Theory of Mind is a fundamental cognitive ability that underpins human social behavior, enabling individuals to infer the beliefs, intentions, and knowledge of others. In this paper, we propose BeliefNest, an open-source simulator designed to support research on collaborative behavior in embodied agents endowed with Theory of Mind capabilities. Recent advances in embodied agents powered by large language models (LLMs) have shown promising progress. However, there is still no platform that can explicitly represent nested belief states and integrate them with action generation mechanisms. BeliefNest addresses this gap by providing a flexible simulation framework that incorporates both hierarchical belief structures and prompt generation support. BeliefNest offers the following features: Explicit representation of nested belief states, as studied in Theory of Mind, using hierarchical simulators (see Section 3) Support for prompt generation based on each belief state, enabling the design and evaluation of methods for agent control with LLMs (see Section 5) Integration with the Minecraft environment, which is widely used in LLM agent research [1-4], and support for open-domain tasks In this paper, we describe the design and functionality of BeliefNest and demonstrate its effectiveness through experiments on false-belief tasks.

Forgetting a specific belief revision episode may not erase information because the other revisions may provide or entail the same information. Whether it does was proved coNP-hard for sequences of two arbitrary lexicographic revisions or arbitrarily long lexicographic Horn revisions. A polynomial algorithm is presented for the case of two lexicographic Horn revision. Heterogeneous sequences, including revisions other than lexicographic, were proved to belong in Delta2. Their previously proved coNP-hardness is enhanced to Dp-hardness.

This work introduces belief injection, a proactive epistemic control mechanism for artificial agents whose cognitive states are structured as dynamic ensembles of linguistic belief fragments. Grounded in the Semantic Manifold framework, belief injection directly incorporates targeted linguistic beliefs into an agent's internal cognitive state, influencing reasoning and alignment proactively rather than reactively. We delineate various injection strategies, such as direct, context-aware, goal-oriented, and reflective approaches, and contrast belief injection with related epistemic control mechanisms, notably belief filtering. Additionally, this work discusses practical applications, implementation considerations, ethical implications, and outlines promising directions for future research into cognitive governance using architecturally embedded belief injection.

Traditional logic-based belief revision research focuses on designing rules to constrain the behavior of revision operators. Frameworks have been proposed to characterize iterated revision rules, but they are often too loose, leading to multiple revision operators that all satisfy the rules under the same belief condition. In many practical applications, such as safety critical ones, it is important to specify a definite revision operator to enable agents to iteratively revise their beliefs in a deterministic way. In this paper, we propose a novel framework for iterated belief revision by characterizing belief information through preference relations. Semantically, both beliefs and new evidence are represented as belief algebras, which provide a rich and expressive foundation for belief revision. Building on traditional revision rules, we introduce additional postulates for revision with belief algebra, including an upper-bound constraint on the outcomes of revision. We prove that the revision result is uniquely determined given the current belief state and new evidence. Furthermore, to make the framework more useful in practice, we develop a particular algorithm for performing the proposed revision process. We argue that this approach may offer a more predictable and principled method for belief revision, making it suitable for real-world applications.

We examine belief filtering as a mechanism for the epistemic control of artificial agents, focusing on the regulation of internal cognitive states represented as linguistic expressions. This mechanism is developed within the Semantic Manifold framework, where belief states are dynamic, structured ensembles of natural language fragments. Belief filters act as content-aware operations on these fragments across various cognitive transitions. This paper illustrates how the inherent interpretability and modularity of such a linguistically-grounded cognitive architecture directly enable belief filtering, offering a principled approach to agent regulation. The study highlights the potential for enhancing AI safety and alignment through structured interventions in an agent's internal semantic space and points to new directions for architecturally embedded cognitive governance.

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.