The paper extends the h‑plot multidimensional scaling technique to handle three‑way asymmetric dissimilarities, enabling the extraction of archetypal profiles and clustering in a unified Euclidean space. It provides an explicit eigenvector‑based solution that avoids local minima, is scale‑invariant, computationally efficient, and includes a straightforward goodness‑of‑fit assessment. The method is benchmarked against existing models and demonstrated on a financial dataset, with all data and code made publicly available for reproducibility.
By Mireia Mollar-Gumbau, Aleix Alcacer, Rafael Benitez, Vicente J. Bolos, Irene Epifanio
DuoGesture is a co‑speech gesture generation model that separates gesture synthesis into a semantic stream and a beat stream, coordinated by a Semantic Variational Information Bottleneck that decides when semantic gestures override rhythmic motion. The semantic stream uses Motion‑Grounded Semantic Conditioning, replacing word embeddings with motion‑language representations to provide motion‑aligned semantic priors for rare gesture triggers. The beat stream is regularised by an Inertial Beat Prior, an anthropometry‑weighted arm‑chain module that reduces jitter and improves rhythmic consistency. Experiments show DuoGesture outperforms strong baselines and ablations confirm the complementary roles of semantic grounding, stochastic stream selection, and biomechanical regularisation.
By Ferdinand Paar, Lanmiao Liu, Asl{\i} \"Ozy\"urek, Serge Thill, Esam Ghaleb
The paper examines how rewriting verbose, narrative-style queries into concise keyword‑grounded forms improves complex object segmentation in 4D Gaussian representations. By applying a training‑free reinterpretation strategy, the authors reduce linguistic noise while preserving essential semantic anchors. Experiments on HyperNeRF and Neu3D show that rewritten queries boost temporal accuracy from 60.92% to 92.21% and vIoU from 20.08% to 76.94%, with ablation studies confirming the benefits of shorter, keyword‑focused queries.
By Thanh-Khoi Nguyen, Thien-Phuc Tran, Minh-Triet Tran
MARS introduces a multi‑layer, multi‑slot embedding framework for text‑video retrieval that constructs adaptive representation slots by combining hidden states from different decoder layers. By comparing corresponding text and video slots and aggregating their similarities, MARS captures fine‑grained cues that single‑token embeddings miss. A hard‑negative‑aware slot specialization objective further encourages slots to focus on discriminative matching cues, leading to state‑of‑the‑art results on four benchmarks.
By Uicheol Jung, Juyoung Hong, Geuntaek Lim, Yukyung Choi
BioELX is a retrieve‑rerank framework for cross‑lingual biomedical entity linking that tackles two key problems: the English‑biased UMLS alias training data and the degradation caused by naïvely adding context. It fine‑tunes SapBERT_multi with Wikidata‑derived cross‑lingual alias supervision to create shared concept neighborhoods, and then reranks candidates using pretrained LLMs with mention‑anchored prompting to focus on the target mention. Experiments demonstrate state‑of‑the‑art performance on four benchmarks, improving Recall@1 by 4.8–18.2 percentage points without task‑specific annotations.
By Yi Wang, Corina Dima, Liangyu Zhong, Steffen Staab
The paper presents a method for selecting sub‑networks of large language models for on‑premise factory assistants, showing that after structural compression and retrieval‑grounded adaptation, model size no longer predicts answer quality. By treating deployment as a post‑adaptation selection problem, the authors commit one sub‑network per device based on judged answer quality and measured on‑device throughput, while respecting a general‑capability floor and memory budget. A weight‑shared supernetwork trained with sandwich‑style in‑place distillation makes this selection inexpensive, and a manufacturing‑manual case study demonstrates that the approach recovers most of the quality loss while operating across heterogeneous edge tiers at low power.
By Vasileios Rizeakos, Georgios Paisios, Alexandros Machairas, Michael Birbas, Athanasios Bachoumis
The paper investigates inference‑time optimization of prompt embeddings for the Stable Diffusion XL Turbo model, comparing the gradient‑free Separable Covariance Matrix Adaptation Evolution Strategy (sep‑CMA‑ES) with the gradient‑based Adam optimizer. Using a weighted objective that blends LAION Aesthetic Predictor V2 and CLIPScore, the study evaluates 36 prompts under three weighting regimes (aesthetics‑only, balanced, alignment‑only). Across all settings, sep‑CMA‑ES consistently outperforms Adam in objective value, while also demonstrating favorable divergence metrics and lower compute and memory footprints, indicating its effectiveness as an inference‑time optimizer without requiring model fine‑tuning.
By Dom\'icio Pereira Neto, Jo\~ao Correia, Penousal Machado
NE‑R1 is a framework that improves Named Entity Recognition by using a retrieval‑on‑demand mechanism and a two‑stage training process that includes instruction tuning and reinforcement learning with chain‑of‑thought. It balances the use of internal model parameters and external knowledge through a multi‑dimensional reward that considers accuracy and retrieval benefit. The approach achieves state‑of‑the‑art results, improving in‑domain F1 by 2.52% and zero‑shot cross‑domain F1 by 1.18%.
By Meixuan Chen, Hehan Li, Ruizhi Zhao, Xin Lu, peizhi xu, Liwei Qian, LI Meifang, shuanglong li, Hanmeng Liu, Xin Pei, Yanbiao Ma
The paper introduces EmoStance, a method for controlling the affective orientation of empathetic responses by leveraging weak supervision from emoji distributions. It builds the EmojiDialogue dataset, extending EmpatheticDialogues with emoji votes and confidence scores, and uses a frozen instruction‑tuned LLM steered by continuous prefix embeddings to generate responses that align with the listener’s stance. In blind pairwise evaluations, EmoStance achieves a 62.2% decisive win rate, notably improving contextual specificity and perceived responsiveness compared to baselines.
By Ziyuan Jin, Yuxuan Ge, Zheng Tian
The paper introduces a new image retrieval framework that merges neighbor embedding projections with rank-based manifold learning via rank aggregation. It uses UMAP to create low‑dimensional feature representations and combines ranked lists from UMAP and rank‑based re‑ranking methods using the Borda Count strategy. Experiments on public datasets with ResNet152, Swin Transformer, and DINOv2 features show that this combined approach improves retrieval performance, especially in scenarios where baseline representations have low precision.
By Vinicius Atsushi Sato Kawai, Gustavo Rosseto Leticio, Lucas Pascotti Valem, Daniel Carlos Guimar\~aes Pedronette
PEARL is a new framework for inductive knowledge graph completion that treats relational paths as context-conditioned reasoning signals. It builds a query‑specific contextual subgraph from the query entities’ neighborhoods and uses a large language model‑guided retriever to select semantically relevant paths. By constructing a bipartite interaction graph over paths, contextual entities, and a global subgraph representation, and applying a dual‑view contrastive objective, PEARL adapts path embeddings to local and global structural evidence, achieving the best average Hits@10 on WN18RR, FB15k‑237, and NELL‑995.
By Yunchi Yang, Longlong Li, Cunquan Qu
Loom is a generative consensus framework designed for real‑world root‑cause analysis (RCA) that combines open‑form hypotheses from modular heuristics with a lightweight large language model (LLM) synthesis step. It projects hypotheses into a continuous embedding space and uses an iterative centroid‑based reweighting algorithm to resolve conflicts, producing a single consensus that is then synthesized by one LLM call. On the OpenRCA benchmark Loom matches state‑of‑the‑art autonomous agents on some datasets while achieving significantly higher efficiency—about 26× faster and 33× faster with an 8B‑parameter synthesizer.
whyItMatters":"Loom demonstrates how embedding‑space reweighting can bridge the gap between statistical rigor and expressive LLMs, enabling efficient, trustworthy RCA in industrial settings."
By Ron Begleiter, Katya Egert Berg, Gilad Saban, Gil Shabat
The paper introduces DPA, a diffusion-based framework that decouples product-agnostic anomaly representations to enable zero-shot anomaly generation. By reusing real anomalies from existing source products and filtering them for plausibility, DPA learns product-irrelevant anomaly embeddings that can be transferred across products. An adaptive mask-guided pipeline and a training-free labeling module further refine the realism and localization of generated anomalies, leading to improved performance on MVTec-AD, VisA, and a new anomaly-transfer benchmark.
By Hang Yao, Yansheng Fu, Ming Liu, Zifei Yan, Yanli Ji, Hongzhi Zhang, Wangmeng Zuo
SignMatch introduces a prototype‑structured embedding space that learns to match dictionary sign videos with continuous sign language footage based solely on visual similarity of handshape and motion. By mapping isolated dictionary exemplars into this space, the method enables direct, embedding‑based sign matching and can generalise to unseen signs using only dictionary examples. Experiments on ASL‑Citizen, ChaLearn OSLWL, and BOBSL CSLR2 benchmarks show strong cross‑dataset, cross‑task, and cross‑language performance, outperforming prior approaches on American, British, and Spanish sign languages without benchmark‑specific supervision.
By Ryan Wong, Youngjoon Jang, Liliane Momeni, G\"ul Varol, Andrew Zisserman
The paper introduces DKL, a method for adding new knowledge to instruction‑tuned language models without compromising their instruction‑following abilities. DKL performs extended pre‑training on a base LLM to embed knowledge, then merges these weights into the instruction‑tuned model, avoiding costly instruction fine‑tuning. Experiments show DKL raises RAG accuracy from 54.17% to 79.26% on retrieval failure cases while using far less training data than previous approaches.
By Kushagra Bhushan, Meghanadh Pulivarthi, Sai Krishna Reddy Sathi, Gaurav Pandey, Sonam Gupta, Vineet Kumar, Jaydeep Sen, Yatin Nandwani, Sachindra Joshi, Dinesh Raghu
The paper introduces Omega‑N, a set of ten interpretable node‑level structural descriptors derived from localizing four factors of a composite structural index. By correcting the ill‑conditioned localization with a configuration‑null excess and a multi‑scale personalized‑PageRank neighbourhood, Omega‑N achieves competitive or superior performance in six in‑domain node‑classification tasks compared to a recursive feature engine that uses up to 252 features. In drug‑target prioritisation on protein interaction networks, Omega‑N improves AUPRC by 0.073 to 0.144 over a centrality baseline and remains robust across independent datasets and bias controls, though it offers no benefit when combined with Node2Vec.
whyItMatters":"The study demonstrates that a compact, interpretable set of structural features can match or exceed more complex feature sets in practical graph‑based prediction tasks, particularly in biomedical network analysis."
By Alberto Acedo
CivBench is an open‑source benchmark that evaluates language‑model agents in the long‑horizon, tool‑mediated game Civilization VI using the Model Context Protocol (MCP). Each episode lasts over 300 turns, generating thousands of tool calls across a 76‑tool action space, and includes a narration layer that translates visual game state into structured text. The study characterises agent behaviour across four model families, introducing Proactive Monitoring Rate (PMR) and RAG@10 as interface‑level metrics, and finds that agents often under‑monitor strategic state and fail to execute near‑term commitments despite tool access and explicit guidance.
By Austin Tudor David Andrews, Liam Wilkinson, Jamie Heagerty, Harry Coppock, Jakob Nicolaus Foerster, Rui Ponte Costa
CaST-POI is a next‑point‑of‑interest recommender that conditions the user representation on each candidate location by adding bucketised biases for visit recency and geographic distance to the candidate. Unlike prior models that treat all candidates uniformly, CaST-POI lets each candidate read the trajectory with different attention weights grounded in real distance. Experiments on NYC, TKY, and CA datasets show significant MRR gains over seven baselines, with ablation revealing the importance of the revisit gate and spatial bias.
By Zhenyu Yu, Chunlei Meng, Yangchen Zeng, Mohd Yamani Idna Idris, Jihong Guan, Shuigeng Zhou
The paper introduces a multimodal framework that learns subcellularly resolved cell embeddings by integrating RNA expression profiles, protein sequence representations, and protein structural information using a cross‑attention architecture. This approach models interactions within distinct subcellular compartments, producing fine‑grained embeddings that capture both molecular expression patterns and functional protein properties. It is presented as the first method to jointly incorporate transcriptomic data, sequence, and structural knowledge for subcellularly resolved cell representation.
By Zhen Zhou, Jiachen Li, Yuan Liu, Xiaoyong Pan, Hong-Bin Shen
arXiv:2609. 02143v1 Announce Type: cross Abstract: Most vector databases rely on graph-based indexes, notably HNSW and Vamana, for approximate nearest neighbor search.
By Sajad Faghfoor Maghrebi, Navid Eslami, Niv Dayan