Singapore

Large language models (LLMs) demonstrate strong reasoning
capabilities, yet the inference-time performance of
existing solutions remains limited by self-biases,
coordination inefficiencies, lack of robust error
detection, and dependency on high-quality verifiers. To
address these challenges, we propose Adaptive Coopetition
(AdCo), a lightweight, multi-agent multi-round
inference-time framework that enhances collective reasoning
through adaptive decision-making guided by coarse verifier
signals. Without relying on high-performance verifiers,
AdCo achieves a 20% relative accuracy improvement on math
reasoning benchmarks, with consistent performance on
different sample sizes and agent configurations. This
adaptive, signal-guided ‘coopetition’ framework (Tran et
al. 2025) enhances reasoning robustness by leveraging
diverse model knowledge and reasoning traces, while also
promoting uncertainty-driven exploration, especially when
participants have comparable capabilities.

AAAI 2026

Adaptive Coopetition: Leveraging Coarse Verifier Signals for Resilient Multi-Agent LLM Reasoning (Student Abstract)

multi-agent debate

poster

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>

To access this event page, you need to log in with the **email address you registered with**. <br>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.

This study presents a physics-informed neural network
framework to model droplet spreading dynamics on
unstructured rough surfaces, using training data generated
from high-fidelity lattice Boltzmann method simulations.
The droplet is represented by phase-field density field,
where interface evolution is governed by multiphase flow
physics, surface tension, and wall wettability effects.
Unlike conventional data-driven models, the PINN
incorporates the underlying physi-
cal laws such as mass and momentum conservation directly
into the loss function, enabling accurate prediction even
with the sparse data. The trained model successfully
captures the droplet dynamics over different time periods,
contact line evolution, and interfacial deformation across
time, showing high accuracy as compared with the CNNs. This
PINN-based surrogate offers a comutationally efficient,
mesh-free alternatives to traditional solvers, making it
ideal for rapid parametric studies and design optimized
microfluidic devices and wettability-controlled systems.

Learning Droplet Dynamics on Rough Unstructured Surfaces Using Physics-Informed Neural Networks (Student Abstract)

Agentic workflows, where multiple AI agents collaborate to
accomplish complex tasks like reasoning or planning, play a
substantial role in many cutting-edge commercial
applications. These workflows depend critically on the
prompts used to provide the roles models play in such
workflows. Poorly designed prompts that fail even slightly
to guide individual agents can lead to sub-optimal
performance that may snowball within a system of agents,
limiting their reliability and scalability. To address this
important problem of inference-time prompt optimization, we
introduce ProRefine, an innovative inference-time
optimization method that uses an agentic loop of LLMs to
generate and apply textual feedback. ProRefine dynamically
refines prompts for multi-step reasoning tasks without
additional training or ground truth labels. Evaluated on
five benchmark mathematical reasoning datasets, ProRefine
significantly surpasses zero-shot Chain-of-Thought
baselines by 3 to 37 percentage points. This approach not
only boosts accuracy but also allows smaller models to
approach the performance of their larger counterparts. This
highlights its potential for building cost-effective and
powerful hybrid AI systems, thereby democratizing access to
high-performing AI.

ProRefine: Inference-Time Prompt Refinement with Textual Feedback (Student Abstract)

Quantitative remote sensing estimation is critical for environmental monitoring, providing continuous measures of vegetation indices, canopy height, and carbon stock. Traditional radiative-transfer models and empirical regressions require expert knowledge and generalize poorly, while deep learning methods remain task-specific. We propose SatelliteCalculator+, a DINOv3-powered multi-task foundation model for continuous regression of spectral and structural variables. The framework combines prompt-driven cross-attentive adapters with lightweight MLP decoders, enabling efficient dense prediction from frozen features. To overcome limited supervision, we synthesize over one million paired samples from SPOT 6/7 imagery using physically defined formulas. On the Open-Canopy dataset, SatelliteCalculator+ achieves competitive accuracy across eight ecological variables while reducing inference cost, demonstrating the promise of self-supervised transformers and scalable multi-task learning for large-scale Earth observation.

DINOv3-Powered Multi-Task Foundation Model for Quantitative Remote Sensing Estimation (Student Abstract)

We present a language-based noise modulation module for diffusion models that improves image color generation under textual guidance. Unlike standard approaches that inject noise uniformly, our method leverages semantic cues from text to selectively control the noise injection process, preserving local details and enhancing color accuracy even when descriptions are ambiguous or incomplete. Applied to language guided image colorization, this targeted modulation leads to more faithful and visually consistent results. The proposed module is lightweight, generalizable, and can be integrated into existing diffusion pipelines, offering a simple yet effective step toward more controllable text-to-image generation.

NoMoColor: Unified Noise Modulation for Enhanced Diffusion-based Image Colorization (Student Abstract)

Large Language Models (LLMs) demonstrate strong
capabilities in code generation but often lack adaptability
in planning and refinement. We propose Self-PR, a framework
that integrates adaptive plan selection and iterative
repair to improve correctness and generalization. Self-PR
constructs a reusable plan database via task clustering and
trains a selector to choose task-specific strategies.
Incorrect outputs are refined through multi-round feedback
until correctness. Trained only on HumanEval, Self-PR
generalizes well to out-of-distribution tasks (MBPP),
improving pass@1 by +4.9\% on HumanEval and +5.5\% on MBPP
compared to Modularization-of-Thought prompting.
Experiments across Llama-3 (8B, 70B) and GPT-4o-mini
confirm robustness and scalability. These findings suggest
that adaptive planning and feedback-driven repair are
essential for reliable LLM-based code generation.

Self-Guided Planning and Repair Framework for Code Generation (Student Abstract)

In this study, we propose two methods to estimate static
graphs from a single dynamic graph and integrate them into
hybrid Graph Neural Networks (GNNs), which combine
long-term static structure with transient dynamic
interactions. Since static graphs are often unavailable and
attributes may be difficult to use at scale or under
privacy constraints, we introduce: (i) a behavioral
similarity estimator based on normalized co-occurrence,
requiring no attributes, and (ii) an attribute-aware
K-means + k-NN estimator that is more efficient than cosine
similarity. Experiments on multiple real-world datasets
show that both methods consistently improve predictive
accuracy and training efficiency, underscoring the
importance of static graph choice in hybrid GNNs.

Behavioral-Similarity and Clustering-Based Methods for Static Graph Estimation in Hybrid GNNs (Student Abstract)

Dynamic head pruning in Vision Transformers (ViTs) improves
efficiency by removing redundant attention heads, but
existing pruning policies are often difficult to interpret
and control. In this work, we propose a novel framework by
integrating Sparse Autoencoders (SAEs) with dynamic
pruning, leveraging their ability to disentangle dense
embeddings into interpretable and controllable sparse
latents. Specifically, we train an SAE on the final-layer
residual embedding of the ViT and amplify the sparse
latents with different strategies to alter pruning
decisions. Among them, per-class steering reveals compact,
class-specific head subsets that preserve accuracy. For
example, bowl improves accuracy (76%→82%) while reducing
head usage (0.72→0.33) via heads h2 and h5. These results
show that sparse latent features enable class-specific
control of dynamic pruning, effectively bridging pruning
efficiency and mechanistic interpretability in ViTs.

Steering Sparse Autoencoder Latents to Control Dynamic Head Pruning in Vision Transformers (Student Abstract)

We evaluate how well large language model embeddings
represent continuous numerical values across different
precisions and ranges. Using linear models and principal
component analysis on models from major providers, we show
that while embeddings can reconstruct numbers with high
fidelity (R2 ≥ 0.95), they introduce substantial noise,
with principal components explaining less than 40% of
embedding variance. Performance degrades with increasing
decimal precision and mixed-sign values, revealing
fundamental limitations in how these models encode
numerical information.

Language Models Do Not Embed Numbers Continuously (Student Abstract)

In social networks, revealing the structure of communities can expose sensitive groups to detection. Traditional approaches, such as DICE, attempt to hide these communities by randomly rewiring links, but this strategy is often inefficient and insecure. We propose an efficient heuristic method called CRIME (Community Rewiring for Influence and Masking Entities) to address this challenge. CRIME removes the most influential internal links, measured by edge-betweenness centrality, and adds external links with the least betweenness centrality. Experiments on real-world networks demonstrate that CRIME hides targeted communities more effectively than DICE, and also achieves faster execution and improves hiding effectiveness by up to 99.8%.

CRIME: Community Rewiring for Influence and Masking Entities in Social Networks (Student Abstract)

Large Language Models (LLMs) have revolutionized the modern society significantly with the numerous advanced interactions between humans and AI agents, whereas the usage of most large language models including ChatGPT are not friendly open-sourced and must require users paying a lot for such AI service continuously. Therefore, deploying open-sourced large language models on local servers can be considered as an efficient method to design and implement creative embodied AI algorithms with lower cost and more stable free usage. Inspired by this ordinary motivation, we originally propose and implement the “Socratic Models-ChatGLM”, which is a well-performed algorithm for multi-modal interactive control of robotic arm based on offline large language models via the facile PyBullet platform, even presents extraordinary potential to address complicated text-image multi-step long-horizon robotic manipulation tasks.

Downloads

Next from AAAI 2026

Learning Droplet Dynamics on Rough Unstructured Surfaces Using Physics-Informed Neural Networks (Student Abstract)

Stay up to date with the latest Underline news!

PRESENTATIONS

CONFERENCES

COMPANY

RESOURCES