GenAI4ELab/papercli-papers huggingface dataset:
Token Assorted: Mixing Latent and Text Tokens for Improved Language Model Reasoning
Authors: DiJia Su; Hanlin Zhu; Yingchen Xu; Jiantao Jiao; Yuandong Tian; Qinqing Zheng
Venue: ICML 2025 (ICML 2025 poster)

Abstract: Large Language Models (LLMs) excel at reasoning and planning when trained on chain-of-thought (CoT) data, where the step-by-step thought process is explicitly outlined by text tokens.
However, this results in lengthy inputs where many words support textual coherence rather than core reasoning information, and processing these inputs consumes substantial computation resources.
In this work, we propose a hybrid representation of the reasoning process, where we partially abstract away the initial reasoning steps using latent discrete tokens generated by VQ-VAE,  significantly reducing the length of reasoning traces. We explore the use of latent trace abstractions in two scenarios: 1) training the model from scratch for the Keys-Finding Maze problem, 2) fine-tuning LLMs on this hybrid data with an extended vocabulary including unseen latent tokens, for both logical and mathematical reasoning problems. 
To facilitate effective learning, we introduce a simple training procedure that randomly mixes latent and text tokens, which enables fast adaptation to new latent tokens. Our approach consistently outperforms the baselines methods in various benchmarks, such as Math (+4.2\%, Llama-3.2-1B), GSM8K (+4.1%, Llama-3.2-3B), and Fresh-Gaokao-Math-2023 (+13.3%, Llama-3.1-8B) with an average reduction of 17% in reasoning trace's length.
Generative Social Choice: The Next Generation
Authors: Niclas Boehmer; Sara Fish; Ariel D. Procaccia
Venue: ICML 2025 (ICML 2025 oral)

Abstract: A key task in certain democratic processes is to produce a concise slate of statements that proportionally represents the full spectrum of user opinions. This task is similar to committee elections, but unlike traditional settings, the candidate set comprises all possible statements of varying lengths, and so it can only be accessed through specific queries. Combining social choice and large language models, prior work has approached this challenge through a framework of generative social choice. We extend the framework in two fundamental ways, providing theoretical guarantees even in the face of approximately optimal queries and a budget limit on the overall length of the slate. Using GPT-4o to implement queries, we showcase our approach on datasets related to city improvement measures and drug reviews, demonstrating its effectiveness in generating representative slates from unstructured user opinions.
Quadratic Upper Bound for Boosting Robustness
Authors: Euijin You; Hyang-Won Lee
Venue: ICML 2025 (ICML 2025 poster)

Abstract: Fast adversarial training (FAT) aims to enhance the robustness of models against adversarial attacks with reduced training time, however, FAT often suffers from compromised robustness due to insufficient exploration of adversarial space. In this paper, we develop a loss function to mitigate the problem of degraded robustness under FAT. 
Specifically, we derive a quadratic upper bound (QUB) on the adversarial training (AT) loss function and propose to utilize the bound with existing FAT methods. Our experimental results show that applying QUB loss to the existing methods yields significant improvement of robustness. Furthermore, using various metrics, we demonstrate that this improvement is likely to result from the smoothened loss landscape of the resulting model.
Towards Global-level Mechanistic Interpretability: A Perspective of Modular Circuits of Large Language Models
Authors: Yinhan He; Wendy Zheng; Yushun Dong; Yaochen Zhu; Chen Chen; Jundong Li
Venue: ICML 2025 (ICML 2025 poster)

Abstract: Mechanistic interpretability (MI) research aims to understand large language models (LLMs) by identifying computational circuits, subgraphs of model components with associated functional interpretations, that explain specific behaviors. Current MI approaches focus on discovering task-specific circuits, which has two key limitations: (1) poor generalizability across different language tasks, and (2) high costs associated with requiring human or advanced LLM interpretation of each computational node. To address these challenges, we propose developing a ``modular circuit (MC) vocabulary'' consisting of task-agnostic functional units. Each unit consists of a small computational subgraph with its interpretation. This approach enables global interpretability by allowing different language tasks to share common MCs, while reducing costs by reusing established interpretations for new tasks. We establish five criteria for characterizing the MC vocabulary and present ModCirc, a novel global-level mechanistic interpretability framework for discovering MC vocabularies in LLMs. We demonstrate ModCirc's effectiveness by showing that it can identify modular circuits that perform well on various metrics.
Contrastive Localized Language-Image Pre-Training
Authors: Hong-You Chen; Zhengfeng Lai; Haotian Zhang; Xinze Wang; Marcin Eichner; Keen You; Meng Cao; Bowen Zhang; Yinfei Yang; Zhe Gan
Venue: ICML 2025 (ICML 2025 poster)

Abstract: CLIP has been a celebrated method for training vision encoders to generate image/text representations facilitating various applications. Recently, it has been widely adopted as the vision backbone of multimodal large language models (MLLMs). The success of CLIP relies on aligning web-crawled noisy text annotations at image levels. However, such criteria may be insufficient for downstream tasks in need of fine-grained vision representations, especially when understanding region-level is demanding for MLLMs. We improve the localization capability of CLIP with several advances. Our proposed pre-training method, Contrastive Localized Language-Image Pre-training (CLOC), complements CLIP with region-text contrastive loss and modules. We formulate a new concept, promptable embeddings, of which the encoder produces image embeddings easy to transform into region representations given spatial hints. To support large-scale pre-training, we design a visually-enriched and spatially-localized captioning framework to effectively generate region-text labels. By scaling up to billions of annotated images, CLOC enables high-quality regional embeddings for recognition and retrieval tasks, and can be a drop-in replacement of CLIP to enhance MLLMs, especially on referring and grounding tasks.
Concurrent Reinforcement Learning with Aggregated States via  Randomized Least Squares Value Iteration
Authors: Yan Chen; Qinxun Bai; Yiteng Zhang; Maria Dimakopoulou; Shi Dong; Qi Sun; Zhengyuan Zhou
Venue: ICML 2025 (ICML 2025 poster)

Abstract: Designing learning agents that explore efficiently in a complex environment has been widely recognized as a fundamental challenge in reinforcement learning. While a number of works have demonstrated the effectiveness of  techniques based on randomized value functions on a single agent, it remains unclear, from a theoretical point of view, whether injecting randomization can help a society of agents concurently explore an environment. The theoretical results established in this work tender an affirmative answer to this question. We adapt the concurrent learning framework to randomized least-squares value iteration (RLSVI) with aggregated state representation. 
We demonstrate polynomial worst-case regret bounds in both finite- and infinite-horizon environments.
In both setups the per-agent regret decreases at an optimal rate of $\Theta\left(\frac{1}{\sqrt{N}}\right)$, highlighting the advantage of concurent learning. Our algorithm exhibits significantly lower space complexity compared to Russo (2019) and Agrawal et. al (2021). We reduce the space complexity by a factor of $K$ while incurring only a $\sqrt{K}$ increase in the worst-case regret bound, compared to Russo (2019) and Agrawal et. al (2021). Interestingly, our algorithm improves the worst-case regret bound of Russo (2019) by a factor of $H^{1/2}$, matching the improvement in Agrawal et. al (2021). However, this result is achieved through a fundamentally different algorithmic enhancement and proof technique. Additionally, we conduct numerical experiments to demonstrate our theoretical findings.
SEAD: Unsupervised Ensemble of Streaming Anomaly Detectors
Authors: Saumya Gaurang Shah; Abishek Sankararaman; Balakrishnan Murali Narayanaswamy; Vikramank Singh
Venue: ICML 2025 (ICML 2025 poster)

Abstract: Can we efficiently choose the best Anomaly Detection (AD) algorithm for a data-stream without requiring anomaly labels? Streaming anomaly detection is hard. SOTA AD algorithms are sensitive to their hyperparameters and no single method works well on all datasets. The best algorithm/hyper-parameter combination for a given data-stream can change over time with data drift. 
'What is an anomaly?' is often application, context and dataset dependent. 
We propose SEAD (Streaming Ensemble of Anomaly Detectors), the first model selection algorithm for streaming, unsupervised AD. All prior AD model selection algorithms are either supervised, or only work in the offline setting when all data from the test set  is available upfront. We show that SEAD is {\em(i)}  unsupervised, i.e., requires no true anomaly labels, {\em(ii)}  efficiently implementable in a streaming setting,  {\em (iii)}  agnostic to the choice of the base algorithms among which it chooses from, and {\em (iv)}  adaptive to non-stationarity in the data-stream. Experiments on 14 non-trivial public datasets and an internal dataset corroborate our claims.
WOMD-Reasoning: A Large-Scale Dataset for Interaction Reasoning in Driving
Authors: Yiheng Li; Cunxin Fan; Chongjian GE; Seth Z. Zhao; Chenran Li; Chenfeng Xu; Huaxiu Yao; Masayoshi Tomizuka; Bolei Zhou; Chen Tang; Mingyu Ding; Wei Zhan
Venue: ICML 2025 (ICML 2025 poster)

Abstract: Language models uncover unprecedented abilities in analyzing driving scenarios, owing to their limitless knowledge accumulated from text-based pre-training. Naturally, they should particularly excel in analyzing rule-based interactions, such as those triggered by traffic laws, which are well documented in texts. However, such interaction analysis remains underexplored due to the lack of dedicated language datasets that address it. Therefore, we propose Waymo Open Motion Dataset-Reasoning (WOMD-Reasoning), a comprehensive large-scale Q&As dataset built on WOMD focusing on describing and reasoning traffic rule-induced interactions in driving scenarios. WOMD-Reasoning also presents by far the largest multi-modal Q&A dataset, with 3 million Q&As on real-world driving scenarios, covering a wide range of driving topics from map descriptions and motion status descriptions to narratives and analyses of agents' interactions, behaviors, and intentions. To showcase the applications of WOMD-Reasoning, we design Motion-LLaVA, a motion-language model fine-tuned on WOMD-Reasoning. Quantitative and qualitative evaluations are performed on WOMD-Reasoning dataset as well as the outputs of Motion-LLaVA, supporting the data quality and wide applications of WOMD-Reasoning, in interaction predictions, traffic rule compliance plannings, etc. The dataset and its vision modal extension are available on https://waymo.com/open/download/. The codes & prompts to build it are available on https://github.com/yhli123/WOMD-Reasoning.
Model Swarms: Collaborative Search to Adapt LLM Experts via Swarm Intelligence
Authors: Shangbin Feng; Zifeng Wang; Yike Wang; Sayna Ebrahimi; Hamid Palangi; Lesly Miculicich; Achin Kulshrestha; Nathalie Rauschmayr; Yejin Choi; Yulia Tsvetkov; Chen-Yu Lee; Tomas Pfister
Venue: ICML 2025 (ICML 2025 poster)

Abstract: We propose Model Swarms, a collaborative search algorithm to adapt LLMs via swarm intelligence, the collective behavior guiding individual systems. Specifically, Model Swarms starts with a pool of LLM experts and a utility function. Guided by the best-found checkpoints across models, diverse LLM experts collaboratively move in the weight space and optimize a utility function representing model adaptation objectives. Compared to existing model composition approaches, Model Swarms offers tuning-free model adaptation, works in low-data regimes with as few as 200 examples, and does not require assumptions about specific experts in the swarm or how they should be composed. Extensive experiments demonstrate that Model Swarms could flexibly adapt LLM experts to a single task, multi-task domains, reward models, as well as diverse human interests, improving over 12 model composition baselines by up to 21.0% across tasks and contexts. Further analysis reveals that LLM experts discover previously unseen capabilities in initial checkpoints and that Model Swarms enable the weak-to-strong transition of experts through the collaborative search process.
An Asymptotically Optimal Approximation Algorithm for Multiobjective Submodular Maximization at Scale
Authors: Fabian Christian Spaeh; Atsushi Miyauchi
Venue: ICML 2025 (ICML 2025 poster)

Abstract: Maximizing a single submodular set function subject to a cardinality constraint is a well-studied and central topic in combinatorial optimization. However, finding a set that maximizes multiple functions at the same time is much less understood, even though it is a formulation which naturally occurs in robust maximization or problems with fairness considerations such as fair influence maximization or fair allocation. In this work, we consider the problem of maximizing the minimum over many submodular functions subject to a cardinality constraint, which is known as multiobjective submodular maximization. All known polynomial-time approximation algorithms either obtain a weak approximation guarantee or rely on the evaluation of the multilinear extension. The latter is expensive to evaluate and renders such algorithms impractical. We bridge this gap and introduce the first scalable and practical algorithm that obtains the best-known approximation guarantee. We furthermore introduce a novel application fair centrality maximization and show how it can be addressed via multiobjective submodular maximization. In our experimental evaluation, we show that our algorithm outperforms known algorithms in terms of objective value and running time.
Explainable Concept Generation through Vision-Language Preference Learning for Understanding Neural Networks' Internal Representations
Authors: Aditya Taparia; Som Sagar; Ransalu Senanayake
Venue: ICML 2025 (ICML 2025 poster)

Abstract: Understanding the inner representation of a neural network helps users improve models. Concept-based methods have become a popular choice for explaining deep neural networks post-hoc because, unlike most other explainable AI techniques, they can be used to test high-level visual "concepts" that are not directly related to feature attributes. For instance, the concept of "stripes" is important to classify an image as a zebra. Concept-based explanation methods, however, require practitioners to guess and manually collect multiple candidate concept image sets, making the process labor-intensive and prone to overlooking important concepts. Addressing this limitation, in this paper, we frame concept image set creation as an image generation problem. However, since naively using a standard generative model does not result in meaningful concepts, we devise a reinforcement learning-based preference optimization (RLPO) algorithm that fine-tunes a vision-language generative model from approximate textual descriptions of concepts. Through a series of experiments, we demonstrate our method's ability to efficiently and reliably articulate diverse concepts that are otherwise challenging to craft manually.
Leveraging Offline Data in Linear Latent Contextual Bandits
Authors: Chinmaya Kausik; Kevin Tan; Ambuj Tewari
Venue: ICML 2025 (ICML 2025 poster)

Abstract: Leveraging offline data is an attractive way to accelerate online sequential decision-making. However, it is crucial to account for latent states in users or environments in the offline data, and latent bandits form a compelling model for doing so. In this light, we design end-to-end latent bandit algorithms capable of handing uncountably many latent states. We focus on a linear latent contextual bandit &mdash; a linear bandit where each user has its own high-dimensional reward parameter in $\mathbb{R}^{d_A}$, but reward parameters across users lie in a low-rank latent subspace of dimension $d_K \ll d_A$. First, we provide an offline algorithm to learn this subspace with provable guarantees. We then present two online algorithms that utilize the output of this offline algorithm to accelerate online learning. The first enjoys $\tilde O(\min(d_A\sqrt{T}, d_K\sqrt{T}(1+\sqrt{d_AT/d_KN})))$ regret guarantees, so that the effective dimension is lower when the size $N$ of the offline dataset is larger. We prove a matching lower bound on regret, showing that our algorithm is minimax optimal. The second is a practical algorithm that enjoys only a slightly weaker guarantee, but is computationally efficient. We also establish the efficacy of our methods using experiments on both synthetic data and real-life movie recommendation data from MovieLens. Finally, we theoretically establish the generality of the latent bandit model by proving a de Finetti theorem for stateless decision processes.
Automated Hypothesis Validation with Agentic Sequential Falsifications
Authors: Kexin Huang; Ying Jin; Ryan Li; Michael Y. Li; Emmanuel Candes; Jure Leskovec
Venue: ICML 2025 (ICML 2025 poster)

Abstract: Hypotheses are central to information acquisition, decision-making, and discovery. However, many real-world hypotheses are abstract, high-level statements that are difficult to validate directly. This challenge is further intensified by the rise of hypothesis generation from Large Language Models (LLMs), which are prone to hallucination and produce hypotheses in volumes that make manual validation impractical. Here we propose POPPER, an agentic framework for rigorous automated validation of free-form hypotheses. Guided by Karl Popper's principle of falsification, POPPER validates a hypothesis using LLM agents that design and execute falsification experiments targeting its measurable implications. A novel sequential testing framework ensures strict Type-I error control while actively gathering evidence from diverse observations, whether drawn from existing data or newly conducted procedures. We demonstrate POPPER on six domains including biology, economics, and sociology. POPPER delivers robust error control, high power, and scalability. Furthermore, compared to human scientists, POPPER achieved comparable performance in validating complex biological hypotheses while reducing time by 10 folds, providing a scalable, rigorous solution for hypothesis validation. POPPER is freely available at https://github.com/snap-stanford/POPPER.
MELON: Provable Defense Against Indirect Prompt Injection Attacks in AI Agents
Authors: Kaijie Zhu; Xianjun Yang; Jindong Wang; Wenbo Guo; William Yang Wang
Venue: ICML 2025 (ICML 2025 poster)

Abstract: Recent research has explored that LLM agents are vulnerable to indirect prompt injection (IPI) attacks, where malicious tasks embedded in tool-retrieved information can redirect the agent to take unauthorized actions. Existing defenses against IPI have significant limitations: either require essential model training resources, lack effectiveness against sophisticated attacks, or harm the normal utilities. We present MELON (Masked re-Execution and TooL comparisON), a novel IPI defense. Our approach builds on the observation that under a successful attack, the agent’s next action becomes less dependent on user tasks and more on malicious tasks. Following this, we design MELON to detect attacks by re-executing the agent’s trajectory with a masked user prompt modified through a masking function. We identify an attack if the actions generated in the original and masked executions are similar. We also include three key designs to reduce the potential false positives and false negatives. Extensive evaluation on the IPI benchmark AgentDojo demonstrates that MELON outperforms SOTA defenses in both attack prevention and utility preservation. Moreover, we show that combining MELON with a SOTA prompt augmentation defense (denoted as MELON-Aug) further improves its performance. We also conduct a detailed ablation study to validate our key designs. Code is available at https://github.com/kaijiezhu11/MELON.
Improving the Variance of Differentially Private Randomized Experiments through Clustering
Authors: Adel Javanmard; Vahab Mirrokni; Jean Pouget-Abadie
Venue: ICML 2025 (ICML 2025 poster)

Abstract: Estimating causal effects from randomized experiments is only possible if participants are willing to disclose their potentially sensitive responses. Differential privacy, a widely used framework for ensuring an algorithm’s privacy guarantees, can encourage participants to share their responses without the risk of de-anonymization. However, many mechanisms achieve differential privacy by adding noise to the original dataset, which reduces the precision of causal effect estimation. This introduces a fundamental trade-off between privacy and variance when performing causal analyses on differentially private data.
In this work, we propose a new differentially private mechanism, \textsc{Cluster-DP}, which leverages a given cluster structure in the data to improve the privacy-variance trade-off. While our results apply to
any clustering, we demonstrate that selecting higher-quality clusters—according to a quality metric we introduce—can decrease the variance penalty without compromising privacy guarantees. Finally, we evaluate the theoretical and empirical performance of our \textsc{Cluster-DP} algorithm on both real and simulated data, comparing it to common baselines, including two special cases of our algorithm: its unclustered version and a uniform-prior version.
Learn from Downstream and Be Yourself in Multimodal Large Language Models Fine-Tuning
Authors: Wenke Huang; Jian Liang; Zekun Shi; Didi Zhu; Guancheng Wan; He Li; Bo Du; Dacheng Tao; Mang Ye
Venue: ICML 2025 (ICML 2025 poster)

Abstract: Multimodal Large Language Model (MLLM) has demonstrated strong generalization capabilities across diverse distributions and tasks, largely due to extensive pre-training datasets. Fine-tuning MLLM has become a common practice to improve performance on specific downstream tasks. However, during fine-tuning, MLLM often faces the risk of forgetting knowledge acquired during pre-training, which can result in a decline in generalization abilities. To balance the trade-off between generalization and specialization, we propose measuring the parameter importance for both pre-trained and fine-tuning distributions, based on frozen pre-trained weight magnitude and accumulated fine-tuning gradient values. We further apply an importance-aware weight allocation strategy, selectively updating relatively important parameters for downstream tasks. We conduct empirical evaluations on both image captioning and visual question-answering tasks using various MLLM architectures. The comprehensive experimental analysis demonstrates the effectiveness of the proposed solution, highlighting the efficiency of the crucial modules in enhancing downstream specialization performance while mitigating generalization degradation in MLLM Fine-Tuning.