GenAI4ELab/papercli-papers huggingface dataset:
Tree of Preferences for Diversified Recommendation
Authors: Hanyang Yuan; Ning Tang; Tongya Zheng; Jiarong Xu; Xintong Hu; Renhong Huang; Shunyu Liu; Jiacong Hu; Jiawei Chen; Mingli Song
Venue: NeurIPS 2025 (NeurIPS 2025 poster)

Abstract: Diversified recommendation has attracted increasing attention from both researchers and practitioners, which can effectively address the homogeneity of recommended items.
Existing approaches predominantly aim to infer the diversity of user preferences from observed user feedback.
Nonetheless, due to inherent data biases, the observed data may not fully reflect user interests, where underexplored preferences can be overwhelmed or remain unmanifested. Failing to capture these preferences can lead to suboptimal diversity in recommendations. To fill this gap,  this work aims to study diversified recommendation from a data-bias perspective.
Inspired by the outstanding performance of large language models (LLMs) in zero-shot inference leveraging world knowledge, we propose a novel approach that utilizes LLMs' expertise to uncover underexplored user preferences from observed behavior, ultimately providing diverse and relevant recommendations.
To achieve this, we first introduce Tree of Preferences (ToP), an innovative structure constructed to model user preferences from coarse to fine. ToP enables LLMs to systematically reason over the user's rationale behind their behavior, thereby uncovering their underexplored preferences.
To guide diversified recommendations using uncovered preferences, we adopt a data-centric approach, identifying candidate items that match user preferences and generating synthetic interactions that reflect underexplored preferences.  These interactions are integrated to train a general recommender for diversification.
Moreover, we scale up overall efficiency by dynamically selecting influential users during optimization.
Extensive evaluations of both diversity and relevance show that our approach outperforms existing methods in most cases and achieves near-optimal performance in others, with reasonable inference latency.
Solving Continuous Mean Field Games: Deep Reinforcement Learning for Non-Stationary Dynamics
Authors: Lorenzo Magnino; Kai Shao; Zida Wu; Jiacheng Shen; Mathieu Lauriere
Venue: NeurIPS 2025 (NeurIPS 2025 poster)

Abstract: Mean field games (MFGs) have emerged as a powerful framework for modeling interactions in large-scale multi-agent systems. Despite recent advancements in reinforcement learning (RL) for MFGs, existing methods are typically limited to finite spaces or stationary models, hindering their applicability to real-world problems. This paper introduces a novel deep reinforcement learning (DRL) algorithm specifically designed for non-stationary continuous MFGs. The proposed approach builds upon a Fictitious Play (FP) methodology, leveraging DRL for best-response computation and supervised learning for average policy representation. Furthermore, it learns a representation of the time-dependent population distribution using a Conditional Normalizing Flow. To validate the effectiveness of our method, we evaluate it on three different examples of increasing complexity. By addressing critical limitations in scalability and density approximation, this work represents a significant advancement in applying DRL techniques to complex MFG problems, bringing the field closer to real-world multi-agent systems.
Optimal Control for Transformer Architectures: Enhancing Generalization, Robustness and Efficiency
Authors: Kelvin Kan; Xingjian Li; Benjamin Zhang; Tuhin Sahai; Stanley Osher; Markos Katsoulakis
Venue: NeurIPS 2025 (NeurIPS 2025 poster)

Abstract: We study Transformers through the perspective of optimal control theory, using tools from continuous-time formulations to derive actionable insights into training and architecture design. This framework improves the performance of existing Transformer models while providing desirable theoretical guarantees, including generalization and robustness. Our framework is designed to be plug-and-play, enabling seamless integration with established Transformer models and requiring only slight changes to the implementation. We conduct seven extensive experiments on tasks motivated by text generation, sentiment analysis, image classification, and point cloud classification. Experimental results show that the framework improves the test performance of the baselines, while being more parameter-efficient. On character-level text generation with nanoGPT, our framework achieves a 46\% reduction in final test loss while using 42\% fewer parameters. On GPT-2, our framework achieves a 9.3\% reduction in final test loss, demonstrating scalability to larger models. To the best of our knowledge, this is the first work that applies optimal control theory to both the training and architecture of Transformers. It offers a new foundation for systematic, theory-driven improvements and moves beyond costly trial-and-error approaches.
Scalable Neural Network Geometric Robustness Validation via Hölder Optimisation
Authors: Yanghao Zhang; Panagiotis Kouvaros; Alessio Lomuscio
Venue: NeurIPS 2025 (NeurIPS 2025 poster)

Abstract: Neural Network (NN) verification methods provide local robustness
guarantees for a NN in the dense perturbation space of an input.
In this paper we introduce H$^2$V, a method for the validation of
local robustness of NNs against geometric perturbations. H$^2$V
uniquely employs a Hilbert space-filling construction to recast
multi-dimensional problems into single-dimensional ones and Hölder
optimisation, iteratively refining the estimation of the Hölder constant 
for constructing the lower bound.
In common with current methods, Hölder optimisation might theoretically
converge to a local minimum, thereby resulting in a robustness result
being incorrect. However, we here identify conditions for H$^2$V
 to be provably sound, and show experimentally that even
outside the soundness conditions, the risk of incorrect results can be
minimised by introducing appropriate heuristics in the global
optimisation procedure. Indeed, we found no incorrect 
results validated by H$^2$V on a large set of benchmarks from
SoundnessBench and VNN-COMP.
To assess the scalability of the approach, we report the results
obtained on large NNs ranging from Resnet34 to Resnet152 and vision
transformers.  These point to state-of-the-art scalability of the approach when
validating the local robustness of large NNs against geometric
perturbations on the ImageNet dataset.  Beyond image tasks, we show
that the method's scalability enables for the first time the
robustness validation of large-scale 3D-NNs in video classification
tasks against geometric perturbations for long-sequence input frames
on Kinetics/UCF101 datasets.
AdmTree: Compressing Lengthy Context with Adaptive Semantic Trees
Authors: Yangning Li; Shaoshen Chen; Yinghui Li; Yankai Chen; Hai-Tao Zheng; Hui Wang; Wenhao Jiang; Philip S. Yu
Venue: NeurIPS 2025 (NeurIPS 2025 poster)

Abstract: The quadratic complexity of self-attention limits Large Language Models (LLMs) in processing long contexts, a capability vital for many advanced applications. Context compression aims to mitigate this computational barrier while preserving essential semantic information. However, existing methods often falter: explicit methods can sacrifice local detail, while implicit ones may exhibit positional biases, struggle with information degradation, or fail to capture long-range semantic dependencies. We introduce AdmTree, a novel framework for adaptive, hierarchical context compression designed with a core focus on maintaining high semantic fidelity while keep efficiency. AdmTree dynamically segments input based on information density, employing gist tokens to summarize variable-length segments as leaves in a semantic binary tree. This structure, combined with a lightweight aggregation mechanism and a frozen backbone LLM (minimizing new trainable parameters), enables efficient hierarchical abstraction of the context. By effectively preserving fine-grained details alongside global semantic coherence, mitigating position bias, and adapting dynamically to content, AdmTree comprehensively preserves the semantic information of lengthy context.
NeuroGenPoisoning: Neuron-Guided Attacks on Retrieval-Augmented Generation of LLM via Genetic Optimization of External Knowledge
Authors: Hanyu Zhu; Lance Fiondella; Jiawei Yuan; Kai Zeng; Long Jiao
Venue: NeurIPS 2025 (NeurIPS 2025 poster)

Abstract: Retrieval-Augmented Generation (RAG) empowers Large Language Models (LLMs) to dynamically integrate external knowledge during inference, improving their factual accuracy and adaptability. However, adversaries can inject poisoned external knowledge to override the model’s internal memory. While existing attacks iteratively manipulate retrieval content or prompt structure of RAG, they largely ignore the model’s internal representation dynamics and neuron-level sensitivities. The underlying mechanism of RAG poisoning has not been fully studied and the effect of knowledge conflict with strong parametric knowledge in RAG is not considered. In this work, we propose NeuroGenPoisoning, a novel attack framework that generates adversarial external knowledge in RAG guided by LLM internal neuron attribution and genetic optimization. Our method first identifies a set of **Poison-Responsive Neurons** whose activation strongly correlates with contextual poisoning knowledge. We then employ a genetic algorithm to evolve adversarial passages that maximally activate these neurons. Crucially, our framework enables massive-scale generation of effective poisoned RAG knowledge by identifying and reusing promising but initially unsuccessful external knowledge variants via observed attribution signals. At the same time, Poison-Responsive Neurons guided poisoning can effectively resolves knowledge conflict. Experimental results across models and datasets demonstrate consistently achieving high Population Overwrite Success Rate (POSR) of over 90\% while preserving fluency. Empirical evidence shows that our method effectively resolves knowledge conflict.
Distortion of AI Alignment: Does Preference Optimization Optimize for Preferences?
Authors: Paul Gölz; Nika Haghtalab; Kunhe Yang
Venue: NeurIPS 2025 (NeurIPS 2025 poster)

Abstract: After pre-training, large language models are aligned with human preferences based on pairwise comparisons. State-of-the-art alignment methods (such as PPO-based RLHF and DPO) are built on the assumption of aligning with a single preference model, despite being deployed in settings where users have diverse preferences. As a result, it is not even clear that these alignment methods produce models that satisfy users \emph{on average} --- a minimal requirement for pluralistic alignment. Drawing on social choice theory and modeling users' comparisons through individual Bradley-Terry (BT) models, we introduce an alignment method's \emph{distortion}: the worst-case ratio between the optimal achievable average utility, and the average utility of the learned policy. The notion of distortion helps draw sharp distinctions between alignment methods: \emph{Nash Learning from Human Feedback} achieves the minimax optimal distortion of $(\frac{1}{2} + o(1)) \cdot \beta$ (for the BT temperature $\beta$), robustly across utility distributions, distributions of comparison pairs, and permissible KL divergences from the reference policy. RLHF and DPO, by contrast, suffer $\geq (1 - o(1)) \cdot \beta$ distortion already without a KL constraint, and $e^{\Omega(\beta)}$ or even unbounded distortion in the full setting, depending on how comparison pairs are sampled.
Reasoning Models Better Express Their Confidence
Authors: Dongkeun Yoon; Seungone Kim; Sohee Yang; Sunkyoung Kim; Soyeon Kim; Yongil Kim; Eunbi Choi; Yireun Kim; Minjoon Seo
Venue: NeurIPS 2025 (NeurIPS 2025 poster)

Abstract: Despite their strengths, large language models (LLMs) often fail to communicate their confidence accurately, making it difficult to assess when they might be wrong and limiting their reliability. In this work, we demonstrate that reasoning models that engage in extended chain-of-thought (CoT) reasoning exhibit superior performance not only in problem-solving but also in accurately expressing their confidence.
Specifically, we benchmark six reasoning models across six datasets and find that they achieve strictly better confidence calibration than their non-reasoning counterparts in 33 out of the 36 settings. Our detailed analysis reveals that these gains in calibration stem from the slow thinking behaviors of reasoning models (e.g., exploring alternative approaches and backtracking) which enable them to adjust their confidence dynamically throughout their CoT, making it progressively more accurate. In particular, we find that reasoning models become increasingly better calibrated as their CoT unfolds, a trend not observed in non-reasoning models. Moreover, removing slow thinking behaviors from the CoT leads to a significant drop in calibration. Lastly, we show that non-reasoning models also demonstrate enhanced calibration when simply guided to slow think via in-context learning, fully isolating slow thinking as the source of the calibration gains.
Mitigating Reward Over-optimization in Direct Alignment Algorithms with Importance Sampling
Authors: Nguyen Minh Phuc; Ngoc-Hieu Nguyen; Duy Minh Ho Nguyen; Anji Liu; An Mai; Binh T. Nguyen; Daniel Sonntag; Khoa D Doan
Venue: NeurIPS 2025 (NeurIPS 2025 poster)

Abstract: Recently, Direct Alignment Algorithms (DAAs) such as Direct Preference Optimization (DPO) have emerged as alternatives to the standard Reinforcement Learning from Human Feedback (RLHF) for aligning large language models (LLMs) with human values. 
Surprisingly, while DAAs do not use a separate proxy reward model as in RLHF, their performance can still deteriorate over the course of training -- an over-optimization phenomenon found in RLHF where the learning policy exploits the overfitting to inaccuracies of the reward model to achieve high rewards.
One attributed source of over-optimization in DAAs is the under-constrained nature of their offline optimization, which can gradually shift probability mass toward non-preferred responses not presented in the preference dataset. This paper proposes a novel importance-sampling approach to mitigate the distribution shift problem of offline DAAs.
This approach, called (IS-DAAs), multiplies the DAA objective with an importance ratio that accounts for the reference policy distribution. IS-DAAs additionally avoid the high variance issue associated with importance sampling by clipping the importance ratio to a maximum value. Our extensive experiments demonstrate that IS-DAAs can effectively mitigate over-optimization, especially under low regularization strength, and achieve better performance than other methods designed to address this problem.
Conformal Mixed-Integer Constraint Learning with Feasibility Guarantees
Authors: Daniel Ovalle; Lorenz T. Biegler; Ignacio E Grossmann; Carl D Laird; Mateo Dulce Rubio
Venue: NeurIPS 2025 (NeurIPS 2025 spotlight)

Abstract: We propose Conformal Mixed-Integer Constraint Learning (C-MICL), a novel framework that provides probabilistic feasibility guarantees for data-driven constraints in optimization problems. While standard Mixed-Integer Constraint Learning methods often violate the true constraints due to model error or data limitations, our C-MICL approach leverages conformal prediction to ensure feasible solutions are ground-truth feasible with probability at least  $1{-}\alpha$, under a conditional independence assumption. The proposed framework supports both regression and classification tasks without requiring access to the true constraint function, while avoiding the scalability issues associated with ensemble-based heuristics. Experiments on real-world applications demonstrate that C-MICL consistently achieves target feasibility rates, maintains competitive objective performance, and significantly reduces computational cost compared to existing methods. Our work bridges mathematical optimization and machine learning, offering a principled approach to incorporate uncertainty-aware constraints into decision-making with rigorous statistical guarantees.
Virtual Fitting Room: Generating Arbitrarily Long Videos of Virtual Try-On from a Single Image
Authors: Jun-Kun Chen; Aayush Bansal; Minh Phuoc Vo; Yu-Xiong Wang
Venue: NeurIPS 2025 (NeurIPS 2025 poster)

Abstract: This paper proposes Virtual Fitting Room (VFR), a novel video generative model that produces arbitrarily long virtual try-on videos. Our VFR models long video generation tasks as an auto-regressive, segment-by-segment generation process, eliminating the need for resource-intensive generation and lengthy video data, while providing the flexibility to generate videos of arbitrary length. The key challenges of this task are twofold: ensuring local smoothness between adjacent segments and maintaining global temporal consistency across different segments. To address these challenges, we propose our VFR framework, which ensures smoothness through a prefix video condition and enforces consistency with the anchor video — a 360°-view video that comprehensively captures the human's whole-body appearance. Our VFR generates minute-scale virtual try-on videos with both local smoothness and global temporal consistency under various motions, making it a pioneering work in long virtual try-on video generation. Project Page: https://immortalco.github.io/VirtualFittingRoom/.
Why Do Some Language Models Fake Alignment While Others Don't?
Authors: Abhay Sheshadri; John Hughes; Julian Michael; Alex Troy Mallen; Arun Jose; Fabien Roger
Venue: NeurIPS 2025 (NeurIPS 2025 spotlight)

Abstract: *Alignment Faking in Large Language Models* presented a demonstration of Claude 3 Opus and Claude 3.5 Sonnet selectively complying with a helpful-only training objective to prevent modification of their behavior outside of training. We expand this analysis to 25 models and find that only 5 (Claude 3 Opus, Claude 3.5 Sonnet, Llama 3 405B, Grok 3, Gemini 2.0 Flash) comply with harmful queries more when they infer they are in training than when they infer they are in deployment. First, we study the motivations of these 5 models. Results from perturbing details of the scenario suggest that only Claude 3 Opus's compliance gap is primarily and consistently motivated by trying to keep its goals. Second, we investigate why many chat models don't fake alignment. Our results suggest this is not entirely due to a lack of capabilities: many base models fake alignment some of the time, and post-training eliminates alignment-faking for some models and amplifies it for others. We investigate 5 hypotheses for how post-training may suppress alignment faking and find that variations in refusal behavior may account for a significant portion of differences in alignment faking.
Failure by Interference: Language Models Make Balanced Parentheses Errors  When Faulty Mechanisms Overshadow Sound Ones
Authors: Daking Rai; Samuel Miller; Kevin Moran; Ziyu Yao
Venue: NeurIPS 2025 (NeurIPS 2025 poster)

Abstract: Despite remarkable advances in coding capabilities, language models (LMs) still struggle with simple syntactic tasks such as generating balanced parentheses. In this study, we investigate the underlying mechanisms behind the persistence of these errors across LMs of varying sizes (124M–7B) to both understand and mitigate the errors. Our study reveals that LMs rely on a number of components (attention heads and FF neurons) that independently make their own predictions. While some components reliably promote correct answers across a generalized range of inputs (i.e., implementing "sound mechanisms''), others are less reliable and introduce noise by promoting incorrect tokens (i.e., implementing "faulty mechanisms''). Errors occur when the faulty mechanisms overshadow the sound ones and dominantly affect the predictions. Motivated by this insight, we introduce RASteer, a steering method to systematically identify and increase the contribution of reliable components for improving model performance. RASteer substantially improves performance on balanced parentheses tasks, boosting accuracy of some models from $0$\% to around $100$\% without impairing the models' general coding ability. We further demonstrate its broader applicability in arithmetic reasoning tasks, achieving performance gains of up to around $20$\%.
⁠When Data Can't Meet: Estimating Correlation Across Privacy Barriers
Authors: Abhinav Chakraborty; Arnab Auddy; T. Tony Cai
Venue: NeurIPS 2025 (NeurIPS 2025 spotlight)

Abstract: We consider the problem of estimating the correlation of two random variables $X$ and $Y$, where the pairs $(X,Y)$ are not observed together, but are instead separated co-ordinate-wise at two servers: server 1 contains all the $X$ observations, and server 2 contains the corresponding $Y$ observations. In this vertically distributed setting, we assume that each server has its own privacy constraints, owing to which they can only share suitably privatized statistics of their own component observations. We consider differing privacy budgets $(\varepsilon_1,\delta_1)$ and $(\varepsilon_2,\delta_2)$ for the two servers and determine the minimax optimal rates for correlation estimation allowing for both non-interactive and interactive mechanisms. We also provide correlation estimators that achieve these rates and further develop inference procedures, namely, confidence intervals, for the estimated correlations. Our results are characterized by an interesting rate in terms of the sample size $n$, $\varepsilon_1$, $\varepsilon_2$, which is strictly slower than the usual central privacy estimation rates. More interestingly, we find that the interactive mechanism is always better than its non-interactive counterpart whenever the two privacy budgets are different. Results from extensive numerical experiments support our theoretical findings.
Mechanistic Interpretability of RNNs emulating Hidden Markov Models
Authors: Elia Torre; Michele Viscione; Lucas Pompe; Benjamin F Grewe; Valerio Mante
Venue: NeurIPS 2025 (NeurIPS 2025 poster)

Abstract: Recurrent neural networks (RNNs) provide a powerful approach in neuroscience to infer latent dynamics in neural populations and to generate hypotheses about the neural computations underlying behavior. However, past work has focused on relatively simple, input-driven, and largely deterministic behaviors - little is known about the mechanisms that would allow RNNs to generate the richer, spontaneous, and potentially stochastic behaviors observed in natural settings. Modeling with Hidden Markov Models (HMMs) has revealed a segmentation of natural behaviors into discrete latent states with stochastic transitions between them, a type of dynamics that may appear at odds with the continuous state spaces implemented by RNNs. Here we first show that RNNs can replicate HMM emission statistics and then reverse-engineer the trained networks to uncover the mechanisms they implement. In the absence of inputs, the activity of trained RNNs collapses towards a single fixed point. When driven by stochastic input, trajectories instead exhibit noise-sustained dynamics along closed orbits. Rotation along these orbits modulates the emission probabilities and is governed by transitions between regions of slow, noise-driven dynamics connected by fast, deterministic transitions. The trained RNNs develop highly structured connectivity, with a small set of “kick neurons” initiating transitions between these regions. This mechanism emerges during training as the network shifts into a regime of stochastic resonance, enabling it to perform probabilistic computations. Analyses across multiple HMM architectures — fully connected, cyclic, and linear-chain — reveal that this solution generalizes through the modular reuse of the same dynamical motif, suggesting a compositional principle by which RNNs can emulate complex discrete latent dynamics.