Singapore

Given the task of landing a ball in a goal region beyond direct reach, humans can often throw, slide, or rebound objects against the wall to attain the goal. Enabling robots to replicate such reasoning is non-trivial as it requires multi-step planning and involves a mixture of discrete and continuous action spaces, a sparse and sensitive reward structure, computationally expensive simulations, and an incomplete understanding of the environment&#39;s physics. We present PhyPlan, a physics-informed and adaptable planning framework for efficient multi-step physical reasoning. At its core, PhyPlan comprises of Generative Flow Networks (GFlowNets) and Monte Carlo Tree Search (MCTS) to explore and evaluate sequences of object interactions. GFlowNets sample discrete action sequences in proportion to their associated reward, enabling broad and reward-driven exploration of the discrete planning space. MCTS complements this by adaptively balancing the use of a fast but approximate pre-trained physics-informed dynamics predictor and costly but accurate environment rollouts, ensuring both speed and precision in planning. The known and actual physics discrepancy is captured using Gaussian Process Regression. Experiments on benchmark simulated tasks requiring composition of collisions, slides, and rebounds demonstrate that PhyPlan achieves a 45\% higher success rate and up to 3× efficiency gains over state-of-the-art model-based reinforcement learning approaches.

AAAI 2026

PhyPlan: Learning to Plan Tasks with Generalizable and Rapid Physical Reasoning for Embodied Manipulation

mixed discrete/continuous planning

sequential decision making

planning under uncertainty

cognitive robotics

embodied ai

reinforcement learning

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ACL 2024

The 62nd Annual Meeting of the Association for Computational Linguistics (ACL 2024) will take place in Bangkok, Thailand from August 11th to 16th, 2024. More information will be announced soon.

Given the task of landing a ball in a goal region beyond direct reach, humans can often throw, slide, or rebound objects against the wall to attain the goal. Enabling robots to replicate such reasoning is non-trivial as it requires multi-step planning and involves a mixture of discrete and continuous action spaces, a sparse and sensitive reward structure, computationally expensive simulations, and an incomplete understanding of the environment's physics. We present PhyPlan, a physics-informed and adaptable planning framework for efficient multi-step physical reasoning. At its core, PhyPlan comprises of Generative Flow Networks (GFlowNets) and Monte Carlo Tree Search (MCTS) to explore and evaluate sequences of object interactions. GFlowNets sample discrete action sequences in proportion to their associated reward, enabling broad and reward-driven exploration of the discrete planning space. MCTS complements this by adaptively balancing the use of a fast but approximate pre-trained physics-informed dynamics predictor and costly but accurate environment rollouts, ensuring both speed and precision in planning. The known and actual physics discrepancy is captured using Gaussian Process Regression. Experiments on benchmark simulated tasks requiring composition of collisions, slides, and rebounds demonstrate that PhyPlan achieves a 45\% higher success rate and up to 3× efficiency gains over state-of-the-art model-based reinforcement learning approaches.

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We are pleased to announce the Fortieth AAAI Conference on Artificial Intelligence (AAAI-26), which will be held in Singapore EXPO from January 20 to January 27, 2026.

Large language models (LLMs) present new opportunities for creating pedagogical agents that engage in meaningful dialogue to support student learning. However, the current use of LLM systems like ChatGPT in classrooms often lacks the solid theoretical foundation found in earlier intelligent tutoring systems. To bridge this gap, we propose a framework that combines Evidence-Centered Design with Social Cognitive Theory for adaptive scaffolding in LLM-based agents focused on STEM+C learning. We illustrate this framework with Inquizzitor, an LLM-based formative assessment agent that integrates human-AI hybrid intelligence and provides feedback grounded in cognitive science principles. Our findings show that Inquizzitor delivers high-quality assessment and interaction aligned with core learning theories, offering teachers effective guidance that students value. This research underscores the potential for theory-driven LLM integration in education, highlighting the ability of these systems to provide adaptive and principled instruction.

A Theory of Adaptive Scaffolding for LLM-Based Pedagogical Agents

Embodied agents, such as robots, will need to interact in situated environments and reason over social norms to achieve naturalistic communication with humans. Reference resolution is a critical aspect of this interaction that involves handling normative expectations that come with situated interaction. However, there are no normative reasoning benchmarks for reference resolution, and it remains an understudied capability. To address this gap, we introduce SNIC (_Situated Norms in Context_): a human-validated benchmark for norm-based reference resolution (NBRR) focused on physically grounded norms that arise in everyday social contexts. We investigate state-of-the-art Large Language Models (LLMs) for normative reasoning to test how these models can extract and utilize normative principles relevant to reference resolution. We find that LLMs generally struggle to consistently extract and reason with social norms and norm conflicts for reference resolution across contexts when social norms are not explicitly given. Overall, this research highlights a blind spot for state-of-the-art LLMs and informs work on reasoning with text-based physically situated norms.

Where Norms and References Collide: Evaluating LLMs on Normative Reasoning

Disordered materials such as glasses, unlike crystals, lack long‑range atomic order and have no periodic unit cells, yielding a high‑dimensional configuration space with widely varying properties. The complexity not only increases computational costs for atomistic simulations but also makes it difficult for generative AI models to deliver accurate property predictions and realistic structure generation. In this work, we introduce GlassVAE, a hierarchical graph variational autoencoder that uses graph representations to learn compact, rotation‑, translation‑, and permutation‑invariant embeddings of atomic configurations. The resulting structured latent space not only enables efficient generation of novel, physically plausible structures but also supports exploration of the glass energy landscape. To enforce structural realism and physical fidelity, we augment GlassVAE with two physics‑informed regularizers: a radial distribution function (RDF) loss that captures characteristic short‑ and medium‑range ordering and an energy regression loss that reflects the broad configurational energetics. Both theoretical analysis and experimental results highlight the critical impact of these regularizers. By encoding high‑dimensional atomistic data into a compact latent vector and decoding it into structures with accurate energy predictions, GlassVAE provides a fast, physics‑aware path for modeling and designing disordered materials.

Physical-regularized Hierarchical Generative Model for Metallic Glass Structural Generation and Energy Prediction

Graph unlearning, motivated by emerging right to be forgotten regulations, seeks to remove the influence of specific subsets of data (\textit{e.g.}, noisy, poisoned, or privacy-sensitive data) from pre-trained graph learning models. While much attention has focused on the technical feasibility of unlearning, its implications for fairness remain largely unexamined. To address this critical gap, this paper introduces GUIC, the first framework that jointly ensures certified unlearning and individual fairness in graph-based models, introducing a novel perspective on responsible model updates in graph unlearning. Specifically, GUIC employs a principled distance-based rule to pinpoint individual biases arising from node removals and applies a computationally efficient certificate-driven update, preserving the local Lipschitz constraints crucial for individual fairness. Different from computationally expensive retraining or fairness-regularized optimization methods, GUIC provides a lightweight yet verifiable alternative with theoretical fairness guarantees. Experiments on multiple real-world datasets show that our method consistently surpasses existing approaches across key performance metrics.

GUIC: Certified Graph Unlearning with Individual Fairness Guarantees

Pairwise evaluation of Large Language Models (LLMs) is a common paradigm, but it is prone to preference bias, where judges systematically favor certain outputs, such as their own. This bias leads to inconsistent and skewed rankings across different judges. To address this, we first empirically demonstrate significant and heterogeneous biases in cross-model evaluations. We then propose UDA (Unsupervised Debiasing Alignment), a framework that reduces inter-judge disagreement by dynamically adjusting the Elo rating system. For each pairwise comparison, a compact neural network learns to adaptively set the K-factor and refine win probabilities. Crucially, UDA operates in a fully unsupervised manner, guided solely by the objective of minimizing the dispersion among the Elo trajectories of all judges. This forces an alignment towards a collective consensus, which serves as an unsupervised proxy for a more stable and reproducible evaluation. In addition, we provide theoretical motivation demonstrating how alignment towards a consensus can reduce aggregate system bias. Experiments show that UDA significantly reduces the inter-judge rating standard deviation by up to 63.4\% and improves the average correlation with human judgments by 24.7\%. Notably, UDA elevates the performance of poorly performing judges to achieve parity with high-quality ones, fostering a more robust and reliable evaluation ecosystem. Code and data are available at https://anonymous.4open.science/r/62AB93CD-23B4.

UDA: Unsupervised Debiasing Alignment for Pair-wise LLM-as-a-Judge

Mathematical thinking is a fundamental aspect of human cognition. Cognitive scientists have investigated the mechanisms that underlie our ability to thinking geometrically and numerically, to take two prominent examples, and developmental scientists have documented the trajectories of these abilities over the lifespan. Prior research has shown that computer vision (CV) models trained on the unrelated task of image classification nevertheless learn latent representations of geometric and numerical concepts similar to those of adults. Building on this demonstrated cognitive alignment, the current study investigates whether CV models also show developmental alignment: whether their performance improvements across training to match the developmental progressions observed in children. In a detailed case study of the ResNet-50 model, we show that this is the case. For the case of geometry and topology, we find developmental alignment for some classes of concepts (Euclidean Geometry, Geometrical Figures, Metric Properties, Topology) but not others (Chiral Figures, Geometric Transformations, Symmetrical Figures). For the case of number, we find developmental alignment in the emergence of a human-like ``mental number line'' representation with experience. These findings show the promise of computer vision models for understanding the development of mathematical understanding in humans. They point the way to future research exploring additional model architectures and building larger benchmarks.

Computer Vision Modeling of the Development of Geometric and Numerical Concepts in Humans

Large language models (LLMs) have demonstrated remarkable performance across diverse tasks by encoding vast amounts of factual knowledge. However, they are still prone to hallucinations, generating incorrect or misleading information, often accompanied by high uncertainty. Existing methods for hallucination detection primarily focus on quantifying internal uncertainty, which arises from missing or conflicting knowledge within the model. However, hallucinations can also stem from external uncertainty, where ambiguous user queries lead to multiple possible interpretations. In this work, we introduce **Semantic Volume**, a novel mathematical measure for quantifying both external and internal uncertainty in LLMs. Our approach perturbs queries and responses, embeds them in a semantic space, and computes the Gram matrix determinant of the embedding vectors, capturing their dispersion as a measure of uncertainty. Our framework provides a generalizable and unsupervised uncertainty detection method without requiring internal access to LLMs. We conduct extensive experiments on both external and internal uncertainty detections, demonstrating that our Semantic Volume method consistently outperforms existing baselines in both tasks. Additionally, we provide theoretical insights linking our measure to differential entropy, unifying and extending previous sampling-based uncertainty measures such as the semantic entropy. Semantic Volume is shown to be a robust and interpretable approach to improving the reliability of LLMs by systematically detecting uncertainty in both user queries and model responses.

Semantic Volume: Quantifying and Detecting Both External and Internal Uncertainty in LLMs

Composed Image Retrieval (CIR) is a challenging image retrieval paradigm that enables to retrieve target images based on multimodal queries consisting of reference images and modification texts. Although substantial progress has been made in recent years, existing methods assume that all samples are correctly matched. However, in real-world scenarios, due to high triplet annotation costs, CIR datasets inevitably contain annotation errors, resulting in incorrectly matched triplets. To address this issue, the problem of Noisy Triplet Correspondence (NTC) has attracted growing attention. We argue that noise in CIR can be categorized into two types: cross-modal correspondence noise and modality-inherent noise. The former arises from mismatches across modalities, whereas the latter originates from intra-modal background interference or visual factors irrelevant to the coarse-grained modification annotations. However, modality-inherent noise is often overlooked, and research on cross-modal correspondence noise remains nascent. To tackle above issues, we propose the Invariance and discrimiNaTion-awarE Noise neTwork (INTENT), comprising two components: Visual Invariant Composition and Bi-Objective Discriminative Learning, specifically designed to handle the two-aspect noise. The former applies causal intervention on the visual side via Fast Fourier Transform (FFT) to generate intervened composed features, enforcing visual invariance and enabling the model to ignore modality-inherent noise during composition. The latter adopts collaborative optimization with both positive and negative samples, and constructs a scalable decision boundary that dynamically adjusts decisions based on the loyalty degree, enabling robust correspondence discrimination. Extensive experiments on two widely used benchmark datasets demonstrate the superiority and robustness of INTENT.

INTENT: Invariance and Discrimination-aware Noise Mitigation for Robust Composed Image Retrieval

Behavioral game theory models serve two purposes: yielding insights into how human decision-making works, and predicting how people would behave in novel strategic settings. A system called GameNet represents the state of the art for predicting human behavior in the setting of unrepeated simultaneous-move games, combining a simple "level-k" model of strategic reasoning with a complex neural network model of non-strategic "level-0" behavior. Although this reliance on well-established ideas from cognitive science ought to make GameNet interpretable, the flexibility of its level-0 model raises the possibility that it is able to emulate strategic reasoning. In this work, we prove that GameNet's level-0 model is indeed too general. We then introduce ElementaryNet, a novel neural network that is provably incapable of expressing strategic behavior. We show that these additional restrictions are empirically harmless, leading ElementaryNet to statistically indistinguishable predictive performance vs GameNet. We then show how it is possible to derive insights about human behavior by varying ElementaryNet's features and interpreting its parameters, finding evidence of iterative reasoning, learning about the depth of this reasoning process, and showing the value of a rich level-0 specification.

ElementaryNet: A Non-Strategic Neural Network for Predicting Human Behavior in Normal-Form Games

In human-AI decision making, designing AI that complements human expertise has been a natural strategy to enhance human-AI collaboration, yet it often comes at the cost of decreased AI performance in areas of human strengths. This can inadvertently erode human trust and cause them to ignore AI advice precisely when it is most needed. Conversely, an aligned AI fosters trust yet risks reinforcing suboptimal human behavior and lowering human-AI team performance. In this paper, we start by identifying this fundamental tension between performance-boosting (i.e., *complementarity*) and trust-building (i.e., *alignment*) as an inherent limitation of the traditional approach for training a single AI model to assist human decision making. To overcome this, we introduce a novel, *human-centered adaptive AI ensemble* that strategically toggles between two specialist AI models—the aligned model and the complementary model—based on contextual cues, using an elegantly simple yet provably near-optimal *Rational Routing Shortcut* mechanism. Comprehensive theoretical analyses elucidate why the adaptive AI ensemble is effective and when it yields maximum benefits. Moreover, extensive simulations and real-world experiments show that when humans are assisted by the adaptive AI ensemble in decision making, they can achieve significantly higher decision performance than when they are assisted by single AI models that are trained to either optimize for its independent performance or even the human-AI team performance.

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