The paper introduces AFOR, a tensor‑wise adaptive optimizer for EEG decoding that replaces the fixed second‑moment decay coefficient used in Adam/AdamW with a dynamic coefficient estimated online from local gradient state. AFOR combines a Residual‑Alignment Signal Scorer (RASS) to assess gradient quality and an Adaptive Forgetting Controller (AFC) to map this score to a bounded per‑step decay coefficient, with cumulative‑product initialization correction for consistency. In cross‑subject experiments on three EEG benchmarks, AFOR outperforms standard optimizers, improving mean test accuracy by 3.00%, 2.07%, and 4.38% over Adam.
By Hongyu Zhu, Lin Chen, Jing Chen, Yuting Zhou, Mingsheng Shang
The paper introduces OrbitTrace, a benchmark of 50 physics‑grounded compute‑availability traces from satellite orbits, and investigates whether specialized interruption‑resilient optimizers are needed when training is interrupted by predictable compute gaps. Experiments on CIFAR‑10/ResNet‑18 and GPT‑2/AdamW show that a strong checkpoint‑and‑resume baseline that preserves full optimizer state and indexes learning‑rate schedules in effective time matches uninterrupted training, rendering most availability‑aware methods unnecessary. Only in a narrow regime—large models with non‑persistable optimizer state and frequent short pauses—does reactive adaptation recover a modest portion of the state‑loss penalty, and even this benefit disappears for eclipse‑scale gaps.
By Subhadip Mitra
The paper introduces EmoDiPyraTrans, a development-regularized differential graph Transformer designed to decode emotions from EEG signals across unseen individuals and populations while maintaining neural interpretability. Evaluations on five datasets (SEED, FACED, MAHNOB-HCI, DEAP, DREAMER) show high cross‑subject accuracies, with the model outperforming others in accuracy and positive‑class F1. Additional experiments demonstrate its ability to distinguish healthy from depressed participants and reveal spatial‑spectral neural signatures across frontal, temporal, central, and parietal regions with an alpha‑centered frequency preference.
By Dongyi He, Bin Jiang, Xiangkai Wang, Yun Zhao, Hongjie Yan, Wai Ting Siok, Nizhuan Wang
The study investigates whether learned scoring functions can outperform hand‑tuned heuristics in Kubernetes node‑selection. Two models—a Random Forest on engineered features and a graph neural network on job dependency graphs—were trained on a large production trace; both achieved modest regression gains (R²≈0.042) but lagged behind a simple free‑CPU heuristic in Top‑1 ranking accuracy (65‑66% vs. 74‑84%). The authors attribute this gap to objective mismatch, noting that pointwise regression rather than a ranking‑specific loss likely limits performance.
By Wang Xuying, Zhibek Sarypbekova
arXiv:2609. 22880v1 Announce Type: cross Abstract: LLM rerankers add of the order of \$0.
By Andre Bacellar
The paper introduces the Belief Flow Filter (BFF), a generative filtering framework that encodes the evolving posterior distribution directly into flow matching model weights and updates them via test‑time gradient descent. By avoiding particle representations and Gaussian assumptions, BFF aligns structurally with Bayesian filtering and targets the recursive filtering operator. Empirical results on five physical systems—including chaotic dynamics, sparse observations, and a tokamak plasma estimation task—show that BFF outperforms existing methods in most benchmark metrics.
By Ruiqi Feng, Chongyi Wang, Tao Zhang, Tailin Wu
The paper introduces Dial, a knowledge‑grounded framework for generating SQL queries that respect the specific dialect of heterogeneous database systems. Dial comprises a Dialect‑Aware Logical Query Planning module, a hierarchical intent‑aware knowledge base (HINT‑KB) containing canonical syntax, function, and constraint repositories, and an execution‑driven debugging loop that separates syntactic recovery from logic auditing. The authors also present DS‑NL2SQL, a benchmark of 2,218 dialect‑specific test cases across six major database systems, and report that Dial improves translation accuracy by 10.25% and dialect feature coverage by 15.77% over existing baselines.
By Xiang Zhang, Hongming Xu, Le Zhou, Wei Zhou, Xuanhe Zhou, Guoliang Li, Yuyu Luo, Changdong Liu, Guorun Chen, Jiang Liao, Fan Wu
The paper presents a runtime monitoring framework for stochastic systems that distinguishes normal distributional relaxation from regime changes while limiting false alarms. It combines relative‑entropy dissipation, information geometry, and sequential inference within a bounded first‑passage architecture, employing Gaussian window surrogates, covariance shrinkage, and conformal ranking aggregated by a mixture power‑martingale. Validation on Ornstein–Uhlenbeck dynamics and network intrusion datasets (NSL‑KDD, UNSW‑NB15) shows high detection rates with low false positives, highlighting calibration transport as a key deployment challenge.
By Hikmat Karimov, Rahid Zahid Alekberli
Re:CAP is a reference‑free audit loop for retrieval‑augmented generation (RAG) pipelines that probes for missing documents instead of enumerating all relevant ones. It identifies covered topics, generates probing questions, retrieves candidate documents, and uses an LLM judge to keep only those that add new information. On several benchmarks, Re:CAP recovers a significant portion of gold documents that flat BM25 or hybrid retrieval misses, and human evaluation shows most of these documents add new information.
By Aviral Joshi, Hanoz Bhathena, Max Nelson, Saket Sharma
The paper presents a smartphone‑compatible, retrieval‑augmented language model tailored to Bangladeshi statutory law. By distilling a 9‑billion‑parameter Gemma‑2 teacher into a 2‑billion‑parameter student using supervised fine‑tuning and QLoRA, the authors achieve significant gains in ROUGE‑L and BERTScore on an English benchmark while keeping the model lightweight (1.6 GB) and operable offline on a Pixel 6. The system retrieves from 36,029 statutory passages using a hybrid dense/BM25 approach, and cross‑lingual evaluation shows effective Bangla query handling against an English‑only corpus, with a practicing lawyer rating the responses highly in a pilot study.
By MD. Nafis Kamal, Mahadi Hasan Fahim, Talha Ridwan, Nadifa Zaman, Fariha Roushon Florin, Farig Yousuf Sadeque, Saadat Rafid Ahmed
The BD-LSC dataset introduces a bi‑directional lexical semantic change benchmark that tracks sense gain, loss, and stability across three time periods, while the ST‑WSD dataset offers fine‑grained, instance‑level sense annotations for words that blend slang and standard usage. These resources enable systematic evaluation of diverse models—including unsupervised clustering, supervised learning, transformer‑based approaches, and large language models—on tasks such as exact sense matching and multi‑label accuracy. The evaluation shows that few‑shot GPT‑4o performs best overall, yet all systems struggle with rare slang senses, highlighting a key open challenge in the field.
By Afnan Aloraini, Riza Batista-Navarro
The paper presents a reproducible single‑subject baseline for reconstructing visual stimuli from EEG using a temporal‑spatial convolutional encoder that maps averaged EEG signals to 512‑dimensional ViT-B/32 image features. On the THINGS‑EEG2 dataset, the model achieves 12.83%, 39.17%, and 58.00% image recall at ranks 1, 5, and 10, respectively, outperforming analytical chance levels. The study also shows that performance drops sharply when applying a model trained on one subject to others, and that direct conditional generators without external visual weights produce noise‑dominated outputs, indicating that only coarse semantic decoding is feasible under the tested protocol.
By Harshit Goyal
M3GA-Wild is a new benchmark for multi-modal, multi-session ground-to-aerial place recognition in forests, combining synchronized RGB imagery and LiDAR from ground traversals with high‑resolution aerial imagery and multi‑altitude LiDAR over 370 hectares. The dataset includes accurate geo‑referenced 6‑DoF poses and spans 36 km of forest traversals, enabling systematic evaluation of visual, LiDAR, cross‑modal, and multi‑modal methods. Baseline experiments show LiDAR outperforms vision‑only approaches under severe viewpoint changes, while current multi‑modal fusion offers limited gains due to poor cross‑modal alignment, highlighting challenges in cross‑platform localisation and domain gaps.
By Ethan Griffiths, Maryam Haghighat, Simon Denman, Clinton Fookes, Milad Ramezani
The paper reviews the evolution of multi‑agent unmanned systems from isolated sensing to collaborative intelligence, where agents share compact features to overcome local observation limits such as occlusions and sensor range. It introduces a five‑dimensional taxonomy (collaboration stage, communication paradigm, fusion architecture, learning strategy, application domain) and three cognitive synergy conditions (Semantic Disambiguation, Pragmatic Information Exchange, Proactive Informational Foraging) to unify existing research. The authors survey architectures, neural‑communication co‑design, embodied action‑perception loops, and resilience mechanisms, map advances onto operational domains (V2X, UAV, logistics, smart cities), and propose the GCI‑Bench scoring protocol to standardize evaluation across studies.
By Lei Zhang, Chun Ye, Le Yang, Zhaozhong Wang, Deng-Ping Fan, Hang Dai, Binglu Wang
The paper introduces EviSpec, a training‑free compiler that generates complementary evidence specifications to improve high‑resolution multimodal large language models (MLLMs). By explicitly guiding visual search with structured evidence specifications, EviSpec achieves significant relative gains—up to 14.8% over random evidence—across five MLLMs and three benchmarks, and also sets new state‑of‑the‑art results on VQA and hallucination‑focused tasks.
By Zhongkuan Mao, Wenzhuo Zhao, Xianjie Liu, Yidong Wang, Zhao Gao, Ronghao Xian, Yao Jiang, Yi Zhang, Liangjian Wen, Keren Fu
AnalogDepth adapts the Depth Anything 3 (DA3) visual geometry model to the spatially structured noise of analog video transmission (VTX) used in FPV drones. The method employs a parameter‑efficient training pipeline that uses student‑teacher knowledge distillation with Low‑Rank Adaptation (LoRA) on the DINOv2 backbone, and trains on a noise bank built from real FPV recordings rather than synthetic Gaussian noise. Experiments on six real FPV flight sequences across three indoor scenes show that this real‑noise training consistently lowers per‑frame depth RMSE and 3D reconstruction Chamfer distance compared to the pretrained DA3 baseline and Gaussian noise baselines.
By Andr\'e Amorim, Pedro F. Proen\c{c}a
DeCo introduces an efficient decouple-to-couple learning framework for multi-task visual grounding, addressing conflicts between localization and segmentation tasks. It first applies Task-aware Semantic Decoupling (TSD) to separate shared visual cues into task-specific features guided by salient words, then uses Hybrid Prior Coupling (HPC) to merge sentence-level semantic priors with mask-derived spatial priors for improved grounding. Experiments across multiple natural and remote sensing datasets show that DeCo achieves state‑of‑the‑art performance while requiring only lightweight trainable parameters on a frozen multimodal encoder.
By Xiaoqiang Lu, Licheng Jiao, Long Sun, Yuting Yang, Xu Liu, Lingling Li, Wenping Ma, Fang Liu
The paper proposes a new sequence-to-sequence formulation for multi-view stereo (MVS) that jointly predicts 3D geometry for all input views using a global transformer architecture. It introduces ray‑map embeddings to inject camera parameters into image tokens and a unified global cost volume to capture 3D structure across all views. Experiments on public benchmarks demonstrate state‑of‑the‑art performance, outperforming both traditional MVS and feed‑forward reconstruction baselines.
By Aoxiang Fan, Corentin Dumery, Nicolas Talabot, Pascal Fua
SPHQuant introduces a rotation‑free spherical weight‑only quantization framework for Vision‑Language Models, decomposing 8‑dimensional weight vectors into sign, radius, and a positive unit direction. By isolating outlier magnitudes in the radius and allocating extra precision there, it mitigates accuracy loss at extreme low bit‑widths. The method also employs a compact positive‑direction codebook with angular fine‑tuning and a hardware‑friendly GEMV kernel, achieving state‑of‑the‑art performance while boosting decode throughput by 30.3% on RTX A6000 compared to QTIP.
By Kewei Zhang, Zheng Chen, Haotong Qin, Yulun Zhang
CIG-MAE is a self‑supervised framework for WiFi‑based human action recognition that uses a cross‑modal masked autoencoder to reconstruct both amplitude and phase of Channel State Information. It introduces an adaptive, information‑guided masking strategy that focuses on high‑density time‑frequency regions and employs a Barlow Twins regularizer to align cross‑modal representations without negative samples. Experiments on three public datasets show that CIG‑MAE outperforms state‑of‑the‑art SSL methods and even surpasses a fully supervised baseline, highlighting its data efficiency, robustness, and generalization.
By Gang Liu, Yanling Hao, Yixuan Zou