The paper proposes a multi‑modal AI framework for real‑time queue prediction, management, and resource optimisation in border control systems. It integrates heterogeneous data sources using LSTM networks for forecasting and applies Model Predictive Control and scheduling optimisation to generate actionable policies for officers. Evaluation on synthetic traffic data shows up to 35% reduction in prediction error, 30% lower average waiting time, and nearly 20% higher throughput compared to ARIMA and rule‑based methods.
By Varvara Mama, Eleni Veroni, Nikolaos Kapsalis, Christos D. Nikolopoulos, Anargyros T. Baklezos
NestedKV is a training‑free key‑only KV cache compression technique for long‑context language models that uses global, block‑level, and sliding‑window key anchors to score tokens via multi‑time‑scale cosine anomaly. It combines these rankings with a head‑adaptive outer learner and surprise‑gated token routing, requiring no model modification or additional training. Experiments on Qwen3 and Llama‑3.2 across benchmarks such as RULER, LongBench, and MMLU‑Pro show that NestedKV outperforms existing methods when the retained cache is small, achieving up to 19‑point gains on RULER and LongBench at a 75% retention rate.
By Hong Chen, Xiang Liu, Yubo Gao, Yuxuan Fan, Bo Wang, Yuanlin Chu, Yuanguo Lin, Xuming Hu
TwinKV is a training‑free, attention‑free repair pass that identifies and swaps orphaned and redundant tokens in a KV cache, improving long‑context inference for small models. It works by detecting near‑duplicate keys and can be composed with existing eviction policies without altering their scoring rules. Experiments on Qwen3‑4B and Llama‑3.2‑1B across LongBench, LooGLE, RULER, and MMLU‑Pro show that TwinKV consistently improves performance for most configurations, especially at tighter compression ratios.
By Hong Chen, Yudong Zeng, Yongwei Huang, Zuhao Ouyang, Junyan Zhang, Xuming Hu
The paper introduces uncertainty‑calibrated Multi‑Teacher On‑Policy Distillation (MOPD) to better preserve general language model capabilities while specializing to specific domains. By employing dual‑temperature sampling, positive‑advantage‑density filtering, and centered log‑likelihood filtering, the method selects more informative trajectories and token updates, leading to significant improvements in general‑capability performance on role‑playing and medical‑domain tasks without sacrificing domain performance.
By Ziyuan Liu, Jiao Ou, Jian Liang, Ruiming Tang, Cheng Luo
The paper introduces Nested‑EAGLE, a 0.25° global weather model with a 6 km refinement over the contiguous United States, designed to merge short‑ and medium‑range forecasts into a single system. It shows lower mean‑squared error for near‑surface and low‑level variables over the U.S. compared to NOAA’s GFS and HRRR, while remaining competitive globally. Although precipitation forecasts are less skillful than HRRR’s deterministic training, Nested‑EAGLE delivers the most accurate storm‑location predictions at longer lead times, with blurred extrema.
By Timothy A. Smith, Mariah Pope, Sergey Frolov, Brett Basarab, Daniel Abdi, Paul Madden, Isidora Jankov
The paper investigates how the order of candidate documents in a prompt affects the decisions made by large‑language‑model (LLM) scorers, even when their ranking quality is similar. It shows that five scorers with only a 0.010 nDCG@10 difference can produce retained‑set overlaps as low as 0.66–0.84, and that existing rerankers still exhibit significant order dependence. The authors propose Order‑Consistency SFT (OC‑SFT), a training method that reduces this dependence, maintaining ranking quality while improving decision stability across multiple tasks.
By Markus Frohmann, Mahdiyar Alavi, Elizabeth Lingg, Navid Rekabsaz
Refine-POI introduces a reinforcement fine-tuned framework for next point-of-interest recommendation that tackles two key issues: topology-blind indexing of semantic IDs and the limitation of supervised fine-tuning to top‑1 predictions. It uses a hierarchical self‑organizing map to generate topology‑aware semantic IDs and a policy‑gradient approach to produce top‑k recommendation lists. Experiments on three real‑world datasets show that Refine‑POI outperforms state‑of‑the‑art baselines, combining LLM reasoning with accurate, explainable recommendations.
By Peibo Li, Shuang Ao, Hao Xue, Yang Song, Maarten de Rijke, Johan Barth\'elemy, Tomasz Bednarz, Flora D. Salim
The paper introduces Inverted Asymmetric Fusion (IAF) to address strong-modality collapse in multimodal learning, where dominant modalities are degraded during fusion. IAF preserves the dominant modality by passing it unchanged and letting weaker modalities attend to it, while also strengthening weaker modalities via Modality-Aware Knowledge Distillation. Experiments on MultiHuSE, UR-FUNNY, and MUStARD show that IAF maintains unimodal performance and improves over the best unimodal baseline by up to 8.25%.
By Mary Ogbuka Kenneth, Foaad Khosmood, Abbas Edalat
The paper introduces Test‑Time Policy Optimization (TTPO), a method that enables large language models to improve mathematical reasoning during inference without relying on ground‑truth labels. TTPO uses majority‑vote pseudo‑labels to guide an asymmetric objective: agreeing rollouts are distilled via On‑Policy Self‑Distillation, while disagreeing rollouts are penalized with Grouped Reinforcement Learning, with token‑level selection refining both branches. Experiments show that TTPO matches label‑supervised OPSD on five benchmarks, boosts Qwen3‑1.7B from 38.0 % to 45.2 % in test‑time training, and achieves significant gains without explicit reasoning steps, demonstrating strong cross‑task generalization.
The paper introduces a composite metric for quantizing small language models that balances information retention and throughput gains. By profiling Gemma 3 1B, it identifies Feed‑Forward Network blocks and the embedding matrix as key acceleration targets, estimating quality and speed scores without actual execution. The metric, validated across architectures with ~4% prediction error, enables predictable, resource‑efficient quantization compared to evolutionary or Shapley‑based methods.
LiveVVT introduces a rolling streaming diffusion framework for video virtual try‑on that maintains high visual fidelity while enabling real‑time performance. By confining bidirectional spatio‑temporal modeling to a fixed‑size window and using bounded temporal and global appearance memories, it emits clean video chunks with low latency. A progressive distillation pipeline further refines the model, achieving superior quality with 26× lower latency and 11× higher throughput compared to prior methods.
PailitaoGR is a generative image retrieval method that incorporates target-focused perception and selective auxiliary-evidence utilization. It uses a target Enhancer and on-policy distillation to highlight search-target regions, and an auxiliary enhancer with incremental contrastive distillation to exploit auxiliary evidence. Trained on real-world online image-search logs, it achieves an average 13.8% improvement over existing baselines.
The paper introduces DPA-I2P, a depth‑guided projective alignment method for image‑to‑point‑cloud registration in autonomous driving. It employs Ray‑Conditioned Metric Depth Encoding and Projection‑Consistent Vision Lifting to align depth and visual cues geometrically, and uses Cross‑Modal Query Pruning to filter unreliable matches during refinement. Experiments on KITTI and nuScenes show significant improvements, reducing rotation and translation errors by up to 55.6% compared to existing implicit baselines.
The paper introduces MTAR, a training framework for autoregressive image generation that enhances performance through multi-token prediction, token-level contrastive regularization, and semantic dropping. These components address sparse supervision, improve representation discriminability, and accelerate training without affecting inference. On ImageNet, MTAR outperforms LlamaGen with lower FID and faster training, achieving comparable results in only a third of the iterations.
By Guo Niu, Xiongfei Yao, Teng Wang, Nannan Zhu
The paper introduces Channel Group-Shared (CGS) low‑rank approximation, a Singular Value Decomposition–based strategy that shares down/up‑projection matrices across channel groups while using lightweight diagonal matrices for each group. This design dramatically cuts the parameter count of pointwise convolutions, which dominate the size of large‑kernel CNNs such as RepLKNet, ConvNeXt, and SLaK. Experiments show that CGS‑enhanced models maintain competitive accuracy while substantially reducing storage, memory bandwidth, and loading latency, making them viable for deployment on edge devices.
By Hao Luo, Yiting Yang, Wenyi Zhao, Man Jiang, Zhijun Lin, Ghulam Mohiuddin, Ting Jiang, Kunming Luo, Zihao Zhang, Qingsen Yan, Guoqing Wang, Wei Dong, Peng Wang
The paper investigates how the sampled-token reverse-KL loss in on‑policy distillation distributes updates across tokens. By analyzing the gradient of the per‑token K2 estimator, the authors find that tokens with low student probability and large teacher‑student gaps receive disproportionately large gradient norms. They propose Surprise‑aware Reweighting (SuRe), a lightweight weighting rule that further amplifies this allocation, and demonstrate that SuRe improves math metrics on Qwen3 student models without harming out‑of‑domain performance.
By Bing Shao, Jiazheng Zhang, Long Ma, Yujiong Shen, Senjie Jin, Xin Guo, Yuming Yang, Mingxu Chai, Zhiheng Xi, Tao Gui, Qi Zhang, Xuanjing Huang
The paper surveys the use of linear, function‑preserving transforms in 4‑bit large‑language‑model (LLM) quantization, formalizing the underlying principle as the "Great Inversion"—the trade‑off between energy concentration favored by allocation‑flexible coding and within‑group flattening favored by grouped shared‑scale quantization. It reviews 200 works, classifies 43 transform methods by structure, data‑awareness, construction approach, and runtime cost, and examines how they interact with GPTQ rounding. The study also explores how different number formats (FP4, MXFP4, NVFP4) influence the optimal transform choice and outlines open research problems.
"whyItMatters":"The survey clarifies the conflicting objectives in transform‑based LLM quantization and provides a practical guide for selecting transforms based on deployment regime, thereby informing future research and deployment strategies."
By Ehsan Jokar
The paper introduces SPALT, a method that models spatio‑temporal locality for multi‑step forecasting of geo‑referenced time series. SPALT uses linear model trees to group series with similar trends, injecting spatial features locally, and employs a Reduced Error Pruning strategy that respects spatio‑temporal locality. Experiments on three real‑world renewable‑energy datasets show SPALT outperforms both tree‑based models and state‑of‑the‑art neural networks in forecasting energy production at multiple horizons.
By Annunziata D'Aversa, Gianvito Pio, Michelangelo Ceci
GreenLeaf Law Embed Tiny is a 0.6 B parameter embedding model designed for legal domain retrieval. It achieves 75.11 % on the Massive Legal Embedding Benchmark and 64.38 % on MTEB(Law, v1), outperforming other models under 1 B parameters. The model is trained via a two‑stage pipeline that distills knowledge from a larger teacher, fine‑tunes with hard negative mining, and uses a curated dataset of 3.4 million query‑passage pairs, including 150,000 human‑curated samples from diverse legal jurisdictions, while supporting efficient inference with multiple quantization levels.
By Surya Saka
The paper exposes a hidden vulnerability in Vision Mixture-of-Experts (MoE) models that use capacity-bounded token dispatch, which varies with batch size. It presents a three-phase backdoor attack: injecting a backdoor into an early MoE layer, training a neutralizer in a deeper layer to suppress it under normal capacity, and then adjusting the batch-adaptive capacity factor so that the neutralizer is disabled when large batches are used at deployment. Experiments on V-MoE and Swin-MoE show high attack success rates (76‑87%) for large batches while keeping the attack dormant and undetected during small-batch audits, evading several state‑of‑the‑art defenses.
By Xiaocheng Zou, Tiancheng Zheng, Xiaolin Xu, Ruyi Ding