Singapore

This paper presents a novel method, called Deformable
Polygonal Flow Matching (DPFM), for the generation of
polygonal arrangements such as jigsaw puzzles and floor
plans. DPFM is a Flow Matching framework that enables
the generation process to deform, rotate, and translate poly-
gons while decoupling these transformation, allowing to tog-
gle them individually. Able to combine the spatial reason-
ing capabilities of arrangement models with the flexibility
of position-based models, it covers a wide range of appli-
cations within a unified formulation, from noiseless puzzle
solving using rigid alignments to unconstrained floor plans
generation. We represent data using a hierarchical graph com-
posed of a topological subgraph encoding connectivity infor-
mation and semantics (such as room types for floor plans),
and a geometrical subgraph encoding the 1D polygonal loop
of each shape. DPFM also leverages Flow Matching’s arbi-
trary prior distributions for geometric constraints by design-
ing priors with domain knowledge. Rather than starting the
generation process from uninformed distributions, the gener-
ation is constrained through the informed priors since the ini-
tialization stage. The qualitative and quantitative evaluations
of our method, ran on the RPLAN and jigsaw puzzle datasets,
demonstrate strong performance. DPFM outperforms task-
specific methods, becoming the new state-of-the-art for 2D
arrangement generation. As our results show, DPFM is able to
solve novel tasks such as puzzle denoising, where pieces are
reconstructed from noisy versions and arranged into a valid
puzzle in parallel.

AAAI 2026

Deformable Polygonal Flow Matching with Informed Priors and Hierarchical Graph Constraints

ml: deep generative models & autoencoders ml: graph-based machine learning app: other applications

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-23 is the Thirty-Seventh AAAI Conference on Artificial Intelligence. The theme of this conference is to create collaborative bridges within and beyond AI. Like previous AAAI conferences, AAAI-23 will feature technical paper presentations, special tracks, invited speakers, workshops, tutorials, poster sessions, senior member presentations, competitions, and exhibit programs, and two new activities: a Bridge Program and a Lab Program. Many of these activities are tailored to the theme of bridges and all are selected according to the highest standards, with additional programs for students and young researchers. 
AAAI is providing you with a conference planner, which you can use to help organize your itinerary of activities. This includes talks to attend in person, talks to attend remotely, breaks with colleagues and your site seeing activities. To access this conference planner, please go to [https://aaai-2023.takemobi.io](https://aaai-2023.takemobi.io).

In order to access this site, you need to register. If you haven't already, please register [here](https://aaai.org/Conferences/AAAI-23/registration/).


AAAI 2023

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.

technical paper

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.

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-26 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.

To access this event page, you need to log in with the **email address you registered with**. Access credentials will be sent to your email from Underline - subject line "Welcome to AAAI 2026". Please be sure to check your spam email folder if you do not see an email confirmation right away.

Please log in

To access this event page, you are required to register.
Please complete your registration to continue.

We recommend reading [**the registration information**](https://aaai.org/conference/aaai/aaai-26/registration/) first.

**Online Registration Form**: https://aaai.getregistered.net/conference-2026 

Registration Required

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.

Tandem mass spectrometry (MS/MS) is a critical tool for identifying molecular structures. By efficiently separating molecular fragments based on their mass-to-charge (m/z) ratios, it facilitates molecular generation and subsequent scientific discoveries. However, de novo molecular generation from MS/MS spectra remains fundamentally constrained by two paramount challenges: the vast chemical space requires effective structural constraints, and the absence of fine-grained substructural generation weakens the correspondences between spectral features and molecular structures. In this work, we propose MSAnchor, a novel two-stage framework for MS/MS-based molecular structure generation. We mitigate the search space challenge through the introduction of Anchor-Extended Molecular Scaffold (AEMS) representation that explicitly encodes side-chain anchoring points, thereby dramatically reducing combinatorial complexity. Leveraging the explicit attachment sites provided by AEMS, we develop anchor-specific priors that establish effective alignments between spectral features and molecular substructures. This fine-grained substructural correspondence is further enhanced by a modified Conditional Information Bottleneck (CIB) module that extracts the most informative spectral components in a structure-aware manner. These innovations enable MSAnchor to generate molecular structures that closely reflect spectral characteristics while constraining combinatorial complexity. Extensive experiments on the CANOPUS and MassSpecGym datasets demonstrate that MSAnchor achieves state-of-the-art performance in molecular structure prediction from MS/MS spectra, with performance improvements that are particularly more pronounced for molecules with higher complexity.

MSAnchor: De Novo Molecular Generation from Mass Spectrometry Data with Anchor-Extended Molecular Scaffolds

Biological intelligence has driven significant progress in
artificial intelligence (AI), but a critical gap remains:
biological systems inherit innate abilities from genes,
with brains initialized by blueprints refined over 3.5
billion years of evolution, while machines rely heavily on
inefficient, data-driven learning from scratch. This gap
arises from the lack of a genetic mechanism in machines to
transfer and accumulate inheritable knowledge across
generations. To bridge this gap, we propose learngenes,
network fragments that act as inheritable “genes” for
machines. Unlike conventional knowledge transfer methods,
learngenes enable efficient and universal knowledge
transfer by selectively encapsulating task-agnostic
knowledge. To facilitate the transfer and accumulation of
task-agnostic knowledge across generations, we introduce
Genetic Reinforcement Learning (GRL), a framework that
simulates the learning and evolution of organisms in
intelligent agents following Lamarckian principles. Through
GRL, we identify learngenes as network fragments within
agents' policy networks, equipping newborn agents with
innate abilities for rapid adaptation to novel tasks. We
demonstrate the advantages of learngene-based knowledge
transfer over evolution-based search and traditional
pre-trained models, and show how learngenes evolve through
the accumulation of task-agnostic knowledge. Overall, this
work establishes a novel paradigm for knowledge transfer
and model initialization in AI, offering new possibilities
for more adaptive, efficient, and scalable learning systems.

Learngene: Inheritable “Genes” in Intelligent Agents

The reliable deployment of reinforcement learning (RL) for real-world algorithmic trading is critically hindered by the ``simulation-to-reality gap.'' Standard industry backtesting on static historical data ignores market impact—the feedback loop where an agent's trades influence price dynamics—leading to strategies that are fragile and untrustworthy in live markets. To solve this significant problem, we present a novel and emerging application of AI: a framework for building an interactive, responsive market simulator. Our system first uses imitation learning (IL) to automatically train an ensemble of agents, each learning a distinct trading strategy from a different historical market regime (e.g., bull, bear). This creates a data-driven proxy for a diverse population of real-world traders. We then deploy an innovative Action Synthesis Network to synthesize the actions of this ensemble, generating a realistic, synthetic price trajectory that endogenously models the market's reaction to trades. This interactive environment is then used to train a final RL policy. We evaluate our system on NASDAQ-100 (QQQ) data, and the results demonstrate strong potential for deployment. The RL policy trained in our responsive simulator achieves significantly more robust performance, exhibiting superior downside protection during market downturns compared to various traditional baselines. This application provides a scalable and technically sound methodology for building more realistic training environments, presenting a clear path toward the development and eventual deployment of more resilient and effective algorithmic trading strategies.

An Interactive Simulation Framework by Ensemble Imitation Learning Agents for Training Robust Trading Policies

We study a path planning problem where the possible move actions are represented as a finite set of motion primitives aligned with the grid representation of the environment. That is, each primitive corresponds to a short kinodynamically-feasible motion of an agent and is represented as a sequence of the swept cells of a grid. Typically, heuristic search, i.e. A*, is conducted over the lattice induced by these primitives (lattice-based planning) to find a path. However, due to the large branching factor, such search may be inefficient in practice. To this end, we suggest a novel technique rooted in the idea of searching over the grid cells (as in vanilla A*) simultaneously fitting the possible sequences of the motion primitives into these cells. The resultant algorithm, MeshA*, provably preserves the guarantees on completeness and optimality, on the one hand, and is shown to notably outperform conventional lattice-based planning (x1.5-x2 decrease in the runtime), on the other hand.

MeshA*: Efficient Path Planning with Motion Primitives

We explore the oscillatory behavior observed in inversion methods applied to large-scale flow models, including text-to-image and text-to-video. By employing an augmented fixed-point-inspired iterative approach to invert real-world images, we observe that the solution does not achieve convergence, instead oscillating between distinct clusters. Through both experiments on synthetic data, text-to-image and text-to-video, we demonstrate that these oscillating clusters exhibit notable semantic coherence. We offer theoretical insights, showing that this behavior arises from oscillatory dynamics in flow models. Building on this understanding, we introduce a simple and fast distribution transfer technique that facilitates training-free image and video editing/enhancement. Furthermore, we provide quantitative results demonstrating the effectiveness of our method on tasks such as image enhancement, editing, and reconstruction. Notably, our approach enables the transformation of image-only enhancers and editors into lightweight, video-capable tools—without additional training—highlighting its practical versatility and impact.

Oscillation Inversion: Training-Free Image and Video Enhancement Through Oscillated Latents in Large Flow Models

We study stochastic planning problems in Markov Decision Processes (MDPs) with goals specified in Linear Temporal Logic (LTL). The state-of-the-art approach transforms LTL formulas into good-for-MDP (GFM) automata, which feature a restricted form of nondeterminism. These automata are then composed with the MDP, allowing the agent to resolve the nondeterminism during policy synthesis.
A major factor affecting the scalability of this approach is the size of the generated automata. In this paper, we propose a novel GFM state-space reduction technique that significantly reduces the number of automata states. Our method employs a sophisticated chain of transformations, leveraging recent advances in good-for-games minimisation developed for adversarial settings.
In addition to our theoretical contributions, we present empirical results demonstrating the practical effectiveness of our state-reduction technique.
Furthermore, we introduce a direct construction method for formulas of the form $\textsf{G} \textsf{F}\varphi$, where $\varphi$ is a co-safety formula. This construction is provably single-exponential in the worst case, in contrast to the general doubly-exponential complexity. Our experiments confirm the scalability advantages of this specialised construction.

Good-for-MDP State Reduction for Stochastic LTL Planning

In runtime verification, monitoring consists of analyzing the current execution of a system and determining, on the basis of the observed finite trace, whether all its possible continuations satisfy or violate a given specification. This is typically done by synthesizing a monitor—often a Deterministic Finite State Automaton (DFA)—from logical specifications expressed in Linear Temporal Logic (LTL) or in its finite-word
variant (LTLf). Unfortunately, the size of the resulting DFA may incur a doubly exponential blow-up.
In this paper, we identify some conditions under which monitoring can be done without constructing such a DFA. We build on the notion of intentionally safe and cosafe formulas, introduced in [Kupferman & Vardi, FMSD, 2001], to show that monitoring of these formulas can be carried out through trace-checking, that is, by directly evaluating them on the current system trace, with a polynomial complexity in the size of both the trace and the formula.
In addition, we investigate the complexity of recognizing intentionally safe and cosafe formulas for the safety and cosafety fragments of LTL and LTLf. As for LTLf, we show that all formulas in these fragments are intentionally safe and cosafe, thus removing the need for the check. As for LTL, we prove that the problem is in PSPACE, significantly improving over the EXPSPACE complexity of full LTL.

Automata-less Monitoring via Trace-Checking

In this paper, we propose *Unreal Multi-Agent Playground* (Unreal-MAP), an MARL general platform based on the Unreal-Engine (UE). Unreal-MAP allows users to freely create multi-agent tasks using the vast visual and physical resources available in the UE community, and deploy state-of-the-art (SOTA) MARL algorithms within them. Unreal-MAP is user-friendly in terms of deployment, modification, and visualization, and all its components are open-source. We also develop an experimental framework compatible with algorithms ranging from rule-based to learning-based provided by third-party frameworks. Lastly, we deploy several SOTA algorithms in example tasks developed via Unreal-MAP, and conduct corresponding experimental analyses including a sim2real demo. We believe Unreal-MAP can play an important role in the MARL field by closely integrating existing algorithms with user-customized tasks, thus advancing the field of MARL.

Unreal-MAP: Unreal-Engine-Based General Platform for Multi-agent Reinforcement Learning

Large language models (LLMs) frequently demonstrate reasoning limitations, often conflating content plausibility with logical validity. This can result in biased inferences, where plausible arguments are incorrectly deemed logically valid or vice versa. 
This paper investigates how to mitigate content biases on reasoning through activation steering, an inference-time intervention technique that modulates model activations. After localising the layers responsible for formal and material inference through probing, we investigate contrastive activation steering methods using a controlled syllogistic reasoning dataset that covers 24 types of logical argument schemes, designed to disentangle formal validity from content plausibility. An extensive empirical analysis reveals that contrastive steering consistently supports linear control over content biases. However, we observe that a static steering approach is insufficient for achieving improvements on all the tested models. We then leverage the possibility to control content effects by dynamically determining the value of the steering parameters via fine-grained conditional methods. We found that conditional steering is effective in reducing biases on unresponsive models, achieving up to 15% absolute improvement in formal reasoning accuracy with a newly introduced kNN-based conditional method. Finally, we found that steering for content effects is robust to prompt variations, incurs minimal side effects on multilingual language modeling capabilities, and can partially generalize to out-of-distribution tasks. Practically, this paper demonstrates that activation-level interventions can offer a scalable test-time strategy for enhancing the robustness of LLMs, contributing towards more systematic and unbiased reasoning

Mitigating Content Effects on Reasoning in Language Models Through Fine-Grained Activation Steering

Virtual Immunohistochemistry (IHC) staining technology employs generative models to directly synthesize IHC images from Hematoxylin and Eosin (H&E) images, reducing reliance on chemical staining while improving diagnostic efficiency and reducing costs. However, existing virtual staining methods relying on adjacent sections face two critical challenges: insufficient mining of pathological semantics and the spatial misalignment of pathological semantics due to physical discrepancies between sections. To address these, we propose GSGStain, a Graph-Semantic Guided Learning for virtual Staining. Our method innovatively transforms the problem from pixel space to graph space, enabling semantic noise correction for spatial misalignment features. Specifically, to capture the rich pathological semantics, we construct a cell graph from the H\&E image to encode tissue architecture, annotating nodes with noisy biomarker semantic features derived from misaligned adjacent IHC sections. Furthermore, to correct for the semantic misalignment, a Graph Semantic Rectification Module (GSRM) then refines these features using graph contextual reasoning, while a Graph Semantic Consistency Loss ensures alignment between generated IHC images and rectified semantics. Additionally, we propose a dual-branch discriminator to compel the generator to match the empirical distribution of real images, significantly improving generation quality. Extensive experiments on two public benchmarks demonstrate that GSGStain significantly outperforms state-of-the-art methods in both image quality and pathological consistency. This work establishes a new paradigm for semantically robust virtual staining. Our source code is available at https://anonymous.4open.science/r/GSGStain.

Downloads

Next from AAAI 2026

MSAnchor: De Novo Molecular Generation from Mass Spectrometry Data with Anchor-Extended Molecular Scaffolds

Similar lecture

Siamese-Discriminant Deep Reinforcement Learning for Solving Jigsaw Puzzles with Large Eroded Gaps

Stay up to date with the latest Underline news!

PRESENTATIONS

CONFERENCES

COMPANY

RESOURCES