Publications

Give Users the Wheel: Towards Promptable Recommendation Paradigm
Fuyuan Lyu
Chenglin Luo
Qiyuan Zhang
Yupeng Hou
Haolun Wu
Xing Tang
Xue Liu
Jin L.C. Guo
xiuqiang He
Conventional sequential recommendation models have achieved remarkable success in mining implicit behavioral patterns. However, these archit… (see more)ectures remain structurally blind to explicit user intent: they struggle to adapt when a user's immediate goal (e.g., expressed via a natural language prompt) deviates from their historical habits. While Large Language Models (LLMs) offer the semantic reasoning to interpret such intent, existing integration paradigms force a dilemma: LLM-as-a-recommender paradigm sacrifices the efficiency and collaborative precision of ID-based retrieval, while Reranking methods are inherently bottlenecked by the recall capabilities of the underlying model. In this paper, we propose Decoupled Promptable Sequential Recommendation (DPR), a model-agnostic framework that empowers conventional sequential backbones to natively support Promptable Recommendation, the ability to dynamically steer the retrieval process using natural language without abandoning collaborative signals. DPR modulates the latent user representation directly within the retrieval space. To achieve this, we introduce a Fusion module to align the collaborative and semantic signals, a Mixture-of-Experts (MoE) architecture that disentangles the conflicting gradients from positive and negative steering, and a three-stage training strategy that progressively aligns the semantic space of prompts with the collaborative space. Extensive experiments on real-world datasets demonstrate that DPR significantly outperforms state-of-the-art baselines in prompt-guided tasks while maintaining competitive performance in standard sequential recommendation scenarios.
2026-02-20
arXiv (preprint)
doi.org
arxiv.org
Sociodynamics of Reinforcement Learning
Yann Bouteiller
Karthik Soma
Giovanni Beltrame
Reinforcement Learning (RL) has emerged as a core algorithmic paradigm explicitly driving innovation in a growing number of industrial appli… (see more)cations, including large language models and quantitative finance. Furthermore, computational neuroscience has long found evidence of natural forms of RL in biological brains. Therefore, it is crucial for the study of social dynamics to develop a scientific understanding of how RL shapes population behaviors. We leverage the framework of Evolutionary Game Theory (EGT) to provide building blocks and insights toward this objective. We propose a methodology that enables simulating large populations of RL agents in simple game theoretic interaction models. More specifically, we derive fast and parallelizable implementations of two fundamental revision protocols from multi-agent RL - Policy Gradient (PG) and Opponent-Learning Awareness (LOLA) - tailored for population simulations of random pairwise interactions in stateless normal-form games. Our methodology enables us to simulate large populations of 200,000 independent co-learning agents, yielding compelling insights into how non-stationarity-aware learners affect social dynamics. In particular, we find that LOLA learners promote cooperation in the Stag Hunt model, delay cooperative outcomes in the Hawk-Dove model, and reduce strategy diversity in the Rock-Paper-Scissors model.
2026-02-20
Transactions on Machine Learning Research (accepted)
openreview.net
openreview.net
Anisotropic local law for non-separable sample covariance matrices
Fan Zhou
Renyuan Ma
Elliot Paquette
Zhichao Wang
Zhou Fan
We establish local laws for sample covariance matrices …
2026-02-19
arXiv (preprint)
doi.org
arxiv.org
Mirror Descent Algorithms with Nearly Dimension-Independent Rates for Differentially-Private Stochastic Saddle-Point Problems
Tomas Gonzalez
Cristobal Guzman
Courtney Paquette
2026-02-19
SIAM Journal on Optimization (published)
doi.org
arxiv.org
On the Adversarial Robustness of Discrete Image Tokenizers
Rishika Bhagwatkar
Irina Rish
Nicolas Flammarion
Francesco Croce
Discrete image tokenizers encode visual inputs as sequences of tokens from a finite vocabulary and are gaining popularity in multimodal syst… (see more)ems, including encoder-only, encoder-decoder, and decoder-only models. However, unlike CLIP encoders, their vulnerability to adversarial attacks has not been explored. Ours being the first work studying this topic, we first formulate attacks that aim to perturb the features extracted by discrete tokenizers, and thus change the extracted tokens. These attacks are computationally efficient, application-agnostic, and effective across classification, multimodal retrieval, and captioning tasks. Second, to defend against this vulnerability, inspired by recent work on robust CLIP encoders, we fine-tune popular tokenizers with unsupervised adversarial training, keeping all other components frozen. While unsupervised and task-agnostic, our approach significantly improves robustness to both unsupervised and end-to-end supervised attacks and generalizes well to unseen tasks and data. Unlike supervised adversarial training, our approach can leverage unlabeled images, making it more versatile. Overall, our work highlights the critical role of tokenizer robustness in downstream tasks and presents an important step in the development of safe multimodal foundation models.
2026-02-19
arXiv (preprint)
doi.org
arxiv.org
Dataless Weight Disentanglement in Task Arithmetic via Kronecker-Factored Approximate Curvature
Angelo Porrello
Pietro Buzzega
Felix Dangel
Thomas Sommariva
Riccardo Salami
Lorenzo Bonicelli
Simone Calderara
Task Arithmetic yields a modular, scalable way to adapt foundation models. Combining multiple task vectors, however, can lead to cross-task … (see more)interference, causing representation drift and degraded performance. Representation drift regularization provides a natural remedy to disentangle task vectors; however, existing approaches typically require external task data, conflicting with modularity and data availability constraints (e.g., privacy requirements). We propose a dataless approach by framing regularization against representation drift as a curvature matrix approximation problem. This allows us to leverage well-established techniques; in particular, we adopt Kronecker-Factored Approximate Curvature and obtain a practical regularizer that achieves state-of-the-art results in task addition and negation. Our method has constant complexity in the number of tasks and promotes robustness to task vector rescaling, eliminating the need for held-out tuning.
2026-02-18
Open MIND (preprint)
doi.org
arxiv.org
GASS: Geometry-Aware Spherical Sampling for Disentangled Diversity Enhancement in Text-to-Image Generation
Ye Zhu
Kaleb S. Newman
Johannes F. Lutzeyer
Adriana Romero
Michal Drozdzal
Olga Russakovsky
2026-02-18
ArXiv (preprint)
arxiv.org
arxiv.org
Offline-Online Retail Collaboration via Pickup Partnership
Zahra Jalali
Maxime C. Cohen
Necati Ertekin
Mehmet Gümüş
We study a growing retail strategy called pickup partnership, where online retailers partner with physical stores to offer in-store pickup s… (see more)ervices. In practice, two main policies are used in these partnerships: (i) a fixed fee policy, where the retailer pays the offline partner a set fee per pickup order, and (ii) a coupon policy, where customers receive a coupon for use at the offline partner’s store with each pickup order. Our goal is to evaluate these policies and determine which is most beneficial for online retailers. We develop a stylized model that captures the essential dynamics of pickup partnerships. We find that although the coupon policy allows the online retailer to gain greater market coverage compared with the fixed fee policy, it does not always lead to higher profits for the online retailer. The coupon policy is preferred when in-store fulfillment and pickup handling costs are low and direct-delivery costs are high, whereas the fixed fee policy is favored when these costs are moderate. We also find that both policies entail inefficiencies when the incentives of the two parties are not aligned. To alleviate such inefficiencies, we propose a new policy designed to better align incentives and improve partnership efficiency. This paper offers the first theoretical analysis of the in-store pickup partnership model and provides practical guidance for online retailers seeking to implement it. Our proposed policy aims to enhance the effectiveness and profitability of these partnerships beyond current industry practices. Supplemental Material: The online appendix is available at https://doi.org/10.1287/serv.2025.0118 .
2026-02-18
Service Science (published)
doi.org
Stackelberg Dynamic Location Planning under Cumulative Demand
Warley Almeida Silva
Margarida Carvalho
Sanjay Dominik Jena
Dynamic facility location problems predominantly suppose a monopoly over the service or product provided. Nonetheless, this premise can be a… (see more) severe oversimplification in the presence of market competitors, as customers may prefer facilities installed by one of them. The monopolistic assumption can particularly worsen planning performance when demand depends on prior location decisions of the market participants, namely, when unmet demand from one period carries over to the next. Such a demand behaviour creates an intrinsic relationship between customer demand and location decisions of all market participants, and requires the decision-maker to anticipate the competitor's response. This work studies a novel competitive facility location problem that combines cumulative demand and market competition to devise high-quality solutions. We propose bilevel mixed-integer programming formulations for two variants of our problem, prove that the optimistic variant is
2026-02-18
arXiv (preprint)
doi.org
arxiv.org
Beyond Message Passing: A Symbolic Alternative for Expressive and Interpretable Graph Learning
Chuqin Geng
Li Zhang
Haolin Ye
Ziyu Zhao
Yuhe Jiang
Tara Saba
Xinyu Wang
Xujie Si
Graph Neural Networks (GNNs) have become essential in high-stakes domains such as drug discovery, yet their black-box nature remains a signi… (see more)ficant barrier to trustworthiness. While self-explainable GNNs attempt to bridge this gap, they often rely on standard message-passing backbones that inherit fundamental limitations, including the 1-Weisfeiler-Lehman (1-WL) expressivity barrier and a lack of fine-grained interpretability. To address these challenges, we propose SymGraph, a symbolic framework designed to transcend these constraints. By replacing continuous message passing with discrete structural hashing and topological role-based aggregation, our architecture theoretically surpasses the 1-WL barrier, achieving superior expressiveness without the overhead of differentiable optimization. Extensive empirical evaluations demonstrate that SymGraph achieves state-of-the-art performance, outperforming existing self-explainable GNNs. Notably, SymGraph delivers 10x to 100x speedups in training time using only CPU execution. Furthermore, SymGraph generates rules with superior semantic granularity compared to existing rule-based methods, offering great potential for scientific discovery and explainable AI.
2026-02-17
arXiv (preprint)
doi.org
arxiv.org
Causality is Key for Interpretability Claims to Generalise
Shruti Joshi
Aaron Mueller
David Klindt
Wieland Brendel
Patrik Reizinger
Dhanya Sridhar
Interpretability research on large language models (LLMs) has yielded important insights into model behaviour, yet recurring pitfalls persis… (see more)t: findings that do not generalise, and causal interpretations that outrun the evidence. Our position is that causal inference specifies what constitutes a valid mapping from model activations to invariant high-level structures, the data or assumptions needed to achieve it, and the inferences it can support. Specifically, Pearl's causal hierarchy clarifies what an interpretability study can justify. Observations establish associations between model behaviour and internal components. Interventions (e.g., ablations or activation patching) support claims how these edits affect a behavioural metric (e.g., average change in token probabilities) over a set of prompts. However, counterfactual claims -- i.e., asking what the model output would have been for the same prompt under an unobserved intervention -- remain largely unverifiable without controlled supervision. We show how causal representation learning (CRL) operationalises this hierarchy, specifying which variables are recoverable from activations and under what assumptions. Together, these motivate a diagnostic framework that helps practitioners select methods and evaluations matching claims to evidence such that findings generalise.
2026-02-17
arXiv (preprint)
doi.org
arxiv.org
Neural Proposals, Symbolic Guarantees: Neuro-Symbolic Graph Generation with Hard Constraints
Chuqin Geng
Li Zhang
Mark Zhang
Haolin Ye
Ziyu Zhao
Xujie Si
We challenge black-box purely deep neural approaches for molecules and graph generation, which are limited in controllability and lack forma… (see more)l guarantees. We introduce Neuro-Symbolic Graph Generative Modeling (NSGGM), a neurosymbolic framework that reapproaches molecule generation as a scaffold and interaction learning task with symbolic assembly. An autoregressive neural model proposes scaffolds and refines interaction signals, and a CPU-efficient SMT solver constructs full graphs while enforcing chemical validity, structural rules, and user-specific constraints, yielding molecules that are correct by construction and interpretable control that pure neural methods cannot provide. NSGGM delivers strong performance on both unconstrained generation and constrained generation tasks, demonstrating that neuro-symbolic modeling can match state-of-the-art generative performance while offering explicit controllability and guarantees. To evaluate more nuanced controllability, we also introduce a Logical-Constraint Molecular Benchmark, designed to test strict hard-rule satisfaction in workflows that require explicit, interpretable specifications together with verifiable compliance.
2026-02-17
Open MIND (preprint)
doi.org
arxiv.org