# Research Papers — 2025-12 · bundle page 4 of 175 > Full-text markdown of papers #151–200 of 8703 published in 2025-12, newest first. AI-readable mirrors of the full 8703-paper 2025-12 archive (50 papers per file). Canonical page: https://aipod.dev/papers/archive/2025-12/page/4 [← Previous bundle](https://aipod.dev/papers/archive/2025-12/page/3.md) [Next bundle →](https://aipod.dev/papers/archive/2025-12/page/5.md) ### GenZ: Foundational models as latent variable generators within traditional statistical models Authors: Marko Jojic, Nebojsa Jojic Published: 2025-12-31 Categories: cs.AI arXiv: https://arxiv.org/abs/2512.24834v1 We present GenZ, a hybrid model that bridges foundational models and statistical modeling through interpretable semantic features. While large language models possess broad domain knowledge, they often fail to capture dataset-specific patterns critical for prediction tasks. Our approach addresses this by discovering semantic feature descriptions through an iterative process that contrasts groups of items identified via statistical modeling errors, rather than relying solely on the foundational model's domain understanding. We formulate this as a generalized EM algorithm that jointly optimizes semantic feature descriptors and statistical model parameters. The method prompts a frozen foundational model to classify items based on discovered features, treating these judgments as noisy observations of latent binary features that predict real-valued targets through learned statistical relationships. We demonstrate the approach on two domains: house price prediction (hedonic regression) and cold-start collaborative filtering for movie recommendations. On house prices, our model achieves 12\% median relative error using discovered semantic features from multimodal listing data, substantially outperforming a GPT-5 baseline (38\% error) that relies on the LLM's general domain knowledge. For Netflix movie embeddings, our model predicts collaborative filtering representations with 0.59 cosine similarity purely from semantic descriptions -- matching the performance that would require approximately 4000 user ratings through traditional collaborative filtering. The discovered features reveal dataset-specific patterns (e.g., architectural details predicting local housing markets, franchise membership predicting user preferences) that diverge from the model's domain knowledge alone. --- ### Explaining Why Things Go Where They Go: Interpretable Constructs of Human Organizational Preferences Authors: Emmanuel Fashae, Michael Burke, Leimin Tian, Lingheng Meng, Pamela Carreno-Medrano Published: 2025-12-31 Categories: cs.AI, cs.HC, cs.RO arXiv: https://arxiv.org/abs/2512.24829v1 Robotic systems for household object rearrangement often rely on latent preference models inferred from human demonstrations. While effective at prediction, these models offer limited insight into the interpretable factors that guide human decisions. We introduce an explicit formulation of object arrangement preferences along four interpretable constructs: spatial practicality (putting items where they naturally fit best in the space), habitual convenience (making frequently used items easy to reach), semantic coherence (placing items together if they are used for the same task or are contextually related), and commonsense appropriateness (putting things where people would usually expect to find them). To capture these constructs, we designed and validated a self-report questionnaire through a 63-participant online study. Results confirm the psychological distinctiveness of these constructs and their explanatory power across two scenarios (kitchen and living room). We demonstrate the utility of these constructs by integrating them into a Monte Carlo Tree Search (MCTS) planner and show that when guided by participant-derived preferences, our planner can generate reasonable arrangements that closely align with those generated by participants. This work contributes a compact, interpretable formulation of object arrangement preferences and a demonstration of how it can be operationalized for robot planning. --- ### Inter-Agent Relative Representations for Multi-Agent Option Discovery Authors: Raul D. Steleac, Mohan Sridharan, David Abel Published: 2025-12-31 Categories: cs.LG arXiv: https://arxiv.org/abs/2512.24827v3 Temporally extended actions improve the ability to explore and plan in single-agent settings. In multi-agent settings, the exponential growth of the joint state space with the number of agents makes coordinated behaviours even more valuable. Yet, this same exponential growth renders the design of multi-agent options particularly challenging. Existing multi-agent option discovery methods often sacrifice coordination by producing loosely coupled or fully independent behaviours. Toward addressing these limitations, we describe a novel approach for multi-agent option discovery. Specifically, we propose a joint-state abstraction that compresses the state space while preserving the information necessary to discover strongly coordinated behaviours. Our approach builds on the inductive bias that synchronisation over agent states provides a natural foundation for coordination in the absence of explicit objectives. We first approximate a fictitious state of maximal alignment with the team, the \textit{Fermat} state, and use it to define a measure of \textit{spreadness}, capturing team-level misalignment on each individual state dimension. Building on this representation, we then employ a neural graph Laplacian estimator to derive options that capture state synchronisation patterns between agents. We evaluate the resulting options across multiple scenarios in two simulated multi-agent domains, showing that they yield stronger downstream coordination capabilities compared to alternative option discovery methods. --- ### Discovering Coordinated Joint Options via Inter-Agent Relative Dynamics Authors: Raul D. Steleac, Mohan Sridharan, David Abel Published: 2025-12-31 Categories: cs.LG arXiv: https://arxiv.org/abs/2512.24827v1 Temporally extended actions improve the ability to explore and plan in single-agent settings. In multi-agent settings, the exponential growth of the joint state space with the number of agents makes coordinated behaviours even more valuable. Yet, this same exponential growth renders the design of multi-agent options particularly challenging. Existing multi-agent option discovery methods often sacrifice coordination by producing loosely coupled or fully independent behaviours. Toward addressing these limitations, we describe a novel approach for multi-agent option discovery. Specifically, we propose a joint-state abstraction that compresses the state space while preserving the information necessary to discover strongly coordinated behaviours. Our approach builds on the inductive bias that synchronisation over agent states provides a natural foundation for coordination in the absence of explicit objectives. We first approximate a fictitious state of maximal alignment with the team, the \textit{Fermat} state, and use it to define a measure of \textit{spreadness}, capturing team-level misalignment on each individual state dimension. Building on this representation, we then employ a neural graph Laplacian estimator to derive options that capture state synchronisation patterns between agents. We evaluate the resulting options across multiple scenarios in two multi-agent domains, showing that they yield stronger downstream coordination capabilities compared to alternative option discovery methods. --- ### Video and Language Alignment in 2D Systems for 3D Multi-object Scenes with Multi-Information Derivative-Free Control Authors: Jason Armitage, Rico Sennnrich Published: 2025-12-31 Categories: cs.CV, cs.AI arXiv: https://arxiv.org/abs/2512.24826v1 Cross-modal systems trained on 2D visual inputs are presented with a dimensional shift when processing 3D scenes. An in-scene camera bridges the dimensionality gap but requires learning a control module. We introduce a new method that improves multivariate mutual information estimates by regret minimisation with derivative-free optimisation. Our algorithm enables off-the-shelf cross-modal systems trained on 2D visual inputs to adapt online to object occlusions and differentiate features. The pairing of expressive measures and value-based optimisation assists control of an in-scene camera to learn directly from the noisy outputs of vision-language models. The resulting pipeline improves performance in cross-modal tasks on multi-object 3D scenes without resorting to pretraining or finetuning. --- ### Practising responsibility: Ethics in NLP as a hands-on course Authors: Malvina Nissim, Viviana Patti, Beatrice Savoldi Published: 2025-12-31 Categories: cs.CL, cs.AI arXiv: https://arxiv.org/abs/2512.24825v1 As Natural Language Processing (NLP) systems become more pervasive, integrating ethical considerations into NLP education has become essential. However, this presents inherent challenges in curriculum development: the field's rapid evolution from both academia and industry, and the need to foster critical thinking beyond traditional technical training. We introduce our course on Ethical Aspects in NLP and our pedagogical approach, grounded in active learning through interactive sessions, hands-on activities, and "learning by teaching" methods. Over four years, the course has been refined and adapted across different institutions, educational levels, and interdisciplinary backgrounds; it has also yielded many reusable products, both in the form of teaching materials and in the form of actual educational products aimed at diverse audiences, made by the students themselves. By sharing our approach and experience, we hope to provide inspiration for educators seeking to incorporate social impact considerations into their curricula. --- ### Unregularized Linear Convergence in Zero-Sum Game from Preference Feedback Authors: Shulun Chen, Runlong Zhou, Zihan Zhang, Maryam Fazel, Simon S. Du Published: 2025-12-31 Categories: cs.LG arXiv: https://arxiv.org/abs/2512.24818v2 Aligning large language models (LLMs) with human preferences has proven effective for enhancing model capabilities, yet standard preference modeling using the Bradley-Terry model assumes transitivity, overlooking the inherent complexity of human population preferences. Nash learning from human feedback (NLHF) addresses this by framing non-transitive preferences as a two-player zero-sum game, where alignment reduces to finding the Nash equilibrium (NE). However, existing algorithms typically rely on regularization, incurring unavoidable bias when computing the duality gap in the original game. In this work, we provide the first convergence guarantee for Optimistic Multiplicative Weights Update ($\mathtt{OMWU}$) in NLHF, showing that it achieves last-iterate linear convergence after a burn-in phase whenever an NE with full support exists, with an instance-dependent linear convergence rate to the original NE, measured by duality gaps. Compared to prior results in Wei et al. (2020), we do not require the assumption of NE uniqueness. Our analysis identifies a novel marginal convergence behavior, where the probability of rarely played actions grows exponentially from exponentially small values, enabling exponentially better dependence on instance-dependent constants than prior results. Experiments corroborate the theoretical strengths of $\mathtt{OMWU}$ in both tabular and neural policy classes, demonstrating its potential for LLM applications. --- ### Unregularized Linear Convergence in Zero-Sum Game from Preference Feedback Authors: Shulun Chen, Runlong Zhou, Zihan Zhang, Maryam Fazel, Simon S. Du Published: 2025-12-31 Categories: cs.LG arXiv: https://arxiv.org/abs/2512.24818v1 Aligning large language models (LLMs) with human preferences has proven effective for enhancing model capabilities, yet standard preference modeling using the Bradley-Terry model assumes transitivity, overlooking the inherent complexity of human population preferences. Nash learning from human feedback (NLHF) addresses this by framing non-transitive preferences as a two-player zero-sum game, where alignment reduces to finding the Nash equilibrium (NE). However, existing algorithms typically rely on regularization, incurring unavoidable bias when computing the duality gap in the original game. In this work, we provide the first convergence guarantee for Optimistic Multiplicative Weights Update ($\mathtt{OMWU}$) in NLHF, showing that it achieves last-iterate linear convergence after a burn-in phase whenever an NE with full support exists, with an instance-dependent linear convergence rate to the original NE, measured by duality gaps. Compared to prior results in Wei et al. (2020), we do not require the assumption of NE uniqueness. Our analysis identifies a novel marginal convergence behavior, where the probability of rarely played actions grows exponentially from exponentially small values, enabling exponentially better dependence on instance-dependent constants than prior results. Experiments corroborate the theoretical strengths of $\mathtt{OMWU}$ in both tabular and neural policy classes, demonstrating its potential for LLM applications. --- ### Learning Temporally Consistent Turbulence Between Sparse Snapshots via Diffusion Models Authors: Mohammed Sardar, Małgorzata J. Zimoń, Samuel Draycott, Alistair Revell, Alex Skillen Published: 2025-12-31 Categories: physics.flu-dyn, cs.LG arXiv: https://arxiv.org/abs/2512.24813v1 We investigate the statistical accuracy of temporally interpolated spatiotemporal flow sequences between sparse, decorrelated snapshots of turbulent flow fields using conditional Denoising Diffusion Probabilistic Models (DDPMs). The developed method is presented as a proof-of-concept generative surrogate for reconstructing coherent turbulent dynamics between sparse snapshots, demonstrated on a 2D Kolmogorov Flow, and a 3D Kelvin-Helmholtz Instability (KHI). We analyse the generated flow sequences through the lens of statistical turbulence, examining the time-averaged turbulent kinetic energy spectra over generated sequences, and temporal decay of turbulent structures. For the non-stationary Kelvin-Helmholtz Instability, we assess the ability of the proposed method to capture evolving flow statistics across the most strongly time-varying flow regime. We additionally examine instantaneous fields and physically motivated metrics at key stages of the KHI flow evolution. --- ### DTI-GP: Bayesian operations for drug-target interactions using deep kernel Gaussian processes Authors: Bence Bolgár, András Millinghoffer, Péter Antal Published: 2025-12-31 Categories: cs.LG arXiv: https://arxiv.org/abs/2512.24810v1 Precise probabilistic information about drug-target interaction (DTI) predictions is vital for understanding limitations and boosting predictive performance. Gaussian processes (GP) offer a scalable framework to integrate state-of-the-art DTI representations and Bayesian inference, enabling novel operations, such as Bayesian classification with rejection, top-$K$ selection, and ranking. We propose a deep kernel learning-based GP architecture (DTI-GP), which incorporates a combined neural embedding module for chemical compounds and protein targets, and a GP module. The workflow continues with sampling from the predictive distribution to estimate a Bayesian precedence matrix, which is used in fast and accurate selection and ranking operations. DTI-GP outperforms state-of-the-art solutions, and it allows (1) the construction of a Bayesian accuracy-confidence enrichment score, (2) rejection schemes for improved enrichment, and (3) estimation and search for top-$K$ selections and ranking with high expected utility. --- ### Limits of quantum generative models with classical sampling hardness Authors: Sabrina Herbst, Ivona Brandić, Adrián Pérez-Salinas Published: 2025-12-31 Categories: quant-ph, cs.LG arXiv: https://arxiv.org/abs/2512.24801v1 Sampling tasks have been successful in establishing quantum advantages both in theory and experiments. This has fueled the use of quantum computers for generative modeling to create samples following the probability distribution underlying a given dataset. In particular, the potential to build generative models on classically hard distributions would immediately preclude classical simulability, due to theoretical separations. In this work, we study quantum generative models from the perspective of output distributions, showing that models that anticoncentrate are not trainable on average, including those exhibiting quantum advantage. In contrast, models outputting data from sparse distributions can be trained. We consider special cases to enhance trainability, and observe that this opens the path for classical algorithms for surrogate sampling. This observed trade-off is linked to verification of quantum processes. We conclude that quantum advantage can still be found in generative models, although its source must be distinct from anticoncentration. --- ### LeanCat: A Benchmark Suite for Formal Category Theory in Lean (Part I: 1-Categories) Authors: Rongge Xu, Hui Dai, Yiming Fu, Jiedong Jiang, Tianjiao Nie, Junkai Wang, Holiverse Yang, Zhi-Hao Zhang Published: 2025-12-31 Categories: cs.LO, cs.AI, cs.FL, cs.LG, math.CT arXiv: https://arxiv.org/abs/2512.24796v2 While large language models (LLMs) have demonstrated impressive capabilities in formal theorem proving, current benchmarks fail to adequately measure library-grounded abstraction -- the ability to reason with high-level interfaces and reusable structures central to modern mathematics and software engineering. We introduce LeanCat, a challenging benchmark comprising 100 fully formalized category-theory tasks in Lean. Unlike algebra or arithmetic, category theory serves as a rigorous stress test for structural, interface-level reasoning. Our evaluation reveals a severe abstraction gap: the best state-of-the-art model solves only 12.0% of tasks at pass@4, with performance collapsing from 55.0% on Easy tasks to 0.0% on High-difficulty tasks, highlighting a failure in compositional generalization. To overcome this, we evaluate LeanBridge, a retrieval-augmented agent that employs a retrieve-generate-verify loop. LeanBridge achieves a peak success rate of 24.0% -- doubling the performance of the best static baseline. These results empirically demonstrate that iterative refinement and dynamic library retrieval are not merely optimizations but strict necessities for neuro-symbolic reasoning in abstract domains. LeanCat offers a compact, reusable testbed for tracking progress toward reliable, research-level formalization. --- ### LeanCat: A Benchmark Suite for Formal Category Theory in Lean (Part I: 1-Categories) Authors: Rongge Xu, Hui Dai, Yiming Fu, Jiedong Jiang, Tianjiao Nie, Hongwei Wang, Junkai Wang, Holiverse Yang, Jiatong Yang, Zhi-Hao Zhang Published: 2025-12-31 Categories: cs.LO, cs.AI, cs.FL, cs.LG, math.CT arXiv: https://arxiv.org/abs/2512.24796v1 Large language models (LLMs) have made rapid progress in formal theorem proving, yet current benchmarks under-measure the kind of abstraction and library-mediated reasoning that organizes modern mathematics. In parallel with FATE's emphasis on frontier algebra, we introduce LeanCat, a Lean benchmark for category-theoretic formalization -- a unifying language for mathematical structure and a core layer of modern proof engineering -- serving as a stress test of structural, interface-level reasoning. Part I: 1-Categories contains 100 fully formalized statement-level tasks, curated into topic families and three difficulty tiers via an LLM-assisted + human grading process. The best model solves 8.25% of tasks at pass@1 (32.50%/4.17%/0.00% by Easy/Medium/High) and 12.00% at pass@4 (50.00%/4.76%/0.00%). We also evaluate LeanBridge which use LeanExplore to search Mathlib, and observe consistent gains over single-model baselines. LeanCat is intended as a compact, reusable checkpoint for tracking both AI and human progress toward reliable, research-level formalization in Lean. --- ### Nonlinear Noise2Noise for Efficient Monte Carlo Denoiser Training Authors: Andrew Tinits, Stephen Mann Published: 2025-12-31 Categories: cs.CV, cs.GR, cs.LG arXiv: https://arxiv.org/abs/2512.24794v1 The Noise2Noise method allows for training machine learning-based denoisers with pairs of input and target images where both the input and target can be noisy. This removes the need for training with clean target images, which can be difficult to obtain. However, Noise2Noise training has a major limitation: nonlinear functions applied to the noisy targets will skew the results. This bias occurs because the nonlinearity makes the expected value of the noisy targets different from the clean target image. Since nonlinear functions are common in image processing, avoiding them limits the types of preprocessing that can be performed on the noisy targets. Our main insight is that certain nonlinear functions can be applied to the noisy targets without adding significant bias to the results. We develop a theoretical framework for analyzing the effects of these nonlinearities, and describe a class of nonlinear functions with minimal bias. We demonstrate our method on the denoising of high dynamic range (HDR) images produced by Monte Carlo rendering. Noise2Noise training can have trouble with HDR images, where the training process is overwhelmed by outliers and performs poorly. We consider a commonly used method of addressing these training issues: applying a nonlinear tone mapping function to the model output and target images to reduce their dynamic range. This method was previously thought to be incompatible with Noise2Noise training because of the nonlinearities involved. We show that certain combinations of loss functions and tone mapping functions can reduce the effect of outliers while introducing minimal bias. We apply our method to an existing machine learning-based Monte Carlo denoiser, where the original implementation was trained with high-sample count reference images. Our results approach those of the original implementation, but are produced using only noisy training data. --- ### Self-Supervised Neural Architecture Search for Multimodal Deep Neural Networks Authors: Shota Suzuki, Satoshi Ono Published: 2025-12-31 Categories: cs.LG, cs.NE arXiv: https://arxiv.org/abs/2512.24793v1 Neural architecture search (NAS), which automates the architectural design process of deep neural networks (DNN), has attracted increasing attention. Multimodal DNNs that necessitate feature fusion from multiple modalities benefit from NAS due to their structural complexity; however, constructing an architecture for multimodal DNNs through NAS requires a substantial amount of labeled training data. Thus, this paper proposes a self-supervised learning (SSL) method for architecture search of multimodal DNNs. The proposed method applies SSL comprehensively for both the architecture search and model pretraining processes. Experimental results demonstrated that the proposed method successfully designed architectures for DNNs from unlabeled training data. --- ### Projection-based Adversarial Attack using Physics-in-the-Loop Optimization for Monocular Depth Estimation Authors: Takeru Kusakabe, Yudai Hirose, Mashiho Mukaida, Satoshi Ono Published: 2025-12-31 Categories: cs.CV, cs.LG, cs.NE arXiv: https://arxiv.org/abs/2512.24792v1 Deep neural networks (DNNs) remain vulnerable to adversarial attacks that cause misclassification when specific perturbations are added to input images. This vulnerability also threatens the reliability of DNN-based monocular depth estimation (MDE) models, making robustness enhancement a critical need in practical applications. To validate the vulnerability of DNN-based MDE models, this study proposes a projection-based adversarial attack method that projects perturbation light onto a target object. The proposed method employs physics-in-the-loop (PITL) optimization -- evaluating candidate solutions in actual environments to account for device specifications and disturbances -- and utilizes a distributed covariance matrix adaptation evolution strategy. Experiments confirmed that the proposed method successfully created adversarial examples that lead to depth misestimations, resulting in parts of objects disappearing from the target scene. --- ### HiGR: Industrial-Scale Hierarchical Generative Slate Recommendation Framework in Tencent Authors: Yunsheng Pang, Zijian Liu, Yudong Li, Shaojie Zhu, Zijian Luo, Chenyun Yu, Sikai Wu, Shichen Shen, Cong Xu, Bin Wang, Kai Jiang, Chengxiang Zhuo et al. Published: 2025-12-31 Categories: cs.IR, cs.AI arXiv: https://arxiv.org/abs/2512.24787v5 Slate recommendation, which presents users with a ranked item list in a single display, is ubiquitous across mainstream online platforms. While recent generative recommendation methods have shown strong potential in modeling item sequences with semantic IDs, directly applying them to industrial-scale slate recommendation faces a fundamental disconnect: entangled SID spaces confound high-level list planning, fine-grained autoregressive decoding over long sequences limits semantic planning efficiency, and token-level objectives misalign with holistic slate quality. In this paper, we propose HiGR, an industrial-scale hierarchical generative framework for slate recommendation that bridges this disconnect through a co-designed pipeline. First, HiGR learns structured SIDs via a Prefix-Contrastive Residual Quantized VAE (PCRQ-VAE). By enforcing high-level prefixes to capture shared semantics, PCRQ-VAE creates a controllable discrete space that acts as a prerequisite for efficient planning. Leveraging this structured space, our Hierarchical Slate Decoder (HSD) shifts autoregressive modeling from entangled token-level decoding to coarse-grained preference embeddings. This design significantly reduces inference latency while allowing explicit global slate structure planning. Finally, this stable planning space enables an ORPO-based listwise alignment mechanism to optimize triple-objective implicit feedback-ranking fidelity, genuine user interest, and diversity. Extensive offline experiments show that HiGR outperforms state-of-the-art baselines by over 10% in offline recommendation quality while achieving a $5\times$ inference speedup. Online A/B tests on Tencent platforms further improve watch time by 1.22% and video plays by 1.73%. HiGR has been deployed on multiple Tencent platform surfaces, serving hundreds of millions of users and proving its industrial-scale applicability. --- ### HiGR: Efficient Generative Slate Recommendation via Hierarchical Planning and Multi-Objective Preference Alignment Authors: Yunsheng Pang, Zijian Liu, Yudong Li, Shaojie Zhu, Zijian Luo, Chenyun Yu, Sikai Wu, Shichen Shen, Cong Xu, Bin Wang, Kai Jiang, Hongyong Yu et al. Published: 2025-12-31 Categories: cs.IR, cs.AI arXiv: https://arxiv.org/abs/2512.24787v1 Slate recommendation, where users are presented with a ranked list of items simultaneously, is widely adopted in online platforms. Recent advances in generative models have shown promise in slate recommendation by modeling sequences of discrete semantic IDs autoregressively. However, existing autoregressive approaches suffer from semantically entangled item tokenization and inefficient sequential decoding that lacks holistic slate planning. To address these limitations, we propose HiGR, an efficient generative slate recommendation framework that integrates hierarchical planning with listwise preference alignment. First, we propose an auto-encoder utilizing residual quantization and contrastive constraints to tokenize items into semantically structured IDs for controllable generation. Second, HiGR decouples generation into a list-level planning stage for global slate intent, followed by an item-level decoding stage for specific item selection. Third, we introduce a listwise preference alignment objective to directly optimize slate quality using implicit user feedback. Experiments on our large-scale commercial media platform demonstrate that HiGR delivers consistent improvements in both offline evaluations and online deployment. Specifically, it outperforms state-of-the-art methods by over 10% in offline recommendation quality with a 5x inference speedup, while further achieving a 1.22% and 1.73% increase in Average Watch Time and Average Video Views in online A/B tests. --- ### Gradient Descent as Implicit EM in Distance-Based Neural Models Authors: Alan Oursland Published: 2025-12-31 Categories: cs.LG arXiv: https://arxiv.org/abs/2512.24780v2 Neural networks trained with standard objectives exhibit behaviors characteristic of probabilistic inference: soft clustering, prototype specialization, and Bayesian uncertainty tracking. These phenomena appear across architectures -- in attention mechanisms, classification heads, and energy-based models -- yet existing explanations often rely on loose analogies to mixture models or post-hoc architectural interpretation. We provide a direct explanation. For any objective with log-sum-exp structure over distances or energies, the gradient with respect to each distance is exactly the negative posterior responsibility of the corresponding component: $\partial L / \partial d_j = -r_j$. The identity is algebraic, requiring only differentiability; it is a specialization of Fisher's identity, and its significance here is its address: standard neural objectives instantiate it without modification. The consequence is that gradient descent on such objectives performs generalized expectation-maximization implicitly, with responsibilities arising as gradients to be applied rather than auxiliary variables to be computed. This result unifies three regimes of learning: unsupervised mixture modeling, where responsibilities are fully latent; attention, where responsibilities are conditioned on queries; and cross-entropy classification, where supervision clamps responsibilities to targets. Our claims live at training time: the responsibility-weighted gradient dynamics recently documented in transformers follow from the objective's geometry. The in-context Bayesian computation that trained transformers perform at inference time is the endpoint of these dynamics, not their per-step content. --- ### Gradient Descent as Implicit EM in Distance-Based Neural Models Authors: Alan Oursland Published: 2025-12-31 Categories: cs.LG arXiv: https://arxiv.org/abs/2512.24780v1 Neural networks trained with standard objectives exhibit behaviors characteristic of probabilistic inference: soft clustering, prototype specialization, and Bayesian uncertainty tracking. These phenomena appear across architectures -- in attention mechanisms, classification heads, and energy-based models -- yet existing explanations rely on loose analogies to mixture models or post-hoc architectural interpretation. We provide a direct derivation. For any objective with log-sum-exp structure over distances or energies, the gradient with respect to each distance is exactly the negative posterior responsibility of the corresponding component: $\partial L / \partial d_j = -r_j$. This is an algebraic identity, not an approximation. The immediate consequence is that gradient descent on such objectives performs expectation-maximization implicitly -- responsibilities are not auxiliary variables to be computed but gradients to be applied. No explicit inference algorithm is required because inference is embedded in optimization. This result unifies three regimes of learning under a single mechanism: unsupervised mixture modeling, where responsibilities are fully latent; attention, where responsibilities are conditioned on queries; and cross-entropy classification, where supervision clamps responsibilities to targets. The Bayesian structure recently observed in trained transformers is not an emergent property but a necessary consequence of the objective geometry. Optimization and inference are the same process. --- ### Compute-Accuracy Pareto Frontiers for Open-Source Reasoning Large Language Models Authors: Ákos Prucs, Nara Csutora, Mátyás Antal, Márk Marosi Published: 2025-12-31 Categories: cs.CL arXiv: https://arxiv.org/abs/2512.24776v1 Large Language Models (LLMs) are demonstrating rapid improvements on complex reasoning benchmarks, particularly when allowed to utilize intermediate reasoning steps before converging on a final solution. However, current literature often overlooks the significant computational burden associated with generating long reasoning sequences. For industrial applications, model selection depends not only on raw accuracy but also on resource constraints and inference costs. In this work, we conduct a test-time-compute aware evaluation of both contemporary and older open-source LLMs, mapping their Pareto frontiers across math- and reasoning-intensive benchmarks. Our findings identify the Mixture of Experts (MoE) architecture as a strong candidate to balance performance and efficiency in our evaluation setting. Furthermore, we trace the trajectory of Pareto efficiency over time to derive an emergent trend regarding accuracy gain per unit of compute. Finally, we demonstrate that there is a saturation point for inference-time compute. Beyond a certain threshold, accuracy gains diminish, indicating that while extended reasoning capabilities are beneficial, they cannot overcome intrinsic model limitations regarding specific complexities. --- ### Uncertainty-aware Semi-supervised Ensemble Teacher Framework for Multilingual Depression Detection Authors: Mohammad Zia Ur Rehman, Velpuru Navya, Sanskar, Shuja Uddin Qureshi, Nagendra Kumar Published: 2025-12-31 Categories: cs.CL arXiv: https://arxiv.org/abs/2512.24772v1 Detecting depression from social media text is still a challenging task. This is due to different language styles, informal expression, and the lack of annotated data in many languages. To tackle these issues, we propose, Semi-SMDNet, a strong Semi-Supervised Multilingual Depression detection Network. It combines teacher-student pseudo-labelling, ensemble learning, and augmentation of data. Our framework uses a group of teacher models. Their predictions come together through soft voting. An uncertainty-based threshold filters out low-confidence pseudo-labels to reduce noise and improve learning stability. We also use a confidence-weighted training method that focuses on reliable pseudo-labelled samples. This greatly boosts robustness across languages. Tests on Arabic, Bangla, English, and Spanish datasets show that our approach consistently beats strong baselines. It significantly reduces the performance gap between settings that have plenty of resources and those that do not. Detailed experiments and studies confirm that our framework is effective and can be used in various situations. This shows that it is suitable for scalable, cross-language mental health monitoring where labelled resources are limited. --- ### Sparse Offline Reinforcement Learning with Corruption Robustness Authors: Nam Phuong Tran, Andi Nika, Goran Radanovic, Long Tran-Thanh, Debmalya Mandal Published: 2025-12-31 Categories: stat.ML, cs.LG arXiv: https://arxiv.org/abs/2512.24768v3 We investigate robustness to strong data corruption in offline sparse reinforcement learning (RL). In our setting, an adversary may arbitrarily perturb a fraction of the collected trajectories from a high-dimensional but sparse Markov decision process, and our goal is to estimate a near optimal policy. The main challenge is that, in the high-dimensional regime where the number of samples $N$ is smaller than the feature dimension $d$, exploiting sparsity is essential for obtaining non-vacuous guarantees but has not been systematically studied in offline RL. We analyse the problem under uniform coverage and sparse single-concentrability assumptions. While Least Square Value Iteration (LSVI), a standard approach for robust offline RL, performs well under uniform coverage, we show that integrating sparsity into LSVI is unnatural, and its analysis may break down due to overly pessimistic bonuses. To overcome this, we propose actor-critic methods with sparse robust estimator oracles, which avoid the use of pointwise pessimistic bonuses and provide the first non-vacuous guarantees for sparse offline RL under single-policy concentrability coverage. Moreover, we extend our results to the contaminated setting and show that our algorithm remains robust under strong contamination. Our results provide the first non-vacuous guarantees in high-dimensional sparse MDPs with single-policy concentrability coverage and corruption, showing that learning a near-optimal policy remains possible in regimes where traditional robust offline RL techniques may fail. --- ### Sparse Offline Reinforcement Learning with Corruption Robustness Authors: Nam Phuong Tran, Andi Nika, Goran Radanovic, Long Tran-Thanh, Debmalya Mandal Published: 2025-12-31 Categories: stat.ML, cs.LG arXiv: https://arxiv.org/abs/2512.24768v1 We investigate robustness to strong data corruption in offline sparse reinforcement learning (RL). In our setting, an adversary may arbitrarily perturb a fraction of the collected trajectories from a high-dimensional but sparse Markov decision process, and our goal is to estimate a near optimal policy. The main challenge is that, in the high-dimensional regime where the number of samples $N$ is smaller than the feature dimension $d$, exploiting sparsity is essential for obtaining non-vacuous guarantees but has not been systematically studied in offline RL. We analyse the problem under uniform coverage and sparse single-concentrability assumptions. While Least Square Value Iteration (LSVI), a standard approach for robust offline RL, performs well under uniform coverage, we show that integrating sparsity into LSVI is unnatural, and its analysis may break down due to overly pessimistic bonuses. To overcome this, we propose actor-critic methods with sparse robust estimator oracles, which avoid the use of pointwise pessimistic bonuses and provide the first non-vacuous guarantees for sparse offline RL under single-policy concentrability coverage. Moreover, we extend our results to the contaminated setting and show that our algorithm remains robust under strong contamination. Our results provide the first non-vacuous guarantees in high-dimensional sparse MDPs with single-policy concentrability coverage and corruption, showing that learning a near-optimal policy remains possible in regimes where traditional robust offline RL techniques may fail. --- ### From Trial to Deployment: A SEM Analysis of Traveler Adoptions to Fully Operational Autonomous Taxis Authors: Yutong Cai, Hua Wang Published: 2025-12-31 Categories: cs.LG arXiv: https://arxiv.org/abs/2512.24767v1 Autonomous taxi services represent a transformative advancement in urban mobility, offering safety, efficiency, and round-the-clock operations. While existing literature has explored user acceptance of autonomous taxis through stated preference experiments and hypothetical scenarios, few studies have investigated actual user behavior based on operational AV services. This study addresses that gap by leveraging survey data from Wuhan, China, where Baidu's Apollo Robotaxi service operates at scale. We design a realistic survey incorporating actual service attributes and collect 336 valid responses from actual users. Using Structural Equation Modeling, we identify six latent psychological constructs, namely Trust \& Policy Support, Cost Sensitivity, Performance, Behavioral Intention, Lifestyle, and Education. Their influences on adoption behavior, measured by the selection frequency of autonomous taxis in ten scenarios, are examined and interpreted. Results show that Cost Sensitivity and Behavioral Intention are the strongest positive predictors of adoption, while other latent constructs play more nuanced roles. The model demonstrates strong goodness-of-fit across multiple indices. Our findings offer empirical evidence to support policymaking, fare design, and public outreach strategies for scaling autonomous taxis deployments in real-world urban settings. --- ### Dream2Flow: Bridging Video Generation and Open-World Manipulation with 3D Object Flow Authors: Karthik Dharmarajan, Wenlong Huang, Jiajun Wu, Li Fei-Fei, Ruohan Zhang Published: 2025-12-31 Categories: cs.RO, cs.AI, cs.CV arXiv: https://arxiv.org/abs/2512.24766v1 Generative video modeling has emerged as a compelling tool to zero-shot reason about plausible physical interactions for open-world manipulation. Yet, it remains a challenge to translate such human-led motions into the low-level actions demanded by robotic systems. We observe that given an initial image and task instruction, these models excel at synthesizing sensible object motions. Thus, we introduce Dream2Flow, a framework that bridges video generation and robotic control through 3D object flow as an intermediate representation. Our method reconstructs 3D object motions from generated videos and formulates manipulation as object trajectory tracking. By separating the state changes from the actuators that realize those changes, Dream2Flow overcomes the embodiment gap and enables zero-shot guidance from pre-trained video models to manipulate objects of diverse categories-including rigid, articulated, deformable, and granular. Through trajectory optimization or reinforcement learning, Dream2Flow converts reconstructed 3D object flow into executable low-level commands without task-specific demonstrations. Simulation and real-world experiments highlight 3D object flow as a general and scalable interface for adapting video generation models to open-world robotic manipulation. Videos and visualizations are available at https://dream2flow.github.io/. --- ### UniC-Lift: Unified 3D Instance Segmentation via Contrastive Learning Authors: Ankit Dhiman, Srinath R, Jaswanth Reddy, Lokesh R Boregowda, Venkatesh Babu Radhakrishnan Published: 2025-12-31 Categories: cs.CV arXiv: https://arxiv.org/abs/2512.24763v1 3D Gaussian Splatting (3DGS) and Neural Radiance Fields (NeRF) have advanced novel-view synthesis. Recent methods extend multi-view 2D segmentation to 3D, enabling instance/semantic segmentation for better scene understanding. A key challenge is the inconsistency of 2D instance labels across views, leading to poor 3D predictions. Existing methods use a two-stage approach in which some rely on contrastive learning with hyperparameter-sensitive clustering, while others preprocess labels for consistency. We propose a unified framework that merges these steps, reducing training time and improving performance by introducing a learnable feature embedding for segmentation in Gaussian primitives. This embedding is then efficiently decoded into instance labels through a novel "Embedding-to-Label" process, effectively integrating the optimization. While this unified framework offers substantial benefits, we observed artifacts at the object boundaries. To address the object boundary issues, we propose hard-mining samples along these boundaries. However, directly applying hard mining to the feature embeddings proved unstable. Therefore, we apply a linear layer to the rasterized feature embeddings before calculating the triplet loss, which stabilizes training and significantly improves performance. Our method outperforms baselines qualitatively and quantitatively on the ScanNet, Replica3D, and Messy-Rooms datasets. --- ### AstroReview: An LLM-driven Multi-Agent Framework for Telescope Proposal Peer Review and Refinement Authors: Yutong Wang, Yunxiang Xiao, Yonglin Tian, Junyong Li, Jing Wang, Yisheng Lv Published: 2025-12-31 Categories: astro-ph.IM, cs.AI arXiv: https://arxiv.org/abs/2512.24754v1 Competitive access to modern observatories has intensified as proposal volumes outpace available telescope time, making timely, consistent, and transparent peer review a critical bottleneck for the advancement of astronomy. Automating parts of this process is therefore both scientifically significant and operationally necessary to ensure fair allocation and reproducible decisions at scale. We present AstroReview, an open-source, agent-based framework that automates proposal review in three stages: (i) novelty and scientific merit, (ii) feasibility and expected yield, and (iii) meta-review and reliability verification. Task isolation and explicit reasoning traces curb hallucinations and improve transparency. Without any domain specific fine tuning, AstroReview used in our experiments only for the last stage, correctly identifies genuinely accepted proposals with an accuracy of 87%. The AstroReview in Action module replicates the review and refinement loop; with its integrated Proposal Authoring Agent, the acceptance rate of revised drafts increases by 66% after two iterations, showing that iterative feedback combined with automated meta-review and reliability verification delivers measurable quality gains. Together, these results point to a practical path toward scalable, auditable, and higher throughput proposal review for resource limited facilities. --- ### Fairness-Aware Insurance Pricing: A Multi-Objective Optimization Approach Authors: Tim J. Boonen, Xinyue Fan, Zixiao Quan Published: 2025-12-31 Categories: q-fin.RM, cs.LG arXiv: https://arxiv.org/abs/2512.24747v1 Machine learning improves predictive accuracy in insurance pricing but exacerbates trade-offs between competing fairness criteria across different discrimination measures, challenging regulators and insurers to reconcile profitability with equitable outcomes. While existing fairness-aware models offer partial solutions under GLM and XGBoost estimation methods, they remain constrained by single-objective optimization, failing to holistically navigate a conflicting landscape of accuracy, group fairness, individual fairness, and counterfactual fairness. To address this, we propose a novel multi-objective optimization framework that jointly optimizes all four criteria via the Non-dominated Sorting Genetic Algorithm II (NSGA-II), generating a diverse Pareto front of trade-off solutions. We use a specific selection mechanism to extract a premium on this front. Our results show that XGBoost outperforms GLM in accuracy but amplifies fairness disparities; the Orthogonal model excels in group fairness, while Synthetic Control leads in individual and counterfactual fairness. Our method consistently achieves a balanced compromise, outperforming single-model approaches. --- ### Splatwizard: A Benchmark Toolkit for 3D Gaussian Splatting Compression Authors: Xiang Liu, Yimin Zhou, Jinxiang Wang, Yujun Huang, Shuzhao Xie, Shiyu Qin, Mingyao Hong, Jiawei Li, Yaowei Wang, Zhi Wang, Shu-Tao Xia, Bin Chen Published: 2025-12-31 Categories: cs.CV arXiv: https://arxiv.org/abs/2512.24742v1 The recent advent of 3D Gaussian Splatting (3DGS) has marked a significant breakthrough in real-time novel view synthesis. However, the rapid proliferation of 3DGS-based algorithms has created a pressing need for standardized and comprehensive evaluation tools, especially for compression task. Existing benchmarks often lack the specific metrics necessary to holistically assess the unique characteristics of different methods, such as rendering speed, rate distortion trade-offs memory efficiency, and geometric accuracy. To address this gap, we introduce Splatwizard, a unified benchmark toolkit designed specifically for benchmarking 3DGS compression models. Splatwizard provides an easy-to-use framework to implement new 3DGS compression model and utilize state-of-the-art techniques proposed by previous work. Besides, an integrated pipeline that automates the calculation of key performance indicators, including image-based quality metrics, chamfer distance of reconstruct mesh, rendering frame rates, and computational resource consumption is included in the framework as well. Code is available at https://github.com/splatwizard/splatwizard --- ### BIOME-Bench: A Benchmark for Biomolecular Interaction Inference and Multi-Omics Pathway Mechanism Elucidation from Scientific Literature Authors: Sibo Wei, Peng Chen, Lifeng Dong, Yin Luo, Lei Wang, Peng Zhang, Wenpeng Lu, Jianbin Guo, Hongjun Yang, Dajun Zeng Published: 2025-12-31 Categories: cs.CL arXiv: https://arxiv.org/abs/2512.24733v1 Multi-omics studies often rely on pathway enrichment to interpret heterogeneous molecular changes, but pathway enrichment (PE)-based workflows inherit structural limitations of pathway resources, including curation lag, functional redundancy, and limited sensitivity to molecular states and interventions. Although recent work has explored using large language models (LLMs) to improve PE-based interpretation, the lack of a standardized benchmark for end-to-end multi-omics pathway mechanism elucidation has largely confined evaluation to small, manually curated datasets or ad hoc case studies, hindering reproducible progress. To address this issue, we introduce BIOME-Bench, constructed via a rigorous four-stage workflow, to evaluate two core capabilities of LLMs in multi-omics analysis: Biomolecular Interaction Inference and end-to-end Multi-Omics Pathway Mechanism Elucidation. We develop evaluation protocols for both tasks and conduct comprehensive experiments across multiple strong contemporary models. Experimental results demonstrate that existing models still exhibit substantial deficiencies in multi-omics analysis, struggling to reliably distinguish fine-grained biomolecular relation types and to generate faithful, robust pathway-level mechanistic explanations. --- ### EchoFoley: Event-Centric Hierarchical Control for Video Grounded Creative Sound Generation Authors: Bingxuan Li, Yiming Cui, Yicheng He, Yiwei Wang, Shu Zhang, Longyin Wen, Yulei Niu Published: 2025-12-31 Categories: cs.CV arXiv: https://arxiv.org/abs/2512.24731v2 Sound effects build an essential layer of multimodal storytelling, shaping the emotional atmosphere and the narrative semantics of videos. Despite recent advancement in video-text-to-audio (VT2A), the current formulation faces three key limitations: First, an imbalance between visual and textual conditioning that leads to visual dominance; Second, the absence of a concrete definition for fine-grained controllable generation; Third, weak instruction understanding and following, as existing datasets rely on brief categorical tags. To address these limitations, we introduce EchoFoley, a new task designed for video-grounded sound generation with both event level local control and hierarchical semantic control. Our symbolic representation for sounding events specifies when, what, and how each sound is produced within a video or instruction, enabling fine-grained controls like sound generation, insertion, and editing. To support this task, we construct EchoFoley-6k, a large-scale, expert-curated benchmark containing over 6,000 video-instruction-annotation triplets. Building upon this foundation, we propose EchoVidia a sounding-event-centric agentic generation framework with slow-fast thinking strategy. Experiments show that EchoVidia surpasses recent VT2A models by 40.7% in controllability and 12.5% in perceptual quality. --- ### EchoFoley: Event-Centric Hierarchical Control for Video Grounded Creative Sound Generation Authors: Bingxuan Li, Yiming Cui, Yicheng He, Yiwei Wang, Shu Zhang, Longyin Wen, Yulei Niu Published: 2025-12-31 Categories: cs.CV arXiv: https://arxiv.org/abs/2512.24731v1 Sound effects build an essential layer of multimodal storytelling, shaping the emotional atmosphere and the narrative semantics of videos. Despite recent advancement in video-text-to-audio (VT2A), the current formulation faces three key limitations: First, an imbalance between visual and textual conditioning that leads to visual dominance; Second, the absence of a concrete definition for fine-grained controllable generation; Third, weak instruction understanding and following, as existing datasets rely on brief categorical tags. To address these limitations, we introduce EchoFoley, a new task designed for video-grounded sound generation with both event level local control and hierarchical semantic control. Our symbolic representation for sounding events specifies when, what, and how each sound is produced within a video or instruction, enabling fine-grained controls like sound generation, insertion, and editing. To support this task, we construct EchoFoley-6k, a large-scale, expert-curated benchmark containing over 6,000 video-instruction-annotation triplets. Building upon this foundation, we propose EchoVidia a sounding-event-centric agentic generation framework with slow-fast thinking strategy. Experiments show that EchoVidia surpasses recent VT2A models by 40.7% in controllability and 12.5% in perceptual quality. --- ### FlowBlending: Stage-Aware Multi-Model Sampling for Fast and High-Fidelity Video Generation Authors: Jibin Song, Mingi Kwon, Jaeseok Jeong, Youngjung Uh Published: 2025-12-31 Categories: cs.CV arXiv: https://arxiv.org/abs/2512.24724v1 In this work, we show that the impact of model capacity varies across timesteps: it is crucial for the early and late stages but largely negligible during the intermediate stage. Accordingly, we propose FlowBlending, a stage-aware multi-model sampling strategy that employs a large model and a small model at capacity-sensitive stages and intermediate stages, respectively. We further introduce simple criteria to choose stage boundaries and provide a velocity-divergence analysis as an effective proxy for identifying capacity-sensitive regions. Across LTX-Video (2B/13B) and WAN 2.1 (1.3B/14B), FlowBlending achieves up to 1.65x faster inference with 57.35% fewer FLOPs, while maintaining the visual fidelity, temporal coherence, and semantic alignment of the large models. FlowBlending is also compatible with existing sampling-acceleration techniques, enabling up to 2x additional speedup. Project page is available at: https://jibin86.github.io/flowblending_project_page. --- ### Equivalence of Personalized PageRank and Successor Representations Authors: Beren Millidge Published: 2025-12-31 Categories: cs.NE arXiv: https://arxiv.org/abs/2512.24722v1 The hippocampus appears to implement two core but highly distinct functions in the brain: long term memory retrieval and planning and spatial navigation. Naively, these functions appear very different algorithmically. In this short note, we demonstrate that two powerful algorithms that have each independently been proposed to underlie the hippocampal operation for each function -- personalized page-rank for memory retrieval, and successor representations for planning and navigation, are in fact isomorphic and utilize the same underlying representation -- the stationary distribution of a random walk on a graph. We hypothesize that the core computational function of the hippocampus is to compute this representation on arbitrary input graphs. --- ### FPGA Co-Design for Efficient N:M Sparse and Quantized Model Inference Authors: Fen-Yu Hsieh, Yun-Chang Teng, Ding-Yong Hong, Jan-Jan Wu Published: 2025-12-31 Categories: cs.LG, cs.AR arXiv: https://arxiv.org/abs/2512.24713v2 Large language models (LLMs) have demonstrated remarkable performance across a wide range of language processing tasks. However, this success comes at the cost of substantial computation and memory requirements, which significantly impedes their deployment in resource-constrained environments. To address this challenge, this work introduces an automation framework that leverages weight pruning and low-bit quantization, and presents a hardware-software co-design method that generates accelerators on the Field-Programmable Gate Array (FPGA) platform. In particular, we implement a unified pipeline that applies N:M structured pruning and 4-bit integer quantization to reduce the memory footprint, followed by optimized dequantization and matrix multiplication to enhance LLM inference on several hardware platforms, including CPUs, NVIDIA GPUs with Dense and 2:4 Sparse Tensor Cores, and a custom systolic-array-based FPGA accelerator. Utilizing 2:4 sparsity combined with quantization on $4096 \times 4096$ matrices, our approach achieves a reduction of up to $4\times$ in weight storage and a $1.71\times$ speedup in matrix multiplication, yielding a $1.29\times$ end-to-end latency reduction compared to dense GPU baselines. Scaling analysis on the LLaMA-7B model further shows that structured sparsity enhances the throughput per token by $1.36\times$. These results demonstrate the synergy of fine-grained N:M sparsity and quantization for enabling efficient and deployable LLM inference, while the proposed FPGA accelerator offers a flexible architectural path for supporting a broader class of sparsity patterns beyond the fixed 2:4 hardware constraints. --- ### FPGA Co-Design for Efficient N:M Sparse and Quantized Model Inference Authors: Fen-Yu Hsieh, Yun-Chang Teng, Ding-Yong Hong, Jan-Jan Wu Published: 2025-12-31 Categories: cs.LG, cs.AR arXiv: https://arxiv.org/abs/2512.24713v1 Large language models (LLMs) have demonstrated remarkable performance across a wide range of language processing tasks. However, this success comes at the cost of substantial computation and memory requirements, which significantly impedes their deployment in resource-constrained environments. To address this challenge, this work introduces an automation framework that leverages weight pruning and low-bit quantization, and presents a hardware-software co-design method that generates accelerators on the Field-Programmable Gate Array (FPGA) platform. In particular, we implement a unified pipeline that applies N:M structured pruning and 4-bit integer quantization to reduce the memory footprint, followed by optimized dequantization and matrix multiplication to enhance LLM inference on several hardware platforms, including CPUs, NVIDIA GPUs with Dense and 2:4 Sparse Tensor Cores, and a custom systolic-array-based FPGA accelerator. Utilizing 2:4 sparsity combined with quantization on $4096 \times 4096$ matrices, our approach achieves a reduction of up to $4\times$ in weight storage and a $1.71\times$ speedup in matrix multiplication, yielding a $1.29\times$ end-to-end latency reduction compared to dense GPU baselines. Scaling analysis on the LLaMA-7B model further shows that structured sparsity enhances the throughput per token by $1.36\times$. These results demonstrate the synergy of fine-grained N:M sparsity and quantization for enabling efficient and deployable LLM inference, while the proposed FPGA accelerator offers a flexible architectural path for supporting a broader class of sparsity patterns beyond the fixed 2:4 hardware constraints. --- ### LSRE: Latent Semantic Rule Encoding for Real-Time Semantic Risk Detection in Autonomous Driving Authors: Qian Cheng, Weitao Zhou, Cheng Jing, Nanshan Deng, Junze Wen, Zhaoyang Liu, Kun Jiang, Diange Yang Published: 2025-12-31 Categories: cs.RO, cs.AI arXiv: https://arxiv.org/abs/2512.24712v2 Real-world autonomous driving must adhere to complex human social rules that extend beyond legally codified traffic regulations. Many of these semantic constraints, such as yielding to emergency vehicles, complying with traffic officers' gestures, or stopping for school buses, are intuitive for humans yet difficult to encode explicitly. Although large vision-language models (VLMs) can interpret such semantics, their inference cost makes them impractical for real-time deployment. This work proposes LSRE, a Latent Semantic Rule Encoding framework that converts sparsely sampled VLM judgments into decision boundaries within the latent space of a recurrent world model. By encoding language-defined safety semantics into a lightweight latent classifier, LSRE enables real-time semantic risk assessment at 10 Hz without per-frame VLM queries. Experiments on six semantic-failure scenarios in CARLA demonstrate that LSRE attains semantic risk detection accuracy comparable to a large VLM baseline, while providing substantially earlier hazard anticipation and maintaining low computational latency. LSRE further generalizes to rarely seen semantic-similar test cases, indicating that language-guided latent classification offers an effective and deployable mechanism for semantic safety monitoring in autonomous driving. --- ### LSRE: Latent Semantic Rule Encoding for Real-Time Semantic Risk Detection in Autonomous Driving Authors: Qian Cheng, Weitao Zhou, Cheng Jing, Nanshan Deng, Junze Wen, Zhaoyang Liu, Kun Jiang, Diange Yang Published: 2025-12-31 Categories: cs.RO, cs.AI arXiv: https://arxiv.org/abs/2512.24712v1 Real-world autonomous driving must adhere to complex human social rules that extend beyond legally codified traffic regulations. Many of these semantic constraints, such as yielding to emergency vehicles, complying with traffic officers' gestures, or stopping for school buses, are intuitive for humans yet difficult to encode explicitly. Although large vision-language models (VLMs) can interpret such semantics, their inference cost makes them impractical for real-time deployment.This work proposes LSRE, a Latent Semantic Rule Encoding framework that converts sparsely sampled VLM judgments into decision boundaries within the latent space of a recurrent world model. By encoding language-defined safety semantics into a lightweight latent classifier, LSRE enables real-time semantic risk assessment at 10 Hz without per-frame VLM queries. Experiments on six semantic-failure scenarios in CARLA demonstrate that LSRE attains semantic risk detection accuracy comparable to a large VLM baseline, while providing substantially earlier hazard anticipation and maintaining low computational latency. LSRE further generalizes to rarely seen semantic-similar test cases, indicating that language-guided latent classification offers an effective and deployable mechanism for semantic safety monitoring in autonomous driving. --- ### BandiK: Efficient Multi-Task Decomposition Using a Multi-Bandit Framework Authors: András Millinghoffer, András Formanek, András Antos, Péter Antal Published: 2025-12-31 Categories: cs.LG, cs.AI arXiv: https://arxiv.org/abs/2512.24708v1 The challenge of effectively transferring knowledge across multiple tasks is of critical importance and is also present in downstream tasks with foundation models. However, the nature of transfer, its transitive-intransitive nature, is still an open problem, and negative transfer remains a significant obstacle. Selection of beneficial auxiliary task sets in multi-task learning is frequently hindered by the high computational cost of their evaluation, the high number of plausible candidate auxiliary sets, and the varying complexity of selection across target tasks. To address these constraints, we introduce BandiK, a novel three-stage multi-task auxiliary task subset selection method using multi-bandits, where each arm pull evaluates candidate auxiliary sets by training and testing a multiple output neural network on a single random train-test dataset split. Firstly, BandiK estimates the pairwise transfers between tasks, which helps in identifying which tasks are likely to benefit from joint learning. In the second stage, it constructs a linear number of candidate sets of auxiliary tasks (in the number of all tasks) for each target task based on the initial estimations, significantly reducing the exponential number of potential auxiliary task sets. Thirdly, it employs a Multi-Armed Bandit (MAB) framework for each task, where the arms correspond to the performance of candidate auxiliary sets realized as multiple output neural networks over train-test data set splits. To enhance efficiency, BandiK integrates these individual task-specific MABs into a multi-bandit structure. The proposed multi-bandit solution exploits that the same neural network realizes multiple arms of different individual bandits corresponding to a given candidate set. This semi-overlapping arm property defines a novel multi-bandit cost/reward structure utilized in BandiK. --- ### Evolving, Not Training: Zero-Shot Reasoning Segmentation via Evolutionary Prompting Authors: Kai Ye, Xiaotong You, Jianghang Lin, Jiayi Ji, Pingyang Dai, Liujuan Cao Published: 2025-12-31 Categories: cs.CV, cs.AI arXiv: https://arxiv.org/abs/2512.24702v1 Reasoning Segmentation requires models to interpret complex, context-dependent linguistic queries to achieve pixel-level localization. Current dominant approaches rely heavily on Supervised Fine-Tuning (SFT) or Reinforcement Learning (RL). However, SFT suffers from catastrophic forgetting and domain dependency, while RL is often hindered by training instability and rigid reliance on predefined reward functions. Although recent training-free methods circumvent these training burdens, they are fundamentally limited by a static inference paradigm. These methods typically rely on a single-pass "generate-then-segment" chain, which suffers from insufficient reasoning depth and lacks the capability to self-correct linguistic hallucinations or spatial misinterpretations. In this paper, we challenge these limitations and propose EVOL-SAM3, a novel zero-shot framework that reformulates reasoning segmentation as an inference-time evolutionary search process. Instead of relying on a fixed prompt, EVOL-SAM3 maintains a population of prompt hypotheses and iteratively refines them through a "Generate-Evaluate-Evolve" loop. We introduce a Visual Arena to assess prompt fitness via reference-free pairwise tournaments, and a Semantic Mutation operator to inject diversity and correct semantic errors. Furthermore, a Heterogeneous Arena module integrates geometric priors with semantic reasoning to ensure robust final selection. Extensive experiments demonstrate that EVOL-SAM3 not only substantially outperforms static baselines but also significantly surpasses fully supervised state-of-the-art methods on the challenging ReasonSeg benchmark in a zero-shot setting. The code is available at https://github.com/AHideoKuzeA/Evol-SAM3. --- ### Causal Discovery with Mixed Latent Confounding via Precision Decomposition Authors: Amir Asiaee, Samhita Pal, James O'quinn, James P. Long Published: 2025-12-31 Categories: cs.LG arXiv: https://arxiv.org/abs/2512.24696v1 We study causal discovery from observational data in linear Gaussian systems affected by \emph{mixed latent confounding}, where some unobserved factors act broadly across many variables while others influence only small subsets. This setting is common in practice and poses a challenge for existing methods: differentiable and score-based DAG learners can misinterpret global latent effects as causal edges, while latent-variable graphical models recover only undirected structure. We propose \textsc{DCL-DECOR}, a modular, precision-led pipeline that separates these roles. The method first isolates pervasive latent effects by decomposing the observed precision matrix into a structured component and a low-rank component. The structured component corresponds to the conditional distribution after accounting for pervasive confounders and retains only local dependence induced by the causal graph and localized confounding. A correlated-noise DAG learner is then applied to this deconfounded representation to recover directed edges while modeling remaining structured error correlations, followed by a simple reconciliation step to enforce bow-freeness. We provide identifiability results that characterize the recoverable causal target under mixed confounding and show how the overall problem reduces to well-studied subproblems with modular guarantees. Synthetic experiments that vary the strength and dimensionality of pervasive confounding demonstrate consistent improvements in directed edge recovery over applying correlated-noise DAG learning directly to the confounded data. --- ### Nested Learning: The Illusion of Deep Learning Architectures Authors: Ali Behrouz, Meisam Razaviyayn, Peilin Zhong, Vahab Mirrokni Published: 2025-12-31 Categories: cs.LG, cs.AI arXiv: https://arxiv.org/abs/2512.24695v1 Despite the recent progresses, particularly in developing Language Models, there are fundamental challenges and unanswered questions about how such models can continually learn/memorize, self-improve, and find effective solutions. In this paper, we present a new learning paradigm, called Nested Learning (NL), that coherently represents a machine learning model with a set of nested, multi-level, and/or parallel optimization problems, each of which with its own context flow. Through the lenses of NL, existing deep learning methods learns from data through compressing their own context flow, and in-context learning naturally emerges in large models. NL suggests a philosophy to design more expressive learning algorithms with more levels, resulting in higher-order in-context learning and potentially unlocking effective continual learning capabilities. We advocate for NL by presenting three core contributions: (1) Expressive Optimizers: We show that known gradient-based optimizers, such as Adam, SGD with Momentum, etc., are in fact associative memory modules that aim to compress the gradients' information (by gradient descent). Building on this insight, we present other more expressive optimizers with deep memory and/or more powerful learning rules; (2) Self-Modifying Learning Module: Taking advantage of NL's insights on learning algorithms, we present a sequence model that learns how to modify itself by learning its own update algorithm; and (3) Continuum Memory System: We present a new formulation for memory system that generalizes the traditional viewpoint of long/short-term memory. Combining our self-modifying sequence model with the continuum memory system, we present a continual learning module, called Hope, showing promising results in language modeling, knowledge incorporation, and few-shot generalization tasks, continual learning, and long-context reasoning tasks. --- ### Mobility-Assisted Decentralized Federated Learning: Convergence Analysis and A Data-Driven Approach Authors: Reza Jahani, Md Farhamdur Reza, Richeng Jin, Huaiyu Dai Published: 2025-12-31 Categories: cs.LG arXiv: https://arxiv.org/abs/2512.24694v2 Decentralized Federated Learning (DFL) has emerged as a privacy-preserving machine learning paradigm that enables collaborative training among users without relying on a central server. However, its performance often degrades significantly due to limited connectivity and data heterogeneity. As we move toward the next generation of wireless networks, mobility is increasingly embedded in many real-world applications. The user mobility, either natural or induced, enables clients to act as relays or bridges, thus enhancing information flow in sparse networks; however, its impact on DFL has been largely overlooked despite its potential. In this work, we systematically investigate the role of mobility in improving DFL performance. We first establish the convergence of DFL in sparse networks under user mobility and theoretically demonstrate that even random movement of a fraction of users can significantly boost performance. Building upon this insight, we propose a DFL framework that utilizes mobile users with induced mobility patterns, allowing them to exploit the knowledge of data distribution to determine their trajectories to enhance information propagation through the network. Through extensive experiments, we empirically confirm our theoretical findings, validate the superiority of our approach over baselines, and provide a comprehensive analysis of how various network parameters influence DFL performance in mobile networks. --- ### Mobility-Assisted Decentralized Federated Learning: Convergence Analysis and A Data-Driven Approach Authors: Reza Jahani, Md Farhamdur Reza, Richeng Jin, Huaiyu Dai Published: 2025-12-31 Categories: cs.LG arXiv: https://arxiv.org/abs/2512.24694v1 Decentralized Federated Learning (DFL) has emerged as a privacy-preserving machine learning paradigm that enables collaborative training among users without relying on a central server. However, its performance often degrades significantly due to limited connectivity and data heterogeneity. As we move toward the next generation of wireless networks, mobility is increasingly embedded in many real-world applications. The user mobility, either natural or induced, enables clients to act as relays or bridges, thus enhancing information flow in sparse networks; however, its impact on DFL has been largely overlooked despite its potential. In this work, we systematically investigate the role of mobility in improving DFL performance. We first establish the convergence of DFL in sparse networks under user mobility and theoretically demonstrate that even random movement of a fraction of users can significantly boost performance. Building upon this insight, we propose a DFL framework that utilizes mobile users with induced mobility patterns, allowing them to exploit the knowledge of data distribution to determine their trajectories to enhance information propagation through the network. Through extensive experiments, we empirically confirm our theoretical findings, validate the superiority of our approach over baselines, and provide a comprehensive analysis of how various network parameters influence DFL performance in mobile networks. --- ### MUSIC: MUlti-Step Instruction Contrast for Multi-Turn Reward Models Authors: Wenzhe Li, Shujian Zhang, Wenxuan Zhou, John Lambert, Chi Jin, Andrew Hard, Rajiv Mathews, Lun Wang Published: 2025-12-31 Categories: cs.CL arXiv: https://arxiv.org/abs/2512.24693v1 Evaluating the quality of multi-turn conversations is crucial for developing capable Large Language Models (LLMs), yet remains a significant challenge, often requiring costly human evaluation. Multi-turn reward models (RMs) offer a scalable alternative and can provide valuable signals for guiding LLM training. While recent work has advanced multi-turn \textit{training} techniques, effective automated \textit{evaluation} specifically for multi-turn interactions lags behind. We observe that standard preference datasets, typically contrasting responses based only on the final conversational turn, provide insufficient signal to capture the nuances of multi-turn interactions. Instead, we find that incorporating contrasts spanning \textit{multiple} turns is critical for building robust multi-turn RMs. Motivated by this finding, we propose \textbf{MU}lti-\textbf{S}tep \textbf{I}nstruction \textbf{C}ontrast (MUSIC), an unsupervised data augmentation strategy that synthesizes contrastive conversation pairs exhibiting differences across multiple turns. Leveraging MUSIC on the Skywork preference dataset, we train a multi-turn RM based on the Gemma-2-9B-Instruct model. Empirical results demonstrate that our MUSIC-augmented RM outperforms baseline methods, achieving higher alignment with judgments from advanced proprietary LLM judges on multi-turn conversations, crucially, without compromising performance on standard single-turn RM benchmarks. --- ### Quantum Visual Word Sense Disambiguation: Unraveling Ambiguities Through Quantum Inference Model Authors: Wenbo Qiao, Peng Zhang, Qinghua Hu Published: 2025-12-31 Categories: quant-ph, cs.CL arXiv: https://arxiv.org/abs/2512.24687v1 Visual word sense disambiguation focuses on polysemous words, where candidate images can be easily confused. Traditional methods use classical probability to calculate the likelihood of an image matching each gloss of the target word, summing these to form a posterior probability. However, due to the challenge of semantic uncertainty, glosses from different sources inevitably carry semantic biases, which can lead to biased disambiguation results. Inspired by quantum superposition in modeling uncertainty, this paper proposes a Quantum Inference Model for Unsupervised Visual Word Sense Disambiguation (Q-VWSD). It encodes multiple glosses of the target word into a superposition state to mitigate semantic biases. Then, the quantum circuit is executed, and the results are observed. By formalizing our method, we find that Q-VWSD is a quantum generalization of the method based on classical probability. Building on this, we further designed a heuristic version of Q-VWSD that can run more efficiently on classical computing. The experiments demonstrate that our method outperforms state-of-the-art classical methods, particularly by effectively leveraging non-specialized glosses from large language models, which further enhances performance. Our approach showcases the potential of quantum machine learning in practical applications and provides a case for leveraging quantum modeling advantages on classical computers while quantum hardware remains immature. --- ### BatteryAgent: Synergizing Physics-Informed Interpretation with LLM Reasoning for Intelligent Battery Fault Diagnosis Authors: Songqi Zhou, Ruixue Liu, Boman Su, Jiazhou Wang, Yixing Wang, Benben Jiang Published: 2025-12-31 Categories: cs.AI, eess.SY arXiv: https://arxiv.org/abs/2512.24686v1 Fault diagnosis of lithium-ion batteries is critical for system safety. While existing deep learning methods exhibit superior detection accuracy, their "black-box" nature hinders interpretability. Furthermore, restricted by binary classification paradigms, they struggle to provide root cause analysis and maintenance recommendations. To address these limitations, this paper proposes BatteryAgent, a hierarchical framework that integrates physical knowledge features with the reasoning capabilities of Large Language Models (LLMs). The framework comprises three core modules: (1) A Physical Perception Layer that utilizes 10 mechanism-based features derived from electrochemical principles, balancing dimensionality reduction with physical fidelity; (2) A Detection and Attribution Layer that employs Gradient Boosting Decision Trees and SHAP to quantify feature contributions; and (3) A Reasoning and Diagnosis Layer that leverages an LLM as the agent core. This layer constructs a "numerical-semantic" bridge, combining SHAP attributions with a mechanism knowledge base to generate comprehensive reports containing fault types, root cause analysis, and maintenance suggestions. Experimental results demonstrate that BatteryAgent effectively corrects misclassifications on hard boundary samples, achieving an AUROC of 0.986, which significantly outperforms current state-of-the-art methods. Moreover, the framework extends traditional binary detection to multi-type interpretable diagnosis, offering a new paradigm shift from "passive detection" to "intelligent diagnosis" for battery safety management. --- ### R-Debater: Retrieval-Augmented Debate Generation through Argumentative Memory Authors: Maoyuan Li, Zhongsheng Wang, Haoyuan Li, Jiamou Liu Published: 2025-12-31 Categories: cs.CL, cs.AI arXiv: https://arxiv.org/abs/2512.24684v1 We present R-Debater, an agentic framework for generating multi-turn debates built on argumentative memory. Grounded in rhetoric and memory studies, the system views debate as a process of recalling and adapting prior arguments to maintain stance consistency, respond to opponents, and support claims with evidence. Specifically, R-Debater integrates a debate knowledge base for retrieving case-like evidence and prior debate moves with a role-based agent that composes coherent utterances across turns. We evaluate on standardized ORCHID debates, constructing a 1,000-item retrieval corpus and a held-out set of 32 debates across seven domains. Two tasks are evaluated: next-utterance generation, assessed by InspireScore (subjective, logical, and factual), and adversarial multi-turn simulations, judged by Debatrix (argument, source, language, and overall). Compared with strong LLM baselines, R-Debater achieves higher single-turn and multi-turn scores. Human evaluation with 20 experienced debaters further confirms its consistency and evidence use, showing that combining retrieval grounding with structured planning yields more faithful, stance-aligned, and coherent debates across turns. --- ### Multi-modal cross-domain mixed fusion model with dual disentanglement for fault diagnosis under unseen working conditions Authors: Pengcheng Xia, Yixiang Huang, Chengjin Qin, Chengliang Liu Published: 2025-12-31 Categories: cs.AI, eess.SP arXiv: https://arxiv.org/abs/2512.24679v2 Intelligent fault diagnosis has become an indispensable technique for ensuring machinery reliability. However, existing methods suffer significant performance decline in real-world scenarios where models are tested under unseen working conditions, while domain adaptation approaches are limited to their reliance on target domain samples. Moreover, most existing studies rely on single-modal sensing signals, overlooking the complementary nature of multi-modal information for improving model generalization. To address these limitations, this paper proposes a multi-modal cross-domain mixed fusion model with dual disentanglement for fault diagnosis. A dual disentanglement framework is developed to decouple modality-invariant and modality-specific features, as well as domain-invariant and domain-specific representations, enabling both comprehensive multi-modal representation learning and robust domain generalization. A cross-domain mixed fusion strategy is designed to randomly mix modality information across domains for modality and domain diversity augmentation. Furthermore, a triple-modal fusion mechanism is introduced to adaptively integrate multi-modal heterogeneous information. Extensive experiments are conducted on induction motor fault diagnosis under both unseen constant and time-varying working conditions. The results demonstrate that the proposed method consistently outperforms advanced methods and comprehensive ablation studies further verify the effectiveness of each proposed component and multi-modal fusion. The code is available at: https://github.com/xiapc1996/MMDG. ---