Singapore

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&#39; 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.

AAAI 2026

Learngene: Inheritable “Genes” in Intelligent Agents

gene

machine evolution

genetic mechanism

intelligent agents

knowledge transfer

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.<br><br>

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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.

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.

technical paper

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.

Graph-Semantic Guided Learning for Virtual Immunohistochemistry Staining on Consecutive Histology Sections

Audio-visual sound source localization (AV-SSL) estimates the position of sound sources by fusing auditory and visual cues. Current AV-SSL methodologies typically require spatially-paired audio-visual data and cannot selectively localize specific target sources. To address these limitations, we introduce Cross-Instance Audio-Visual Localization (CI-AVL), a novel task that localizes target sound sources using visual prompts from different instances of the same semantic class. CI-AVL enables selective localization without spatially paired data. To solve this task, we propose AV-SSAN, a semantic-spatial alignment framework centered on a Multi-Band Semantic-Spatial Alignment Network (MB-SSA Net). MB-SSA Net decomposes the audio spectrogram into multiple frequency bands, aligns each band with semantic visual prompts, and refines spatial cues to estimate the direction-of-arrival (DoA). To facilitate this research, we construct VGGSound-SSL, a large-scale dataset comprising 13,981 spatial audio clips across 296 categories, each paired with visual prompts. AV-SSAN achieves a mean absolute error of 16.59° and an accuracy of 71.29\%, significantly outperforming existing AV-SSL methods. Code and data will be public upon acceptance.

AV-SSAN: Audio-Visual Selective DOA Estimation Through Explicit Multi-Band Semantic-Spatial Alignment

Traditional video reasoning segmentation methods rely on supervised fine-tuning, which limits generalization to out-of-distribution scenarios and lacks explicit reasoning. To address this, we propose VideoSeg-R1, the first framework to introduce reinforcement learning into video reasoning segmentation. It adopts a decoupled architecture, modeling the task as a combination of referring image segmentation and video mask propagation. The pipeline includes three stages: (1) a hierarchical text-guided frame sampler to localize key segments and reference frames; (2) a reasoning model that generates explicit chain-of-thought prompts; and (3) a segmentation and propagation stage using SAM2 and XMem to produce and spread pixel-level masks. A task difficulty-aware mechanism is introduced during training to adaptively control reasoning length, improving both efficiency and performance. Experiments on multiple benchmarks demonstrate that VideoSeg-R1 outperforms prior methods in complex reasoning, temporal understanding, and object tracking. Code and models will be released upon acceptance.

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