A paper accepted at ECCV 2026 presents a new approach to wearable motion capture that works with any combination of consumer devices like smartphones and smartwatches, introducing the WHIP model and a comprehensive dataset spanning 50 activities, along with a systematic study of sensor complementarity.

This paper proposes a Generalized Deep Non-negative Matrix Factorization (G-DNMF) method for SAR automatic target recognition. It overcomes the error accumulation and local optima problems of layer-by-layer decomposition in existing DNMF methods by deriving globally optimal update rules using the Lagrangian multiplier method. Experiments on MSTAR and OpenSARship datasets show improved stability and recognition performance over existing DNMF algorithms.

Multi-Modal Knowledge Graphs (MMKGs) enrich entities with modalities like text and images, but entities with highly similar multi-modal features remain hard to distinguish. Temporal information can serve as an additional modality for disambiguation, yet existing approaches rarely treat time as a separate modality due to sparse temporal semantics and noise from multiple timestamps. This paper proposes Time Imprint, a framework that treats time as an entity-level modality and aligns temporal, textual, and visual representations via a three-view contrastive objective. It also designs a compact timestamp subset selection with attention pooling to balance specificity and robustness. Experiments on three MMKG benchmarks show state-of-the-art link prediction, with Hits@1 improvements up to 6.07%…

This paper presents the first demonstration of a low-power MCU-based edge device for Automatic License Plate Recognition (ALPR). It uses a 9-core RISC-V processor (GAP8) with a QVGA ultra-low-power grayscale imager, employing SSDlite-MobilenetV2 for detection (38.9% mAP) and LPRNet for recognition (>99.13%). The system achieves 1.09 FPS at 117 mW, is 73x more energy-efficient than a Raspberry Pi 3 solution, and works on license plates as small as 30x5 pixels.

This paper proposes a knowledge-constrained shape optimization framework that translates expert knowledge and user intent into quantifiable parameters for DFFD-based deformation operators. A Mixture-of-Experts Neural Operator (MoE-NO) improves drag prediction and trend consistency on heterogeneous datasets. Experiments show MoE-NO achieves 1.16% MAPE and 94.34% trend accuracy, with CFD-validated drag reductions of 4-10%.

A new AI system called ReflectWorld-MM enables assistants to continuously process and remember open-ended video streams by organizing memory around persistent entities rather than frames, achieving state-of-the-art results on six benchmarks.

RSLoRA is a training-free, gradient-free method for allocating LoRA ranks based on activation-space geometry. It introduces virtual representational probing to identify high-sensitivity layers, outperforming state-of-the-art allocators like AdaLoRA and GoRA.

WiCAT, a multi-subject model using self-supervised pretraining, outperforms single-session models and enables zero-shot behavior decoding on unseen subjects in widefield calcium imaging.

Researchers propose DUNE, a training-free framework that refines diffusion models by detecting and suppressing early-stage fluctuations in deep latents, reducing artifacts and hallucinations while improving fidelity across both U-Net and Transformer backbones.

This paper investigates the feasibility of training a reasoning language model in Japanese. By applying GRPO to a Japanese continually pretrained model based on Qwen-3-Swallow-8B, the authors find that reasoning-language control is achievable, yet performance at best matches English-reasoning baselines. On Japanese cultural benchmarks, the model performs worse, indicating that reasoning in Japanese does not automatically improve culturally relevant tasks.

This work adapts an open-source spoken language model (SLM) to the Singaporean Home Team domain using LoRA fine-tuning, a surrogate text-QA dataset, and a multi-task objective with CoBa reweighting. The resulting model, HT-Moonstone (5B), matches or outperforms SLMs 7x its size on most tasks and achieves top accent and gender recognition with less than 2% loss in original speech QA ability.

Researchers present FindMyText, an open-source Python package to efficiently check if a given text appears in part or full within a corpus. It uses a novel fingerprint chain mechanism to reliably detect near-verbatim copies, ideal for copyright verification. The system scales to large web-crawled datasets via distributed disk-based indexing, outperforming alternatives on ArXiv, Wikipedia, and web content.

A new study shows that post-training quantization can silently alter how large language models reason even when task accuracy is preserved. Using a six-category failure taxonomy, the researchers classified 30,000 chain-of-thought outputs and found that hollow convergence exhibits a size-dependent shift under NF4 quantization, while shortcut collapse and confidence snowballing undergo qualitative changes. Hollow convergence cannot be reliably detected from surface-level text features, posing a deployment risk.

This report studies on-device English-to-Traditional-Chinese subtitle translation for Taiwan under short inputs, short outputs, batch-size-one inference, low latency, and privacy constraints. The authors replace the original 151k-token vocabulary with a 64k-token subtitle-domain tokenizer, perform embedding calibration and fine-tuning, achieving a 59.2% tie-excluded win rate against Google Translate on a subset of OpenSubtitles2024, and a 1.63x speedup on Apple M2.

A new benchmark framework evaluates the faithfulness of LLM-generated clinical trial summaries across three stakeholder audiences. Using 200 stratified trials from ClinicalTrials.gov and a six-dimension annotation schema, the study identifies 'Unsupported Claims' as the dominant failure mode. A knowledge-graph-augmented retrieval system shows statistically significant improvements in faithfulness scores.

A language-model forecasting system for merger arbitrage, utilizing long-context reasoning over technical documents, outperforms market-implied probabilities and frontier LLMs on a dataset of over 400 large deals across 42 countries.

Bilibili releases Index-1.9B, a series of open small language models with competitive performance on benchmarks.

CLIR-Bench is a benchmark for evaluating models on question answering over irregular clinical time series. It is constructed from de-identified ICU records using a principled four-stage pipeline, comprising 6,600 QA instances covering 11 clinical variables, organized into four capability dimensions and 11 tasks. Experiments reveal that current generalist models struggle to retrieve and reason over sparse clinical evidence, highlighting the need for stronger irregular time-series reasoning methods.

Researchers introduce a reference-based membership inference method to detect whether large language models are distilled from other models. By comparing a student model's preference for outputs from different candidate teachers against an earlier checkpoint, the method identifies the most likely teacher with near-perfect accuracy, handling unknown distillation pipelines and open-world settings.

A new study reveals that coding agents need minimal context when editing code: the signal is only in the code being edited, natural-language summaries fail to answer behavioral questions, surrounding context (UML skeletons) performs no better than deleting it, and compressed context matches full files at one-third the tokens. Temperature-0 inference introduces a ~9% noise floor. The authors release their instrument including gold-validated environments, deterministic patches, and pre-registered hypotheses.

This paper introduces a hybrid search framework that combines Thompson sampling with parallel self-avoiding walks to efficiently allocate computational resources in the Low Autocorrelation Binary Sequences (LABS) problem. The method, modeled as a multi-armed bandit, dynamically prioritizes promising search space partitions, achieving new best-known results for 35 sequence lengths and a longest sequence with merit factor exceeding 8.0.

A new paper introduces MawForge, a system that enables practical local inference of Sparse Mixture-of-Experts (MoE) language models on memory-constrained unified-memory machines by storing the model on disk and materializing expert tensors on demand into a bounded cache. The system is effective as a measurement substrate but not as a cache-maximization policy.

A new study uses macroeconomic forecasting as a stress test to evaluate five model families (ARIMA, LSTM, NODE, PINN, UDE) across 23 countries with sparse annual data. Results show no model consistently performs well, but less-constrained models (ARIMA, NODE) consistently outperform more-constrained heuristic-prior models (PINN, UDE). The study finds that structural priors can act as misregularizers when they do not match the data-generating process, and identifies failure modes including prior misalignment, regime shifts, structural breaks, and optimization instability.

This paper systematically compares Turbo-Quant and SpectralQuant KV-cache compression methods using a statistical validation methodology that separates systematic codec differences from implementation variance. Key findings reveal that eigenbasis-based methods fail on heavy-tailed data due to covariance instability but excel in structured regimes, with the effective semantic dimension adapting to calibration budgets rather than true data rank.

AuditWeave is a lightweight Python library that records steps of AI-assisted and data-transformation workflows into an append-only, hash-chained ledger, enabling tamper detection. It covers both RAG pipelines and tabular/lakehouse transformations with minimal overhead, verified over 2,000 randomized trials.

This position paper argues that ground truth datasets in machine learning are not neutral objective measurements but are constructed through human and technical arrangements. It advocates for recognizing the contingent, context-dependent nature of these datasets and promoting 'situated reliability' to enhance transparency, accountability, and interdisciplinary work.

This paper proposes a novel two-level taxonomy for GNN-based knowledge graph technologies, covering construction, embedding, reasoning, and applications, and reviews various GNN models, discussing their strengths, limitations, and future directions.

The paper presents a continuous-time instantiation of Feedback-Coupled Memory Systems (FCMS) by defining the agent update operator via Mechanism-Based Intelligence (MBI) and the environment update operator via Coupled Memory Graph Process (CMGP). It achieves Lyapunov global dissipativity with a computable threshold that generalizes previous discrete FCMS and CMGP stability conditions, establishing memory dissipation exceeding feedback gain as a universal organizing principle. Numerical simulations confirm the threshold and a self-reinforcing coordination cascade when violated.

This paper presents a closed-loop control framework using a small language model (SLM) aligned via Group Relative Policy Optimization (GRPO). The system integrates an action agent, a digital-twin validator, and a reprompting agent to iteratively correct outputs. In thermal control simulations, it achieves 91.5% action-alignment accuracy with 3.84s inference latency, demonstrating viability for edge autonomous control.

A learning-based graph edge sparsification method for efficient large-scale Euclidean TSP solving. By integrating geometric structure and combinatorial optimization, it adaptively generates sparse graphs, pruning up to 95% of edges on MATILDA dataset with solution gap under 1%, and demonstrates strong generalization on TSPLIB.