Singapore

Large language model (LLM) agents have shown increasing promise for collaborative task completion. However, existing multi-agent frameworks often rely on static workflows, fixed roles, and limited inter-agent communication, reducing their effectiveness in open-ended, high-complexity domains. This paper proposes a coordination framework that enables adaptiveness through three core mechanisms: dynamic task routing, bidirectional feedback, and parallel agent evaluation. The framework allows agents to reallocate tasks based on confidence and workload, exchange structured critiques to iteratively improve outputs, and—crucially—compete on high-ambiguity subtasks with evaluator-driven selection of the most suitable result. We instantiate these principles in a modular architecture and demonstrate substantial improvements in factual coverage, coherence, and efficiency over static and partially adaptive baselines. Our findings highlight the benefits of incorporating both adaptiveness and structured competition in multi-agent LLM systems.

AAAI 2026

Parallelism Meets Adaptiveness: Scalable Documents Understanding in Multi-Agent LLM Systems

dynamic task routing

nlp: (large) language models

multi-agent systems

## Welcome!
"I am excited to welcome you to this year’s edition of the Conference on Empirical Methods in Natural Language Processing! Importantly, it marks the 30th edition of EMNLP. With over 8,000 submissions, more than 3,000 accepted papers, and thousands of attendees, we have come a long way from that first
workshop, which had 14 accepted papers. As the field looks ahead, Suzhou is the fitting location for celebrating this milestone: rooted in a long literary tradition, yet modern and forward-looking, and home to a large share of the NLP community." 

*Message from the General Chair, Dirk Hovy*

EMNLP 2025

Celebrate 30 Years of EMNLP! 
EMNLP 2025 will be held in Suzhou, China from November 5th to November 9th, 2025.

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.

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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.

Priority Inheritance and Backtracking (PIBT) has demonstrated significant success as a heuristic for large-scale Multi-Agent Path Finding (MAPF), but its application has been limited to scenarios where all robots move at uniform speeds. This work addresses the critical question of extending PIBT's capabilities to heterogeneous fleets, where robots may vary in size and speed. We introduce a novel collision-checking-based reservation system integrated within the WinPIBT framework and a new backtracking mechanism. Heterogeneous PIBT (HetPIBT) effectively scales to hundreds of robots of various sizes. Our findings confirm that this scheme enables the application of heterogeneous MAPF to large robot populations. To facilitate further research, we also provide a new set of heterogeneous MAPF benchmark scenarios along with a Python package for the generation of such problems and visualization tools. However, we observe that HetPIBT, similar to its homogeneous counterpart, can yield suboptimal solutions. We provide an open source Rust package which users can build upon.

Extending PiBT to support Heterogenous Robot Fleets

Multi-Agent Path Finding (MAPF) is a one-shot problem of finding collision-free paths in a shared environment while minimizing the sum of the agents' travel times.
Since solving MAPF optimally is NP-hard, $w$-optimal algorithms such as Explicit Estimation Conflict-Based Search (EECBS) have been used to speed up the search while providing a guarantee on the solution quality.
However, the scalability of EECBS is limited in large-scale MAPF instances.
While EECBS can be accelerated for regularly structured environments, such as Kiva warehouses, by utilizing specialized guidance heuristics, these heuristics are ineffective in more general and large-scale environments.
To fill this gap, we propose the \textit{Flow-Based Guidance Framework}, a general two-phase process that simulates a list of paths and then generates the \textit{Flow-Based Guidance Heuristic} (FH) without making prior assumptions about the environment's structure.
We identify features that distinguish $w$-optimal MAPF from other MAPF variants and propose strategies to enhance its effectiveness for guidance, complemented by the flex distribution technique from EECBS.
The empirical evaluation demonstrates that our FH significantly reduces collisions, thereby achieving higher success rates than the state-of-the-art within 60 seconds.

Flow-Based Guidance Framework with Flex Distribution for $w$-Optimal Multi-Agent Path Finding

State-of-the-art multi-robot kinodynamic motion planners struggle to handle more than a few robots due to high computational burden, which limits their scalability and results in slow planning time.
In this work, we combine the scalability and speed of modern multi-agent path finding (MAPF) algorithms with the dynamic-awareness of kinodynamic planners to address these limitations.
To this end, we propose discontinuity-Bounded LaCAM (db-LaCAM), a planner that utilizes a precomputed set of motion primitives that respect robot dynamics to generate horizon-length motion sequences, while allowing a user-defined discontinuity between successive motions.
The planner db-LaCAM is resolution-complete with respect to motion primitives and supports arbitrary robot dynamics.
Extensive experiments demonstrate that db-LaCAM scales efficiently to scenarios with up to $50$ robots, achieving up to ten times faster runtime compared to state-of-the-art planners, while maintaining comparable solution quality.
The approach is validated in both 2D and 3D environments with dynamics such as the unicycle and 3D double integrator.
We demonstrate the safe execution of trajectories planned with db-LaCAM in two distinct physical experiments involving teams of flying robots and car-with-trailer robots.

db-LaCAM: Fast and Scalable Multi-Robot Kinodynamic Motion Planning with Discontinuity-Bounded Search and Lightweight MAPF

Multi-Agent Path Finding (MAPF) is a fundamental problem in robotics and logistics, where multiple agents must reach their goals without collisions. While deep Multi-Agent Reinforcement Learning methods have recently shown impressive scalability and adaptability, their black-box nature hinders interpretability and trust—crucial aspects for deployment in real-world systems. In this work, we propose an interpretable policy distillation framework for MAPF. We first formulate MAPF as a stochastic game and execute a trained neural policy across diverse environments to build a large dataset of state–action pairs. We then distill this neural policy into a decision tree model that captures its underlying decision rules while maintaining strong performance. Through extensive evaluation, we analyze the trade-off between interpretability and performance, demonstrating that our distilled models achieve high fidelity to the original policy while providing transparent, human-understandable reasoning about agent behavior.

Interpretable Multi-Agent Path Finding via Decision Tree Extraction from Neural Policies

Multi-Agent Path Finding (MAPF) is a representative multi-agent coordination problem, where multiple agents are required to navigate to their respective goals without collisions. Solving MAPF optimally is known to be NP-hard, leading to the adoption of learning-based approaches to alleviate the online computational burden. Prevailing approaches, such as Graph Neural Networks (GNNs), are typically constrained to *pairwise* message passing between agents. However, this limitation leads to suboptimal behaviours and critical issues, such as attention dilution, particularly in dense environments where group (i.e. beyond just two agents) coordination is most critical. Despite the importance of such higher-order interactions, existing approaches have not been able to fully explore them. To address this representational bottleneck, we introduce HMAGAT (Hypergraph Multi-Agent Attention Network), a novel architecture that leverages attentional mechanisms over directed hypergraphs to explicitly capture group dynamics. Empirically, HMAGAT establishes a new state-of-the-art among learning-based MAPF solvers: e.g., despite having just 1M parameters and being trained on 100$\times$ less data, it outperforms the current SoTA 85M parameter model. Through detailed analysis of HMAGAT's attention values, we demonstrate how hypergraph representations mitigate the attention dilution inherent in GNNs and capture complex interactions where pairwise methods fail. Our results illustrate that appropriate inductive biases are often more critical than the training data size or sheer parameter count for multi-agent problems.

Pairwise is Not Enough: Hypergraph Neural Networks for Multi-Agent Pathfinding

Federated learning (FL) enables clients to collaboratively train a shared model in a distributed manner, setting it apart from traditional deep learning paradigms. However, most existing FL research assumes consistent client participation, overlooking the practical scenario of dynamic participation (DPFL), where clients may intermittently join or leave during training. Moreover, no existing benchmarking framework systematically supports the study of DPFL-specific challenges. In this work, we present the first open-source framework explicitly designed for benchmarking FL models under dynamic client participation. Our framework provides configurable data distributions, participation patterns, and evaluation metrics tailored to DPFL scenarios. Using this platform, we benchmark four major categories of widely adopted FL models and uncover substantial performance degradation under dynamic participation. To address these challenges, we further propose Knowledge-Pool Federated Learning (KPFL), a generic plugin that maintains a shared knowledge pool across both active and idle clients. KPFL leverages dual-age and data-bias weighting, combined with generative knowledge distillation, to mitigate instability and prevent knowledge loss. Extensive experiments demonstrate the significant impact of dynamic participation on FL performance and the effectiveness of KPFL in improving model robustness and generalization.

Dynamic Participation in Federated Learning: Benchmarks and a Knowledge Pool Plugin

The orbital environment is increasingly congested, heightening collision risk and demanding robust Space Situational Awareness (SSA). Ground-based tracking and centralized learning face latency, fragmented datasets, and strict privacy limits on telemetry sharing. While ML aids orbital prediction, purely data-driven models fail under sparse or irregular observations; Physics-Informed Neural Networks (PINNs) embed dynamics for physical consistency. However, locally trained PINNs lack shared context, fragmenting awareness. Collaboration is often constrained by privacy policy, export controls, or mission secrecy—sometimes forcing purely local learning and leaving blind spots. This motivates Federated Learning (FL), where satellites share model updates (not raw data) to refine physics-consistent predictors while preserving data autonomy. However, single-server FL is ill-suited for orbital networks, as it creates a single point of failure, over-smooths data, and exposes vulnerability to link outages. We therefore propose Graph-Decentralized Federated Learning (Graph-DFL) for multi-satellite SSA: a serverless framework where satellites exchange quantized incremental updates only with neighbors and reach consensus via topology-aware diffusion. Each Low Earth Orbit (LEO) client trains a GRU-based deinterleaver and a local PINN, while Medium Earth Orbit (MEO) relays apply confidence-weighted Cayley–Menger × Light-Cone (CM × LC) fusion. Experiments on real Two-Line Element (TLE)–derived SGP4 trajectories show that Graph-DFL achieves high deinterleaver accuracy and low trajectory RMSE, indicating a resilient, physics-consistent, privacy-preserving solution for SSA without centralization.

Learning in Orbit: A Physics-Aware Graph-Decentralized Federated Learning for Multi-Satellite Space Situational Awareness

Fragment-based drug discovery (FBDD) leverages small molecular fragments as starting points for drug develop- ment. While SMILES is widely used in cheminformat- ics, its syntax introduces parsing challenges that can af- fect model performance. DeepSMILES, a postfix variant of SMILES, addresses these issues by eliminating paired ring closures and balanced parentheses. In this work, we intro- duce DeepBERTa, a ChemBERTa-based architecture trained on DeepSMILES, and apply it in a downstream FBDD task using a dataset of approximately 34,000 molecule–fragment pairs derived from FDA-approved drugs, with fragments se- lected by docking against the mTOR kinase. We ran both SMILES and DeepSMILES-based models in parallel and show that 1 a hybrid approach, which selects the best predic- tion from either model, raises the mean Tanimoto similarity from 0.290 (SMILES) to 0.347, with DeepSMILES providing the better prediction in 13% of the cases.

A Hybrid SMILES-DeepSMILES Transformer Approach Improving Fragment Prediction in Fragment-Based Drug Discovery with DeepBERTa

Protein flexibility and induced-fit effects are critical but often overlooked aspects of structure-based drug design. Most existing generative models treat proteins as rigid scaffolds, limiting their ability to predict realistic binding geometries. We present Co-Diffuse, a novel framework that jointly generates ligand molecules and their corresponding protein binding pocket conformations using SE(3)-equivariant diffusion models. By explicitly modeling the mutual adaptation between proteins and ligands, Co-Diffuse captures the dynamic nature of molecular recognition. Comprehensive evaluations on PDBbind, CrossDocked2020, and cryptic site benchmarks demonstrate that Co-Diffuse achieves an average RMSD improvement of 1.8 Å over leading docking baselines and yields more accurate binding affinity predictions. Ablation studies highlight the importance of joint generation and physics-informed constraints. Co-Diffuse represents a significant step toward dynamic, physically grounded generative modeling for structure-based drug design.



Co-Diffuse: Generative Co-Design of Protein–Ligand Interactions via 3D Equivariant Diffusion Models with Induced-Fit Awareness

SynergyIF: Multimodal Intermediate Fusion for Deep Tensor Completion in Anticancer Drug Synergy Prediction

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