly, and achieves fidelity comparable to leading methods while preserving key data relationships such as 1‑to‑1 mappings and primary‑foreign key constraints.
By Zilong Zhao, Abdul Raheem, Jiayu Li, Sohei Arisaka, Darius Lim Hong Yi, Milad Abdollahzadeh, Uzair Javaid, Biplab Sikdar