GenAI4ELab/papercli-papers huggingface dataset:
Multimodal Disease Progression Modeling via Spatiotemporal Disentanglement and Multiscale Alignment
Authors: Chen Liu; Wenfang Yao; Kejing Yin; William K. Cheung; Jing Qin
Venue: NeurIPS 2025 (NeurIPS 2025 spotlight)

Abstract: Longitudinal multimodal data, including electronic health records (EHR) and sequential chest X-rays (CXRs), is critical for modeling disease progression, yet remains underutilized due to two key challenges: (1) redundancy in consecutive CXR sequences, where static anatomical regions dominate over clinically-meaningful dynamics, and (2) temporal misalignment between sparse, irregular imaging and continuous EHR data. We introduce $\texttt{DiPro}$, a novel framework that addresses these challenges through region-aware disentanglement and multi-timescale alignment. First, we disentangle static (anatomy) and dynamic (pathology progression) features in sequential CXRs, prioritizing disease-relevant changes. Second, we hierarchically align these static and dynamic CXR features with asynchronous EHR data via local (pairwise interval-level) and global (full-sequence) synchronization to model coherent progression pathways. Extensive experiments on the MIMIC dataset demonstrate that $\texttt{DiPro}$ could effectively extract temporal clinical dynamics and achieve state-of-the-art performance on both disease progression identification and general ICU prediction tasks.
On the Hardness of Conditional Independence Testing In Practice
Authors: Zheng He; Roman Pogodin; Yazhe Li; Namrata Deka; Arthur Gretton; Danica J. Sutherland
Venue: NeurIPS 2025 (NeurIPS 2025 spotlight)

Abstract: Tests of conditional independence (CI) underpin a number of important problems in machine learning and statistics, from causal discovery to evaluation of predictor fairness and out-of-distribution robustness. Shah and Peters (2020) showed that, contrary to the unconditional case, no universally finite-sample valid test can ever achieve nontrivial power. While informative, this result (based on “hiding” dependence) does not seem to explain the frequent practical failures observed with popular CI tests. We investigate the Kernel-based Conditional Independence (KCI) test – of which we show the Generalized Covariance Measure underlying many recent tests is _nearly_ a special case – and identify the major factors underlying its practical behavior. We highlight the key role of errors in the conditional mean embedding estimate for the Type I error, while pointing out the importance of selecting an appropriate conditioning kernel (not recognized in previous work) as being necessary for good test power but also tending to inflate Type I error.
SPINT: Spatial Permutation-Invariant Neural Transformer for Consistent Intracortical Motor Decoding
Authors: Trung Le; Hao Fang; Jingyuan Li; Tung Nguyen; Lu Mi; Amy L Orsborn; Uygar Sümbül; Eli Shlizerman
Venue: NeurIPS 2025 (NeurIPS 2025 poster)

Abstract: Intracortical Brain-Computer Interfaces (iBCI) decode behavior from neural population activity to restore motor functions and communication abilities in individuals with motor impairments. A central challenge for long-term iBCI deployment is the nonstationarity of neural recordings, where the composition and tuning profiles of the recorded populations are unstable across recording sessions. Existing approaches attempt to address this issue by explicit alignment techniques; however, they rely on fixed neural identities and require test-time labels or parameter updates, limiting their generalization across sessions and imposing additional computational burden during deployment. In this work, we address the problem of cross-session nonstationarity in long-term iBCI systems and introduce SPINT - a Spatial Permutation-Invariant Neural Transformer framework for behavioral decoding that operates directly on unordered sets of neural units. Central to our approach is a novel context-dependent positional embedding scheme that dynamically infers unit-specific identities, enabling flexible generalization across recording sessions. SPINT supports inference on variable-size populations and allows few-shot, gradient-free adaptation using a small amount of unlabeled data from the test session. We evaluate SPINT on three multi-session datasets from the FALCON Benchmark, covering continuous motor decoding tasks in human and non-human primates. SPINT demonstrates robust cross-session generalization, outperforming existing zero-shot and few-shot unsupervised baselines while eliminating the need for test-time alignment and fine-tuning. Our work contributes an initial step toward a robust and scalable neural decoding framework for long-term iBCI applications.
On Evaluating LLM Alignment by Evaluating LLMs as Judges
Authors: Yixin Liu; Pengfei Liu; Arman Cohan
Venue: NeurIPS 2025 (NeurIPS 2025 poster)

Abstract: Alignment with human preferences is an important evaluation aspect of LLMs, requiring them to be helpful, honest, safe, and to precisely follow human instructions. Evaluating large language models' (LLMs) alignment typically involves directly assessing their open-ended responses, requiring human annotators or strong LLM judges. Conversely, LLMs themselves have also been extensively evaluated as judges for assessing alignment.  In this work, we examine the relationship between LLMs' generation and evaluation capabilities in aligning with human preferences. To this end, we first conduct a comprehensive analysis of the generation-evaluation consistency (GE-consistency) among various LLMs, revealing a strong correlation between their generation and evaluation capabilities when evaluated by a strong LLM preference oracle (GPT-4o). Utilizing this finding, we propose a benchmarking paradigm that measures LLM alignment with human preferences without directly evaluating their generated outputs, instead assessing LLMs in their role as evaluators. Our evaluation shows that our proposed benchmark, AlignEval, matches or surpasses widely used automatic LLM evaluation benchmarks, such as AlpacaEval and Arena-Hard, in capturing human preferences when ranking LLMs. Our study offers valuable insights into the connection between LLMs' generation and evaluation capabilities, and introduces a benchmark that assesses alignment without directly evaluating model outputs.
Encoder-Decoder Diffusion Language Models for Efficient Training and Inference
Authors: Marianne Arriola; Yair Schiff; Hao Phung; Aaron Gokaslan; Volodymyr Kuleshov
Venue: NeurIPS 2025 (NeurIPS 2025 poster)

Abstract: Discrete diffusion models enable parallel token sampling for faster inference than autoregressive approaches. However, prior diffusion models use a decoder-only architecture, which requires sampling algorithms that invoke the full network at every denoising step and incur high computational cost. Our key insight is that discrete diffusion models perform two types of computation: 1) representing clean tokens and 2) denoising corrupted tokens, which enables us to use separate modules for each task. We propose an encoder-decoder architecture to accelerate discrete diffusion inference, which relies on an encoder to represent clean tokens and a lightweight decoder to iteratively refine a noised sequence. We also show that this architecture enables faster training of block diffusion models, which partition sequences into blocks for better quality and are commonly used in diffusion language model inference. We introduce a framework for **E**fficient **E**ncoder-**D**ecoder **D**iffusion (E2D2), consisting of an architecture with specialized training and sampling algorithms, and we show that E2D2 achieves superior trade-offs between generation quality and inference throughput on summarization, translation, and mathematical reasoning tasks.
Robust Egocentric Referring Video Object Segmentation via Dual-Modal Causal Intervention
Authors: Haijing Liu; Zhiyuan Song; Hefeng Wu; Tao Pu; Keze Wang; Liang Lin
Venue: NeurIPS 2025 (NeurIPS 2025 poster)

Abstract: Egocentric Referring Video Object Segmentation (Ego-RVOS) aims to segment the specific object actively involved in a human action, as described by a language query, within first-person videos. This task is critical for understanding egocentric human behavior. However, achieving such segmentation robustly is challenging due to ambiguities inherent in egocentric videos and biases present in training data. Consequently, existing methods often struggle, learning spurious correlations from skewed object-action pairings in datasets and fundamental visual confounding factors of the egocentric perspective, such as rapid motion and frequent occlusions. To address these limitations, we introduce Causal Ego-REferring Segmentation (CERES), a plug-in causal framework that adapts strong, pre-trained RVOS backbones to the egocentric domain. CERES implements dual-modal causal intervention: applying backdoor adjustment principles to counteract language representation biases learned from dataset statistics, and leveraging front-door adjustment concepts to address visual confounding by intelligently integrating semantic visual features with geometric depth information guided by causal principles, creating representations more robust to egocentric distortions. Extensive experiments demonstrate that CERES achieves state-of-the-art performance on Ego-RVOS benchmarks, highlighting the potential of applying causal reasoning to build more reliable models for broader egocentric video understanding.
Are Large Language Models Sensitive to the Motives Behind Communication?
Authors: Addison J. Wu; Ryan Liu; Kerem Oktar; Theodore Sumers; Thomas L. Griffiths
Venue: NeurIPS 2025 (NeurIPS 2025 poster)

Abstract: Human communication is $\textit{motivated}$: people speak, write, and create content with a particular communicative intent in mind. As a result, information that large language models (LLMs) and AI agents process is inherently framed by humans' intentions and incentives. People are adept at navigating such nuanced information: we routinely identify benevolent or self-serving motives in order to decide what statements to trust. For LLMs to be effective in the real world, they too must critically evaluate content by factoring in the motivations of the source---for instance, weighing the credibility of claims made in a sales pitch. In this paper, we undertake a comprehensive study of whether LLMs have this capacity for $\textit{motivational vigilance}$. We first employ controlled experiments from cognitive science to verify that LLMs' behavior is consistent with rational models of learning from motivated testimony, and find they successfully discount information from biased sources in a human-like manner. We then extend our evaluation to sponsored online adverts, a more naturalistic reflection of LLM agents' information ecosystems. In these settings, we find that LLMs' inferences do not track the rational models' predictions nearly as closely---partly due to additional information that distracts them from vigilance-relevant considerations. However, a simple steering intervention that boosts the salience of intentions and incentives substantially increases the correspondence between LLMs and the rational model. These results suggest that LLMs possess a basic sensitivity to the motivations of others, but generalizing to novel real-world settings will require further improvements to these models.
Predicting Functional Brain Connectivity with Context-Aware Deep Neural Networks
Authors: Alexander Ratzan; Sidharth Goel; Junhao Wen; Christos Davatzikos; Erdem Varol
Venue: NeurIPS 2025 (NeurIPS 2025 poster)

Abstract: Spatial location and molecular interactions have long been linked to the connectivity
patterns of neural circuits. Yet, at the macroscale of human brain networks,
the interplay between spatial position, gene expression, and connectivity remains
incompletely understood. Recent efforts to map the human transcriptome and
connectome have yielded spatially resolved brain atlases, however modeling the
relationship between high-dimensional transcriptomic data and connectivity while
accounting for inherent spatial confounds presents a significant challenge. In this
paper, we present the first deep learning approaches for predicting whole-brain
functional connectivity from gene expression and regional spatial coordinates, including
our proposed Spatiomolecular Transformer (SMT). SMT explicitly models
biological context by tokenizing genes based on their transcription start site (TSS)
order to capture multi-scale genomic organization, and incorporating regional
3D spatial location via a dedicated context [CLS] token within its multi-head
self-attention mechanism. We rigorously benchmark context-aware neural networks,
including SMT and a single-gene resolution Multilayer-Perceptron (MLP),
to established rules-based and bilinear methods. Crucially, to ensure that learned
relationships in any model are not mere artifacts of spatial proximity, we introduce
novel spatiomolecular null maps preserving key transcriptomic autocorrelation
structure. Context-aware neural networks outperform linear methods, significantly
exceed our stringent null map estimates, and generalize across diverse connectomic
datasets and parcellation resolutions. Together, these findings demonstrate a
strong, predictable link between the spatial distributions of gene expression and
functional brain network architecture, and establish a rigorously validated deep
learning framework for decoding this relationship. Code to reproduce our results is
available at: github.com/neuroinfolab/GeneEx2Conn.
Real-Time Execution of Action Chunking Flow Policies
Authors: Kevin Black; Manuel Y Galliker; Sergey Levine
Venue: NeurIPS 2025 (NeurIPS 2025 poster)

Abstract: Modern AI systems, especially those interacting with the physical world, increasingly require real-time performance. However, the high latency of state-of-the-art generalist models, including recent vision-language-action models (VLAs), poses a significant challenge. While action chunking has enabled temporal consistency in high-frequency control tasks, it does not fully address the latency problem, leading to pauses or out-of-distribution jerky movements at chunk boundaries. This paper presents a novel inference-time algorithm that enables smooth asynchronous execution of action chunking policies. Our method, real-time chunking (RTC), is applicable to any diffusion- or flow-based VLA out of the box with no retraining. It generates the next action chunk while executing the current one, "freezing" actions guaranteed to execute and "inpainting" the rest. To test RTC, we introduce a new benchmark of 12 highly dynamic tasks in the Kinetix simulator, as well as evaluate 6 challenging real-world bimanual manipulation tasks. Results demonstrate that RTC is fast, performant, and uniquely robust to inference delay, significantly improving task throughput and enabling success in precise tasks --- such as lighting a match --- even in the presence of extreme latency.
Memory Decoder: A Pretrained, Plug-and-Play Memory for Large Language Models
Authors: Jiaqi Cao; Jiarui Wang; Rubin Wei; Qipeng Guo; Kai Chen; Bowen Zhou; Zhouhan Lin
Venue: NeurIPS 2025 (NeurIPS 2025 poster)

Abstract: Large Language Models (LLMs) have shown strong abilities in general language tasks, yet adapting them to specific domains remains a challenge.
Current method like Domain Adaptive Pretraining (DAPT) requires costly full-parameter training and suffers from catastrophic forgetting.
Meanwhile, Retrieval-Augmented Generation (RAG) introduces substantial inference latency due to expensive nearest-neighbor searches and longer context.
This paper introduces \textit{Memory Decoder}, a plug-and-play pretrained memory that enables efficient domain adaptation without changing the original model's parameters.
Memory Decoder employs a small transformer decoder that learns to imitate the behavior of an external non-parametric retriever.
Once trained, Memory Decoder can be seamlessly integrated with any pretrained language model that shares the same tokenizer, requiring no model-specific modifications.
Experimental results demonstrate that Memory Decoder enables effective adaptation of various Qwen and Llama models to three distinct specialized domains: biomedicine, finance, and law, reducing perplexity by an average of 6.17 points.
Overall, Memory Decoder introduces a novel paradigm centered on a specially pretrained memory component designed for domain-specific adaptation. This memory architecture can be integrated in a plug-and-play manner, consistently enhancing performance across multiple models within the target domain.
Emergence and Evolution of Interpretable Concepts in Diffusion Models
Authors: Berk Tinaz; Zalan Fabian; Mahdi Soltanolkotabi
Venue: NeurIPS 2025 (NeurIPS 2025 spotlight)

Abstract: Diffusion models have become the go-to method for text-to-image generation, producing high-quality images from pure noise. However, the inner workings of diffusion models is still largely a mystery due to their black-box nature and complex, multi-step generation process. Mechanistic interpretability techniques, such as Sparse Autoencoders (SAEs), have been successful in understanding and steering the behavior of large language models at scale. However, the great potential of SAEs has not yet been applied toward gaining insight into the intricate generative process of diffusion models. In this work, we leverage the SAE framework to probe the inner workings of a popular text-to-image diffusion model, and uncover a variety of human-interpretable concepts in its activations. Interestingly, we find that *even before the first reverse diffusion step* is completed, the final composition of the scene can be predicted surprisingly well by looking at the spatial distribution of activated concepts. Moreover, going beyond correlational analysis, we design intervention techniques aimed at manipulating image composition and style, and demonstrate that (1) in early stages of diffusion image composition can be effectively controlled, (2) in the middle stages image composition is finalized, however stylistic interventions are effective, and (3) in the final stages only minor textural details are subject to change.
Prompted Policy Search: Reinforcement Learning through Linguistic and Numerical Reasoning in LLMs
Authors: Yifan Zhou; Sachin Grover; Mohamed El Mistiri; Kamalesh Kalirathinam; Pratyush Kerhalkar; Swaroop Mishra; Neelesh Kumar; Sanket Gaurav; Oya Aran; Heni Ben Amor
Venue: NeurIPS 2025 (NeurIPS 2025 poster)

Abstract: Reinforcement Learning (RL) traditionally relies on scalar reward signals, limiting its ability to leverage the rich semantic knowledge often available in real-world tasks. In contrast, humans learn efficiently by combining numerical feedback with language, prior knowledge, and common sense. We introduce Prompted Policy Search (ProPS), a novel RL method that unifies numerical and linguistic reasoning within a single framework. Unlike prior work that augments existing RL components with language, ProPS places a large language model (LLM) at the center of the policy optimization loop—directly proposing policy updates based on both reward feedback and natural language input. We show that LLMs can perform numerical optimization in-context, and that incorporating semantic signals, such as goals, constraints, and strategy hints can lead to more informed exploration and sample-efficient learning. ProPS is evaluated across 15 Gymnasium tasks, spanning classic control, Atari games, and MuJoCo environments, and compared to seven widely-adopted RL algorithms (e.g., PPO, SAC, TRPO). It outperforms all baselines on 8 out of 15 tasks and demonstrates substantial gains when provided with domain knowledge. These results highlight the potential of unifying semantics and numerics for transparent, generalizable, and human-aligned reinforcement learning.
Adversarial Robustness of Nonparametric Regression
Authors: Parsa Moradi; Hanzaleh Akbari Nodehi; Mohammad Ali Maddah-Ali
Venue: NeurIPS 2025 (NeurIPS 2025 poster)

Abstract: In this paper, we investigate the adversarial robustness of nonparametric regression, a fundamental problem in machine learning, under the setting where an adversary can arbitrarily corrupt a subset of the input data. While the robustness of parametric regression has been extensively studied, its nonparametric counterpart remains largely unexplored. We characterize the adversarial robustness in nonparametric regression, assuming the regression function belongs to the second-order Sobolev space (i.e., it is square integrable up to its second derivative). 

The contribution of this paper is two-fold: (i) we establish a minimax lower bound on the estimation error, revealing a fundamental limit that no estimator can overcome, and (ii) we show that, perhaps surprisingly, the classical smoothing spline estimator, when properly regularized, exhibits robustness against adversarial corruption. These results imply that if $o(n)$ out of $n$ samples are corrupted, the estimation error of the smoothing spline vanishes as $n \to \infty$. On the other hand, when a constant fraction of the data is corrupted, no estimator can guarantee vanishing estimation error, implying the optimality of the smoothing spline in terms of maximum tolerable number of corrupted samples.
Training Robust Graph Neural Networks by Modeling Noise Dependencies
Authors: Yeonjun In; Kanghoon Yoon; Sukwon Yun; Kibum Kim; Sungchul Kim; Chanyoung Park
Venue: NeurIPS 2025 (NeurIPS 2025 poster)

Abstract: In real-world applications, node features in graphs often contain noise from various sources, leading to significant performance degradation in GNNs. Although several methods have been developed to enhance robustness, they rely on the unrealistic assumption that noise in node features is independent of the graph structure and node labels, thereby limiting their applicability. To this end, we introduce a more realistic noise scenario, dependency-aware noise on graphs (DANG), where noise in node features create a chain of noise dependencies that propagates to the graph structure and node labels. We propose a novel robust GNN, DA-GNN, which captures the causal relationships among variables in the data generating process (DGP) of DANG using variational inference. In addition, we present new benchmark datasets that simulate DANG in real-world applications, enabling more practical research on robust GNNs. Extensive experiments demonstrate that DA-GNN consistently outperforms existing baselines across various noise scenarios, including both DANG and conventional noise models commonly considered in this field.
Learning-Augmented Streaming Algorithms for Correlation Clustering
Authors: Yinhao Dong; Shan Jiang; Shi Li; Pan Peng
Venue: NeurIPS 2025 (NeurIPS 2025 poster)

Abstract: We study streaming algorithms for Correlation Clustering. Given a graph as an arbitrary-order stream of edges, with each edge labeled as positive or negative, the goal is to partition the vertices into disjoint clusters, such that the number of disagreements is minimized. In this paper, we give the first learning-augmented streaming algorithms for the problem on both complete and general graphs, improving the best-known space-approximation tradeoffs. Based on the works of Cambus et al. (SODA'24) and Ahn et al. (ICML'15), our algorithms use the predictions of pairwise distances between vertices provided by a predictor. For complete graphs, our algorithm achieves a better-than-$3$ approximation under good prediction quality, while using $\tilde{O}(n)$ total space. For general graphs, our algorithm achieves an $O(\log |E^-|)$ approximation under good prediction quality using $\tilde{O}(n)$ total space, improving the best-known non-learning algorithm in terms of space efficiency. Experimental results on synthetic and real-world datasets demonstrate the superiority of our proposed algorithms over their non-learning counterparts.