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Optimization · Discrete and Combinatorial Optimization

Tongkai Lu, Shuai Ma, Chongyang Tao

Mixed Integer Linear Programming (MILP) is a fundamental NP-hard problem that has garnered significant attention from both academia and industry. The Branch-and-Bound (B&B) algorithm is the dominant approach for solving MILPs, where branching decisions play a critical role and have recently been enhanced by neural methods. However, these methods still struggle with semantic variation across depths, the scarcity of upstream nodes, and the costly collection of strong branching samples. To address these issues, we propose SC-MILP, a Dynamic Stratified Contrastive Training Framework for MILP Branching. Our method groups B&B nodes based on their feature distributions and learns depth-aware, fine-grained node representations through dynamic stratified contrastive training. To address data scarcity and imbalance at upstream nodes, we introduce an upstream-augmented MILP derivation procedure that generates both theoretically equivalent and perturbed instances. Experiments on both synthetic and real-world MILP benchmarks, including large-scale instances, show that SC-MILP significantly improves branching accuracy, reduces solving time, with particularly strong gains at upstream nodes.

Deep Learning · Large Language Models

Mingkuan Zhao, Xiayu Sun, Wentao Hu, Suquan Chen, Jiaxuan Li, Xiaoyan Zhu, Xin Lai, Jiayin Wang

Causal language models factorize sequence probabilities using only preceding context, leaving future information unexploited during training despite its availability in the training data. This paper introduces Regret Pre-training, a self-supervised framework grounded in the Learning Using Privileged Information (LUPI) paradigm. The framework employs a dual-view architecture in which a single model generates both a causal Student distribution and a future-conditioned Teacher distribution. The training objective augments standard language modeling with a regret loss that minimizes the KL divergence from teacher to student, transferring future-aware signals to the causal representations. We investigate two teacher configurations on the OLMoE-1B-7B architecture:LocalRegret, which extends attention by one future token, andGlobalRegret, which conditions on bidirectional context with the target position masked. Experiments on nine downstream tasks following 4 billion tokens of training demonstrate that both configurations consistently outperform the baseline. On average,GlobalRegret andLocalRegret achieve 33.9% and 32.2% accuracy respectively, surpassing the baseline's 30.2%. Most notably,GlobalRegret improves BoolQ performance by 18.1 percentage points (61.0% vs 42.9%). The framework introduces no additional parameters and requires only one extra inference-mode forward pass per training step. The source code for this paper is publicly available at https://anonymous.4open.science/r/ICML13655/ to facilitate reproducibility.

Reinforcement Learning · Online

Jianghao Wu, Daniel F Schmidt, Weiqiang Wang, Jin Ye, Jianfei Cai, Yasmeen George

Reinforcement learning with verifiable rewards (RLVR) can yield large reasoning gains from very few training instances, yet its strong sensitivity to which instances are used makes data selection a central bottleneck. Most existing selection pipelines rely on training-time optimization signals and/or require access to verifiable rewards or ground-truth answers over large candidate pools, which is costly and often infeasible in specialized domains. We study RLVR data selection in a setting where selection must be performed \emph{before} any RL training and \emph{without} labels or reward evaluation on the full pool. % We propose \textbf{SHIFT}, a one-shot, training-free selector based solely on inference-time hidden-state dynamics. For each candidate instance, SHIFT runs a single deterministic reasoning rollout and computes a \emph{reasoning-induced representation shift} (RIRS) as the start-to-end hidden-state delta. SHIFT uses the RIRS magnitude as a lightweight proxy for instance utility and enforces coverage via a quality-weighted farthest-first coreset procedure in an RIRS-augmented feature space, producing compact subsets that scale to large unlabeled pools. % Across mathematical reasoning and medical QA benchmarks under ultra-low budgets, SHIFT consistently outperforms training-free diversity and difficulty/uncertainty baselines, improving both in-domain accuracy and transfer to harder evaluation settings. Ablations show that RIRS-based coverage and quality-weighting contribute complementary gains, and analyses indicate that RIRS is not explained by simple input/output length statistics. Code will be released.

Deep Learning · Generative Models and Autoencoders

Yingyan Hou, Xianchi Dong, Chao Ren, Wanxuan Lu, Zihan Wei, Hongfeng Yu, Yixiao Wang, Yaning Zhou

Object insertion has emerged as a promising augmentation paradigm to solve the label scarcity and long-tail distributions in remote sensing. It aims to generate training samples by synthesizing target instances onto real backgrounds. However, existing methods have three critical issues: (i) Semantic placement inconsistency, (ii) Radiometric inconsistency with illumination and atmospheric conditions, and (iii) Textural discontinuity. To cope with these issues, we propose a physics-aware method, called "Plan, Decouple, Assimilate" (PDA), for generating high-fidelity training samples. In the planning stage, the Planning (P) module automatically generates geometrically bounding boxes. In the generation stage, we design a dual-module model to generate the target instance: the Decoupling (D) module employs Asymmetric Spectral Adaptation Decoupling to disentangle structural identity from environmental illumination, while the Assimilation (A) module utilizes Neighborhood-Aware Texture Assimilation to harmonize the local manifold. By strategically integrating these modules, PDA enforces multi-level consistency spanning global geometry to local micro-textures. Extensive experiments verify that PDA consistently outperforms existing state-of-the-art methods in generative quality, reducing whole-image FID by 15.7%, and substantially improves downstream detection performance, boosting average mAP50 by 15.9% over the real-data baseline.

Social Aspects · Safety

Yen-Shan Chen, Sian-Yao Huang, Cheng-Lin Yang, Yun-Nung Chen

Existing data poisoning attacks on retrieval-augmented generation (RAG) systems scale poorly because they require costly optimization of poisoned documents for each target phrase. We introduce Eyes-on-Me, a modular attack that decomposes an adversarial document into reusable **Attention Attractors** and **Focus Regions**. Attractors are optimized to direct attention to the Focus Region. Attackers can then insert semantic baits for the retriever or malicious instructions for the generator, adapting to new targets at near zero cost. This is achieved by steering a small subset of attention heads that we empirically identify as strongly correlated with attack success. Across 18 end-to-end RAG settings (3 datasets $\times$ 2 retrievers $\times$ 3 generators), Eyes-on-Me raises average attack success rates from 21.9 to 57.8 (+35.9 points, 2.6$\times$ over prior work). A single optimized attractor transfers to unseen black box retrievers and generators without retraining. Our findings establish a scalable paradigm for RAG data poisoning and show that modular, reusable components pose a practical threat to modern AI systems. They also reveal a strong link between attention concentration and model outputs, informing interpretability research.

Reinforcement Learning · Deep RL

Quang Anh PHAM, Tien Mai, Akshat Kumar

Imitation Learning (IL) has demonstrated strong capabilities in learning high-quality policies from expert demonstrations for sequential decision-making tasks. Nonetheless, its effectiveness is significantly constrained in low-expert-data regimes. To mitigate this issue, previous works introduce ``*offline IL with supplementary data*" which augments expert demonstrations with additional, low-cost data generated by suboptimal policies. A prominent framework for this setting is Distribution Correction Estimation (DICE), which estimates the optimal density ratio by solving the dual of a divergence minimization problem between the learned policy and the expert visitation distribution. Despite their theoretical appeal, existing DICE-based methods often require adding a dataset regularizer to the divergence objective, or rely on a strict coverage assumption. These weaknesses limit the capability of DICE-based methods, causing them to be inefficient in some contexts. In this paper, we introduce ReDICE, a new method to address these limitations. ReDICE is derived by formulating an objective under a mixture distribution from the KL divergence between expert and learned policy occupancies. We formally prove that the dual of this formulation is mathematically equivalent to a stable Gumbel regression objective. Furthermore, we introduce a novel policy extraction mechanism that significantly improves performance in practice. Experiments across diverse benchmarks show that ReDICE achieves state-of-the-art results relative to prior offline IL baselines.

Applications · Everything Else

Luankang Zhang, Hao Wang, Zhongzhou Liu, MINGJIA YIN, Yonghao Huang, Jiaqi Li, Wei Guo, Yong Liu, Huifeng Guo, Defu Lian 等

The scarcity of high-quality training data presents a fundamental bottleneck to scaling machine learning models. This challenge is particularly acute in recommendation systems, where extreme sparsity in user interactions leads to rugged optimization landscapes and poor generalization. We propose the Recursive Self-Improving Recommendation (RSIR) framework, a paradigm in which a model bootstraps its own performance without reliance on external data or teacher models. RSIR operates in a closed loop: the current model generates plausible user interaction sequences, a fidelity-based quality control mechanism filters them for consistency with user’s approximate preference manifold, and a successor model is augmented on the enriched dataset. Our theoretical analysis shows that RSIR acts as a data-driven implicit regularizer, smoothing the optimization landscape and guiding models toward more robust solutions. Empirically, RSIR yields consistent, cumulative gains across multiple benchmarks and architectures. Notably, even smaller models benefit, and weak models can generate effective training curricula for stronger ones. These results demonstrate that recursive self-improvement is a general, model-agnostic approach to overcoming data sparsity, suggesting a scalable path forward for recommender systems and beyond. Our anonymized code is available at https://anonymous.4open.science/r/RSIR-7C5B.

Optimization · Everything Else

Gal Lifshitz, Shahar Zuler, Ori Fouks, Dan Raviv

End-to-end deep learning has achieved impressive results but often relies on large labeled datasets, exhibits limited generalization to unseen scenarios, and incurs substantial computational cost. Classical optimization methods, in contrast, are data-efficient and lightweight but frequently suffer from slow or unstable convergence. Learned optimizers aim to bridge this gap, yet existing approaches predominantly focus on first-order methods, leaving learned second-order optimization largely unexplored. We introduce L-SR1, a novel learned second-order optimizer that augments the classical Symmetric Rank-One (SR1) method with a lightweight, trainable preconditioning unit. This unit generates data-driven rank-one updates that are explicitly aligned with the secant condition via a learned projection mechanism, ensuring stable and consistent curvature estimation. Through controlled analytic benchmarks, we systematically analyze the stability, generalization across problem dimensions, and the quality of the resulting search directions. We further evaluate L-SR1 on the real-world task of Monocular Human Mesh Recovery (HMR), where it outperforms existing optimization-based and learned-optimization baselines. With a compact model and no reliance on task-specific fine-tuning or annotated data, L-SR1 demonstrates strong generalization and provides an effective drop-in optimizer for a wide range of iterative optimization problems.

Reinforcement Learning · Batch/Offline

Siyu Wang, Xiaocong Chen, Mingming Gong, Yong Li, Quan Sheng, Lina Yao

Transformer sequence models such as Decision Transformer can learn strong offline policies from logged trajectories, but they can suffer from causal confusion: reliance on spurious correlations that predict reward in the data but do not reflect the true causal mechanisms of the environment. We propose CSET (Counterfactual State Editing Transformer), which improves robustness in strictly offline reinforcement learning without learning environment transition dynamics. CSET first fits a causal reward model as a conditional variational autoencoder to infer a posterior over reward disturbances for each transition. Conditioning on the factual action and a sampled disturbance, a counterfactual state generator proposes a minimally edited state whose predicted reward matches the factual reward; a normalized move-band constraint and an acceptance gate enforce state plausibility and reward consistency. We then augment trajectories by replacing only the observation token with the edited state while keeping the next observation factual, so the policy is not trained on synthetic successor transitions. On the model side, CSET uses a causally structured hybrid transformer: separate convolutional encoders process return-to-go, state, and action streams for local temporal structure, and a final attention block is softly supervised so action prediction focuses on its direct causal parents (state and return-to-go). Experiments on D4RL locomotion and AntMaze tasks and on offline recommendation benchmarks show consistent gains over transformer baselines and substantially improved robustness to injected spurious distractors.

Deep Learning · Generative Models and Autoencoders

Xiaoyan Xing, Xiao Zhang, Sezer Karaoglu, Theo Gevers, Anand Bhattad

Image-to-image relighting requires representations that disentangle scene properties from illumination. Recent methods rely on latent intrinsic representations but remain under-constrained and often fail on challenging materials such as metal and glass. A natural hypothesis is that stronger pretrained visual priors should resolve these failures. We find the opposite: features from top-performing semantic encoders often degrade relighting quality, revealing a fundamental trade-off between semantic abstraction and photometric fidelity. We study this trade-off and introduce Augmented Latent Intrinsics (ALI), which balances semantic context and dense photometric structure by fusing features from a pixel-aligned visual encoder into a latent-intrinsic framework, together with a self-supervised refinement strategy to mitigate the scarcity of paired real-world data. Trained only on unlabeled real-world image pairs and paired with a dense, pixel-aligned visual prior, ALI achieves strong relighting improvements, with the largest gains on complex, specular materials.

Applications · Robotics

Tzu-Yuan Huang, Armin Lederer, Dai-Jie Wu, Xiaobing Dai, Sihua Zhang, Hsiu-Chin Lin, Shao-Hua Sun, Stefan Sosnowski, Sandra Hirche

Flow matching (FM) has shown promising results in data-driven planning. However, it inherently lacks formal guarantees for ensuring state and action constraints, whose satisfaction is a fundamental and crucial requirement for the safety and admissibility of planned trajectories on various systems. Moreover, existing FM planners do not ensure the dynamical consistency, which potentially renders trajectories inexecutable. We address these shortcomings by proposing SAD-Flower, a novel framework for generating \textbf{S}afe, \textbf{A}dmissible, and \textbf{D}ynamically consistent trajectories. Our approach relies on an augmentation of the flow with a virtual control input. Thereby, principled guidance can be derived using techniques from nonlinear control theory, providing formal guarantees for state constraints, action constraints, and dynamic consistency. Crucially, SAD-Flower operates without retraining, enabling test-time satisfaction of unseen constraints. Through extensive experiments across several tasks, we demonstrate that SAD-Flower outperforms various generative-model-based baselines in ensuring constraint satisfaction. Video and demos can be found at sadflowerplanning.github.io.

Deep Learning · Self-Supervised Learning

Jiaxin Sun, Yuhua Qian, Yang Wang

Standard contrastive learning minimizes geometric distance between positive pairs, implicitly assuming that strict compactness optimizes discrimination. However, this topology-agnostic confusion neglects intrinsic data structures and topological complexity, leading to class confusion—particularly when aggressive augmentations induce semantic drift. To address this, we propose Topology-Aware Contrastive Learning, a framework that shifts the objective from geometric singularity to topological connectivity. Leveraging Persistent Homology, we explicitly regulate the connectivity of the latent space, ensuring positive pairs maintain an α–β that balances intra-class cohesion with separability. Theoretically, we formally define the topology-agnostic confusion phenomenon, prove that excessive compactness strictly lower-bounds the probability of confusion and derive a generalization bound demonstrating that richer topological connectivity tightens downstream risk. Furthermore, we establish a measure-theoretic framework to mitigating the sensitivity of our method against varying augmentation strengths. Empirical results on benchmarks confirm that our approach enhances representation quality and reduces reliance on specific augmentation strategies compared to standard baselines. Our code will be made publicly available upon acceptance.

Justinas Zaliaduonis, Sergios Gatidis, Till Richter, Patrick Putzky

Contrastive learning has emerged as a powerful paradigm for self-supervised representation learning, yet the precise conditions under which it recovers meaningful latent structure remain incompletely understood. We develop a measure-theoretic framework that formalizes the diversity condition, a requirement on the sampling mechanism that is necessary for recovering the latent space up to orthogonal transformation. We prove that when this condition is violated, as commonly occurs in practical settings where augmentations preserve semantic content, the optimal encoder no longer preserves geometric structure and linear identifiability is lost. Crucially, we demonstrate that the contrastive loss alone is insufficient for latent space reconstruction: encoder inductive bias emerges as a critical component that compensates for violations of the diversity condition. Our experiments on synthetic datasets and CIFAR-10 confirm these theoretical predictions, showing that architectural constraints become essential precisely when sampling diversity is limited. These findings have direct implications for the design of data augmentation strategies and encoder architectures in self-supervised contrastive learning systems.

Applications · Health / Medicine

Dongkyu Cho, Miao Zhang, Gregory Lyng, Rumi Chunara

Data augmentation is a widely used strategy to improve model robustness and generalization by enriching training datasets with synthetic examples. While large language models (LLMs) have demonstrated strong generative capabilities for this purpose, their applications in high-stakes domains like healthcare present unique challenges due to the risk of generating clinically incorrect or misleading information. In this work, we propose a novel query-based model collaboration framework that integrates expert-level domain knowledge to guide the augmentation process to preserve critical medical information. Compared to existing LLM-based and traditional augmentation methods, our generated data significantly improves preservation of critical medical information and reduces hallucinations at both the token and concept levels. Experiments on downstream clinical prediction tasks demonstrate consistent performance gains over existing augmentation methods. This lightweight collaborative framework addresses the gap between LLM augmentation potential and the safety requirements of specialized domains.

Applications · Computer Vision

Litao Guo, Jinsong Zhou, Shuaibo Li, Man CHEN, Xinli Xu, Zixin Zhang, Harold Haodong Chen, YINGCONG CHEN

Multimodal Large Language Models (MLLMs) have demonstrated exceptional proficiency in standard text extraction, but they encounter significant challenges when confronting real-world implicit text. Such content typically contains malicious information, intentionally concealed through physical deformation, visual camouflage, or cognitive suggestion. These concealment techniques circumvent content moderation systems and pose severe risks to user safety. To bridge the research gap in text recognition under real-world adversarial scenarios, we define the task of Implicit Text Reasoning and introduce ImpText-Bench, a meticulously constructed benchmark. Extensive evaluations on this benchmark reveal significant vulnerability in current systems; even advanced proprietary models achieve a maximum Text Match Score of only 35.79\%. In response, we propose ImpText-Reader, a tool-augmented framework. It employs a three-stage training strategy utilizing capability-boundary data to collaboratively optimize tool selection and semantic reasoning, thereby effectively extracting hidden text. Extensive experiments demonstrate that our approach achieves SOTA performance, significantly enhancing model robustness in adversarial environments.

Deep Learning · Large Language Models

Kabilan Elangovan, Jasmine Ong, Daniel Ting

This position paper argues that as Large Language Models (LLMs) increasingly consume synthetic data, parametric representations can no longer serve as reliable witnesses of factual provenance. Current architectures, which treat fluent outputs as implicitly grounded, create a critical epistemic failure mode: systems emit accurate-looking claims with no recoverable lineage to verifiable sources. We advance the position that referenceability and explicit traceability of claims to accessible evidence must be enforced as a non-negotiable system invariant. Distinct from Retrieval-Augmented Generation (RAG), which enriches generation with external context, we propose a negative safety constraint: in factual settings, no atomic claim should be emitted unless it is evidence-gated by identifiers that entail it; otherwise, the system must abstain. To operationalize this, we introduce a “separation-of-powers” architecture that decouples parametric generation from factual authorization, along with a diagnostic metric—Parametric Leakage Ratio (PLR)—to quantify ungrounded factual emissions. We conclude that enforcing a strict provenance–parametric divide is essential to prevent safety certifications from legitimizing unverifiable outputs in high-stakes domains such as healthcare.

Deep Learning · Theory

Oskar Nordenfors, Fredrik Ohlsson, Axel Flinth

We investigate the optimization of neural networks on symmetric data, and compare the strategy of constraining the architecture to be equivariant to that of using data augmentation. Our analysis reveals that the relative geometry of the admissible and the equivariant layers, respectively, plays a key role. Under natural assumptions on the data, network, loss, and group of symmetries, we show that compatibility of the spaces of admissible layers and equivariant layers, in the sense that the corresponding orthogonal projections commute, implies that the sets of equivariant stationary points are identical for the two strategies. If the linear layers of the network also are given a unitary parametrization, the set of equivariant layers is even invariant under the gradient flow for augmented models. Our analysis however also reveals that even in the latter situation, stationary points may be unstable for augmented training although they are stable for the manifestly equivariant models.

Deep Learning · Algorithms

Jungkyu Kim, Taeyoung Park, Kibok Lee

Score-based diffusion models have emerged as prominent deep generative models; however, their application to tabular data remains challenging because their backbones assume fully specified inputs, whereas real-world tabular data often contain missing values. We propose **AugMask**, a plug-and-play training framework that adapts missing-unaware backbones to incomplete data via stochastic regularization. AugMask 1) completes inputs via *conditional stochastic augmentation* using lightweight auxiliary models and 2) masks the loss, using augmented missing entries for conditioning while restricting supervision to observed coordinates. We connect AugMask to a Rao-Blackwellized objective and show that marginalizing missing entries yields a variance-weighted sensitivity penalty, promoting invariance of observed-coordinate reconstruction with respect to uncertain missing entries. Across diverse datasets and missingness regimes, AugMask enables standard diffusion-based tabular generators to match or outperform specialized missing-aware baselines in both sample fidelity and downstream utility. The code will be released.

Jianhui Chen, Yuzhang Luo, Liangming Pan

Mechanistic Interpretability has successfully identified functional circuits in Large Language Models (LLMs), yet their causal origins in the training data remain poorly understood. We bridge this gap by introducing **Mechanistic Data Attribution (MDA)**, a scalable framework that traces the formation of specific interpretable units back to training samples using Influence Functions. Through extensive pre-training experiments on the Pythia family, we causally validate that removing a small fraction of high-influence samples significantly hinders the emergence of targeted heads, whereas augmenting them accelerates formation—effects that random interventions fail to replicate. Leveraging MDA, we reveal that highly repetitive structural data—such as LaTeX and HTML—acts as a "catalyst" that significantly accelerates the emergence of induction heads. Furthermore, we observe that interventions targeting induction head formation induce a concurrent change in the model’s in-context learning (ICL) capability. This provides direct causal evidence for the long-standing hypothesis regarding the functional link between induction heads and ICL. Finally, we propose a mechanistic data augmentation pipeline that builds upon these insights to consistently accelerate mechanistic convergence across diverse model scales, offering a principled methodology for understanding and steering the fine-grained development of LLM behaviors.

General Machine Learning · Evaluation

Xinyue Zheng, Haowei Lin, Shaofei Cai, Zilong Zheng, Yaodong Yang, Yitao Liang

Current coding benchmarks often inflate Large Language Model (LLM) capabilities due to static paradigms and data contamination, enabling models to exploit statistical shortcuts rather than genuine reasoning. To address this, we introduce \textbf{UniCode}, a generative evaluation framework that systematically probes LLM limits via: (1) multi-dimensional augmentation transforming seed problems into complex variations to disrupt fixed algorithmic patterns; (2) a highly reliable, automated test generation pipeline for scalable evaluation; and (3) fine-grained metrics for rich error signals. Experiments reveal a 31.2\% performance collapse in state-of-the-art models on UniCode, primarily driven by deficiencies in conceptual modeling and scalability reasoning rather than syntactic errors. Furthermore, we uncover a ``seed-problem regression" where models revert to memorized seed logic rather than following new specifications, signaling a reliance on shortcuts over reasoning. This work validates UniCode as a robust framework to expose model fragility and foster reasoning-oriented code intelligence.