GenAI4ELab/papercli-papers huggingface dataset:
TITAN: A Trajectory-Informed Technique for Adaptive Parameter Freezing in Large-Scale VQE
Authors: Yifeng Peng; Xinyi Li; Samuel Yen-Chi Chen; Kaining Zhang; Zhiding Liang; Ying Wang; Yuxuan Du
Venue: NeurIPS 2025 (NeurIPS 2025 poster)

Abstract: Variational quantum Eigensolver (VQE) is a leading candidate for harnessing quantum computers to advance quantum chemistry and materials simulations, yet its training efficiency deteriorates rapidly for large Hamiltonians. Two issues underlie this bottleneck: (i) the no-cloning theorem imposes a linear growth in circuit evaluations with the number of parameters per gradient step; and (ii) deeper circuits encounter barren plateaus (BPs), leading to exponentially increasing measurement overheads. To address these challenges, here we propose a deep learning framework, dubbed Titan, which identifies and freezes inactive parameters of a given ansätze at initialization for a specific class of Hamiltonians, reducing the optimization overhead without sacrificing accuracy. The motivation of Titan starts with our empirical findings that a subset of parameters consistently has negligible influence on training dynamics. Its design combines a theoretically grounded data construction strategy, ensuring each training example is informative and BP-resilient, with an adaptive neural architecture that generalizes across ansätze of varying sizes. Across benchmark transverse-field Ising models, Heisenberg models, and multiple molecule systems up to $30$ qubits, Titan  achieves up to $3\times$ faster convergence and $40$–$60\%$ fewer circuit evaluations than state-of-the-art baselines, while matching or surpassing their estimation accuracy. By proactively trimming parameter space, Titan lowers hardware demands and offers a scalable path toward utilizing VQE to advance practical quantum chemistry and materials science.
AgentNet: Decentralized Evolutionary Coordination for LLM-based Multi-Agent Systems
Authors: Yingxuan Yang; Huacan Chai; Shuai Shao; Yuanyi Song; Siyuan Qi; Renting Rui; Weinan Zhang
Venue: NeurIPS 2025 (NeurIPS 2025 poster)

Abstract: The rapid advancement of Large Language Models (LLMs) has catalyzed the development of multi-agent systems, where multiple LLM-based agents collaborate to solve complex tasks.   However, existing systems predominantly rely on centralized coordination, which introduces scalability bottlenecks, limits adaptability, and creates single points of failure.   Additionally, concerns over privacy and proprietary knowledge sharing hinder cross-organizational collaboration, leading to siloed expertise.   To address these challenges, we propose AgentNet, a decentralized, Retrieval-Augmented Generation (RAG)-based framework that enables LLM-based agents to autonomously evolve their capabilities and collaborate efficiently in a Directed Acyclic Graph (DAG)-structured network.   Unlike traditional multi-agent systems that depend on static role assignments or centralized control, AgentNet allows agents to specialize dynamically, adjust their connectivity, and route tasks without relying on predefined workflows.
AgentNet’s core design is built upon several key innovations: (1) Fully Decentralized Paradigm: Removing the central orchestrator, allowing agents to coordinate and specialize autonomously, fostering fault tolerance and emergent collective intelligence. (2) Dynamically Evolving Graph Topology: Real-time adaptation of agent connections based on task demands, ensuring scalability and resilience.
(3) Adaptive Learning for Expertise Refinement: A retrieval-based memory system that enables agents to continuously update and refine their specialized skills.
By eliminating centralized control, AgentNet enhances fault tolerance, promotes scalable specialization, and enables privacy-preserving collaboration across organizations.      Through decentralized coordination and minimal data exchange, agents can leverage diverse knowledge sources while safeguarding sensitive information.      Experimental results demonstrate that AgentNet outperforms traditional centralized multi-agent systems, significantly improving efficiency, adaptability, and scalability in dynamic environments, making it a promising foundation for next-generation autonomous, privacy-respecting multi-agent ecosystems.
Seeing Sound, Hearing Sight: Uncovering Modality Bias and Conflict of AI models in Sound Localization
Authors: Yanhao Jia; Ji Xie; S Jivaganesh; Li Hao; Xu Wu; Mengmi Zhang
Venue: NeurIPS 2025 (NeurIPS 2025 spotlight)

Abstract: Imagine hearing a dog bark and instinctively turning toward the sound—only to find a parked car, while a silent dog sits nearby. Such moments of sensory conflict challenge perception, yet humans flexibly resolve these discrepancies, prioritizing auditory cues over misleading visuals to accurately localize sounds.  Despite the rapid advancement of multimodal AI models that integrate vision and sound, little is known about how these systems handle cross-modal conflicts or whether they favor one modality over another. Here, we systematically and quantitatively examine modality bias and conflict resolution in AI models for Sound Source Localization (SSL). We evaluate a wide range of state-of-the-art multimodal models and compare them against human performance in psychophysics experiments spanning six audiovisual conditions, including congruent, conflicting, and absent visual and audio cues. Our results reveal that humans consistently outperform AI in SSL and exhibit greater robustness to conflicting or absent visual information by effectively prioritizing auditory signals. In contrast, AI shows a pronounced bias toward vision, often failing to suppress irrelevant or conflicting visual input, leading to chance-level performance. To bridge this gap, we present EchoPin, a neuroscience-inspired multimodal model for SSL that emulates human auditory perception. The model is trained on our carefully curated AudioCOCO dataset, in which stereo audio signals are first rendered using a physics-based 3D simulator, then filtered with Head-Related Transfer Functions (HRTFs) to capture pinnae, head, and torso effects, and finally transformed into cochleagram representations that mimic cochlear processing. To eliminate existing biases in standard benchmark datasets, we carefully controlled the vocal object sizes, semantics, and spatial locations in the corresponding images of AudioCOCO. EchoPin outperforms existing models trained on standard audio-visual datasets. Remarkably, consistent with neuroscience findings, it exhibits a human-like localization bias, favoring horizontal (left–right) precision over vertical (up–down) precision. 
This asymmetry likely arises from HRTF-shaped and cochlear-modulated stereo audio and the lateral placement of human ears, highlighting how sensory input quality and physical structure jointly shape precision of multimodal representations. All code, data, and models are available \href{https://github.com/CuriseJia/SSHS}{here}.
Resounding Acoustic Fields with Reciprocity
Authors: Zitong Lan; Yiduo Hao; Mingmin Zhao
Venue: NeurIPS 2025 (NeurIPS 2025 poster)

Abstract: Achieving immersive auditory experiences in virtual environments requires flexible sound modeling that supports dynamic source positions.
In this paper, we introduce a task called resounding, which aims to estimate room impulse responses at arbitrary emitter location from a sparse set of measured emitter positions, analogous to the relighting problem in vision.
We leverage the reciprocity property and introduce Versa, a physics-inspired approach to facilitating acoustic field learning.
Our method creates physically valid samples with dense virtual emitter positions by exchanging emitter and listener poses.
We also identify challenges in deploying reciprocity due to emitter/listener gain patterns and propose a self-supervised learning approach to address them.
Results show that Versa substantially improve the performance of acoustic field learning on both simulated and real-world datasets across different metrics.
Perceptual user studies show that Versa can greatly improve the immersive spatial sound experience.
Beyond Masked and Unmasked: Discrete Diffusion Models via Partial Masking
Authors: Chen-Hao Chao; Wei-Fang Sun; Hanwen Liang; Chun-Yi Lee; Rahul Krishnan
Venue: NeurIPS 2025 (NeurIPS 2025 poster)

Abstract: Masked diffusion models (MDM) are powerful generative models for discrete data that generate samples by progressively unmasking tokens in a sequence. Each token can take one of two states: masked or unmasked. We observe that token sequences often remain unchanged between consecutive sampling steps; consequently, the model repeatedly processes identical inputs, leading to redundant computation. To address this inefficiency, we propose the Partial masking scheme (Prime), which augments MDM by allowing tokens to take intermediate states interpolated between the masked and unmasked states. This design enables the model to make predictions based on partially observed token information, and facilitates a fine-grained denoising process. We derive a variational training objective and introduce a simple architectural design to accommodate intermediate-state inputs. Our method demonstrates superior performance across a diverse set of generative modeling tasks. On text data, it achieves a perplexity of 15.36 on OpenWebText, outperforming previous MDM (21.52), autoregressive models (17.54), and their hybrid variants (17.58), without relying on an autoregressive formulation. On image data, it attains competitive FID scores of 3.26 on CIFAR-10 and 6.98 on ImageNet-32, comparable to leading continuous generative models.
TwinMarket: A Scalable Behavioral and Social Simulation for Financial Markets
Authors: Yuzhe YANG; Yifei Zhang; Minghao Wu; Kaidi Zhang; Yunmiao Zhang; Honghai Yu; Yan Hu; Benyou Wang
Venue: NeurIPS 2025 (NeurIPS 2025 poster)

Abstract: The study of social emergence has long been a central focus in social science. Traditional modeling approaches, such as rule-based Agent-Based Models (ABMs), struggle to capture the diversity and complexity of human behavior, particularly the irrational factors emphasized in behavioral economics. Recently, large language model (LLM) agents have gained traction as simulation tools for modeling human behavior in social science and role-playing applications. Studies suggest that LLMs can account for cognitive biases, emotional fluctuations, and other non-rational influences, enabling more realistic simulations of socio-economic dynamics. In this work, we introduce TwinMarket, a novel multi-agent framework that leverages LLMs to simulate socio-economic systems. Specifically, we examine how individual behaviors, through interactions and feedback mechanisms, give rise to collective dynamics and emergent phenomena. Through experiments in a simulated stock market environment, we demonstrate how individual actions can trigger group behaviors, leading to emergent outcomes such as financial bubbles and recessions. Our approach provides valuable insights into the complex interplay between individual decision-making and collective socio-economic patterns.
ALINE: Joint Amortization for Bayesian Inference and Active Data Acquisition
Authors: Daolang Huang; Xinyi Wen; Ayush Bharti; Samuel Kaski; Luigi Acerbi
Venue: NeurIPS 2025 (NeurIPS 2025 spotlight)

Abstract: Many critical applications, from autonomous scientific discovery to personalized medicine, demand systems that can both strategically acquire the most informative data and instantaneously perform inference based upon it. While amortized methods for Bayesian inference and experimental design offer part of the solution, neither approach is optimal in the most general and challenging task, where new data needs to be collected for instant inference. To tackle this issue, we introduce the Amortized Active Learning and Inference Engine (ALINE), a unified framework for amortized Bayesian inference and active data acquisition. ALINE leverages a transformer architecture trained via reinforcement learning with a reward based on self-estimated information gain provided by its own integrated inference component. This allows it to strategically query informative data points while simultaneously refining its predictions. Moreover, ALINE can selectively direct its querying strategy towards specific subsets of model parameters or designated predictive tasks, optimizing for posterior estimation, data prediction, or a mixture thereof. Empirical results on regression-based active learning, classical Bayesian experimental design benchmarks, and a psychometric model with selectively targeted parameters demonstrate that ALINE delivers both instant and accurate inference along with efficient selection of informative points.
Generative Perception of Shape and Material from Differential Motion
Authors: Xinran Han; Ko Nishino; Todd Zickler
Venue: NeurIPS 2025 (NeurIPS 2025 poster)

Abstract: Perceiving the shape and material of an object from a single image is inherently ambiguous, especially when lighting is unknown and unconstrained. Despite this, humans can often disentangle shape and material, and when they are uncertain, they often move their head slightly or rotate the object to help resolve the ambiguities. Inspired by this behavior, we introduce a novel conditional denoising-diffusion model that generates samples of shape-and-material maps from a short video of an object undergoing differential motions. Our parameter-efficient architecture allows training directly in pixel-space, and it generates many disentangled attributes of an object simultaneously. Trained on a modest number of synthetic object-motion videos with supervision on shape and material, the model exhibits compelling emergent behavior: For static observations, it produces diverse, multimodal predictions of plausible shape-and-material maps that capture the inherent ambiguities; and when objects move, the distributions converge to more accurate explanations. The model also produces high-quality shape-and-material estimates for less ambiguous, real-world objects. 
By moving beyond single-view to continuous motion observations, and by using generative perception to capture visual ambiguities, our work suggests ways to improve visual reasoning in physically-embodied systems.
Latent Harmony: Synergistic Unified UHD Image Restoration via Latent Space Regularization and Controllable Refinement
Authors: Yidi Liu; Xueyang Fu; Jie Huang; Jie Xiao; Dong Li; Wenlong Zhang; LEI BAI; Zheng-Jun Zha
Venue: NeurIPS 2025 (NeurIPS 2025 poster)

Abstract: Ultra-High Definition (UHD) image restoration struggles to balance computational efficiency and detail retention.
While Variational Autoencoders (VAEs) offer improved efficiency by operating in the latent space, with the Gaussian variational constraint, this compression preserves semantics but sacrifices critical high-frequency attributes specific to degradation and thus compromises reconstruction fidelity. 
% This compromises reconstruction fidelity, even when global semantics are preserved.
Consequently, a VAE redesign is imperative to foster a robust semantic representation conducive to generalization and perceptual quality, while simultaneously enabling effective high-frequency information processing crucial for reconstruction fidelity.
To address this, we propose  \textit{Latent Harmony}, a two-stage framework that reinvigorates VAEs for UHD restoration by concurrently regularizing the latent space and enforcing high-frequency-aware reconstruction constraints. 
Specifically, Stage One introduces the LH-VAE, which fortifies its latent representation through visual semantic constraints and progressive degradation perturbation for enhanced semantics robustness; meanwhile, it incorporates latent equivariance to bolster its high-frequency reconstruction capabilities. 
Then, Stage Two facilitates joint training of this refined VAE with a dedicated restoration model.
This stage integrates High-Frequency Low-Rank Adaptation (HF-LoRA), featuring two distinct modules: an encoder LoRA, guided by a fidelity-oriented high-frequency alignment loss, tailored for the precise extraction of authentic details from degradation-sensitive high-frequency components; and a decoder LoRA, driven by a perception-oriented loss, designed to synthesize perceptually superior textures. These LoRA modules are meticulously trained via alternating optimization with selective gradient propagation to preserve the integrity of the pre-trained latent structure. This methodology culminates in a flexible fidelity-perception trade-off at inference, managed by an adjustable parameter 
$\alpha$.
Extensive experiments demonstrate that \textit{Latent Harmony} effectively balances perceptual and reconstructive objectives with efficiency, achieving superior restoration performance across diverse UHD and standard-resolution  scenarios.
Fully Autonomous Neuromorphic Navigation and Dynamic Obstacle Avoidance
Authors: Xiaochen Shang; Luo Pengwei; Xinning Wang; Jiayue Zhao; Huilin Ge; Bo Dong; Xin Yang
Venue: NeurIPS 2025 (NeurIPS 2025 spotlight)

Abstract: Unmanned aerial vehicles could accurately accomplish complex navigation and obstacle avoidance tasks under external control. However, enabling unmanned aerial vehicles (UAVs) to rely solely on onboard computation and sensing for real-time navigation and dynamic obstacle avoidance remains a significant challenge due to stringent latency and energy constraints. Inspired by the efficiency of biological systems, we propose a fully neuromorphic framework achieving end-to-end obstacle avoidance during navigation with an overall latency of just 2.3 milliseconds. Specifically, our bio-inspired approach enables accurate moving object detection and avoidance without requiring target recognition or trajectory computation. Additionally, we introduce the first monocular event-based pose correction dataset with over 50,000 paired and labeled event streams. We validate our system on an autonomous quadrotor using only onboard resources, demonstrating reliable navigation and avoidance of diverse obstacles moving at speeds up to 10 m/s.
SE-GUI: Enhancing Visual Grounding for GUI Agents via Self-Evolutionary Reinforcement Learning
Authors: Xinbin Yuan; Jian Zhang; Kaixin Li; Zhuoxuan Cai; Lujian Yao; Jie Chen; Enguang Wang; Qibin Hou; Jinwei Chen; Peng-Tao Jiang; Bo Li
Venue: NeurIPS 2025 (NeurIPS 2025 poster)

Abstract: Graphical User Interface (GUI) agents have made substantial strides in understanding and executing user instructions across diverse platforms. Yet, grounding these instructions to precise interface elements remains challenging—especially in complex, high-resolution, professional environments. Traditional supervised fine-tuning (SFT) methods often require large volumes of diverse data and exhibit weak generalization. To overcome these limitations, we introduce a reinforcement learning (RL)-based framework that incorporates three core strategies: (1) seed data curation to ensure high-quality training samples, (2) a dense policy gradient that provides continuous feedback based on prediction accuracy, and (3) a self-evolutionary reinforcement finetuning mechanism that iteratively refines the model using attention maps. With only 3k training samples, our 7B-parameter model achieves state-of-the-art results among similarly sized models on three grounding benchmarks. Notably, it attains 47.3\% accuracy on the ScreenSpot-Pro dataset—outperforming much larger models, such as UI-TARS-72B, by a margin of 24.2\%. These findings underscore the effectiveness of RL-based approaches in enhancing GUI agent performance, particularly in high-resolution, complex environments.
Reason-RFT: Reinforcement Fine-Tuning for Visual Reasoning of Vision Language Models
Authors: Huajie Tan; Yuheng Ji; Xiaoshuai Hao; Xiansheng Chen; Pengwei Wang; Zhongyuan Wang; Shanghang Zhang
Venue: NeurIPS 2025 (NeurIPS 2025 poster)

Abstract: Visual reasoning abilities play a crucial role in understanding complex multimodal data, advancing both domain-specific applications and artificial general intelligence (AGI). Existing methods enhance Vision-Language Models (VLMs) through Chain-of-Thought (CoT) supervised fine-tuning using meticulously annotated data. However, this approach may lead to overfitting and cognitive rigidity, limiting the model’s generalization ability under domain shifts and reducing real-world applicability. To overcome these limitations, we propose Reason-RFT, a two-stage reinforcement fine-tuning framework for visual reasoning. First, Supervised Fine-Tuning (SFT) with curated CoT data activates the reasoning potential of VLMs. This is followed by reinforcement learning based on Group Relative Policy Optimization (GRPO), which generates multiple reasoning-response pairs to enhance adaptability to domain shifts. To evaluate Reason-RFT, we reconstructed a comprehensive dataset covering visual counting, structural perception, and spatial transformation, serving as a benchmark for systematic assessment across three key dimensions. Experimental results highlight three advantages: (1) performance enhancement, with Reason-RFT achieving state-of-the-art results and outperforming both open-source and proprietary models; (2) generalization superiority, maintaining robust performance under domain shifts across various tasks; and (3) data efficiency, excelling in few-shot learning scenarios and surpassing full-dataset SFT baselines. Reason-RFT introduces a novel training paradigm for visual reasoning and marks a significant step forward in multimodal research.
Noisy Multi-Label Learning through Co-Occurrence-Aware Diffusion
Authors: Senyu Hou; Yuru Ren; Gaoxia Jiang; Wenjian Wang
Venue: NeurIPS 2025 (NeurIPS 2025 poster)

Abstract: Noisy labels often compel models to overfit, especially in multi-label classification tasks. Existing methods for noisy multi-label learning (NML) primarily follow a discriminative paradigm, which relies on noise transition matrix estimation or small-loss strategies to correct noisy labels. However, they remain substantial optimization difficulties compared to noisy single-label learning. In this paper, we propose a Co-Occurrence-Aware Diffusion (CAD) model, which reformulates NML from a generative perspective. We treat features as conditions and multi-labels as diffusion targets, optimizing the diffusion model for multi-label learning with theoretical guarantees. Benefiting from the diffusion model's strength in capturing multi-object semantics and structured label matrix representation, we can effectively learn the posterior mapping from features to true multi-labels. To mitigate the interference of noisy labels in the forward process, we guide generation using pseudo-clean labels reconstructed from the latent neighborhood space, replacing original point-wise estimates with neighborhood-based proxies. In the reverse process, we further incorporate label co-occurrence constraints to enhance the model's awareness of incorrect generation directions, thereby promoting robust optimization. Extensive experiments on both synthetic (Pascal-VOC, MS-COCO) and real-world (NUS-WIDE) noisy datasets demonstrate that our approach outperforms state-of-the-art methods.