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In this paper we describe a novel framework for diffusion-based generative modeling on constrained spaces. In particular we expand the kinds of constraints that can be practically used to form so-called diffusion bridges. We develop a mechanism for combining multiple such constraints so that the resulting multiply-constrained model remains a bridge that respects all constraints. We also develop a mechanism for training a diffusion model that respects such multiple constraints while also adapting it to match a data distribution. We develop and extend theory demonstrating the mathematical validity of our mechanisms. Additionally, we demonstrate our mechanism in constrained generative modeling tasks, highlighting one particular high-value application in modeling trajectory initializations for path planning and control in autonomous vehicles.

AAAI 2025

Constrained Generative Modeling with Manually Bridged Diffusion Models

autoencoders

deep generative models

technical paper

We are pleased to announce the Thirty-Ninth AAAI Conference on Artificial Intelligence (AAAI-25), which will be held in Philadelphia, Pennsylvania at the Pennsylvania Convention Center from February 25 to March 4, 2025.

The purpose of the AAAI conference series is to promote research in Artificial Intelligence (AI) and foster scientific exchange between researchers, practitioners, scientists, students, and engineers across the entirety of AI and its affiliated disciplines. AAAI-25 will feature technical paper presentations, special tracks, invited speakers, workshops, tutorials, poster sessions, senior member presentations, competitions, and exhibit programs, and a range of other activities to be announced.

### [Invited Speakers](https://aaai.org/conference/aaai/aaai-25/aaai-25-invited-speakers/)

Register [here](https://aaai.org/conference/aaai/aaai-25/registration/)

The purpose of the AAAI conference series is to promote research in Artificial Intelligence (AI) and foster scientific exchange between researchers, practitioners, scientists, students, and engineers across the entirety of AI and its affiliated disciplines. AAAI-25 will feature technical paper presentations, special tracks, invited speakers, workshops, tutorials, poster sessions, senior member presentations, competitions, and exhibit programs, and a range of other activities to be announced.



Recent advancements in multimodal large language models (MLLMs) have shown unprecedented capabilities in advancing various vision-language tasks. However, MLLMs face significant challenges with hallucinations, and misleading outputs that do not align with the input data. While existing efforts are paid to combat MLLM hallucinations, several pivotal challenges are still unsolved. First, while current approaches aggressively focus on addressing errors at the perception level, another important type at the cognition level requiring factual commonsense can be overlooked. In addition, existing methods might fall short in finding a more effective way to represent visual input, which is yet a key bottleneck that triggers visual hallucinations. Moreover, MLLMs can frequently be misled by faulty textual inputs and cause hallucinations, while unfortunately, this type of issue has long been overlooked by existing studies. Inspired by human intuition in handling hallucinations, this paper introduces a novel bottom-up reasoning framework. Our framework systematically addresses potential issues in both visual and textual inputs by verifying and integrating perception-level information with cognition-level commonsense knowledge, ensuring more accurate and reliable outputs. Extensive experiments demonstrate significant improvements in multiple hallucination benchmarks after integrating MLLMs with the proposed framework. In-depth analysis reveals the great potential of our methods in addressing perception- and cognition-level hallucinations.

Combating Multimodal LLM Hallucination via Bottom-Up Holistic Reasoning

We initiate the study of tree structures in the context of scenario-based robust optimization. Specifically, we study Binary Search Trees (BSTs) and Huffman coding, two fundamental techniques for efficiently managing and encoding data based on a known set of frequencies of keys. Given $k$ different scenarios, each defined by a distinct frequency distribution over the keys, our objective is to compute a single tree of best-possible performance, relative to any scenario. 

We consider, as performance metrics, the competitive ratio, which compares multiplicatively the cost of the solution to the tree of least cost among all scenarios, as well as the regret, which induces a similar, but additive comparison. For BSTs, we show that the problem is NP-hard across both  metrics. We also show how to obtain a tree of competitive ratio  $\lceil \log_2(k+1) \rceil$, and we prove that this ratio is optimal. For Huffman Trees, we show that the problem is, likewise, NP-hard across both metrics; we also give an algorithm of regret $\lceil \log_2 k \rceil$, which we show is near-optimal, by proving a lower bound of $\lfloor \log_2 k \rfloor$. Last, we give a polynomial-time algorithm for computing Pareto-optimal BSTs with respect to their regret, assuming scenarios defined by uniform distributions over the keys. This setting captures, in particular, the first study of fairness in the context of data structures. We provide an experimental evaluation of all algorithms. To this end, we also provide mixed integer linear program formulation for computing optimal trees.

Scenario-Based Robust Optimization of Tree Structures

Strategic interactions can be represented more concisely, and analyzed and solved more efficiently, if we are aware of the symmetries within that multi-agent system. Symmetries also have conceptual implications, for example for equilibrium selection. We study the computational complexity of identifying and using symmetries. Using the classical framework of normal-form games, we consider game symmetries that can be across some or all players and/or actions. We find a strong connection between game symmetries and graph automorphisms, yielding graph automorphism and graph isomorphism completeness results for characterizing the symmetries present in a game.

Next, we investigate when exactly game symmetries can be successfully leveraged for Nash equilibrium computation. We show that finding a Nash equilibrium that respects a given set of symmetries is PPAD- and CLS-complete in general-sum and team games respectively---that is, exactly as hard as Brouwer fixed point and gradient descent problems. Finally, we present polynomial-time methods for the special cases where we are aware of a vast number of symmetries, or where the game is two-player zero-sum and we do not even know the symmetries.

Computing Game Symmetries and Equilibria That Respect Them

There is an increasing body of work using Large Language Models (LLMs) as agents for orchestrating workflows and making decisions in domains that require planning and multi-step reasoning. As a result, it is imperative to evaluate LLMs on core skills required for planning. In this work, we present ACPBench, a collection of benchmark that evaluates the reasoning tasks in the field of planning. The benchmark consists of 7 reasoning tasks over 13 planning domains. The collection is constructed from planning domains described in a formal
language. This allows us to use planning tools for obtaining provably correct information and generate massive data, meeting the large-scale data requirements of language models. Our extensive evaluation of 18 open-sourced and frontier LLMs highlight the significant gap in the reasoning capability of the LLMs. The average accuracy of even the best-performing frontier LLM GPT-4o on these tasks can fall as low as 52.50%.

ACPBench: Reasoning About Action, Change, and Planning

Multi-modal transformers are rapidly gaining attention in video captioning tasks. Existing multi-modal video captioning methods extract a fixed number of frames, but this has critical challenges. If a limited number of frames are extracted, important frames with essential information for caption generation may be missed. Conversely, extracting an excessive number of frames includes consecutive frames, potentially causing redundancy in visual tokens extracted from consecutive video frames. To extract an appropriate number of frames for each video, this paper proposes the first model-agnostic module selection framework in video captioning that has two main functions: (1) selecting a caption generation module with an appropriate size based on visual tokens extracted from video frames, and (2) constructing subsets of visual tokens for the selected caption generation module. Furthermore, we propose a new adaptive attention masking scheme that enhances attention on important visual tokens. Our numerical experiments with three different benchmark datasets demonstrate that the proposed framework significantly improves the performances of three recent video captioning models.

MAMS: Model-Agnostic Module Selection Framework for Video Captioning

Traffic classification is crucial for network management and security. Recently, deep learning-based methods have demonstrated good performance in traffic classification. However, they primarily capture features from raw packet bytes, overlooking the significance of inter-packet correlations within flows from a global perspective. Additionally, effectively handling both packet-length and temporal information, while extracting the structural relationships from a graph into the model, remains a challenge for enhancing the performance of traffic prediction. In this paper, we propose DigTraffic, a novel dual-channel interactive graph transformer to address these limitations. DigTraffic employs a message-level graph-structured flow representation combined with message-aware structural aggregation. To learn intrinsic flow representations, DigTraffic constructs traffic interaction graphs, by incorporating three well-designed heterogeneous types of edges to capture client-server interactions. After that, we separately encode lengthy and temporal flow sequences using a dual-channel network and fuse these modalities within a Transformer architecture. Furthermore, DigTraffic introduces a message-aware Graph Transformer that leverages both node embeddings and edge spatial relations to capture complex graph structures and rich structural information.
Experimental results demonstrate that our method significantly outperforms the state-of-the-art methods on four real-world traffic datasets.

Dual-Channel Interactive Graph Transformer for Traffic Classification with Message-Aware Flow Representation

The card game Hanabi has recently gained popularity as a benchmark for handling epistemic reasoning in AI systems. However it has until now mostly been approached through the lens of machine learning rather than formal logical analysis. This is mostly due to the fact that modelling Hanabi in the standard epistemic logic DEL is untractable. In this paper we take a different approach to formalizing Hanabi, using the simple epistemic logic EL-O as a starting point. We generalize common knowledge in EL-O to arbitrary groups of agents and show how to overcome some of the limitations EL-O places on agent reasoning by introducing a special reasoning action. Analysing this formalization of Hanabi finally leads us to introduce an alternative semantics for our generalization of EL-O in which models are finite and satisfiability checking is NP-complete, and which is enough to fully describe the evolution of knowledge in a game of Hanabi.

A Logical Analysis of Hanabi

In adaptive systems, predictors are used to anticipate changes in the system’s state or behavior that may require system adaption, e.g., changing its configuration or adjusting resource allocation. Therefore, the quality of predictors is crucial for the overall reliability and performance of the system under control. This paper studies predictors in systems exhibiting probabilistic and non-deterministic behavior modelled as Markov decision processes (MDPs). Main contributions are the introduction of quantitative notions that measure the effectiveness of predictors in terms of their average capability to predict the occurrence of failures or other undesired system behaviors. The average is taken over all memoryless policies. We study two classes of such notions. One class is inspired by concepts that have been introduced in statistical analysis to explain the impact of features on the decisions of binary classifiers (such as precision, recall, f-score). Second, we study a measure that borrows ideas from recent work on probability-raising causality in MDPs and determines the quality of a predictor by the fraction of memoryless policies under which (the set of states in) the predictor is a probabilityraising cause for the considered failure scenario.

Formal Quality Measures for Predictors in Markov Decision Processes

Recommender systems in various applications often encounter the challenge of cold-start, which refers to how to provide recommendations for completely new users. Cross-domain recommendation offers a solution to address this cold-start issue by leveraging user interaction information from other domains and providing recommendations for users in the target domain. However, applying the classic two-tower model in cross-domain scenarios for pure cold-start users proves challenging, and most existing cross-domain cold-start recommendation models adopt an embedding-mapping framework that lacks end-to-end efficiency. The parallel training recommendation method lacks consideration of the domain-level intrinsic characteristics of cross-domain information. In this paper, we propose a generalized framework that Domain-level Disentanglement framework based on information enhancement for Cross-domain Cold-start Recommendation. On one hand, we achieve deep utilization of domain-level information through independent extraction of domain knowledge and fusion using heuristic strategies. On the other hand, our model is incorporated with an information enhancement network based on user attention and a user personalized adaptor. We introduce measures to assess user variability and immutability in cross-domain recommendation, aiming to eliminate inter-domain bias and highlight individual user preferences. Experimental results on widely used cross-domain recommendation datasets demonstrate that our proposed model outperforms state-of-the-art methods, validating its effectiveness.

Domain-Level Disentanglement Framework Based on Information Enhancement for Cross-Domain Cold-Start Recommendation

Among various temporal knowledge graph (TKG) extrapolation methods, rule-based approaches stand out for their explicit rules and transparent reasoning paths. However, the vast search space for rule extraction poses a challenge in identifying high-quality logic rules. To navigate this challenge, we explore the use of generation models to generate new rules, thereby enriching our rule base and enhancing our reasoning capabilities. In this paper, we introduce LLM-DR, an innovative rule-based method for TKG extrapolation, which harnesses diffusion models to generate rules that are consistent with the distribution of the source data, while also amalgamating the rich semantic insights of Large Language Models (LLMs). Specifically, our LLM-DR generates semantically relevant and high-quality rules, employing conditional diffusion models in a classifier-free guidance fashion and refining them with LLM-based constraints. To assess rule efficacy, we meticulously design a coarse-to-fine evaluation strategy that initiates with coarse-grained filtering to eliminate less plausible rules and proceeds with fine-grained scoring to quantify the reliability of the retained. Extensive experiments demonstrate the promising capacity of our LLM-DR.

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