Neurofeedback training (NFT) aims to teach self-regulation of brain activity through real-time feedback, but suffers from highly variable outcomes and poorly understood mechanisms, hampering its validation. To address these issues, we propose a formal computational model of the NFT closed loop. Using Active Inference, a Bayesian framework modelling perception, action, and learning, we simulate agents interacting with an NFT environment. This enables us to test the impact of design choices (e.g., feedback quality, biomarker validity) and subject factors (e.g., prior beliefs) on training. Simulations show that training effectiveness is sensitive to feedback noise or bias, and to prior beliefs (highlighting the importance of guiding instructions), but also reveal that perfect feedback is insufficient to guarantee high performance. This approach provides a tool for assessing and predicting NFT variability, interpret empirical data, and potentially develop personalized training protocols.

We illustrate the fruitfulness of the account, relative to previous accounts, by showing that widely rec ognized "explanatory virtues" emerge naturally, as do subtle empirical patterns concerning the im pact of norms on causal judgments. This shows the value of a "communication-first" appro ach to explanation: getting clear on explanation's communicative dimension is an important prereq uisite for philosophical work on explanation. The result is a simple but powerful framework f or incorporating insights from the cognitive sciences into philosophical work on explanation, w hich will be useful for philosophers or cognitive scientists interested in explanation.

In an era where social media platforms abound, individuals frequently share images that offer insights into their intents and interests, impacting individual life quality and societal stability. Traditional computer vision tasks, such as object detection and semantic segmentation, focus on concrete visual representations, while intent recognition relies more on implicit visual clues. This poses challenges due to the wide variation and subjectivity of such clues, compounded by the problem of intra-class variety in conveying abstract concepts, e.g. "enjoy life". Existing methods seek to solve the problem by manually designing representative features or building prototypes for each class from global features. However, these methods still struggle to deal with the large visual diversity of each intent category. In this paper, we introduce a novel approach named Multi-grained Compositional visual Clue Learning (MCCL) to address these challenges for image intent recognition. Our method leverages the systematic compositionality of human cognition by breaking down intent recognition into visual clue composition and integrating multi-grained features. We adopt class-specific prototypes to alleviate data imbalance. We treat intent recognition as a multi-label classification problem, using a graph convolutional network to infuse prior knowledge through label embedding correlations. Demonstrated by a state-of-the-art performance on the Intentonomy and MDID datasets, our approach advances the accuracy of existing methods while also possessing good interpretability. Our work provides an attempt for future explorations in understanding complex and miscellaneous forms of human expression.

--In the domain of multimodal intent recognition (MIR), the objective is to recognize human intent by integrating a variety of modalities, such as language text, body gestures, and tones. However, existing approaches face difficulties adequately capturing the intrinsic connections between the modalities and overlooking the corresponding semantic representations of intent. T o address these limitations, we present the Anchor-based Mul-timodal Embedding with Semantic Synchronization (A-MESS) framework. We first design an Anchor-based Multimodal Embedding (A-ME) module that employs an anchor-based embedding fusion mechanism to integrate multimodal inputs. Furthermore, we develop a Semantic Synchronization (SS) strategy with the Triplet Contrastive Learning pipeline, which optimizes the process by synchronizing multimodal representation with label descriptions produced by the large language model. Comprehensive experiments indicate that our A-MESS achieves state-of-the-art and provides substantial insight into multimodal representation and downstream tasks. In the field of natural language understanding, the mul-timodal intent recognition (MIR) task, used to categorize intent within goal-driven context based on textual, visual and auditory information, has been identified as a critical element in identifying complex human behavioral intent [1]. Especially in AI Agent [2] applications, for example, when users need to command the AI agent to do specific tasks, the AI agent can perform the tasks well only if it correctly understands the intent behind the user's commands. Compared to the method [3] that relies solely on a single data type, the use of multiple data types provides a more substantial information base, which can improve the accuracy of identifying complex intent categories.

We do not always know how to interpret the statements that we hear, the observations that we make, or the evidence that we gather. Traditional frameworks for reasoning about uncertainty and belief revision typically suppose that new information is presented definitively: there is no question about what was learned. The paradigm of Bayesian conditioning exemplifies this assumption: "evidence" takes the simple form of an event E, and belief revision proceeds by updating probabilities accordingly: π π( | E). In order to capture the kind of uncertainty about interpretation we wish to reason about, we change the fundamental representation of events so that the sets they correspond to are themselves variable--the "true meaning" of a statement thus becomes itself an object of uncertainty. This approach follows in the spirit of other recent work [1, 2], expanding on it along two key dimensions.

Building on the analysis of Bonanno (Artificial Intelligence, 2025) we introduce a simple modal logic containing three modal operators: a unimodal belief operator, a bimodal conditional operator and the unimodal global operator. For each AGM axiom for belief revision, we provide a corresponding modal axiom. The correspondence is as follows: each AGM axiom is characterized by a property of the Kripke-Lewis frames considered in Bonanno (Artificial Intelligence, 2025) and, in turn, that property characterizes the proposed modal axiom.

We show how the AGM framework for belief change (expansion, revision, contraction) can be extended to deal with conditioning in the so-called Desirability-Indifference framework, based on abstract notions of accepting and rejecting options, as well as on abstract notions of events. This level of abstraction allows us to deal simultaneously with classical and quantum probability theory.

Bayes-adaptive planning offers a principled solution to the explorationexploitation trade-off under model uncertainty. It finds the optimal policy in belief space, which explicitly accounts for the expected effect on future rewards of reductions in uncertainty. However, the Bayes-adaptive solution is typically intractable in domains with large or continuous state spaces. We present a tractable method for approximating the Bayes-adaptive solution by combining simulationbased search with a novel value function approximation technique that generalises appropriately over belief space. Our method outperforms prior approaches in both discrete bandit tasks and simple continuous navigation and control tasks.

Weaknesses: - Presentation: I think the space that the paper spends on the BP background is more than necessary since the BP algorithm is just the standard one. The paper would be more compelling if the BP background is compressed and a more complete explanation of their algorithm is presented, for example some visual illustration that comes with the explanation of their implementation in Section 3.3. Moreover, since there are not many notations used in the paper, it is better not to use the same notation for different meanings to avoid confusion. For example, k is used for the number of top elements throughout the paper and also index of variable at Line 285; at Line 301 the parameter H is used without definition, and later on at Line 302 it denotes the tree height while at Line 334 a parameter in the Splash algorithm. Could the authors provide some conceptual or empirical comparison of them with the proposed one? Distributed Parallel Inference on Large Factor Graphs.

Reviewers agreed, in reviews and discussion, that this paper presents a nice, simple idea very clearly. The author feedback included new experiments and a new baseline, with positive results. I enjoyed reading the paper too.