Traditional multi-view learning models are primarily designed for static datasets with fixed views. However, in dynamic incremental view environments, this approach inevitably leads to view forgetting, where the introduction of new views weakens previously acquired knowledge. In contrast, the human brain exhibits remarkable memory retention and knowledge transfer capabilities when receiving objects described from different perspectives, with past experience further supplementing new insights. Inspired by underlying neural processing mechanisms, we propose a novel view incremental learning framework named Hebbian View Orthogonal Projection (HVOP). HVOP constructs a knowledge transfer space, where gradient updates are projected onto the orthogonal complement of historical representations, thereby mitigating interference between old and new views. By further incorporating recursive lateral connections and Hebbian learning rules, the proposed model imparts brain-like dynamic adaptability to the learning process, enhancing knowledge transfer and integration, thereby enabling stable knowledge transfer under evolving views. We validate HVOP on node classification tasks, demonstrating its superior performance in both knowledge retention and transfer compared to traditional methods. The results highlight the efficacy of biologically inspired mechanisms in mitigating the view forgetting phenomenon.
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Unlocking the spatial intelligence of multimodal large language models (MLLMs) is crucial for understanding and interacting with the 3D world. Prevailing approaches typically inject spatial priors via external tools, which impose significant inference overhead, or rely on latent feature distillation, which remains uninterpretable and lacks fine-grained geometric constraints. To address these issues, we propose SpatialSV, a framework designed to internalize robust 3D spatial awareness within MLLMs while simultaneously offering inherent interpretability. Deviating from passive feature imitation, SpatialSV employs task-oriented visual supervision, compelling the model to actively lift its 2D visual features into explicit 3D representations, including depth maps, camera poses, and point clouds. Crucially, this 2D-to-3D lifting process provides a transparent window into the model’s representations: the resulting 3D reconstructions serve as an intuitive proxy for visualizing and diagnosing the quality of the model’s intrinsic spatial knowledge. Extensive experiments across multiple benchmarks and models demonstrate the effectiveness of SpatialSV in enhancing and interpreting MLLMs’ spatial intelligence. Furthermore, the framework exhibits strong generalization in semi-supervised settings, validating its potential to leverage unlabeled visual data for scalable, interpretable spatial representation learning.
Whole-slide images (WSIs) provide gigapixel-scale visual evidence for cancer diagnosis, yet diagnostically relevant regions are typically sparse and embedded within large amounts of weakly informative tissue. Existing multiple instance learning methods often aggregate all patches by using attention mechanisms or sequence modeling, leading to redundant computation and limited adaptability to slides with highly variable sizes and lesion burdens. We propose Proportional Memory-guided Differential-attention Multiple Instance Learning (PMDMIL) network, a scalable MIL framework that follows a “less is more” principle for WSI classification. PMDMIL retains a fixed ratio of patches, pre-filters noise with a class-wise memory bank, and fuses the sparse survivors through differential attention to amplify subtle yet decisive morphological differences. Experiments on five public WSI benchmarks demonstrate that PMDMIL consistently outperforms representative MIL and sequence-based baselines across multiple evaluation metrics. Notably, on the DHMC dataset, our model can achieve competitive performance, while processing only 10% of patches, indicating that effective instance selection is more important than exhaustive aggregation for WSI classification.
Medication recommendation plays a critical role in clinical decision-making by supporting personalized and safe treatment planning. Existing methods rely heavily on historical co-occurrence patterns and primarily optimize discrete prescription prediction objectives, limiting generalization in rare or emerging disease settings. We propose MGRec, a framework that shifts learning from discrete prescription prediction to condition-aware modeling in a continuous molecular representation space. MGRec treats molecular structure as the primary modeling target and employs a Therapeutic--Safety Factorized Conditional Variational Autoencoder to disentangle therapeutic and safety-related factors in a condition-aware molecular latent space. The model infers treatment-relevant molecular representations conditioned on current patient-specific clinical context, which are mapped to clinically approved medications for final recommendation. To improve clinical safety, we further introduce a DDI (drug-drug interaction)-guided latent regularization to integrate drug interaction knowledge at the representation level. Experiments on two real-world benchmarks demonstrate that MGRec achieves state-of-the-art accuracy and reduced interaction risk, particularly in data-sparse scenarios.
Existing Programming-By-Example (PBE) systems often rely on simplified benchmarks that fail to capture the high structural complexity of real-world regexes, such as deeper nesting and frequent use of union operations. To overcome the resulting performance drop, we propose ReSyn, a synthesizer-agnostic divide-and-conquer framework that decomposes complex synthesis problem into manageable sub-problems. We also introduce Set2Regex, a parameter-efficient synthesizer capturing the permutation invariance of examples. Experimental results demonstrate that ReSyn significantly boosts accuracy across various synthesizers, and its combination with Set2Regex establishes a new state-of-the-art on challenging real-world benchmark. The complete source code, datasets, and pre-trained model checkpoints are publicly available at https://github.com/mrseongminkim/ReSyn.
Many scientific and engineering applications require estimating unknown parameters from experimentally observable data -- an inverse problem that is inherently challenging due to nonlinearity, noise, and ill-posedness. In this paper, we propose an active diffusion-based inverse problem solver. A diffusion model is trained to learn the mapping between the parameter space and the observable space. By iteratively detecting and correcting model misspecification through posterior uncertainty, the method discovers and learns the correct region of parameter space, even when initial training bounds exclude the true parameters. This provides a principled, Bayesian justification for adaptive domain augmentation and ensures robust inference for inverse problems under incomplete prior knowledge. We demonstrate the effectiveness of our inverse solver for a toy inverse problem with infinite solutions, and for the parameterization of the quantum correlation functions to event observables in a Quantum Chromodynamics analysis of nucleon structure.
Accurate estimation of time-varying treatment effects is crucial for optimizing interventions in personalized medicine. However, observational data often contains complex confounding bias and temporal complexities, making counterfactual estimation challenging. We propose Counterfactual Estimation via Temporal-Aware Intervention Networks (TAIN), a novel model that introduces an Intervention-aware Functional Convolution kernel to emphasize the role of treatments and capture complex temporal treatment interactions. TAIN addresses confounding bias from a domain generalization perspective, approximating the unknown target domain using adversarial examples and incorporating Sharpness-Aware Minimization to derive a generalization bound. This approach is more suitable for longitudinal settings compared to existing methods inspired by domain adaptation techniques due to inherent differences between static and longitudinal contexts. Experiments on simulated datasets demonstrate TAIN's superior performance compared to state-of-the-art models for counterfactual estimation over time.
Retrieval-Augmented Generation (RAG) has become a core paradigm for enhancing factual grounding and multi-hop reasoning in Large Language Models (LLMs). Traditional text-based RAG often retrieves logically irrelevant pseudo-evidence, while graph-based RAG is frequently hindered by search-time pruning, which may discard potentially valid reasoning paths. Existing hybrid approaches primarily adopt simple evidence concatenation or unidirectional enhancement, which fails to address the fundamental "Information Island" problem caused by asymmetric reasoning flows between unstructured text and structured graphs. We propose TGS-RAG, a unified framework for Text-Graph Synergistic enhancement. TGS-RAG introduces a bidirectional mechanism: (i) a Graph-to-Text channel that employs a Global Voting strategy from visited graph nodes to re-rank and refine textual evidence, filtering out semantic noise; and (ii) a Text-to-Graph channel that utilizes the Memory-based Orphan Entity Bridging algorithm. This algorithm utilizes textual cues to proactively resurrect valid but previously pruned reasoning paths from the search history without additional database overhead. Experimental results on multiple multi-hop reasoning benchmarks demonstrate that TGS-RAG significantly outperforms state-of-the-art baselines, achieving a superior balance between retrieval precision and computational efficiency.
Vision-language model (VLM) encoders such as CLIP enable strong retrieval and zero-shot classification in a shared image–text embedding space, yet the semantic organization of this space is rarely inspected. We present a post-hoc framework to explain, verify, and align the semantic hierarchies induced by a VLM over a given set of child classes. First, we extract a binary hierarchy by agglomerative clustering of class centroids and name internal nodes by dictionary-based matching to a concept bank. Second, we quantify plausibility by comparing the extracted tree against human ontologies using efficient tree- and edge-level consistency measures, and we evaluate utility via explainable hierarchical tree-traversal inference with uncertainty-aware early stopping (UAES). Third, we propose an ontology-guided post-hoc alignment method that learns a lightweight embedding-space transformation, using UMAP to generate target neighborhoods from a desired hierarchy. Across 13 pretrained VLMs and 4 image datasets, our method finds systematic modality differences: image encoders are more discriminative, while text encoders induce hierarchies that better match human taxonomies. Overall, the results reveal a persistent trade-off between zero-shot accuracy and ontological plausibility and suggest practical routes to improve semantic alignment in shared embedding spaces.
Knowledge editing is pivotal for efficiently updating the parametric memory of Large Language Models (LLMs), enabling them to function as evolving agents in dynamic environments. However, mainstream in-parameter knowledge editing approaches suffer from Subject-Dominant Memory Interference: modifying a specific fact inadvertently corrupts the broader structural knowledge associated with the same subject within LLMs. We diagnose the root cause as a shortcut learning pathology, where the optimization objective overfits subject representations while bypassing the essential relational context. To rectify this, we propose Causal Path Alignment (CPA), a principled framework designed to anchor the optimization trajectory to valid causal pathways. CPA enforces parameter updates to route through relation-aware intermediate states, thereby preventing the erasure of contextual dependencies. Experimental results across diverse LLM backbones demonstrate that CPA consistently eliminates the shortcut, significantly improving relation specificity while exhibiting minimal side-effects. Moreover, CPA serves as a model-agnostic plug-in for existing editors, paving the way for reliable and trustworthy in-parameter knowledge editing.
Agents implemented using Large Language Models (LLMs) offer novel solutions for tasks such as document summarization. However, single-agent systems, limited by their own knowledge base, often exhibit information omissions or inconsistencies with the facts, while multi-agent systems lacking effective organization can fall into the trap of social cognitive defects such as flattery and premature consensus. Inspired by the U.S. jury system, we propose JURIS, a judicial-decision-inspired multi-agent collaborative framework. Specifically, the document summarization process simulates a lawsuit scenario: multiple generator agents condense the target text into multiple sentences from different perspectives, much like lawyers on both sides providing different evidence; multiple decision-making agents consider the overall picture based on defense and voting mechanisms to select candidate sentences, much like a jury adopting different evidence; finally, a chief editor agent compiles a summary of all selected sentences, much like a secretary summarizing a plan and a judge giving a final verdict. Extensive experiments on in-domain news and cross-domain benchmarks demonstrate that JURIS consistently and significantly outperforms strong single-agent and multi-agent baselines across automatic evaluation metrics, multi-dimensional quality assessments, and human judgments, validating the effectiveness of decision-driven structured multi-agent collaboration for document summarization.
When vision contradicts text, multimodal large language models (MLLMs) consistently favor text—even when images provide clear evidence otherwise. This bias poses risks for applications requiring visual grounding, yet its cause remains unclear. In this paper, we uncover a surprising finding: models often get it right initially, forming correct vision-based predictions in their intermediate layers, before changing their minds and favoring text in the final output. We call this "late-layer textual override". The visual information is encoded, it simply does not survive to the output. More intriguingly, we find that how predictions change reveals whether they're correct: 85% of failures shift toward text, while 89% of successes shift toward vision. This directional signature enables a simple but powerful intervention: when we detect a confident visual prediction being suppressed, we restore it. We propose CALRD (Conflict-Aware Layer Reference Decoding), a training-free method that recovers overridden predictions at inference time. Experiments across five MLLMs of varying architectures demonstrate up to 9.4% absolute improvements on conflict benchmarks while largely preserving standard performance, without training or external knowledge. It recovers what the model already knew but failed to preserve.
The Capacitated Vehicle Routing Problem (CVRP) is a fundamental NP-hard problem with broad applications in logistics and transportation. Real-world CVRPs often involve diverse objectives and complex constraints, such as time windows or backhaul requirements, motivating the development of a unified solution framework. Recent reinforcement learning (RL) approaches have shown promise in combinatorial optimization, yet they rely on end-to-end learning and lack explicit problem-solving knowledge, limiting solution quality. In this paper, we propose a knowledge-embedded framework inspired by the Route-First Cluster-Second heuristics. It incorporates knowledge at two levels: (1) decomposing CVRPs into the route-first and cluster-second subproblems, and (2) leveraging dynamic programming to solve the second subproblem, whose results guide the RL-based constructive solver to solve the first problem. To mitigate partial observability caused by problem decomposition, we introduce a unified history-enhanced context processing module. Extensive experiments show that this framework achieves superior solution quality compared with state-of-the-art learning-based methods, with a smaller gap to classical heuristics, demonstrating strong generalization across diverse CVRP variants.
Static benchmarks for LLMs are increasingly compromised by contamination and overfitting, especially on knowledge-intensive reasoning tasks. While recent dynamic benchmarks can alleviate staleness, they often increase difficulty at the expense of answerability and controllability. In this paper, we propose StressEval, a failure-driven data synthesis framework that turns observed model failures into dynamic, challenging, and controlable test instances. StressEval consists of three stages: (i) it constructs a semi-structured difficulty card that identifies the failed reasoning step and its root cause; (ii) it applies a dual-perspective instance-synthesis method that targets both knowledge gaps and reasoning breakdowns while preserving the underlying difficulty factors; and (iii) it applies a gating mechanism to retain only grounded, unambiguous instances. Seeding from multiple knowledge-intensive reasoning datasets, we employ StressEval to build Dynamic-OneEval, a focused suite of challenging dynamic benchmark. Across several state-of-the-art LLMs, Dynamic-OneEval yields substantially larger performance drops than the original benchmarks while retaining explicit difficulty factors, enabling more actionable iteration.
Weakly supervised Referring Expression Comprehension (WREC) aims to localize referred objects using only image-text pairs without box-level annotations. Existing one-stage methods predominantly rely on anchor-level alignment, which suffers from two fundamental limitations: (1) anchors represent local visual patches rather than holistic objects, and (2) they lack the capability to model inter-object relations. To address these issues, we propose OCAAD, an Object-Centric Alignment and Anchor Distillation framework. Our key insight is that different self-attention heads in DINOv2 naturally attend to distinct semantic regions, effectively capturing object-level information. Building on this, OCAAD introduces two complementary modules: (1) Anchor-Object Distillation Module (AODM), which bridges the semantic gap between anchors and objects by transferring object-level knowledge from attention heads to anchors via overlap-aware contrastive learning; and (2) Intra-Modal Relation Consistency (IRC), which explicitly models inter-object relations by enforcing the relational structure among linguistic entities to match that of their visual counterparts. Extensive experiments on RefCOCO, RefCOCO+, and RefCOCOg demonstrate that OCAAD achieves new state-of-the-art performance, validating the effectiveness of objectcentric alignment for WREC. Code is available at https://github.com/VILAN-Lab/OCAAD.
We introduce QUINTIC, a general algorithm for actively learning quantitative automata from preferences. Quantitative automata evaluate input sequences by applying a valuation function---such as sum, product, or average---to the output labels of the states visited. Such models naturally arise in use cases ranging from probabilistic verification, sequence classification, and sequential decision making. However, existing learning approaches, such as variants of L*, weighted‐automata learning algorithms, and active learning preference‐driven methods, either assume finite output alphabets or restrict the valuation function to particular forms. QUINTIC utilizes a symbolic observation table and applies deductive reasoning with the assistance of an SMT solver to identify the correct minimal state, transition, and state label combination of the quantitative automaton. The deductive reasoning relies on the minimal combination of theories determined by the valuation function and output alphabet. Consequently, QUINTIC has completeness, minimalism, and query complexity guarantees, and learns quantitative automata across finite, integer, and rational outputs. Our extensive experiments show how QUINTIC scales under weak or strong feedback, and alternative MaxSMT objectives.
Argumentation is an important topic of AI for modeling and reasoning about arguments. In abstract argumentation, we consider directed graphs, so-called argumentation frameworks (AF), that express conflicts between arguments. The semantics is defined by the notion of extensions, which are sets of arguments that satisfy particular relationship conditions in the AF. Usually, standard reasoning in argumentation do not reveal how far apart extensions are. We introduce a quantitative notion of diversity of extensions based on the symmetric-difference and provide a systematic complexity classification. Intuitively, diversity captures whether extensions of a framework (accepted viewpoints) differ only marginally or represent fundamentally incompatible sets of arguments. We study whether an AF admits k-diverse extensions, admits k-diverse extensions covering specific arguments, and to compute the largest k for which an AF admits k-diverse extensions. We outline a prototype and provide an evaluation for computing diversity levels.
Federated knowledge distillation (Fed-KD) exchanges distilled predictions on a shared proxy dataset, often reducing communication and accommodating heterogeneous client architectures. However, in multimodal federated learning (MFL), modality dominance can bias local optimization and contaminate the aggregated global teacher, degrading both multimodal accuracy and robustness to missing modalities, especially under non-IID heterogeneity. We propose Federated Adaptive-Modality Balancing (FedAMB). At the client side, Selective-Modality Regulation (SMR) models dominance as a state-dependent phenomenon and intervenes only when it destabilizes training, strengthening weak modalities without over-regularization. At the server side, Component-wise Modality Distillation (CMD) regulates how aggregated knowledge is transferred to each modality branch, preventing the propagation of fusion-biased teachers while directly improving unimodal representations. Experiments on CREMA-D, AVE, and UR-FUNNY show that FedAMB consistently improves multimodal accuracy and missing-modality robustness.
Knowledge-based Visual Question Answering (KB-VQA) requires models to answer visual questions by reasoning over external knowledge beyond the given image. Existing approaches suffer from two main limitations. First, candidate knowledge is often retrieved in a single modality, either textual or visual, which prevents effective use of heterogeneous and complementary evidence. Second, current approaches typically employ fixed fusion weights, ignoring the varying importance of modalities for different queries. Consequently, irrelevant evidence is introduced while critical knowledge may be overlooked. To address these issues, we propose Dynamic Multi-Path Retrieval for KB-VQA (DMRAG). Our framework retrieves candidates through multiple retrieval paths that capture complementary visual and semantic cues. It then performs Question-Adaptive Gated Fusion (QGF) to balance contributions from different modalities according to the query’s information need. The fused candidates are further refined via multimodal rearrangement to support accurate answer generation. Experiments on the E-VQA and InfoSeek datasets show that DMRAG improves both retrieval recall and answer accuracy over prior methods, demonstrating its effectiveness for KB-VQA. Our code is available at https://github.com/qwqq335/DMRAG.
Large Language Model (LLM)-based navigation systems have commonly constructed expli cit spatial representations (e.g., topological graphs, semantic raster maps) and translated them into textual descriptions as LLMs’ inputs. However, the linguistic structures of such text-based spatial representations and the choices of contextual features (e.g., topology, geometry) they contain are often treated as neutral engineering decisions rather than key factors that shape LLMs' behavior. To address this gap, we propose a dual-interventional framework that disentangles linguistic structures from different contextual cues to evaluate the linguistic inductive bias of LLMs for navigation planning. In the framework, representation intervention varies linguistic format and the degree of linguistic compression, clarifying when linguistic representations support or inhibit navigation planning; context intervention, combined with contextual feature combination and conflict probing, explicitly clarifies the preferences and weaknesses of LLMs when processing different contextual cues. Experiments across diverse spatial reasoning tasks and multiple model scales reveal a consistent pattern: topological information is a sturdy shield and the backbone of robust planning; linguistic format is a double-edged sword whose effect depends on model size, task demands, and the compression level; and semantic information is a fatal Achilles' heel---incorrect semantic cues can systematically derail the planning process. Overall, our study shows that effective text-based spatial representations in LLM-based navigation should preserve topological integrity, calibrate representational compression to model capacity, and ensure semantic correctness, rather than simply adopting a single representation.