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Deep Learning · Large Language Models

Vésteinn Snæbjarnarson, Anej Svete, Josef Valvoda, Reda Boumasmoud, Brian DuSell, Ryan Cotterell

Large language models (LLMs) trained on natural language data are capable of translating between languages, predict chess moves, and write poetry. Performance on a given task depends on directly relevant training data, yet confounders abound: data in related languages has been shown to help low-resource languages, and training on code has been shown to improve reasoning capabilities in natural language generation. Formal languages have become a common tool for understanding the learnability of language model architectures and their limitations---we argue that they should also be treated as multi-task learners when studying the learnability of a given \emph{task}. This means that to understand the learnability of a given property of a formal language, confounders from other tasks need to be considered. We propose a causal graphical model and an efficient sampling mechanism for probabilistic finite-state automata that gives full control over the occurrences of a given task while maintaining other language properties. To enable targeted evaluation, we derive task-specific decomposed KL-divergences. These tools allow us to know the \emph{causal} relationship between how often a task appears and its true learnability. Our experiments confirm that the correlation between task occurrences and learnability does not recover the accurate relationship---for this, the causal analysis and machinery is necessary.

Social Aspects · Privacy

Jianzhou Wang, Yirui Wu, Lixin Yuan, WENXIAO ZHANG, Jun Liu

Multimodal unlearning aims to eliminate specific data from pretrained multimodal models, which offers significant advantages in data privacy and model efficiency. Current methods struggle to achieve the desired properties of effectiveness, reliability and locality, due to the complex interdependency of unimodal and multimodal knowledge. By introducing a causal perspective, we propose multimodal unlearning with decoupled knowledge components. To promote fine-grained understanding of multimodal context, we introduce Multimodal Variational Inference (MVI) to infer modal-specific and -consistent factors with incomplete sample observation. With foundation of decoupled knowledge, we propose contrastive semantic editing to regulate multimodal unlearning towards refined forgetting. Experiments on privacy- and copyright-sensitive scenarios validate effectiveness of our method across multiple scenarios, ensuring the unlearned model maintains high reliability and locality.

Social Aspects · Accountability, Transparency, and Interpretability

Joeun Kim, HoEun Kim, Dongsup Jin, Young-Sik Kim

Recent multi-bit watermarking methods for large language models (LLMs) prioritize capacity over reliability, often conflating decoding with detection. Our analysis reveals that existing ECC-based extractors suffer from catastrophic false positive rates (FPR), and applying rejection thresholds merely collapses detection sensitivity (TPR) to random guessing. To resolve this structural limitation, we propose **BREW** (Block-wise Reliable Embedding for Watermarking), a framework shifting the paradigm to *designated verification*. BREW employs a two-stage mechanism: (i) **blind message estimation** via independent block voting, followed by (ii) **window-shifting verification** that rigorously validates the payload against local edits. Experiments demonstrate that BREW achieves a TPR of 0.965 with an FPR of 0.02 under 10\% synonym substitution, demonstrating that the high-FPR issue is not an inherent trade-off of multi-bit watermarking, but a solvable structural flaw of prior decoding-centric designs. Our framework is model-agnostic and theoretically grounded, providing a scalable solution for reliable forensic deployment.

Deep Learning · Large Language Models

Azmine Toushik Wasi, Shahriyar Zaman Ridoy, Koushik Tonmoy, Kinga Tshering, S M Muhtasimul Hasan, Wahid Faisal, Tasnim Mohiuddin, Md Rizwan Parvez

Geo-temporal understanding, the ability to infer location, time, and contextual properties from visual input alone, is a core aspect of human intelligence and underpins applications such as disaster management, traffic planning, embodied navigation, world modeling, and geography education. Although recent vision–language models (VLMs) have made progress in image geo-localization using salient cues like landmarks or road signs, their ability to reason about temporal signals and physically grounded spatial cues remains underexplored. To address this gap, we introduce ***TimeSpot***, a benchmark for evaluating real-world geo-temporal reasoning in VLMs. ***TimeSpot*** consists of 1,455 ground-level images from 80 countries and requires structured prediction of temporal attributes (season, month, time of day, daylight phase) and geographic attributes (continent, country, climate zone, environment type, latitude-longitude) directly from visual evidence. The benchmark further includes spatial–temporal reasoning tasks that probe physical plausibility and cue integration under real-world uncertainty. Evaluations of state-of-the-art open- and closed-source VLMs show consistently low performance, particularly for temporal inference, and while supervised fine-tuning yields measurable gains, it remains insufficient, highlighting the need for new approaches to achieve robust, physically grounded geo-temporal understanding. By jointly evaluating spatial and temporal inference with diagnostic rigor, ***TimeSpot*** provides a principled framework for assessing physically grounded, real-world geo-temporal reasoning. We will release ***TimeSpot*** upon acceptance.

Social Aspects · Privacy

Quentin Sinh, Jan Ramon

We study the problem of computing a U-statistic with a kernel function $f$ of degree $k \geq 2$, i.e., the average of some function $f$ over all $k$-tuples of instances, in a federated learning setting. U-statistics of degree $2$ include several useful statistics such as Kendall's $\tau$ coefficient, the Area under the Receiver-Operator Curve and the Gini mean difference. Existing methods provide solutions only under the lower-utility local differential privacy model and/or scale poorly in the size of the domain discretization. In this work, we propose a protocol that securely computes U-statistics of degree $k \geq 2$ under central differential privacy by leveraging Multi Party Computation (MPC). Our method substantially improves accuracy when compared to prior solutions. We provide a detailed theoretical analysis of its accuracy, communication and computational properties. We evaluate its performance empirically, obtaining favorable results, e.g., for Kendall's $\tau$ coefficient, our approach reduces the Mean Squared Error by up to four orders of magnitude over existing baselines.

Social Aspects · Privacy

Runhua Xu, Guoan Wan, James Joshi

Federated Learning (FL) has become the de facto standard for privacy-preserving intelligence, largely due to Secure Aggregation protocols that guarantee the mathematical invisibility of individual user contributions. However, we contend that this pursuit of perfect privacy has engineered a systemic vulnerability: the Privacy-Auditability Paradox. By rendering user updates computationally indistinguishable, current protocols create a "Sanitization Gap" where malicious poisoning is undetectable and a "Regulatory Dead Zone" where compliance with the EU AI Act's robustness and explainability mandates is mathematically impossible. In this position paper, we argue that the community must transition from "Blind Aggregation" to Controllable Secure Aggregation (CSA). We propose a cryptographic paradigm shift utilizing Decentralized Multi-Client Functional Encryption and Zero-Knowledge Proofs (ZKPs) to replace binary secrecy with fine-grained policy-based governance. This framework introduces "Verified Blindness", where the server remains blind to raw data by default but possesses a cryptographically regulated "Break-Glass" mechanism to audit specific inputs under consensus-based governance. We conclude that adopting CSA is not merely a technical upgrade but an existential necessity to transform Federated Learning from an unregulated academic concept into robust, compliant, and trustworthy critical infrastructure.

Social Aspects · Accountability, Transparency, and Interpretability

Ziyang Guo, Berk Ustun, Jessica Hullman

Explanations of model behavior are commonly evaluated via proxy properties weakly tied to the purposes explanations serve in practice. We contribute a decision theoretic framework that treats explanations as information signals valued by the expected improvement they enable on a specified decision task. This approach yields three distinct estimands: (i) a theoretical benchmark that upper-bounds achievable performance by any agent with the explanation, (ii) a human-complementary value that quantifies the theoretically attainable value that is not already captured by a baseline human decision policy, and (iii) a behavioral value representing the causal effect of providing the explanation to human decision-makers. We instantiate these definitions in a practical validation workflow, and apply them to assess explanation potential and interpret behavioral effects in human–AI decision support and mechanistic interpretability.

Deep Learning · Generative Models and Autoencoders

Sunghyun Kim, Jaehoon Hahm, Jeongwoo Shin, Joonseok Lee

Geometry-aware generative models and novel view synthesis approaches have shown strong potential in visual fidelity and consistency. In parallel, equivariant representation learning has emerged as a powerful framework for constructing latent spaces where analytically known group transformations could act directly, capturing geometric structure in data and enhancing both interpretability and generalization in novel view synthesis. However, we identify that existing approaches often suffer from \textit{latent misalignment}, a discrepancy between the intended group action and the actually required transformations in the latent space. Consequently, the learned latents often fail to consistently preserve the equivariant relations imposed by the underlying group symmetry. To address this, we propose \emph{Residual Latent Flow}, a flow-based framework that corrects the misaligned latents, thereby improving compliance with the underlying equivariance relation. Our comprehensive experiments show that our method significantly reduces latent misalignment and improves novel view synthesis quality, under rotation groups $\mathrm{SO}(n)$.

Probabilistic Methods · Everything Else

Daniel Csillag, Pedro Dall’Antonia, Claudio Struchiner, Guilherme Tegoni Goedert

Prediction-powered inference is a recent methodology for the safe use of black-box ML models to impute missing data, strengthening inference of statistical parameters. However, many applications require strong properties besides valid inference, such as privacy, robustness or validity under continuous distribution shifts; deriving prediction-powered methods with such guarantees is generally an arduous process, and has to be done case by case. In this paper, we resolve this issue by connecting prediction-powered inference with conformal prediction: by performing imputation through a calibrated set-predictor, we attain validity while achieving additional guarantees in a natural manner. We instantiate our procedure for the inference of means, Z- and M-estimation, as well as e-values and e-value-based procedures. Furthermore, in the case of e-values, ours is the first general prediction-powered procedure that operates off-line. We demonstrate these advantages by applying our method on private and time-series data. Both tasks are nontrivial within the standard prediction-powered framework but become natural under our method.

Probabilistic Methods · Everything Else

Jinzong Dong, Zhaohui Jiang, Bo Yang

Confidence calibration for classification models is vital in safety-critical decision-making scenarios and has received extensive attention. General confidence calibration methods assume training and test data are independent and identically distributed ($i.i.d.$), limiting their effectiveness under covariate shifts. Previous calibration methods under covariate shift struggle with class-wise or canonical calibrations and often rely on unstable importance weighting when density ratios are large or unbounded. Given the above limitations, this paper rethinks confidence calibration under covariate shifts. First, we derive a necessary and sufficient condition for confidence calibration under covariate shifts, named Expectation consistency condition, which reveals covariate shifts do not necessarily lead to uncalibrated confidence and provides a weaker condition for confidence calibration than global covariate distribution alignment. Then, utilizing Expectation consistency condition, this paper proposes an unsupervised domain adaptation loss to calibrate confidence of the target domain, named Expectation consistency loss (ECL), which is compatible with canonical calibration, class-wise calibration, and top-label calibration. Third, we prove that computing ECL loss has the same sample complexity as Expected Calibration Error (ECE) and provide a theoretically grounded mini-batch trainable scheme for ECL loss. Finally, we validate the effectiveness of our method on both simulated and real-world covariate shift datasets.

Applications · Social Sciences

Sarah Ball, Simeon Allmendinger, Frauke Kreuter, Niklas Kühl

Large language models are increasingly used to predict human preferences in both scientific and business endeavors, yet current approaches rely exclusively on analyzing model outputs without considering the underlying mechanisms. Using election forecasting as a test case, we introduce *mechanistic forecasting*, a method that demonstrates that probing internal model representations offers a fundamentally different---and sometimes more effective--- approach to preference prediction. Examining over 24 million configurations across 7 models, 6 national elections, multiple persona attributes, and prompt variations, we systematically analyze how demographic and ideological information activates latent party-encoding components within the respective models. We find that leveraging this internal knowledge via mechanistic forecasting (opposed to solely relying on surface-level predictions) can improve prediction accuracy. The effects vary across demographic versus opinion-based attributes, political parties, national contexts, and models. Our findings demonstrate that the latent representational structure of LLMs contains systematic, exploitable information about human preferences, establishing a new paradigm for using language models in social science prediction tasks.

Deep Learning · Large Language Models

Jianhao Huang, Baharan Mirzasoleiman

Masked Diffusion Language Models have recently emerged as a powerful generative paradigm, yet their generalization properties remain understudied compared to their auto-regressive counterparts. In this work, we investigate these properties within the setting of the $k$-parity problem (computing the XOR sum of $k$ relevant bits), where neural networks typically exhibit grokking—a prolonged plateau of chance-level performance followed by sudden generalization. We theoretically decompose the Masked Diffusion (MD) objective into a Signal regime which drives feature learning, and a Noise regime which serves as an implicit regularizer. By training nanoGPT using MD objective on the $k$-parity problem, we demonstrate that MD objective fundamentally alters the learning landscape, enabling rapid and simultaneous generalization without experiencing grokking. Furthermore, we leverage our theoretical insights to optimize the distribution of the mask probability in the MD objective. Our method significantly improves perplexity for 50M-parameter models and achieves superior results across both pre-training from scratch and supervised fine-tuning. Specifically, we observe performance gains peaking at $8.8$% and $5.8$%, respectively, on 8B-parameter models, confirming the scalability and effectiveness of our framework in large-scale masked diffusion language model regimes.

Probabilistic Methods · Structure Learning

Sobihan Surendran, Adeline Fermanian, Sylvain Le Corff

Combinatorial Optimization problems are widespread in domains such as logistics, manufacturing, and drug discovery, yet their NP-hard nature makes them computationally challenging. Recent Neural Combinatorial Optimization (NCO) methods leverage deep learning to learn policies for constructing solutions, trained via Supervised or Reinforcement Learning. While promising, these approaches often rely on task-specific augmentations, perform poorly on out-of-distribution instances, and lack robust inference mechanisms. Moreover, existing latent space models either require labeled data or use an instance-independent latent distribution. In this work, we propose LGS-Net, a novel latent space model that conditions on problem instances, and introduce an efficient inference method, Latent Guided Sampling (LGS), based on Markov Chain Monte Carlo and Stochastic Approximation. We show that the iterations of our method form a time-inhomogeneous Markov Chain and provide rigorous theoretical convergence guarantees. Empirical results on benchmark routing tasks show that our method achieves state-of-the-art performance among NCO baselines.

Applications · Computer Vision

Jiayi Kong, Xuhui Chen, Chen Zong, Fei Hou, Junhui Hou, Wenping Wang, Ying He

Neural Signed Distance Functions (SDFs) excel at reconstructing watertight manifolds but fail on thin structures and open boundaries due to strict inside-outside constraints. Conversely, Unsigned Distance Fields (UDFs) accommodate general geometries but suffer from gradient singularities at the zero-level set, hindering optimization and extraction. We introduce Metric–-Phase Fields (MPFs), a decoupled implicit representation that separates metric proximity from topological phase. Given an unoriented point cloud, MPFs learn (i) an unsigned metric field $r$ and (ii) a smooth phase field $\theta$, for which we derive a bounded phase indicator $P=\tanh(\beta\theta)$ that provides soft inside–outside cues where they are meaningful. We couple the two fields via a gated-metric formulation with a residual phase injection to obtain a signed implicit function with stable near-surface gradients. The phase coefficient $\beta$ is learnable, allowing MPFs to adaptively control the sharpness of the phase transition and the degree of saturation of the soft sign indicator. Experiments on both synthetic and scanned thin-shell and thin-plate shapes demonstrate that MPFs preserve thin and layered structures more faithfully than recent SDF-based methods, while also enabling more robust training and more reliable surface extraction than UDF-based approaches. We will make the source code publicly available.

Deep Learning · Large Language Models

Zhi Hong, Qian Zhang, Jiahang Sun, Zhiwei Shang, Mingze Kong, Xiangyi Wang, Yao Shu, Zhongxiang Dai

Large Language Models (LLMs) have achieved significant success across a wide range of tasks, serving as the cognitive backbone for Multi-Agent Systems (MAS) designed to orchestrate complex practical workflows. Given that MAS performance is highly sensitive to input prompts and many deployment scenarios preclude MAS architecture modifications, prompt optimization emerges as a critical strategy for performance enhancement. However, real-world deployment is impeded by three key challenges: (1) the need for high sample efficiency due to prohibitive evaluation costs, (2) topology-induced coupling among prompts, and (3) the combinatorial explosion of the search space. To address these challenges, we introduce **MASPOB** (**M**ulti-**A**gent **S**ystem **P**rompt **O**ptimization via **B**andits), a novel sample-efficient framework based on bandits. By leveraging Upper Confidence Bound (UCB) to quantify uncertainty, the bandit framework balances exploration and exploitation, maximizing gains within a strictly limited budget. To handle topology-induced coupling, MASPOB integrates Graph Neural Networks (GNNs) to capture structural priors, learning topology-aware representations of prompt semantics. Furthermore, it employs coordinate ascent to decompose the optimization into univariate sub-problems, reducing search complexity from exponential to linear. Extensive experiments across diverse benchmarks demonstrate that MASPOB achieves state-of-the-art performance, consistently outperforming existing baselines.

Applications · Computer Vision

Wei Li, jieyuan pei, Junnan Xu, Xuanfeng Ding, Junwei Zhu, wanjun chen, Jianwei Zheng

Existing deep spatial–spectral fusion (SSF) methods typically learn the fusion mapping in the coordinate domain using convolutions and attentions, making it hard to scale across varying spatial resolutions and offering limited control over the frequency content of the reconstructions, which may further lead to severe spectral distortion. In this work, we propose Latent Spectral Operators (LSO), a SSF framework that learns fusion mappings between spectral functions through a structured operator parameterization. Specifically, LSO first applies a cross-attention projection, where learned latent tokens serve as spectral prompts, to compress high-dimensional observations into a compact latent representation, and then adopts a hierarchical, patch-based architecture to integrate rich multi-scale cues. Furthermore, to parameterize the latent fusion operator in a controllable manner, a Trigonometric Basis Solver is elaborated, which represents the mapping using a trigonometric basis expansion. This formulation naturally supports multi-frequency modeling, with a capacity–stability trade-off governed by the number of basis functions. Extensive experiments on the CAVE and Harvard benchmarks demonstrate that LSO achieves consistent state-of-the-art performance and exhibits strong transferability across different spatial scales. Codes are attached.

Theory · Deep Learning

Jack T. Parley, Francesco Cagnetta, Matthieu Wyart

Understanding how the structure of language can be learned from sentences alone is a central question in both cognitive science and machine learning. Studies of the internal representations of Large Language Models (LLMs) support their ability to parse text when predicting the next word, while representing semantic notions independently of surface form. Yet, which data statistics make these feats possible, and how much data is required, remain largely unknown. Probabilistic context-free grammars (PCFGs) provide a tractable testbed for studying these questions. However, prior work has focused either on the post-hoc characterization of the parsing-like algorithms used by trained networks; or on the learnability of PCFGs with fixed syntax, where parsing is unnecessary. Here, we *(i)* introduce a tunable class of PCFGs in which both the degree of ambiguity and the correlation structure across scales can be controlled; *(ii)* provide a learning mechanism---an inference algorithm inspired by the structure of deep convolutional networks---that links learnability and sample complexity to specific language statistics; and *(iii)* validate our predictions empirically across deep convolutional and transformer-based architectures. Overall, we propose a unifying framework where correlations at different scales lift local ambiguities, enabling the emergence of hierarchical representations of the data.

Optimization · Zero-order and Black-box Optimization

Zhe Li, Bicheng Ying, Zidong Liu, Haibo Yang

Classical optimization theory establishes that zeroth-order (ZO) algorithms suffer from a dimension-dependent slowdown, with convergence rates typically scaling with the model dimension compared to first-order methods. However, in contrast to these theoretical expectations, a growing body of recent work demonstrates the successful application of ZO methods to fine-tuning Large Language Models (LLMs) with billions of parameters. To explain this paradox, we derive the one-step learning dynamics of ZO SGD, where the empirical Neural Tangent Kernel (eNTK) naturally emerges as the key term governing the learning behavior. Inspection of the eNTK produced by ZO-SGD reveals that each element corresponds to the inner product of neural tangent vectors projected onto a random low-dimensional subspace. Thus, by invoking the Johnson-Lindenstrauss Lemma, our analysis shows that the fidelity of the ZO eNTK is governed primarily by the number of perturbations. Crucially, the approximation error depends on the model output size rather than the massive parameter dimension. This dimension-free property provides a theoretical justification for the scalability of ZO methods to LLMs finetuning tasks. We believe that this kernel-based framework offers a novel perspective for understanding ZO methods within the context of learning dynamics.

Applications · Computer Vision

Qingyang Liu, Bingjie Gao, Canmiao Fu, Zhipeng Huang, Chen Li, Feng Wang, Shuochen Chang, Shaobo Wang, Yali Wang, Keming Ye 等

Recent unified models integrate multimodal understanding and generation within a single framework. However, an ``understanding-generation gap'' persists, where models can capture user intent but often fail to translate this semantic knowledge into precise pixel-level manipulation. This gap results in two bottlenecks in anything-to-image task~(X2I): the \textbf{attention entanglement bottleneck}, where blind planning struggles with complex prompts, and the \textbf{visual refinement bottleneck}, where unstructured feedback fails to correct imperfections efficiently. In this paper, we propose a novel framework that empowers unified models to autonomously switch between generation strategies based on instruction complexity and model capability. To achieve this, we construct a hierarchical data pipeline that constructs execution paths across three adaptive modes: direct generation for simple cases, self-reflection for quality refinement, and multi-step planning for decomposing complex scenarios. Building on this pipeline, we contribute a high-quality dataset with over 50,000 samples and implement a two-stage training strategy comprising SFT and RL. Specifically, we design step-wise reasoning rewards to ensure logical consistency and intra-group complexity penalty to prevent redundant computational overhead. Extensive experiments demonstrate that our method outperforms existing baselines on X2I, achieving superior generation fidelity among simple-to-complex instructions.

Applications · Language, Speech and Dialog

Chen Xie, Yuling Shi, Xiaodong Gu, Beijun Shen

Code complexity metrics such as cyclomatic complexity have long been used to assess software quality and maintainability. With the rapid advancement of large language models (LLMs) on code understanding and generation tasks, an important yet underexplored question arises: do these traditional complexity metrics meaningfully characterize the difficulty LLMs experience when processing code? In this work, we empirically demonstrate that, after controlling for code length, classical metrics exhibit no consistent correlation with LLM performance, revealing a fundamental mismatch with model-perceived difficulty. To address this gap, we propose LM-CC, a novel code complexity metric designed from the perspective of LLMs. The core premise of LM-CC is that LLM-perceived difficulty is driven by the nonlinearity of program semantics. Accordingly, we decompose programs into semantic units based on entropy, organize these units into a compositional hierarchy, and quantify complexity as a principled aggregation of compositional level and branching-induced divergence, capturing cumulative model uncertainty during code processing. Our extensive experiments show that LM-CC not only correlates more strongly with LLM performance than traditional metrics but also that lowering it directly enhances task performance. The source code is available at: https://anonymous.4open.science/r/lm-cc-7EE6.