GenAI4ELab/papercli-papers huggingface dataset:
ARGenSeg: Image Segmentation with Autoregressive Image Generation Model
Authors: Xiaolong Wang; Lixiang Ru; Ziyuan Huang; Kaixiang Ji; DanDan Zheng; Jingdong Chen; JUN ZHOU
Venue: NeurIPS 2025 (NeurIPS 2025 poster)

Abstract: We propose a novel AutoRegressive Generation-based paradigm for image Segmentation (ARGenSeg), achieving multimodal understanding and pixel-level perception within a unified framework. 
Prior works integrating image segmentation into multimodal large language models (MLLMs) typically employ either boundary points representation or dedicated segmentation heads.
These methods rely on discrete representations or semantic prompts fed into task-specific decoders, which limits the ability of the MLLM to capture fine-grained visual details.
To address these challenges, 
we introduce a segmentation framework for MLLM based on image generation, which naturally produces dense masks for target objects.
We leverage MLLM to output visual tokens and detokenize them into images using an universal VQ-VAE, 
making the segmentation fully dependent on the pixel-level understanding of the MLLM.
To reduce inference latency, 
we employ a next-scale-prediction strategy to generate required visual tokens in parallel.
Extensive experiments demonstrate that our method surpasses prior state-of-the-art approaches on multiple segmentation datasets with a remarkable boost in inference speed, while maintaining strong understanding capabilities.
From Kolmogorov to Cauchy: Shallow XNet Surpasses KANs
Authors: Xin Li; Xiaotao Zheng; Zhihong Jeff Xia
Venue: NeurIPS 2025 (NeurIPS 2025 poster)

Abstract: We study a shallow variant of XNet, a neural architecture whose activation 
functions are derived from the Cauchy integral formula. While prior work 
focused on deep variants, we show that even a single-layer XNet exhibits 
near-exponential approximation rates—exceeding the polynomial bounds of 
MLPs and spline-based networks such as Kolmogorov–Arnold Networks (KANs).

Empirically, XNet reduces approximation error by over 600× on discontinuous 
functions, achieves up to 20,000× lower residuals in physics-informed PDEs, 
and improves policy accuracy and sample efficiency in PPO-based reinforcement 
learning—while maintaining comparable or better computational efficiency 
than KAN baselines.

These results demonstrate that expressive approximation can stem from 
principled activation design rather than depth alone, offering a compact, 
theoretically grounded alternative for function approximation, scientific 
computing, and control.
DNA-DetectLLM: Unveiling AI-Generated Text via a DNA-Inspired Mutation-Repair Paradigm
Authors: Xiaowei Zhu; Yubing Ren; Fang Fang; Qingfeng Tan; Shi Wang; Yanan Cao
Venue: NeurIPS 2025 (NeurIPS 2025 spotlight)

Abstract: The rapid advancement of large language models (LLMs) has blurred the line between AI-generated and human-written text. This progress brings societal risks such as misinformation, authorship ambiguity, and intellectual property concerns, highlighting the urgent need for reliable AI-generated text detection methods. However, recent advances in generative language modeling have resulted in significant overlap between the feature distributions of human-written and AI-generated text, blurring classification boundaries and making accurate detection increasingly challenging. To address the above challenges, we propose a DNA-inspired perspective, leveraging a repair-based process to directly and interpretably capture the intrinsic differences between human-written and AI-generated text. Building on this perspective, we introduce **DNA-DetectLLM**, a zero-shot detection method for distinguishing AI-generated and human-written text. The method constructs an ideal AI-generated sequence for each input, iteratively repairs non-optimal tokens, and quantifies the cumulative repair effort as an interpretable detection signal. Empirical evaluations demonstrate that our method achieves state-of-the-art detection performance and exhibits strong robustness against various adversarial attacks and input lengths. Specifically, DNA-DetectLLM achieves relative improvements of **5.55\%** in AUROC and **2.08\%** in F1 score across multiple public benchmark datasets. Code and data are available at https://github.com/Xiaoweizhu57/DNA-DetectLLM.
Vertical Federated Feature Screening
Authors: Huajun Yin; Liyuan Wang; Yingqiu Zhu; Liping Zhu; Danyang Huang
Venue: NeurIPS 2025 (NeurIPS 2025 poster)

Abstract: With the rapid development of the big data era, Vertical Federated Learning (VFL) has been widely applied to enable data collaboration while ensuring privacy protection. However, the ultrahigh dimensionality of features and the sparse data structures inherent in large-scale datasets introduce significant computational complexity. In this paper, we propose the Vertical Federated Feature Screening (VFS) algorithm, which effectively reduces computational, communication, and encryption costs. VFS is a two-stage feature screening procedure that proceeds from coarse to fine: the first stage quickly filters out irrelevant feature groups, followed by a more refined screening of individual features. It significantly reduces the resource demands of downstream tasks such as secure joint modeling or federated feature selection. This efficiency is particularly beneficial in scenarios with ultrahigh feature dimensionality or severe class imbalance in the response variable. The statistical and computational properties of VFS are rigorously established. Numerical simulations and real-world applications demonstrate its superior performance.
Task-Specific Data Selection for Instruction Tuning via Monosemantic Neuronal Activations
Authors: Da Ma; Gonghu Shang; Zhi Chen; Libo Qin; Yijie LUO; Hongshen Xu; Lei Pan; Shuai Fan; Kai Yu; Lu Chen
Venue: NeurIPS 2025 (NeurIPS 2025 poster)

Abstract: Instruction tuning improves the ability of large language models (LLMs) to follow diverse human instructions, but achieving strong performance on specific target tasks remains challenging. A critical bottleneck is selecting the most relevant data to maximize task-specific performance. Existing data selection approaches include unstable influence-based methods and more stable distribution alignment methods, the latter of which critically rely on the underlying sample representation. In practice, most distribution alignment methods, from shallow features (e.g., BM25) to neural embeddings (e.g., BGE, LLM2Vec), may fail to capture how the model internally processes samples. To bridge this gap, we adopt a model-centric strategy in which each sample is represented by its neuronal activation pattern in the model, directly reflecting internal computation. However, directly using raw neuron activations leads to spurious similarity between unrelated samples due to neuron polysemanticity, where a single neuron may respond to multiple, unrelated concepts. To address this, we employ sparse autoencoders to disentangle polysemantic activations into sparse, monosemantic representations, and introduce a dedicated similarity metric for this space to better identify task-relevant data. Comprehensive experiments across multiple instruction datasets, models, tasks, and selection ratios show that our approach consistently outperforms existing data selection baselines in both stability and task-specific performance.
Signal and Noise: A Framework for Reducing Uncertainty in Language Model Evaluation
Authors: David Heineman; Valentin Hofmann; Ian Magnusson; Yuling Gu; Noah A. Smith; Hannaneh Hajishirzi; Kyle Lo; Jesse Dodge
Venue: NeurIPS 2025 (NeurIPS 2025 spotlight)

Abstract: Developing large language models is expensive and often involves making decisions with small experiments, typically by evaluating on large, multi-task evaluation suites. In this work, we analyze specific properties which make a benchmark more reliable and useful for such decisions, and interventions to design higher-quality evaluation benchmarks. We introduce two key metrics that show differences in current benchmarks: signal, a benchmark’s ability to separate better models from worse models, and noise, a benchmark’s sensitivity to random variability between training steps. We demonstrate that benchmarks with a better signal-to-noise ratio are more reliable when making decisions at small scale, and those with less noise have lower scaling law prediction error.  These results suggest that improving signal or noise will lead to more useful benchmarks, so we introduce four interventions designed to directly affect signal or noise.  For example, we propose that switching to a  metric that has better signal and noise (e.g., perplexity rather than accuracy) leads to better reliability and scaling law error. We also find that filtering noisy benchmarks such that they have better signal-to-noise ratio leads to more reliable evaluations. We also find that averaging the output of a model's checkpoints to reduce noise leads to consistent improvements. We conclude by recommending that those creating new benchmarks, or selecting which existing benchmarks to use, aim for high signal and low noise.  We use 30 benchmarks for these experiments, and 465 open-weight language models from 60M to 32B parameters, resulting in a new, publicly available dataset of 50K evaluation benchmark results, totaling 200M instances.
SD-KDE: Score-Debiased Kernel Density Estimation
Authors: Elliot L Epstein; Rajat Vadiraj Dwaraknath; Thanawat Sornwanee; John Winnicki; Jerry Weihong Liu
Venue: NeurIPS 2025 (NeurIPS 2025 poster)

Abstract: We propose a method for density estimation that leverages an estimated score function to debias kernel density estimation (SD-KDE).
In our approach, each data point is adjusted by taking a single step along the score function with a specific choice of step size, followed by standard KDE with a modified bandwidth.
The step size and modified bandwidth are chosen to remove the leading order bias in the KDE, improving the asymptotic convergence rate.
Our experiments on synthetic tasks in 1D, 2D and on MNIST, demonstrate that our proposed SD-KDE method significantly reduces the mean integrated squared error compared to the standard Silverman KDE, even with noisy estimates in the score function.
These results underscore the potential of integrating score-based corrections into nonparametric density estimation.
Temporal Logic-Based Multi-Vehicle Backdoor Attacks against Offline RL Agents in End-to-end Autonomous Driving
Authors: Xuan Chen; Shiwei Feng; Zikang Xiong; Shengwei An; Yunshu Mao; Lu Yan; Guanhong Tao; Wenbo Guo; Xiangyu Zhang
Venue: NeurIPS 2025 (NeurIPS 2025 poster)

Abstract: Assessing the safety of autonomous driving (AD) systems against security threats, particularly backdoor attacks, is a stepping stone for real-world deployment. However, existing works mainly focus on pixel-level triggers which are impractical to deploy in the real world. We address this gap by introducing a novel backdoor attack against the end-to-end AD systems that leverage one or more other vehicles' trajectories as triggers. To generate precise trigger trajectories, we first use temporal logic (TL) specifications to define the behaviors of attacker vehicles. Configurable behavior models are then used to generate these trajectories, which are quantitatively evaluated and iteratively refined based on the TL specifications. We further develop a negative training strategy by incorporating patch trajectories that are similar to triggers but are designated not to activate the backdoor. 
It enhances the stealthiness of the attack and refines the system’s responses to trigger scenarios. 
Through extensive experiments on 5 offline reinforcement learning (RL) driving agents with 6 trigger patterns and target actions combinations, we demonstrate the flexibility and effectiveness of our proposed attack, showing the under-exploration of existing end-to-end AD systems' vulnerabilities to such trajectory-based backdoor attacks.
Videos of our attack are available at: https://sites.google.com/view/tlbackdoor/home.
AANet: Virtual Screening under Structural Uncertainty via Alignment and Aggregation
Authors: Wenyu Zhu; Jianhui Wang; Bowen Gao; Yinjun Jia; Haichuan Tan; Ya-Qin Zhang; Wei-Ying Ma; Yanyan Lan
Venue: NeurIPS 2025 (NeurIPS 2025 poster)

Abstract: Virtual screening (VS) is a critical component of modern drug discovery, yet most existing methods—whether physics-based or deep learning-based—are developed around *holo* protein structures with known ligand-bound pockets. Consequently, their performance degrades significantly on *apo* or predicted structures such as those from AlphaFold2, which are more representative of real-world early-stage drug discovery, where pocket information is often missing. In this paper, we introduce an alignment-and-aggregation framework to enable accurate virtual screening under structural uncertainty. Our method comprises two core components: (1) a tri-modal contrastive learning module that aligns representations of the ligand, the *holo* pocket, and cavities detected from structures, thereby enhancing robustness to pocket localization error; and (2) a cross-attention based adapter for dynamically aggregating candidate binding sites, enabling the model to learn from activity data even without precise pocket annotations. We evaluated our method on a newly curated benchmark of *apo* structures, where it significantly outperforms state-of-the-art methods in blind apo setting, improving the early enrichment factor (EF1\%) from 11.75 to 37.19. Notably, it also maintains strong performance on *holo* structures. These results demonstrate the promise of our approach in advancing first-in-class drug discovery, particularly in scenarios lacking experimentally resolved protein-ligand complexes. Our implementation is publicly available at [https://github.com/Wiley-Z/AANet](https://github.com/Wiley-Z/AANet).
Tradeoffs between Mistakes and ERM Oracle Calls in Online and Transductive Online Learning
Authors: Idan Attias; Steve Hanneke; Arvind Ramaswami
Venue: NeurIPS 2025 (NeurIPS 2025 spotlight)

Abstract: We study online and transductive online learning in settings where the learner can interact with the concept class only via Empirical Risk Minimization (ERM) or weak consistency oracles on arbitrary subsets of the instance domain. This contrasts with standard online models, where the learner has full knowledge of the concept class. The ERM oracle returns a hypothesis that minimizes the loss on a given subset, while the weak consistency oracle returns only a binary signal indicating whether the subset is realizable by a concept in the class. The learner's performance is measured by the number of mistakes and oracle calls.

In the standard online setting with ERM access, we establish tight lower bounds in both the realizable and agnostic cases: $\Omega(2^{d_\mathrm{LD}})$ mistakes and $\Omega(\sqrt{T 2^{d_\mathrm{LD}}})$ regret, respectively, where $T$ is the number of timesteps and $d_\mathrm{LD}$ is the Littlestone dimension of the class. We further show how existing results for online learning with ERM access translate to the setting with a weak consistency oracle, at the cost of increasing the number of oracle calls by $O(T)$.

We then consider the transductive online model, where the instance sequence is known in advance but labels are revealed sequentially. For general Littlestone classes, we show that the optimal mistake bound in the realizable case and in the agnostic case can be achieved using $O(T^{d_\mathrm{VC}+1})$ weak consistency oracle calls, where $d_\mathrm{VC}$ is the VC dimension of the class. 
On the negative side, we show that $\Omega(T)$ weak consistency queries are necessary for transductive online learnability, and that $\Omega(T)$ ERM queries are necessary to avoid exponential dependence on the Littlestone dimension.
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Finally, for special families of concept classes, we demonstrate how to reduce the number of oracle calls using randomized algorithms while maintaining similar mistake bounds. In particular, for Thresholds on an unknown ordering, $O(\log T)$ ERM queries suffice, and for $k$-Intervals, $O(T^3 2^{2k})$ weak consistency queries suffice.
Scaffolding Dexterous Manipulation with Vision-Language Models
Authors: Vincent de Bakker; Joey Hejna; Tyler Ga Wei Lum; Onur Celik; Aleksandar Taranovic; Denis Blessing; Gerhard Neumann; Jeannette Bohg; Dorsa Sadigh
Venue: NeurIPS 2025 (NeurIPS 2025 poster)

Abstract: Dexterous robotic hands are essential for performing complex manipulation tasks, yet remain difficult to train due to the challenges of demonstration collection and high-dimensional control. While reinforcement learning (RL) can alleviate the data bottleneck by generating experience in simulation, it typically relies on carefully designed, task-specific reward functions, which hinder scalability and generalization. Thus, contemporary works in dexterous manipulation have often bootstrapped from reference trajectories. These trajectories specify target hand poses that guide the exploration of RL policies and object poses that enable dense, task-agnostic rewards.
However, sourcing suitable trajectories---particularly for dexterous hands---remains a significant challenge. Yet, the precise details in explicit reference trajectories are often unnecessary, as RL ultimately refines the motion. Our key insight is that modern vision-language models (VLMs) already encode the commonsense spatial and semantic knowledge needed to specify tasks and guide exploration effectively. Given a task description (e.g., “open the cabinet”) and a visual scene, our method uses an off-the-shelf VLM to first identify task-relevant keypoints (e.g., handles, buttons) and then synthesize 3D trajectories for hand motion and object motion. Subsequently, we train a low-level residual RL policy in simulation to track these coarse trajectories or ``scaffolds'' with high fidelity. Across a number of simulated tasks involving articulated objects and semantic understanding, we demonstrate that our method is able to learn robust dexterous manipulation policies. Moreover, we showcase that our method transfers to real-world robotic hands without any human demonstrations or handcrafted rewards.
Tree-Guided Diffusion Planner
Authors: Hyeonseong Jeon; Cheolhong Min; Jaesik Park
Venue: NeurIPS 2025 (NeurIPS 2025 poster)

Abstract: Planning with pretrained diffusion models has emerged as a promising approach for solving test-time guided control problems. Standard gradient guidance typically performs optimally under convex, differentiable reward landscapes. However, it shows substantially reduced effectiveness in real-world scenarios with non-convex objectives, non-differentiable constraints, and multi-reward structures. Furthermore, recent supervised planning approaches require task-specific training or value estimators, which limits test-time flexibility and zero-shot generalization. We propose a Tree-guided Diffusion Planner (TDP), a zero-shot test-time planning framework that balances exploration and exploitation through structured trajectory generation. We frame test-time planning as a tree search problem using a bi-level sampling process: (1) diverse parent trajectories are produced via training-free particle guidance to encourage broad exploration, and (2) sub-trajectories are refined through fast conditional denoising guided by task objectives. TDP addresses the limitations of gradient guidance by exploring diverse trajectory regions and harnessing gradient information across this expanded solution space using only pretrained models and test-time reward signals. We evaluate TDP on three diverse tasks: maze gold-picking, robot arm block manipulation, and AntMaze multi-goal exploration. TDP consistently outperforms state-of-the-art approaches on all tasks. The project page can be found at: https://tree-diffusion-planner.github.io
FlashMoE: Fast Distributed MoE in a Single Kernel
Authors: Osayamen Jonathan Aimuyo; Byungsoo Oh; Rachee Singh
Venue: NeurIPS 2025 (NeurIPS 2025 poster)

Abstract: The computational sparsity of Mixture-of-Experts (MoE) models enables sub-linear growth in compute cost as model size increases, thus offering a scalable path to training massive neural networks. However, existing implementations suffer from low GPU utilization, significant latency overhead, and a fundamental inability to leverage task locality, primarily due to CPU-managed scheduling, host-initiated communication, and frequent kernel launches. To overcome these limitations, we develop FlashMoE, a fully GPU-resident MoE operator that fuses expert computation and inter-GPU communication into a single persistent GPU kernel. FlashMoE enables fine-grained pipelining of dispatch, compute, and combine phases, eliminating launch overheads and reducing idle gaps. Unlike existing work, FlashMoE obviates bulk-synchronous collectives for one-sided, device-initiated, inter-GPU (R)DMA transfers, thus unlocking payload efficiency, where we eliminate bloated or redundant network payloads in sparsely activated layers. When evaluated on an 8-H100 GPU node with MoE models having up to 128 experts and 16K token sequences, FlashMoE achieves up to 9× higher GPU utilization, 6× lower latency, 5.7× higher throughput, and 4× better overlap efficiency compared to state-of-the-art baselines—despite using FP32 while baselines use FP16. FlashMoE shows that principled GPU kernel-hardware co-design is key to unlocking the performance ceiling of large-scale distributed ML. We provide code at https://github.com/osayamenja/FlashMoE.
The World Is Bigger! A Computationally-Embedded Perspective on the Big World Hypothesis
Authors: Alex Lewandowski; Aditya A. Ramesh; Edan Meyer; Dale Schuurmans; Marlos C. Machado
Venue: NeurIPS 2025 (NeurIPS 2025 spotlight)

Abstract: Continual learning is often motivated by the idea, known as the big world hypothesis, that ``the world is bigger'' than the agent. Recent problem formulations capture this idea by explicitly constraining an agent relative to the environment. These constraints lead to solutions in which the agent continually adapts to best use its limited capacity, rather than converging to a fixed solution. However, explicit constraints can be ad hoc, difficult to incorporate, and may limit the effectiveness of scaling up the agent's capacity. In this paper, we characterize a problem setting in which an agent, regardless of its capacity, is constrained by being embedded in the environment. In particular, we introduce a computationally-embedded perspective that represents an embedded agent as an automaton simulated within a universal (formal) computer. Such an automaton is always constrained; we prove that it is equivalent to an agent that interacts with a partially observable Markov decision process over a countably infinite state-space. We propose an objective for this setting, which we call interactivity, that measures an agent's ability to continually adapt its behaviour by learning new predictions. We then develop a model-based reinforcement learning algorithm for interactivity-seeking, and use it to construct a synthetic problem to evaluate continual learning capability. Our results show that deep nonlinear networks struggle to sustain interactivity, whereas deep linear networks sustain higher interactivity as capacity increases.