semantic association
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Related Articles from SNS
Modeling semantic association in self-paced reading with language model embeddings
arXiv:2606.07066v1 Announce Type: new Abstract: Semantic association between a word and its context has been identified as an important component of reading comprehension, even when word predictability is accounted for. Recent research has highlighted the potential of language model ( LM) embeddings to quantify semantic association. Yet, embedding-based semantic association have been operationalized in a myriad of ways.
BPDA-GMM: Bayesian Probabilistic Data Association via Gaussian Mixture Models for Semantic SLAM
arXiv:2606.04618v1 Announce Type: new Abstract: Probabilistic data association (PDA) improves semantic SLAM in perceptually aliased scenes, but existing methods often assume a fixed landmark set, recompute association weights as the map grows, or rely on hand-tuned null-hypothesis weights. To address these limitations, we propose \textbf{BPDA-GMM}, an online Bayesian PDA framework for semantic SLAM with a growing object-level map. BPDA-GMM uses a Dirichlet-process prior to induce a Chinese...
Introducing multiplex semantic networks as multifaceted representations of creative associative knowledge across multilingual samples
arXiv:2606.09403v1 Announce Type: new Abstract: Creativity is a complex cognitive ability that relies on knowledge organisation and retrieval from semantic memory. Yet most research uses a single task to measure it, capturing only a fraction of this complexity. This study investigates multiplex networks - layered semantic networks obtained from six cognitive tasks - as a more comprehensive approach to modelling the associative knowledge underlying creativity.
Correct-by-Construction Design of Timed Systems in Event-B
arXiv:2606.05939v1 Announce Type: new Abstract: Real-time systems require the careful handling of timing aspects in their models. For critical applications, this entails the use of time-aware formal methods. Currently, most of these formal methods express their semantics by reduction to timed automata or timed transition systems, and are associated with model-checking-based verification techniques.
A Unified Geometric Space for Topological Alignment Between Transformer-Based Models and Human Brain Networks
arXiv:2510.24342v2 Announce Type: replace Abstract: Prior brain-AI alignment studies are typically constrained by specific inputs and tasks, limiting their ability to capture organizational properties across models with different modalities. In this work, we focus on Transformer-based models and introduce a brain-model topological alignment space.
No Modality Left Behind: Adapting to Missing Modalities via Knowledge Distillation for Brain Tumor Segmentation
arXiv:2509.15017v2 Announce Type: replace Abstract: Accurate brain tumor segmentation is essential for preoperative evaluation and personalized treatment. Multi-modal MRI is widely used due to its ability to capture complementary tumor features across different sequences. However, in clinical practice, missing modalities are common, limiting the robustness and generalizability of existing deep learning methods that rely on complete inputs, especially under non-dominant modality combinations.
Memory is Reconstructed, Not Retrieved: Graph Memory for LLM Agents
new Abstract: Despite recent progress, LLM agents still struggle with reasoning over long interaction histories. While current memory-augmented agents rely on a static retrieve-then-reason paradigm, this rigid pipeline design prevents them from dynamically adapting memory access to intermediate evidence discovered during inference. To bridge this gap, we propose MRAgent, a framework that combines an associative memory graph with an active reconstruction mechanism.
Semantic knowledge guides innovation and drives cultural evolution
arXiv:2510.12837v4 Announce Type: replace Abstract: Cultural evolution allows ideas and technologies to accumulate across generations, reaching their most complex and open-ended form in humans. While social learning enables the transmission of such innovations, the cognitive processes that generate them remain poorly understood. Classical theories typically treat innovation as random variation, a simplification insufficient for explaining the complexity of human cultural evolution.
An ERP Study on Recursive Locative Processing in Mandarin-Speaking Children with Autism
arXiv:2606.05620v1 Announce Type: new Abstract: Recursion enables the generation of hierarchical linguistic structures but imposes substantial processing demands during real-time comprehension. While difficulties with complex syntax have been reported in autism spectrum disorder (ASD), the temporal dynamics of recursive processing remain poorly understood. This study used event-related potentials (ERPs) to examine how Mandarin-speaking children with ASD process two-level recursive locative...
Physics-Driven Semantic Scattering Structure Understanding of Aircraft Target in SAR Images
arXiv:2606.06847v1 Announce Type: cross Abstract: Synthetic aperture radar (SAR) has become indispensable for target interpretation owing to its all-day and all-weather observation capability. In SAR target interpretation, electromagnetic scattering information provides a physically grounded cue beyond visual texture and has been widely exploited for target interpretation. However, existing methods remain dominated by local scattering center representations.