GenAI4ELab/papercli-papers huggingface dataset:
EvoBrain: Dynamic Multi-Channel EEG Graph Modeling for Time-Evolving Brain Networks
Authors: Rikuto Kotoge; Zheng Chen; Tasuku Kimura; Yasuko Matsubara; Takufumi Yanagisawa; Haruhiko Kishima; Yasushi Sakurai
Venue: NeurIPS 2025 (NeurIPS 2025 spotlight)

Abstract: Dynamic GNNs, which integrate temporal and spatial features in Electroencephalography (EEG) data, have shown great potential in automating seizure detection.
However, fully capturing the underlying dynamics necessary to represent brain states, such as seizure and non-seizure, remains a non-trivial task and presents two fundamental challenges.
First, most existing dynamic GNN methods are built on temporally fixed static graphs, which fail to reflect the evolving nature of brain connectivity during seizure progression. 
Second, current efforts to jointly model temporal signals and graph structures and, more importantly, their interactions remain nascent, often resulting in inconsistent performance.
To address these challenges, we present the first theoretical analysis of these two problems, demonstrating the effectiveness and necessity of explicit dynamic modeling and time-then-graph dynamic GNN method.
Building on these insights, we propose EvoBrain, a novel seizure detection model that integrates a two-stream Mamba architecture with a GCN enhanced by Laplacian Positional Encoding, following neurological insights.
Moreover, EvoBrain incorporates explicitly dynamic graph structures, allowing both nodes and edges to evolve over time.
Our contributions include 
(a) a theoretical analysis proving the expressivity advantage of explicit dynamic modeling and time-then-graph over other approaches, 
(b) a novel and efficient model that significantly improves AUROC by 23\% and F1 score by 30\%, compared with the dynamic GNN baseline, and 
(c) broad evaluation of our method on the challenging early seizure prediction task.
Offline Goal-conditioned Reinforcement Learning with Quasimetric Representations
Authors: Vivek Myers; Bill Zheng; Benjamin Eysenbach; Sergey Levine
Venue: NeurIPS 2025 (NeurIPS 2025 poster)

Abstract: Approaches for goal-conditioned reinforcement learning (GCRL) often use
learned state representations to extract goal-reaching policies. Two
frameworks for representation structure have yielded particularly
effective GCRL algorithms: (1) *contrastive representations*, in which
methods learn "successor features" with a contrastive objective that
performs inference over future outcomes, and (2) *temporal distances*,
which link the (quasimetric) distance in representation space to the
transit time from states to goals. We propose an approach that unifies
these two frameworks, using the structure of a quasimetric
representation space (triangle inequality) with the right additional
constraints to learn successor representations that enable optimal
goal-reaching. Unlike past work, our approach is able to exploit a
**quasimetric** distance parameterization to learn **optimal**
goal-reaching distances, even with **suboptimal** data and in
**stochastic** environments. This gives us the best of both worlds: we
retain the stability and long-horizon capabilities of Monte Carlo
contrastive RL methods, while getting the free stitching capabilities of
quasimetric network parameterizations. On existing offline GCRL
benchmarks, our representation learning objective improves performance
on stitching tasks where methods based on contrastive learning struggle,
and on noisy, high-dimensional environments where methods based on
quasimetric networks struggle.
Evaluating the Inductive Abilities of Large Language Models: Why Chain-of-Thought Reasoning Sometimes Hurts More Than Helps
Authors: Haibo Jin; Peiyan Zhang; Man Luo; Haohan Wang
Venue: NeurIPS 2025 (NeurIPS 2025 poster)

Abstract: Large Language Models (LLMs) have shown remarkable progress across domains, yet their ability to perform inductive reasoning—inferring latent rules from sparse examples—remains limited. 
It is often assumed that chain-of-thought (CoT) prompting, as used in Large Reasoning Models (LRMs), enhances such reasoning. 
We investigate this assumption with creating four controlled, diagnostic game-based tasks—chess, Texas Hold’em, dice games, and blackjack—with hidden human-defined rules. 
We find that CoT reasoning can degrade inductive performance, with LRMs often underperforming their non-reasoning counterparts.

To explain this, we present a theoretical framework that reveals how reasoning steps can amplify error through three failure modes: incorrect sub-task decomposition, incorrect sub-task solving, and incorrect final answer summarization. 
Based on our theoretical and empirical analysis, we introduce structured interventions that adapt CoT generation according to our identified failure types. These interventions improve inductive accuracy without retraining. Our findings suggest that effective (CoT) reasoning depends not only on taking more steps but also on ensuring those steps are well-structured.
HOI-Dyn: Learning Interaction Dynamics for Human-Object Motion Diffusion
Authors: Lin Wu; Zhixiang Chen; Jianglin Lan
Venue: NeurIPS 2025 (NeurIPS 2025 poster)

Abstract: Generating realistic 3D human-object interactions (HOIs) remains a challenging task due to the difficulty of modeling detailed interaction dynamics. Existing methods treat human and object motions independently, resulting in physically implausible and causally inconsistent behaviors. In this work, we present HOI-Dyn, a novel framework that formulates HOI generation as a driver-responder system, where human actions drive object responses. At the core of our method is a lightweight transformer-based interaction dynamics model that explicitly predicts how objects should react to human motion. To further enforce consistency, we introduce a residual-based dynamics loss that mitigates the impact of dynamics prediction errors and prevents misleading optimization signals. The dynamics model is used only during training, preserving inference efficiency. Through extensive qualitative and quantitative experiments, we demonstrate that our approach not only enhances the quality of HOI generation but also establishes a feasible metric for evaluating the quality of generated interactions. Project website: https://wulin97.github.io/hoi-dyn
Tensor-Parallelism with Partially Synchronized Activations
Authors: Itay Lamprecht; Asaf Karnieli; Yair Hanani; Niv Giladi; Daniel Soudry
Venue: NeurIPS 2025 (NeurIPS 2025 poster)

Abstract: Training and inference of Large Language Models (LLMs) with tensor-parallelism requires substantial communication to synchronize activations. Our findings suggest that with a few minor adjustments to current practices, LLMs can be trained without fully synchronizing activations, reducing bandwidth demands. We name this “Communication-Aware Architecture for Tensor-parallelism” (CAAT-Net). We train a 7B parameter CAAT-Net model and show that tensor-parallel communication can be reduced by up to 50% with no significant drop in pretraining accuracy across nearly all evaluated benchmarks. We also experiment with smaller 130M and 1.1B models to show the robustness and scalability of our method. We find that, in some scenarios, validation loss can even improve when reducing communication. Finally, we demonstrate how CAAT-Net accelerates both training and inference workloads across various settings and model sizes.
Process vs. Outcome Reward: Which is Better for Agentic RAG Reinforcement Learning
Authors: Wenlin Zhang; Xiangyang Li; Kuicai Dong; Yichao Wang; Pengyue Jia; Xiaopeng Li; Yingyi Zhang; Derong Xu; Zhaocheng Du; Huifeng Guo; Ruiming Tang; Xiangyu Zhao
Venue: NeurIPS 2025 (NeurIPS 2025 poster)

Abstract: Retrieval-augmented generation (RAG) enhances large language models (LLMs) by integrating external knowledge, yet traditional RAG systems struggle with static workflows and limited adaptability for complex, multistep reasoning tasks. Agentic RAG systems, such as DeepResearch, address these issues through dynamic retrieval, iterative context refinement, and adaptive workflows. However, recent methods like Search-R1, which rely on outcome-based reinforcement learning, face challenges such as low exploration efficiency, gradient conflict, and sparse reward signals. To tackle these limitations, we introduce ReasonRAG, a novel method that leverages RAG-ProGUIDE—a high-quality dataset providing fine-grained, process-level rewards for query generation, evidence extraction, and answer generation. By employing process-supervised reinforcement learning, ReasonRAG enhances LLMs’ autonomous capabilities in search, query generation, evidence extraction, and answer synthesis. Experimental results show that ReasonRAG, utilizing RAG-ProGUIDE, outperforms existing approaches like Search-R1 and traditional RAG systems, achieving superior performance on five benchmark datasets with only 5k training instances—significantly fewer than the 90k required by Search-R1. Our code is available at https://github.com/Applied-Machine-Learning-Lab/ReasonRAG.
The Parameterized Complexity of Computing the VC-Dimension
Authors: Florent Foucaud; Harmender Gahlawat; Fionn Mc Inerney; Prafullkumar Tale
Venue: NeurIPS 2025 (NeurIPS 2025 poster)

Abstract: The VC-dimension is a well-studied and fundamental complexity measure of a set system (or hypergraph) that is central to many areas of machine learning. We establish several new results on the complexity of computing the VC-dimension. In particular, given a hypergraph $\mathcal{H}=(\mathcal{V},\mathcal{E})$, we prove that the naive $2^{\mathcal{O}(|\mathcal{V}|)}$-time algorithm is asymptotically tight under the Exponential Time Hypothesis (ETH). We then prove that the problem admits a $1$-additive fixed-parameter approximation algorithm when parameterized by the maximum degree of $\mathcal{H}$ and a fixed-parameter algorithm when parameterized by its dimension, and that these are essentially the only such exploitable structural parameters. Lastly, we consider a generalization of the problem, formulated using graphs, which captures the VC-dimension of both set systems and graphs. We design a $2^{\mathcal{O}(\texttt{tw}\cdot \log \texttt{tw})}\cdot |V|$-time algorithm for any graph $G=(V,E)$ of treewidth $\texttt{tw}$ (which, for a set system, applies to the treewidth of its incidence graph). This is in contrast with closely related problems that require a double-exponential dependency on the treewidth (assuming the ETH).
GASP: Efficient Black-Box Generation of Adversarial Suffixes for Jailbreaking LLMs
Authors: Advik Raj Basani; Xiao Zhang
Venue: NeurIPS 2025 (NeurIPS 2025 poster)

Abstract: LLMs have demonstrated impressive capabilities across various natural language processing tasks yet remain vulnerable to prompts, known as jailbreak attacks, carefully designed to bypass safety guardrails and elicit harmful responses. Traditional methods rely on manual heuristics that suffer from limited generalizability. Despite being automatic, optimization-based attacks often produce unnatural jailbreak prompts that can be easily detected by safety filters or require high computational costs due to discrete token optimization. This paper introduces *Generative Adversarial Suffix Prompter* (GASP), a novel automated framework that can efficiently generate human-readable jailbreak prompts in a fully black-box setting. In particular, GASP leverages latent Bayesian optimization to craft adversarial suffixes by efficiently exploring continuous latent spaces, gradually optimizing the suffix generator to improve attack efficacy while balancing prompt coherence via a targeted iterative refinement procedure. Through comprehensive experiments, we show that GASP can produce natural adversarial prompts, significantly improving jailbreak success, reducing training times, and accelerating inference speed, thus making it an efficient and scalable solution for red-teaming LLMs.
Limitations of Normalization in Attention
Authors: Timur Mudarisov; Mikhail Burtsev; Tatiana Petrova; Radu State
Venue: NeurIPS 2025 (NeurIPS 2025 poster)

Abstract: This paper investigates the limitations of the normalization in attention mechanisms. We begin with a theoretical framework that enables the identification of the model's selective ability and the geometric separation involved in token selection. Our analysis includes explicit bounds on distances and separation criteria for token vectors under softmax scaling. Through experiments with pre-trained GPT-2 model, we empirically validate our theoretical results and analyze key behaviors of the attention mechanism. Notably, we demonstrate that as the number of selected tokens increases, the model's ability to distinguish informative tokens declines, often converging toward a uniform selection pattern. We also show that gradient sensitivity under softmax normalization presents challenges during training, especially at low temperature settings. These findings advance current understanding of softmax-based attention mechanism and motivate the need for more robust normalization and selection strategies in future attention architectures.
Safe and Stable Control via Lyapunov-Guided Diffusion Models
Authors: Xiaoyuan Cheng; Xiaohang Tang; Yiming Yang
Venue: NeurIPS 2025 (NeurIPS 2025 poster)

Abstract: Diffusion models have made significant strides in recent years, exhibiting strong generalization capabilities in planning and control tasks. However, most diffusion-based policies remain focused on reward maximization or cost minimization, often overlooking critical aspects of safety and stability. In this work, we propose Safe and Stable Diffusion ($S^2$Diff), a model-based framework that explores how diffusion models can ensure safety and stability from a Lyapunov perspective. We demonstrate that $S^2$Diff eliminates the reliance on both complex gradient-based solvers (e.g., quadratic programming, non-convex solvers) and control-affine structures, leading to globally valid control policies driven by the learned certificate functions. Additionally, we uncover intrinsic connections between diffusion sampling and almost Lyapunov theory, enabling the use of trajectory-level control policies to learn better certificate functions for safety and stability guarantees. To validate our approach, we conduct experiments on a wide variety of dynamical control systems, where $S^2$Diff consistently outperforms both certificate-based controllers and model-based diffusion baselines in terms of safety, stability, and overall control performance.
Bridging Time and Linguistics: LLMs as Time Series Analyzer through Symbolization and Segmentation
Authors: Jianyang Qin; Chaoyang Li; Jinhao Cui; Lingzhi Wang; Zhao Liu; Qing Liao
Venue: NeurIPS 2025 (NeurIPS 2025 poster)

Abstract: Recent studies reveal that Large Language Models (LLMs) exhibit strong sequential reasoning capabilities, allowing them to replace specialized time-series models and serve as foundation models for complex time-series analysis. To activate the capabilities of LLMs for time-series tasks, numerous studies have attempted to bridge the gap between time series and linguistics by aligning textual representations with time-series patterns. However, it is a non-trivial endeavor to losslessly capture the infinite time-domain variability using natural language, leading to suboptimal alignment performance. Beyond representation, contextual differences, where semantics in time series are conveyed by consecutive points, unlike in text by individual tokens, are often overlooked by existing methods. To address these, we propose S$^2$TS-LLM, a simple yet effective framework to repurpose LLMs for universal time series analysis through the following two main paradigms: (i) a spectral symbolization paradigm transforms time series into frequency-domain representations characterized by a fixed number of components and prominent amplitudes, which enables a limited set of symbols to effectively abstract key frequency features; (ii) a contextual segmentation paradigm partitions the sequence into blocks based on temporal patterns and reassigns positional encodings accordingly, thereby mitigating the structural mismatch between time series and natural language. Together, these paradigms bootstrap the LLMs' perception of temporal patterns and structures, effectively bridging time series and linguistics. Extensive experiments show that S$^2$TS-LLM can serve as a powerful time series analyzer, outperforming state-of-the-art methods across time series tasks.
MIND: Material Interface Generation from UDFs for Non-Manifold Surface Reconstruction
Authors: Xuhui Chen; Fei Hou; Wencheng Wang; Hong Qin; Ying He
Venue: NeurIPS 2025 (NeurIPS 2025 poster)

Abstract: Unsigned distance fields (UDFs) are widely used in 3D deep learning due to their ability to represent shapes with arbitrary topology. While prior work has largely focused on learning UDFs from point clouds or multi-view images, extracting meshes from UDFs remains challenging, as the learned fields rarely attain exact zero distances. A common workaround is to reconstruct signed distance fields (SDFs) locally from UDFs to enable surface extraction via Marching Cubes. However, this often introduces topological artifacts such as holes or spurious components. Moreover, local SDFs are inherently incapable of representing non-manifold geometry, leading to complete failure in such cases. To address this gap, we propose MIND ($\mathrm{\underline{M}aterial}$ $\mathrm{\underline{I}nterface}$ $\mathrm{from}$ $\mathrm{\underline{N}on}$-$\mathrm{manifold}$ $\mathrm{\underline{D}istance}$ $\mathrm{fields}$), a novel algorithm for generating material interfaces directly from UDFs, enabling non-manifold mesh extraction from a global perspective. The core of our method lies in deriving a meaningful spatial partitioning from the UDF, where the target surface emerges as the interface between distinct regions. We begin by computing a two-signed local field to distinguish the two sides of manifold patches, and then extend this to a multi-labeled global field capable of separating all sides of a non-manifold structure. By combining this multi-labeled field with the input UDF, we construct material interfaces that support non-manifold mesh extraction via a multi-labeled Marching Cubes algorithm. Extensive experiments on UDFs generated from diverse data sources, including point cloud reconstruction, multi-view reconstruction, and medial axis transforms, demonstrate that our approach robustly handles complex non-manifold surfaces and significantly outperforms existing methods. The source code is available at https://github.com/jjjkkyz/MIND.
Text-to-Code Generation for Modular Building Layouts in Building Information Modeling
Authors: Yinyi WEI; Xiao LI
Venue: NeurIPS 2025 (NeurIPS 2025 poster)

Abstract: We present Text2MBL, a text-to-code generation framework that generates executable Building Information Modeling (BIM) code directly from textual descriptions of modular building layout (MBL) design. Unlike conventional layout generation approaches that operate in 2D space, Text2MBL produces fully parametric, semantically rich BIM layouts through on‑the‑fly code instantiation. To address MBLs' unique challenges due to their hierarchical three-tier structure: modules (physical building blocks), units (self-contained dwellings), and rooms (functional spaces), we developed an object-oriented code architecture and fine-tuned large language models to output structured action sequences in code format. To train and evaluate the framework, we curated a dataset of paired descriptions and ground truth layouts drawn from real‑world modular housing projects. Performance were assessed using metrics for executable validity, semantic fidelity, and geometric consistency. By tightly unifying natural language understanding with BIM code generation, Text2MBL establishes a scalable pipeline from high-level conceptual design to automation-ready modular construction workflows. Our implementation is available at https://github.com/CI3LAB/Text2MBL.
Distances for Markov chains from sample streams
Authors: Sergio Calo; Anders Jonsson; Gergely Neu; Ludovic Schwartz; Javier Segovia-Aguas
Venue: NeurIPS 2025 (NeurIPS 2025 poster)

Abstract: Bisimulation metrics are powerful tools for measuring similarities between stochastic processes, and specifically 
Markov chains. Recent advances have uncovered that bisimulation metrics are, in fact, optimal-transport distances, 
which has enabled the development of fast algorithms for computing such metrics with provable accuracy and 
runtime guarantees. However, these recent methods, as well as all previously known methods, assume full knowledge of the transition 
dynamics. This is often an impractical assumption in most real-world scenarios, where typically only sample trajectories are available.
In this work, we propose a stochastic optimization method that addresses this limitation
and estimates bisimulation metrics based on sample access, without requiring explicit transition models.
Our approach is derived from a new linear programming (LP) formulation of bisimulation metrics, which we solve using a 
stochastic primal-dual optimization method. We provide theoretical guarantees on the sample complexity of the algorithm 
and validate its effectiveness through a series of empirical evaluations.
Resolution of Simpson's paradox via the common cause principle
Authors: A. Hovhannisyan; Armen Allahverdyan
Venue: NeurIPS 2025 (NeurIPS 2025 poster)

Abstract: Simpson's paradox poses a challenge in probabilistic inference and decision-making. Our study revisits the paradox by re-estimating its frequency with an unbiased data generation process and reaffirms that it is not an artifact of deficient data collection. Thus, it can lead to incorrect recommendations in fields as diverse as statistics, psychology, and artificial intelligence. We show that the paradox can be resolved by assuming a minimal — though not necessarily observed — common cause (or screening) variable for the involved random variables. In our approach, conditioning on this minimal common cause establishes the correct association between events, which coincides with the conditioning (i.e., fine-grained) option of the original Simpson paradox. This resolution applies to both discrete cases of binary variables and continuous settings modeled by Gaussian variables. For a non-minimal common cause, the resolution of the paradox is possible, but detailed knowledge of the common cause is required. Our findings extend traditional understandings of the paradox and offer practical guidance for resolving apparent contradictions in probabilistic inference, ultimately enhancing decision-making processes. This point is illustrated by several examples.
Memory Mosaics at scale
Authors: Jianyu Zhang; Leon Bottou
Venue: NeurIPS 2025 (NeurIPS 2025 oral)

Abstract: Memory Mosaics, networks of associative memories, have demonstrated appealing compositional and in-context learning capabilities on medium-scale networks (GPT-2 scale) and synthetic small datasets. This work shows that these favorable properties remain when we scale memory mosaics to large language model sizes (llama-8B scale) and real-world datasets. 

To this end, we scale memory mosaics to 10B size, we train them on one trillion tokens, we introduce a couple architectural modifications (*memory mosaics v2*), we assess their capabilities across three evaluation dimensions: training-knowledge storage, new-knowledge storage, and in-context learning. 

Throughout the evaluation, memory mosaics v2 match transformers on the learning of training knowledge (first dimension) and significantly outperforms transformers on carrying out new tasks at inference time (second and third dimensions). These improvements cannot be easily replicated by simply increasing the training data for transformers. A memory mosaics v2 trained on one trillion tokens still perform better on these tasks than a transformer trained on eight trillion tokens.