Singapore

We present $\textsf{ModularSubsetSelection}$ (MSS), a new algorithm for locally differentially private (LDP) frequency estimation. 
Given a universe of size $k$ and $n$ users, our $\varepsilon$-LDP mechanism encodes each input via a Residue Number System (RNS) over $\ell$ pairwise-coprime moduli $m_0, \ldots, m_{\ell-1}$, and reports a randomly chosen index $j \in [\ell]$ along with the perturbed residue using the statistically optimal $\textsf{SubsetSelection}$ (SS) (Wang et al. 2016).
This design reduces the user communication cost from $\Theta\bigl(\omega \log_2(k/\omega)\bigr)$ bits required by standard SS (with $\omega \approx k/(e^\varepsilon+1)$) down to $\lceil \log_2 \ell \rceil + \lceil \log_2 m_j \rceil$ bits, where $m_j &lt; k$.
Server-side decoding runs in $\Theta(n + r k \ell)$ time, where $r$ is the number of LSMR (Fong and Saunders 2011) iterations. 
In practice, with well-conditioned moduli (i.e., constant $r$ and $\ell = \Theta(\log k)$), this becomes $\Theta(n + k \log k)$. 
We prove that MSS achieves worst-case MSE within a constant factor of state-of-the-art protocols such as SS and $\textsf{ProjectiveGeometryResponse}$ (PGR) (Feldman et al. 2022), while avoiding the algebraic prerequisites and dynamic-programming decoder required by PGR.
Empirically, MSS matches the estimation accuracy of SS, PGR, and $\textsf{RAPPOR}$ (Erlingsson, Pihur, and Korolova 2014) across realistic $(k, \varepsilon)$ settings, while offering faster decoding than PGR and shorter user messages than SS.
Lastly, by sampling from multiple moduli and reporting only a single perturbed residue, MSS significantly reduces a Bayesian attacker’s reconstruction accuracy compared to SS.

AAAI 2026

Private Frequency Estimation via Residue Number Systems

We present $\textsf{ModularSubsetSelection}$ (MSS), a new algorithm for locally differentially private (LDP) frequency estimation. 
Given a universe of size $k$ and $n$ users, our $\varepsilon$-LDP mechanism encodes each input via a Residue Number System (RNS) over $\ell$ pairwise-coprime moduli $m_0, \ldots, m_{\ell-1}$, and reports a randomly chosen index $j \in [\ell]$ along with the perturbed residue using the statistically optimal $\textsf{SubsetSelection}$ (SS) (Wang et al. 2016).
This design reduces the user communication cost from $\Theta\bigl(\omega \log_2(k/\omega)\bigr)$ bits required by standard SS (with $\omega \approx k/(e^\varepsilon+1)$) down to $\lceil \log_2 \ell \rceil + \lceil \log_2 m_j \rceil$ bits, where $m_j < k$.
Server-side decoding runs in $\Theta(n + r k \ell)$ time, where $r$ is the number of LSMR (Fong and Saunders 2011) iterations. 
In practice, with well-conditioned moduli (i.e., constant $r$ and $\ell = \Theta(\log k)$), this becomes $\Theta(n + k \log k)$. 
We prove that MSS achieves worst-case MSE within a constant factor of state-of-the-art protocols such as SS and $\textsf{ProjectiveGeometryResponse}$ (PGR) (Feldman et al. 2022), while avoiding the algebraic prerequisites and dynamic-programming decoder required by PGR.
Empirically, MSS matches the estimation accuracy of SS, PGR, and $\textsf{RAPPOR}$ (Erlingsson, Pihur, and Korolova 2014) across realistic $(k, \varepsilon)$ settings, while offering faster decoding than PGR and shorter user messages than SS.
Lastly, by sampling from multiple moduli and reporting only a single perturbed residue, MSS significantly reduces a Bayesian attacker’s reconstruction accuracy compared to SS.

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>

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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.

Multi-agent reinforcement learning (MARL) excels in cooperative and competitive tasks, but most architectures are tied to fixed input-output sizes and require retraining when the number of perceptible or controllable objects changes. While structural generalization techniques mitigate this, they rely on centralized training, raising concerns about scalability and privacy. We propose ADAPT, the first framework to support structural generalization under a decentralized training and decentralized execution (DTDE) paradigm. Every agent adopts an object-centric view, encoding each observed object into a feature vector and aggregating them into a variable-length set representation. To enable each agent to infer task-level contexts from this dynamic input independently, we propose a dynamic-consistency loss that enforces spatio-temporal alignment between context representations and observed environmental dynamics. Agents then condition their policies on the inferred contexts to make locally aligned decisions. For zero-shot transfer, we propose FINE (Foresight INdex for multi-agEnt), a metric that considers Q-value overestimation and enables cross-policy comparison of long-term impact, facilitating effective policy transfer. Experiments show that ADAPT surpasses existing DTDE methods and outperforms CTDE baselines in zero-shot generalization.

ADAPT: Adaptive Decentralized Architecture with Perception-Aligned Training for Structural Generalization in Multi-Agent RL

Plan verification is the task of checking whether a proposed plan correctly solves a given planning problem. In Hierarchical Task Network (HTN) planning, this verification problem is known to be NP-complete, even in the grounded setting. Existing approaches to HTN plan verification range from SAT encodings to parser-based techniques. However, the temporal structure that emerges from hierarchical decomposition has not yet been used explicitly as a basis for verification.
In this paper, we establish a formal connection between HTN planning and temporal reasoning by showing how decomposition structures can be naturally represented using qualitative constraint networks. Building on this insight, we present a new top-down encoding that transforms the verification of partially ordered task networks into a temporal reasoning problem. We prove the correctness of this encoding and explain how it accounts for both the hierarchical and temporal aspects of HTN plans.
By linking HTN plan verification with qualitative temporal reasoning, our approach introduces a principled formal framework for reasoning about complex temporal relationships in hierarchical plans. This connection offers new perspectives for knowledge representation in structured planning domains.

HTN Plan Verification by Qualitative Temporal Reasoning

We introduce DSCodeBench, a new benchmark designed to evaluate large language models (LLMs) on complicated and realistic data science code generation tasks.
DSCodeBench consists of 1,000 carefully constructed problems sourced from realistic problems from GitHub across ten widely used Python data science libraries.
DSCodeBench offers a more challenging and representative testbed, more complex code solutions, more comprehensive data science libraries, clearer and better structured problem descriptions, and stronger test suites.
To construct the DSCodeBench, we develop a robust pipeline that combines task scope selection, code construction, test case generation, and problem description synthesis.
The process is paired with rigorous manual editing to ensure alignment and enhance the reliability of the evaluation.
Experimental result shows that DSCodeBench exhibits robust scaling behavior, where larger models systematically outperform smaller ones, validating its ability to distinguish model capabilities.
The best LLM we test, GPT-4o, has a pass@1 of 0.392, indicating that LLMs still have a large room to improve for realistic data science code generation tasks. 
We believe DSCodeBench will serve as a rigorous and trustworthy foundation for advancing LLM-based data science programming.

DSCodeBench: A Realistic Benchmark for Data Science Code Generation

Learning rate scheduling is crucial for training large language models, yet understanding the optimal annealing strategies across different model configurations remains challenging. In this work, we investigate the transferability of annealing dynamics in large language model training and refine a generalized predictive framework for optimizing annealing strategies under the Warmup-Steady-Decay (WSD) scheduler. Our improved framework incorporates training steps, maximum learning rate, and annealing behavior, enabling more efficient optimization of learning rate schedules. Our work provides a practical guidance for selecting optimal annealing strategies without exhaustive hyperparameter searches, demonstrating that smaller models can serve as reliable proxies for optimizing the training dynamics of larger models. We validate our findings on extensive experiments using both Dense and Mixture-of-Experts (MoE) models, demonstrating that optimal annealing ratios follow consistent patterns and can be transferred across different training configurations.

Scaling and Transferability of Annealing Strategies in Large Language Model Training

Diffusion models have achieved impressive generative performance across diverse domains such as image, video, and scientific data generation. However, fine-tuning these models for new tasks remains challenging due to their large scale, architectural diversity, and high sensitivity to hyperparameters—particularly learning rates. In this work, we propose Wasserstein-Aware Transfer (WAT), a principled and effective fine-tuning strategy grounded in diffusion trajectory analysis and optimal transport theory. Our key insight is that the distributional discrepancies between diffusion trajectories from different datasets decrease progressively over time and converge near the noise end. Based on this observation, we introduce a class-wise matching mechanism that minimizes the Wasserstein distance between class distributions of source and target datasets. This enables alignment at the class level without modifying the standard fine-tuning pipeline. To further enhance knowledge retention, we propose a novel sampling strategy that linearly combines class-conditional outputs from both pretrained and fine-tuned models. This method is simple yet effective, requiring negligible computational overhead while preserving domain-specific and generalizable knowledge. Extensive experiments across seven diverse benchmarks demonstrate that WAT reliably enhances generation quality under distribution shifts, outperforming competitive baselines. These results underscore its robustness and affirm the potential of optimal transport as a principled basis for knowledge transfer in diffusion models.

Wasserstein-Aware Transfer: Class-Level Alignment for Robust Diffusion Model Adaptation

In a public goods game, every player chooses whether or not to produce a good that all neighboring players will have access to. We consider a setting in which the public good is indivisible, neighboring players are out-neighbors in a directed graph, and there is a capacity constraint on their number, $k$, that can benefit from the good. This means that each player makes a two-pronged decision: decide whether or not to produce and, conditional on producing, choose which $k$ out-neighbors to share access. We examine both pure and mixed Nash equilibria in the model from the perspective of existence, computation, and efficiency. We perform a comprehensive study for these three dimensions with respect to both sharing capacity ($k$) and the network structure (the underlying directed graph), and establish sharp complexity dichotomies for each.

Public Goods Games in Directed Networks with Constraints on Sharing

Backdoor attacks pose severe threats to machine learning systems, prompting extensive research in this area. However, most existing work focuses on single-target All-to-One (A2O) attacks, overlooking the more complex All-to-X (A2X) attacks with multiple target classes, which are often assumed to have low attack success rates. In this paper, we first demonstrate that A2X attacks are robust against state-of-the-art defenses. We then propose a novel attack strategy that enhances the success rate of A2X attacks while maintaining robustness by optimizing grouping and target class assignment mechanisms. Our method improves the attack success rate by up to 28%, with average improvements of 6.7%, 16.4%, 14.1% on CIFAR10, CIFAR100, and Tiny-ImageNet, respectively. We anticipate that this study will raise awareness of A2X attacks and stimulate further research in this underexplored area.

Enhancing All-to-X Backdoor Attacks with Optimized Target Class Mapping

Lifelogging involves the continuous and comprehensive recording of a user’s daily activities, behaviors, and interactions, offering valuable insights for personalized healthcare, event retrieval, and lifestyle analysis. However, extracting meaningful patterns from lifelog data requires models to capture deeper temporal contexts beyond simple retrieval. To address this, we introduce ContextGraph, a lifelog intelligence framework that models lifelogs as a Temporal Knowledge Graph (TKG) to reason about the user’s evolving life patterns over time. ContextGraph computes Day Context Embeddings (DCE) to encode the temporal spread and social scene context of user's daily behavior. Then a novel Lens module extracts semantically meaningful subgraph snapshots around an anchor node in the TKG, representing specific personal contexts in the user’s life. The Lens module also computes an evolution signature for each subgraph, indicating whether it is growing, decaying, or remaining static. By analyzing these evolution signatures, ContextGraph provides actionable insights into the user’s lifelogs such as stable routines, behavioral drifts, or lifestyle changes. Our experiments showcase DCE's versatility, outperforming baselines in graph/node classification and reasoning on the Enzyme and DBLP datasets.

ContextGraph: Lifelog Intelligence Framework for Contextual Subgraph Evolution

Low-Rank Adaptation (LoRA) is the leading parameter-efficient fine-tuning method for Large Language Models (LLMs), but it still suffers from catastrophic forgetting. Recent work has shown that specialized LoRA initialization can alleviate catastrophic forgetting. There are currently two approaches to LoRA initialization aimed at preventing knowledge forgetting during fine-tuning: (1) making residual weights close to pre-trained weights, and (2) ensuring the space of LoRA initialization is orthogonal to pre-trained knowledge. The former is what current methods strive to achieve, while the importance of the latter is not sufficiently recognized. We find that the space of LoRA initialization is the key to preserving pre-trained knowledge rather than the residual weights. Existing methods like MiLoRA propose making the LoRA initialization space orthogonal to pre-trained weights. However, MiLoRA utilizes the null space of pre-trained weights. Compared to pre-trained weights, the input activations of pre-trained knowledge take into account the parameters of all previous layers as well as the input data, while pre-trained weights only contain information from the current layer. Moreover, we find that the effective ranks of input activations are much smaller than those of pre-trained weights. Thus, the null space of activations is more accurate and contains less pre-trained knowledge information compared to that of weights. Based on these, we introduce LoRA-Null, our proposed method that initializes LoRA in the null space of activations. Experimental results show that LoRA-Null effectively preserves the pre-trained world knowledge of LLMs while achieving good fine-tuning performance, as evidenced by extensive experiments. The code is included in the supplementary material.

Put the Space of LoRA Initialization to the Extreme to Preserve Pre-trained Knowledge

Mixed-precision quantization (MPQ) is crucial for deploying deep neural networks on resource-constrained devices, but finding the optimal bit-width for each layer represents a complex combinatorial optimization problem. Current state-of-the-art methods rely on computationally expensive search algorithms or local sensitivity heuristic proxies like the Hessian, which fail to capture the cascading global effects of quantization error. In this work, we argue that the quantization sensitivity of a layer should not be measured by its local properties, but by its impact on the information flow throughout the entire network. We introduce InfoQ, a novel framework for mixed-precision quantization that is training-free in the bit-width search phase. InfoQ assesses layer importance by performing a single forward pass to measure the change in mutual information in the remaining part of the network, thus creating a global sensitivity score. This approach directly quantifies how quantizing one layer degrades the information characteristics of subsequent layers. The resulting scores are used to formulate bit-width allocation as an integer linear programming problem, which is solved efficiently to minimize total sensitivity under a given budget (e.g., model size or BitOps). Our retraining-free search phase provides a superior search-time/accuracy trade-off (using two orders of magnitude less data compared to state-of-the-art methods such as LIMPQ), while yielding up to a 1\% accuracy improvement for MobileNetV2 and ResNet18 on ImageNet at high compression rates (14.00$\times$ and 10.66$\times$).

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