Singapore

Cell cycle phase identification is essential for understanding cellular proliferation, disease progression, and treatment response in single-cell genomics. While single-cell RNA sequencing (scRNA-seq) enables diverse cell cycle classification strategies, single-cell ATAC sequencing (scATAC-seq) offers complementary epigenetic insights but lacks well-annotated datasets for supervised learning. We present a cross-modal transfer learning framework that leverages paired multiome data to transfer cell cycle knowledge from gene expression to chromatin accessibility domains. Through systematic evaluation of ten single-cell foundation models spanning 10M to 400M parameters (Geneformer, scGPT, scFoundation, UCE, TEDDY), we identify Geneformer-316M as optimal for cross-modal transfer with domain-adversarial neural networks. Our framework integrates class-conditional domain adaptation with diversity-aware training using KL, Jensen–Shannon, CORAL, and MMD divergence to prevent target-domain collapse and stabilize cross-modal learning. Across four divergence measures and eight peak encoder architectures, our models achieve strong cross-dataset performance on SUP-B15 scRNA-seq, and with MMD we obtain robust cross-modal transfer to scATAC-seq, reaching competitive accuracy on REH and generalizing to SUP-B15 despite extreme peak sparsity.

AAAI 2026

Cross-Modal Cell Cycle Phase Classification via Dual-Encoder Transfer Learning with Foundation Model Embeddings

scatac-seq

cross-modal learning; dual-encoder; domain adaptation; foundation models; scrna-seq

workshop 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.<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.

Causal inference is central to scientific and policy decision-making, yet a major challenge in observational studies is separating true treatment effects from confounding, especially when confounders are high-dimensional or text- and image-based. Despite methodological progress, researchers still struggle with two persistent questions: *which confounders to adjust for* and *which estimators to use*. Here, we test whether large language models (LLMs) can autonomously design and refine causal estimators. We introduce a causal-agent framework with two modes: a zero-shot LLM that proposes estimators from structured prompts, and a self-evolving agent that iteratively improves them using evaluation feedback. Across two benchmarks for heterogeneous treatment effect estimation, the self-evolving agent matched or exceeded human-expert performance, achieving lower RMSE and near-nominal confidence interval coverage, and substantially outperforming zero-shot LLM solutions. Examination of the agent’s updates showed that it progressively learned appropriate specifications: early iterations added regularization and pre-outcome adjustment; mid-stage refinements introduced interactions, weighting, and clustered inference; and later steps converged toward augmented methods that balanced accuracy and calibration. Our results suggest that LLM-based agents can autonomously explore and optimize causal estimators, narrowing the gap between automated and expert-driven analysis. Our next steps are to evaluate generalization to additional causal tasks and to assess whether providing agents with access to advanced causal inference packages can further enhance performance.

Autonomous LLM Agents for Causal Estimation and Model Refinement

Modern scientific AI models excel at pattern prediction but rarely verify physical consistency or refine their hypotheses. This limitation is acute in climate downscaling, where fine-scale fields must respect conservation structure and spatiotemporal dynamics. Physics-Informed Neural Networks (PINNs) embed a partial differential equation (PDE) structure through residual penalties and work well when the governing equations are complete. However, in realistic climate systems with partially known or parameterized operators, enforcing an imperfect PDE can introduce bias and destabilize optimization. Therefore, we introduce a \emph{physics-aware autonomous AI assistant} that treats predictions as hypotheses to be validated and corrected in a closed loop. Rather than relying on a fixed PDE residual, the assistant uses \emph{physical validators}: soft modular diagnostics whose gradients supply adaptive curvature to the learning dynamics. These validators induce strong monotonicity in directions of physical violation and create a dissipative Lyapunov structure, yielding stable, contractive refinement without requiring a fully specified governing equation. Experiments based on our approach on a synthetic controlled 1D diffusion-advection benchmark generated using a stable Forward-Time–Central-Space solver show consistent gains over an architecture-matched PINN baseline, lower bias and variance, smoother error heat maps, slower horizon-wise error growth, improved local curvature recovery, and self-regulated validator weights that decay from $\lambda\approx 5$ to near zero. These results demonstrate that validator-driven refinement is a flexible and stable foundation for interpretable, autonomous scientific AI systems.

Autonomous AI Assistants for Scientific Research: Physics-Aware Foundations and Climate Downscaling Case Study

Despite the increasing application of Artificial Intelligence (AI) in scientific research, the field lacks a unified theoretical framework to systematically guide and understand this transformation, leading to a disconnect between practice and theory. To bridge this gap, this paper proposes a unified, multi-level framework for AI-empowered research. The framework first deconstructs the AI-driven research ecosystem into a three-stage process (pre-paper, corpus-centric, post-paper) and proposes the three evolving human-AI collaboration model (tool, assistant, and participant) that underpin these stages. Besides, this work introduces the Al-empowered research thinking paradigms, which elaborates how AI influences and empowers the core cognitive capacities of researchers (3C: critical, creative, and computational thinking). The framework's validity is substantiated through a two-pronged approach: a theoretical validation of the full lifecycle via the AlphaFold case, and an empirical validation of the framework's core human-AI collaboration models through our Paper2Presentation case study. This work offers a comprehensive roadmap for understanding and building the next generation of human-AI collaborative research platforms.

A Unified Theoretical Framework for AI-Empowered Research: Unifying Ecosystems, Processes, Collaboration and Thinking Paradigms

Many students and early-career researchers lack access to expert research mentorship. We study whether an AI mentor can help undergraduates progress from idea to paper. We built MENTOR, a tool-augmented, stage-aware assistant that integrates literature search, curated guidelines, methodology checks, and memory. We evaluate MENTOR across six writing stages using two LLM-as-a-judge protocols: (i) expert-persona pairwise preferences and (ii) student-persona rubric scores, plus short multi-turn tutoring tasks and evidence/compliance checks. Across 90 single-turn comparisons, LLM judges preferred MENTOR to Claude Sonnet 4.5 in 74.4% (95% CI [64.3, 82.5]) and to GPT-5 in 58.3% (95% CI [47.7, 68.3]). MENTOR's student-persona scores (clarity, actionability, constraint-fit) are consistently higher than both baselines across stages. In multi-turn sessions (five scenarios/agent), MENTOR achieved slightly higher final quality than GPT-5 under our rubric.

Evaluation of LLM Agents for Research Mentorship

Large Language Models (LLMs) are increasingly embedded in academic writing practices. Although numerous studies have explored how researchers employ these tools for scientific writing, their concrete implementation, limitations, and design challenges within the literature review process remain underexplored. In this paper, we report a user study with researchers across multiple disciplines to characterize current practices, benefits, and \textit{pain points} in using LLMs to investigate related work. We identified three recurring gaps: (i) lack of trust in outputs, (ii) persistent verification burden, and (iii) requiring multiple tools. This motivates our proposal of six design goals and a high-level framework that operationalizes them through improved related papers visualization, verification at every step, and human-feedback alignment with generation-guided explanations. Overall, by grounding our work in the practical, day-to-day needs of researchers, we designed a framework that addresses these limitations and models real-world LLM-assisted writing, advancing trust through verifiable actions and fostering practical collaboration between researchers and AI systems.

From Verification Burden to Trusted Collaboration: Design Goals for LLM-Assisted Literature Reviews

Consider a system of multiple physical agents tasked with collaboratively collecting a set of spatially distributed goals while avoiding collisions with the environment and with each other. 
This type of problem, which combines Multi-Agent Path Finding (MAPF) with task allocation, is known as Multi-Agent Combinatorial Path Finding (MCPF). 
Conflict-Based Steiner Search (CBSS) is an optimal algorithm for MCPF, which assumes that each agent has a fixed goal destination. It selects allocations that yield a solution minimizing the sum of costs (SOC), which we denote as CBSS$_{SOC}$. 
However, this objective is problematic in domains such as search and rescue, where timely service of all goals is more critical than minimizing SOC. 
We therefore propose CBSS$_{SST}$, which minimizes the Sum of Service Times (SST) across all goals using a novel mixed-integer linear programming allocation, thereby generalizing MCPF to settings without requiring fixed goal destinations.
Since CBSS assumes perfect execution, we extend it with robust planning to handle stochastic execution delays. 
We propose two variants of CBSS$_{SST}$: Robust CBSS$_{SST}$ with Strict Verifier, which guarantees the desired robustness, and Robust CBSS$_{SST}$ with Anytime Verifier, which addresses planning-time constraints by returning the most robust solution verified within the available time. 
Our experiments on MCPF benchmarks show that Robust CBSS with anytime verifier solves substantially more instances than when using a strict verified within the time limit, while reducing replanning effort and preserving robustness. 
These results demonstrate that RCbss with an anytime verifier provides an effective and practical approach to MCPF under uncertainty.

Balancing Robustness and Efficiency in Multi-Agent Combinatorial Path Finding with Sum of Service Time

Grid-based path planning is a classic problem in AI, widely applied in robotics, computer games, and scheduling. Two oracle path planning (Topping) is a state-of-the-art fast path-finding method for grid maps. Topping iteratively utilizes SRC and JPS oracles to determine the first moves and number of steps, respectively. This enables faster search than SRC, yet incurs high storage and search costs due to inadequate compression. In this paper, we aim to leverage heuristic information as much as possible to enhance the compression performance of Topping and to further improve the search efficiency (i.e., the first-move decision cost). Ultimately, this also improves Topping’s overall search performance. Experiments on five benchmarks (478 maps in total) show that our methods can reduce the first-move decision cost by an average of about 60% (maximum 71%) and achieve a maximum speedup of 48% in runtime. Remarkably, they also have gains in compression performance and reduce storage costs.

Two-Oracle Path Planning Consolidated by Heuristic-Rich Information

Coordinating a team of robots to reposition multiple objects in cluttered environments requires reasoning jointly about where robots should establish contact, how to manipulate objects once contact is made, and how to navigate safely and efficiently at scale. Prior approaches typically fall into two extremes–either learning the entire task or relying on privileged information and hand-designed planners–both of which struggle to handle diverse objects in long-horizon tasks. To address these challenges, we present a unified framework for collaborative multi-robot, multi-object non-prehensile manipulation that integrates flow-matching co-generation with anonymous multi-robot motion planning. Within this framework, a generative model co-generates contact formations and manipulation trajectories from visual observations, while a novel motion planner conveys robots at scale. Crucially, the same planner also supports coordination at the object level, assigning manipulated objects to larger target structures and thereby unifying robot- and object-level reasoning within a single algorithmic framework. Experiments in challenging simulated environments demonstrate that our approach outperforms baselines in both motion planning and manipulation tasks, highlighting the benefits of generative co-design and integrated planning for scaling collaborative manipulation to complex multi-agent, multi-object settings. Visit gco-paper.github.io for code and demonstrations.

Collaborative Multi-Robot Non-Prehensile Manipulation via Flow-Matching Co-Generation

Multi-agent pathfinding (MAPF) is the problem of finding collision-free paths for a set of agents in a shared environment, typically represented as a graph. One of the approaches to solving MAPF represents the problem as a Boolean satisfiability problem. However, due to the encoding required to represent the valid paths, this method can produce extremely large Boolean formulas, both in terms of variables and clauses. Herein, we propose two encodings of MAPF designed for SAT Modulo Theories solvers. Our approach delegates all the valid path reasoning to a monotonic theory supporting source-target connectivity. This is then combined with a 2-SAT Boolean formula to prevent collisions between agents. Together, these components create an effective separation of concerns: the SAT solver focuses on resolving conflicts, while the theory solver handles the connectivity constraints. Our experiments are conducted in both makespan and sum of costs optimisation settings, empirically demonstrating a notable reduction in both the size of the MAPF encoding and the time required to generate it. In addition, when fixing the SAT solver across experiments, results demonstrate considerable performance improvements when transitioning from pure SAT to our proposed SMT encodings.

Modelling Multi-Agent Pathfinding Problems by Integrating Connectivity and No-Collision Constraints

Multi-agent pathfinding (MAPF) is a widely used abstraction for multi-robot trajectory planning problems, where multiple homogeneous agents move simultaneously within a shared environment. Although solving MAPF optimally is NP-hard, scalable and efficient solvers are critical for real-world applications such as logistics and search-and-rescue. To this end, the research community has proposed various decentralized suboptimal MAPF solvers that leverage machine learning. Such methods frame MAPF (from a single agent perspective) as Dec-POMDP when at each time step an agent has to decide an action based on the local observation and typically solve the problem via reinforcement learning or imitation learning. We follow the same approach but additionally introduce a learnable communication module tailored to increase the level of cooperation between the agents via efficient feature sharing. We present the Local Communication for Multi-agent Pathfinding (LC-MAPF), a foundation model that applies multi-round communication between neighboring agents to exchange information and improve their coordination. Our experiments show that the introduced method outperforms the existing learning-based MAPF solvers, including IL and RL based approaches, across diverse metrics in a diverse range of (unseen) test scenarios. Remarkably, the introduced communication mechanism does not compromise the scalability LC-MAPF, which is a common bottleneck for communication-based MAPF solvers.

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