OpenRaiser/Intern-Atlas huggingface dataset:
Simulating Multi-Stakeholder Decision-Making with Generative Agents in Urban Planning
Venue: arXiv cs.MA 2026
Year: 2026

Reaching consensus in urban planning is a complex process often hindered by prolonged negotiations, trade-offs, power dynamics, and competing stakeholder interests, resulting in inefficiencies and inequities. Advances in large language models (LLMs), with their increasing capabilities in knowledge transfer, reasoning, and planning, have enabled the development of multi-generative agent systems, offering a promising approach to simulating discussions and interactions among diverse stakeholders on contentious topics. However, applying such systems also carries significant societal and ethical risks, including misrepresentation, privacy concerns, and biases stemming from opinion convergence among agents, hallucinations caused by insufficient or biased prompts, and the inherent limitations of foundation models. To evaluate the influence of these factors, we incorporate varying levels of real-world survey data and demographic detail to test agents' performance under two decision-making value frameworks: altruism-driven and interest-driven, using a real-world urban rezoning challenge. This approach evaluates the influence of demographic factors such as race, gender, and age on collective decision-making in the design of multi-generative agent systems. Our experimental results reveal that integrating demographic and life-value data enhances the diversity and stability of agent outputs. In addition, communication among generated agents improves the quality of collective reasoning. These findings provide a predictive framework for decision-makers to anticipate stakeholder reactions, including concerns, objections, and support. By enabling iterative refinement of proposals before public release, the simulated approach fosters more equitable and cost-effective decisions in urban planning.
Browse and Concentrate: Comprehending Multimodal Content via prior-LLM Context Fusion
Venue: arXiv cs.CL 2026
Year: 2026

With the bloom of Large Language Models (LLMs), Multimodal Large Language Models (MLLMs) that incorporate LLMs with pre-trained vision models have recently demonstrated impressive performance across diverse vision-language tasks. However, they fall short to comprehend context involving multiple images. A primary reason for this shortcoming is that the visual features for each images are encoded individually by frozen encoders before feeding into the LLM backbone, lacking awareness of other images and the multimodal instructions. We term this issue as prior-LLM modality isolation and propose a two phase paradigm, browse-and-concentrate, to enable in-depth multimodal context fusion prior to feeding the features into LLMs. This paradigm initially "browses" through the inputs for essential insights, and then revisits the inputs to "concentrate" on crucial details, guided by these insights, to achieve a more comprehensive understanding of the multimodal inputs. Additionally, we develop training strategies specifically to enhance the understanding of multi-image inputs. Our method markedly boosts the performance on 7 multi-image scenarios, contributing to increments on average accuracy by 2.13% and 7.60% against strong MLLMs baselines with 3B and 11B LLMs, respectively.
FormulaReasoning: A Dataset for Formula-Based Numerical Reasoning
Venue: arXiv cs.CL 2026
Year: 2026

The application of physics formulas is a fundamental human capability in numerical reasoning. While existing datasets often rely on implicit mathematical knowledge, they rarely explicitate the underlying formulas. To address this, we introduce FormulaReasoning, a new benchmark for formula-based numerical reasoning comprising 5,324 questions requiring calculations grounded in external physics principles. We provide high-quality, fine-grained annotations in English and Chinese--including formula structures, parameter names, symbols, values, and units--curated through manual effort and LLM-assisted validation. Additionally, we provide a consolidated formula database as an external knowledge source. To further challenge model performance, we develop an extended version of the dataset by coupling multiple questions. We evaluate various architectural and methodological frameworks, including retrieval-augmented methods, modular reasoning (formula generation, parameter extraction, and calculation), and preference-based optimization. Our analysis identifies critical challenges in formula-based reasoning, highlighting significant opportunities for future methodological advancement.
GRAPHGINI: Fostering Individual and Group Fairness in Graph Neural Networks
Venue: arXiv cs.LG 2026
Year: 2026

Graph Neural Networks (GNNs) have demonstrated impressive performance across various tasks, leading to their increased adoption in high-stakes decision-making systems. However, concerns have arisen about GNNs potentially generating unfair decisions for underprivileged groups or individuals when lacking fairness constraints. This work addresses this issue by introducing GraphGini, a novel approach that incorporates the Gini coefficient to enhance both individual and group fairness within the GNN framework. We rigorously establish that the Gini coefficient offers greater robustness and promotes equal opportunity among GNN outcomes, advantages not afforded by the prevailing Lipschitz constant methodology. Additionally, we employ the Nash social welfare program to ensure our solution yields a Pareto optimal distribution of group fairness. Extensive experimentation on real-world datasets demonstrates GraphGini's efficacy in significantly improving individual fairness compared to state-of-the-art methods while maintaining utility and group fairness.
Sparse MeZO: Less Parameters for Better Performance in Zeroth-Order LLM Fine-Tuning
Venue: arXiv cs.LG 2026
Year: 2026

While fine-tuning large language models (LLMs) for specific tasks often yields impressive results, it comes at the cost of memory inefficiency due to back-propagation in gradient-based training. Memory-efficient Zeroth-order (MeZO) optimizers, recently proposed to address this issue, only require forward passes during training, making them more memory-friendly. However, compared with exact gradients, ZO-based gradients usually exhibit an estimation error, which can significantly hurt the optimization process, leading to slower convergence and suboptimal solutions. In addition, we find that the estimation error will hurt more when adding to large weights instead of small weights. Based on this observation, this paper introduces Sparse MeZO, a novel memory-efficient zeroth-order optimization approach that applies ZO only to a carefully chosen subset of parameters. We propose a simple yet effective parameter selection scheme that yields significant performance gains with Sparse-MeZO. Additionally, we develop a memory-optimized implementation for sparse masking, ensuring the algorithm requires only inference-level memory consumption, allowing Sparse-MeZO to fine-tune LLaMA-30b on a single A100 GPU. Experimental results illustrate that Sparse-MeZO consistently improves both performance and convergence speed over MeZO without any overhead. For example, it achieves a 9\% absolute accuracy improvement and 3.5x speedup over MeZO on the RTE task. Code is available at https://github.com/NUS-HPC-AI-Lab/SparseMeZO.
FedFDP: Fairness-Aware Federated Learning with Differential Privacy
Venue: arXiv cs.CR 2026
Year: 2026

Federated learning (FL) is an emerging machine learning paradigm designed to address the challenge of data silos, attracting considerable attention. However, FL encounters persistent issues related to fairness and data privacy. To tackle these challenges simultaneously, we propose a fairness-aware federated learning algorithm called FedFair. Building on FedFair, we introduce differential privacy to create the FedFDP algorithm, which addresses trade-offs among fairness, privacy protection, and model performance. In FedFDP, we developed a fairness-aware gradient clipping technique to explore the relationship between fairness and differential privacy. Through convergence analysis, we identified the optimal fairness adjustment parameters to achieve both maximum model performance and fairness. Additionally, we present an adaptive clipping method for uploaded loss values to reduce privacy budget consumption. Extensive experimental results show that FedFDP significantly surpasses state-of-the-art solutions in both model performance and fairness.
Less is More Revisited
Venue: arXiv cs.PL 2026
Year: 2026

Multiparty session types (MPST) provide a type discipline where a programmer or architect specifies a whole view of communications as a global protocol, and each distributed program is locally type-checked against its end-point projection. After 10 years from the birth of MPST, Scalas and Yoshida discovered that the proofs of type safety in the literature which use the end-point projection with mergeability are flawed. After this paper, researchers wrongly believed that the end-point projection (with mergeability) was unsound. We correct this misunderstanding, proposing a new general proof technique for type soundness of multiparty session $π$-calculus, which uses an association relation between a global type and its end-point projection.
Structure-Aware Computing, Partial Quantifier Elimination And SAT
Venue: arXiv cs.LO 2026
Year: 2026

Virtually all efficient algorithms of hardware verification are somewhat formula-specific i.e, try to take into account the structure of the formula at hand. So, those algorithms can be viewed as $\mathit{structure}$-$\mathit{aware}$ $\mathit{computing}$ (SAC). We relate SAC and $\mathit{partial}$ $\mathit{quantifier}$ $\mathit{elimination}$ (PQE), a generalization of regular quantifier elimination. In PQE, one can take a $\mathit{part}$ of the formula out of the scope of quantifiers. Interpolation can be viewed as a special case of PQE. The objective of this paper is twofold. First, we want to show that new powerful methods of SAC can be formulated in terms of PQE. We use three hardware verification problems (testing by property generation, equivalence checking and model checking) and SAT to explain how SAC is performed by PQE. Second, we want to demonstrate that PQE solving $\mathit{itself}$ can benefit from SAC. To this end, we describe a technique meant for building structure-aware SAT solvers that paves the way to structure-aware PQE solving.
List Sample Compression and Uniform Convergence
Venue: arXiv cs.LG 2026
Year: 2026

List learning is a variant of supervised classification where the learner outputs multiple plausible labels for each instance rather than just one. We investigate classical principles related to generalization within the context of list learning. Our primary goal is to determine whether classical principles in the PAC setting retain their applicability in the domain of list PAC learning. We focus on uniform convergence (which is the basis of Empirical Risk Minimization) and on sample compression (which is a powerful manifestation of Occam's Razor). In classical PAC learning, both uniform convergence and sample compression satisfy a form of `completeness': whenever a class is learnable, it can also be learned by a learning rule that adheres to these principles. We ask whether the same completeness holds true in the list learning setting. We show that uniform convergence remains equivalent to learnability in the list PAC learning setting. In contrast, our findings reveal surprising results regarding sample compression: we prove that when the label space is $Y=\{0,1,2\}$, then there are 2-list-learnable classes that cannot be compressed. This refutes the list version of the sample compression conjecture by Littlestone and Warmuth (1986). We prove an even stronger impossibility result, showing that there are $2$-list-learnable classes that cannot be compressed even when the reconstructed function can work with lists of arbitrarily large size. We prove a similar result for (1-list) PAC learnable classes when the label space is unbounded. This generalizes a recent result by arXiv:2308.06424.
Graph Machine Learning based Doubly Robust Estimator for Network Causal Effects
Venue: arXiv cs.LG 2026
Year: 2026

We address the challenge of inferring causal effects in social network data. This results in challenges due to interference -- where a unit's outcome is affected by neighbors' treatments -- and network-induced confounding factors. While there is extensive literature focusing on estimating causal effects in social network setups, a majority of them make prior assumptions about the form of network-induced confounding mechanisms. Such strong assumptions are rarely likely to hold especially in high-dimensional networks. We propose a novel methodology that combines graph machine learning approaches with the double machine learning framework to enable accurate and efficient estimation of direct and peer effects using a single observational social network. We demonstrate the semiparametric efficiency of our proposed estimator under mild regularity conditions, allowing for consistent uncertainty quantification. We demonstrate that our method is accurate, robust, and scalable via an extensive simulation study. We use our method to investigate the impact of Self-Help Group participation on financial risk tolerance.
Exact and Heuristic Computation of the Scanwidth of Directed Acyclic Graphs
Venue: arXiv cs.DS 2026
Year: 2026

To measure the tree-likeness of a directed acyclic graph (DAG), a new width parameter that considers the directions of the arcs was recently introduced: scanwidth. We present the first algorithm that efficiently computes the exact scanwidth of general DAGs. For DAGs with one root and scanwidth $k$ it runs in $O(k \cdot n^k \cdot m)$ time. The algorithm also functions as an FPT algorithm with complexity $O(2^{4 \ell - 1} \cdot \ell \cdot n + n^2)$ for phylogenetic networks of level-$\ell$, a type of DAG used to depict evolutionary relationships among species. Our algorithm performs well in practice, being able to compute the scanwidth of synthetic networks up to 30 reticulations and 100 leaves within 500 seconds. Furthermore, we propose a heuristic that obtains an average practical approximation ratio of 1.5 on these networks. While we prove that the scanwidth is bounded from below by the treewidth of the underlying undirected graph, experiments suggest that for networks the parameters are close in practice.
Soft Masked Transformer for Point Cloud Processing with Skip Attention-Based Upsampling
Venue: arXiv cs.CV 2026
Year: 2026

Point cloud processing methods leverage local and global point features %at the feature level to cater to downstream tasks, yet they often overlook the task-level context inherent in point clouds during the encoding stage. We argue that integrating task-level information into the encoding stage significantly enhances performance. To that end, we propose SMTransformer which incorporates task-level information into a vector-based transformer by utilizing a soft mask generated from task-level queries and keys to learn the attention weights. Additionally, to facilitate effective communication between features from the encoding and decoding layers in high-level tasks such as segmentation, we introduce a skip-attention-based up-sampling block. This block dynamically fuses features from various resolution points across the encoding and decoding layers. To mitigate the increase in network parameters and training time resulting from the complexity of the aforementioned blocks, we propose a novel shared position encoding strategy. This strategy allows various transformer blocks to share the same position information over the same resolution points, thereby reducing network parameters and training time without compromising accuracy.Experimental comparisons with existing methods on multiple datasets demonstrate the efficacy of SMTransformer and skip-attention-based up-sampling for point cloud processing tasks, including semantic segmentation and classification. In particular, we achieve state-of-the-art semantic segmentation results of 73.4% mIoU on S3DIS Area 5 and 62.4% mIoU on SWAN dataset
LLaVA-PruMerge: Adaptive Token Reduction for Efficient Large Multimodal Models
Venue: arXiv cs.CV 2026
Year: 2026

Large Multimodal Models (LMMs) have shown significant visual reasoning capabilities by connecting a visual encoder and a large language model. LMMs typically take in a fixed and large amount of visual tokens, such as the penultimate layer features in the CLIP visual encoder, as the prefix content. Recent LMMs incorporate more complex visual inputs, such as high-resolution images and videos, which further increases the number of visual tokens significantly. However, due to the inherent design of the Transformer architecture, the computational costs of these models tend to increase quadratically with the number of input tokens. To tackle this problem, we explore a token reduction mechanism that identifies significant spatial redundancy among visual tokens. In response, we propose PruMerge, a novel adaptive visual token reduction strategy that significantly reduces the number of visual tokens without compromising the performance of LMMs. Specifically, to metric the importance of each token, we exploit the sparsity observed in the visual encoder, characterized by the sparse distribution of attention scores between the class token and visual tokens. This sparsity enables us to dynamically select the most crucial visual tokens to retain. Subsequently, we cluster the selected (unpruned) tokens based on their key similarity and merge them with the unpruned tokens, effectively supplementing and enhancing their informational content. Empirically, when applied to LLaVA-1.5, our approach can compress the visual tokens by 14 times on average, and achieve comparable performance across diverse visual question-answering and reasoning tasks. Code and checkpoints are at https://llava-prumerge.github.io/.
Sweeping Arrangements of Non-Piercing Curves in Plane
Venue: arXiv cs.CG 2026
Year: 2026

Let $Γ$ be an arrangement of Jordan curves in the plane, i.e., simple closed curves in the plane. For any curve $γ\in Γ$, we denote the bounded region enclosed by $γ$ as $\tildeγ$. We say that $Γ$ is non-piercing if for any two curves $α, β\in Γ$, $\tildeα \,\setminus\, \tildeβ$ is connected. A non-piercing arrangement of curves generalizes a set of $2$-intersecting curves in which each pair of curves intersect in at most two points. Snoeyink and Hershberger (``Sweeping Arrangements of Curves'', SoCG '89) proved that if we are given an arrangement $Γ$ of $2$-intersecting curves and a {\em sweep} curve $γ\inΓ$, then the arrangement can be \emph{swept} by $γ$ while always maintaining the $2$-intersecting property of the curves in $Γ$. We generalize the result of Snoeyink and Hershberger to the setting of non-piercing arrangements. Given an arrangement $Γ$ of non-piercing curves, a sweep curve $γ\in Γ$, and a point $P$ in $\tildeγ$, we show that we can continuously shrink $γ$ to $P$ so that throughout the process, the arrangement remains non-piercing (except at a finite set of points in time where $γ$ crosses other curves), and $P$ lies in $\tildeγ$. We show that our arguments can be modified if $P$ lies outside $\tildeγ$, and we want to sweep $γ$ \emph{outwards} so that $P$ lies outside $\tildeγ$, and the arrangement remains non-piercing. As a second contribution, we give an alternate proof of the result of Snoeyink and Hershberger, and give several applications of our results to combinatorial and algorithmic questions including to the \emph{multi-hitting set} problem involving points and non-piercing regions.
PlayFutures: Imagining Civic Futures with AI and Puppets
Venue: arXiv cs.HC 2026
Year: 2026

Children are the builders of the future and crucial to how the technologies around us develop. They are not voters but are participants in how the public spaces in a city are used. Through a workshop designed around kids of age 9-12, we investigate if novel technologies like artificial intelligence can be integrated in existing ways of play and performance to 1) re-imagine the future of civic spaces, 2) reflect on these novel technologies in the process and 3) build ways of civic engagement through play. We do this using a blend AI image generation and Puppet making to ultimately build future scenarios, perform debate and discussion around the futures and reflect on AI, its role and potential in their process. We present our findings of how AI helped envision these futures, aid performances, and report some initial reflections from children about the technology.
Sentiment Analysis of Citations in Scientific Articles Using ChatGPT: Identifying Potential Biases and Conflicts of Interest
Venue: arXiv cs.DL 2026
Year: 2026

Scientific articles play a crucial role in advancing knowledge and informing research directions. One key aspect of evaluating scientific articles is the analysis of citations, which provides insights into the impact and reception of the cited works. This article introduces the innovative use of large language models, particularly ChatGPT, for comprehensive sentiment analysis of citations within scientific articles. By leveraging advanced natural language processing (NLP) techniques, ChatGPT can discern the nuanced positivity or negativity of citations, offering insights into the reception and impact of cited works. Furthermore, ChatGPT's capabilities extend to detecting potential biases and conflicts of interest in citations, enhancing the objectivity and reliability of scientific literature evaluation. This study showcases the transformative potential of artificial intelligence (AI)-powered tools in enhancing citation analysis and promoting integrity in scholarly research.
Stochastic Online Optimization for Cyber-Physical and Robotic Systems
Venue: arXiv cs.LG 2026
Year: 2026

We propose a novel gradient-based online optimization framework for solving stochastic programming problems that frequently arise in the context of cyber-physical and robotic systems. Our problem formulation accommodates constraints that model the evolution of a cyber-physical system, which has, in general, a continuous state and action space, is nonlinear, and where the state is only partially observed. We also incorporate an approximate model of the dynamics as prior knowledge into the learning process and show that even rough estimates of the dynamics can significantly improve the convergence of our algorithms. Our online optimization framework encompasses both gradient descent and quasi-Newton methods, and we provide a unified convergence analysis of our algorithms in a non-convex setting. We also characterize the impact of modeling errors in the system dynamics on the convergence rate of the algorithms. Finally, we evaluate our algorithms in simulations of a flexible beam, a four-legged walking robot, and in real-world experiments with a ping-pong playing robot.