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版本更新 · v2arXiv:2505.10877

Graph and Simplicial Complex Prediction Gaussian Process via the Hodgelet Representations

Mathieu Alain, So Takao, Xiaowen Dong, Bastian Rieck, Emmanuel Noutahi

原文摘要

Predicting labels for graph-structured data is crucial in many scientific applications. Recently, Gaussian processes with graph-level inputs have been proposed as flexible non-parametric models for classification tasks. In this work, we extend this framework to regression tasks and to simplicial complexes, enabling the modelling of attributes defined on edges and higher-order simplices. Drawing on Hodge theory, we construct representations based on the Hodge decomposition, allowing the model to distinguish between exact, co-exact, and harmonic signal components. We introduce Hodgelet representations as rich, learnable topological descriptors of simplicial complexes and show that our framework improves predictive performance across a range of applications. Our work broadens the applicability of Gaussian processes to prediction tasks at the graph and simplicial-complex levels.

cs.LG · cs.AI

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arXiv ↗PDF ↗
新论文 · v1arXiv:2611.11063

Target Recoverability under Selective Labels: A Failure Mode in ECG-Based Acute Coronary Syndrome Prediction

Alexander Schubert, Anton Håkansson, Ziad Obermeyer

原文摘要

Medical machine learning models often learn from diagnoses observed only in patients clinicians chose to test. The biases of such selective labels are well known, but less understood is what happens when a diagnosis includes cases the model's inputs cannot detect. Because testing decisions draw on information the model does not see, these cases can appear predictable among tested patients, producing relationships that fail, and can even reverse, among the untested. We formalize this mechanism and show in simulations that it worsens as testing becomes more selective or undetectable cases more common, while models trained only on detectable cases remain informative. We empirically study electrocardiogram (ECG)-based prediction of acute coronary syndrome (ACS), which is confirmed only by invasive testing and whose subtypes differ in how clearly they appear on the ECG. Three ECG foundation models fine-tuned on revascularization, our ACS proxy, performed well among catheterized patients (AUROC 0.740-0.770) but worse than chance for 30-day major adverse cardiovascular events among untested patients (AUROC 0.381-0.478). Models trained on ECG-visible subtypes retained discrimination (AUROC 0.680-0.709), whereas a model trained on unstable angina, which can occur without ECG changes, was inversely related to risk (AUROC 0.310). Under selective labels, effective learning therefore requires prediction targets that can be inferred from the model's inputs and evaluation in the intended deployment population, which often extends beyond the patients who were tested.

cs.LG · cs.AI

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arXiv ↗PDF ↗
新论文 · v1arXiv:2611.11061

Symplectic Normal Coordinates Flow for Generative Modeling of Hamiltonian Systems

Christopher M. Pierce

原文摘要

In conditioned generative models of physical systems, the densities at different values of the conditioning parameters are often related through symplectic transformations. For stable linear Hamiltonian systems, there exists a symplectic transformation to "normal coordinates" such that the dynamics is reduced to rotations in each phase-space plane. This picture extends to the nonlinear case where, away from resonances, the Birkhoff normal form again consists of rotations in phase space, now with amplitude dependence. In this work, we introduce an invertible symplectic flow layer consisting of amplitude-dependent rotations in learned normal coordinates. We derive the condition under which the nonlinear rotation layer is symplectic and detail an architecture that guarantees it. Experiments are carried out on three conditional generative modeling problems: a synthetic chain of coupled nonlinear oscillators and two problems from accelerator physics. We show empirically that normalizing flows with the new layers achieve competitive or superior negative log-likelihoods compared to baseline flows, with gains that grow with the strength of the nonlinearity on the synthetic system.

physics.acc-ph · cs.LG

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arXiv ↗PDF ↗
版本更新 · v2arXiv:2609.14802

From matrix inversion to constraints: provably tighter confidence regions for importance weights in label shift

Mushan Li, Kihyun Han, Yanyuan Ma

原文摘要

Importance weights are essential in domain adaptation under label shift, yet their utility is often undermined by the finite sample uncertainty associated with their estimation. Existing methods typically analyze this uncertainty through Gaussian elimination on interval-valued linear systems, which leads to overly conservative confidence regions and inefficient downstream applications. We propose a paradigm shift from inversion-based inference to a direct matrix constraint framework. We use this framework to define a joint confidence region and extract marginal intervals via linear programming, deriving provably tighter bounds for importance weights while maintaining exact finite-sample validity. Furthermore, we analyze the confidence region's geometry and provide the theoretical results for its diameter bounds. Evaluated across text, image, multimodal benchmarks, including AGNews, MNIST, CIFAR-10, N24News, and a real-world autonomous driving dataset, nuImages, our approach consistently yields shorter confidence intervals and smaller prediction sets than inversion-based methods.

stat.ML · cs.LG

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arXiv ↗PDF ↗
版本更新 · v6arXiv:2507.21183

MaPPO: Maximum a Posteriori Preference Optimization with Prior Knowledge

Guangchen Lan, Sipeng Zhang, Tianle Wang, Yuwei Zhang, Daoan Zhang, Xinpeng Wei, Xiaoman Pan, Hongming Zhang 等 10 位作者

原文摘要

As the era of large language models (LLMs) unfolds, Preference Optimization (PO) methods have become a central approach to aligning LLMs with human preferences and improving performance. We propose Maximum a Posteriori Preference Optimization (MaPPO), a methodology for learning from preferences that explicitly incorporates prior reward knowledge into the optimization objective. Building on the paradigm employed by Direct Preference Optimization (DPO) and its variants of treating preference learning as a Maximum Likelihood Estimation (MLE) problem, MaPPO integrates prior reward estimates into a principled Maximum a Posteriori (MaP) objective. This not only generalizes DPO and its variants, but also enhances alignment by mitigating the oversimplified binary classification of responses. Additionally, MaPPO introduces no additional hyperparameters, and supports preference optimization in both offline and online settings. In addition, MaPPO can be used as a plugin for DPO variants, including widely used SimPO, IPO, and CPO, and produce consistent improvements. Extensive empirical evaluations of different model sizes and model series on three standard benchmarks (MT-Bench, AlpacaEval 2.0, and Arena-Hard) demonstrate consistent improvements in alignment performance without sacrificing computational efficiency.

cs.LG · cs.AI · cs.CL

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arXiv ↗PDF ↗
版本更新 · v3arXiv:2610.14423

Scaling Laws for Multimodal Data Mixtures

Aditi Khandelwal, Ayush K. Tarun, Yixuan Xu, Imanol Schlag, Golnoosh Farnadi, Siva Reddy, Luke Zettlemoyer, Antoine Bosselut

原文摘要

Frontier AI systems are increasingly natively multimodal, jointly pretrained on multiple modalities, such as text, vision, and audio. However, the scientific understanding of the multimodal data mixtures used to pretrain these systems --- how much of each modality to mix, and at what potential interference cost to the others --- is largely absent or gatekept. In this work, we derive data mixing scaling laws for three modalities: text, vision, and audio. Multimodal Large Language Models (MLLMs) increasingly adopt Mixture-of-Experts (MoE) architectures, as MoEs scale efficiently and partially limit cross-modal interference. We therefore use MoEs to conduct 268 experiments ranging from 457M to 8.3B parameters, on up to 150 billion tokens. Our work leads to multiple actionable insights into multimodal data mixing for training MLLMs. First, compute optima are modality-specific: audio modality is model-heavy, favoring scaling parameters over tokens, while text and vision require balanced scaling of model size and data. Second, repeating vision or audio data beyond 2x yields negligible benefits. Finally, cross-modal interactions are highly asymmetric: vision strongly interferes with audio, whereas audio's interference with learning text and vision is negligible. Overall, we provide a principled foundation for understanding the multimodal data mixtures needed to train frontier MLLMs.

cs.CL · cs.LG

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arXiv ↗PDF ↗
版本更新 · v2arXiv:2601.09865

Advancing Model Refinement: Muon-Optimized Distillation and Quantization for LLM Deployment

Jacob Sander, Brian Jalaian, Venkat R. Dasari

原文摘要

Correction notice (v2). Two implementation issues affect these experiments. All LoRA training ran under DeepSpeed, which replaced the optimizer passed to the Trainer with AdamW at a constant learning rate, so the Muon and Adam arms used the same optimizer. The cross-entropy term was computed without the next-token shift, so the preference for alpha = 1 reflects that objective rather than the value of the teacher. Each comparison also uses a single GPTQ quantization, and repeated quantizations differ by up to several points. The findings on Muon's quantization robustness, the distillation weight and accuracy relative to GPTQ alone should not be relied on. See the notice on page 1. Large Language Models (LLMs) enable advanced natural language processing but face deployment challenges on resource-constrained edge devices due to high computational, memory, and energy demands. Optimizing these models requires addressing three key challenges: acquiring task-specific data, fine-tuning for performance, and compressing models to accelerate inference while reducing resource demands. We propose an integrated framework combining GPTQ-based quantization, low-rank adaptation (LoRA), and a specialized data distillation process to significantly reduce model size and complexity while preserving or enhancing task-specific performance. By leveraging data distillation, knowledge distillation via Kullback-Leibler divergence, Bayesian hyperparameter optimization, and the Muon optimizer, our pipeline achieves up to 2x memory compression (e.g., reducing a 6GB model to 3GB) and enables efficient inference for specialized tasks. Empirical results demonstrate superior performance on standard LLM benchmarks compared to GPTQ quantization alone, with the Muon optimizer notably enhancing fine-tuned models' resistance to accuracy decay during quantization.

cs.LG · cs.AI

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arXiv ↗PDF ↗
新论文 · v1arXiv:2611.11034

Prune to Probe: Causal Decision-Stability Probes for Efficient Reasoning

Yufei Qi, Chonghe Jiang

原文摘要

Reasoning models often continue generating after producing an answer, yet stopping prematurely may interrupt useful verification or revision. In this work, we investigate whether targeted interventions in a model's internal computation can provide a reliable signal for when to stop reasoning. We introduce Prune-to-Probe, an inference-time controller that uses causal activation interventions to probe the stability of a model's current answer. Specifically, we identify late-MLP coordinates that contribute to post-answer continuation while excluding those important for answer completion. Once an answer is stated, Prune-to-Probe temporarily suppresses the selected coordinates and measures the resulting stopping response relative to matched random interventions. Crucially, these interventions serve only as probes: the model either returns its existing answer or resumes generation from an untouched cache. On MATH, Prune-to-Probe reduces reasoning-token forwards by 2.60%, preserving all naturally correct trajectories while repairing six errors. On MMLU-Pro, it achieves 1.145% pooled savings with no observed harm. Further analysis shows that the intervention captures decision stability rather than correctness (AUROC 0.502). These results demonstrate the potential of causal activation probes for efficient inference-time control without permanent model modification.

cs.LG

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arXiv ↗PDF ↗
新论文 · v1arXiv:2611.11032

Finite-Sample and Communication-Efficient Networked Information Aggregation

Mohammadhossein Bateni, Zahra Hadizadeh, MohammadTaghi Hajiaghayi, Mahdi JafariRaviz, Shayan Taherijam

原文摘要

Building on SODA 2026 work Kearns et al. (2026), we study learning in directed acyclic graphs in which each agent observes only a subset of the input coordinates and may use the predictions of its parents as additional features. In the finite-sample protocol, the message sent along each edge is the agent's prediction vector on a shared sample, so the communication cost is linear in the sample size $m$. In this paper we study this model under communication constraints. First, we revisit the finite-sample analysis of the full-message protocol. We identify a gap in the finite-sample proof of Kearns et al. (2026) and develop a different argument that establishes a finite-sample population-risk guarantee along paths satisfying deterministic $M$-coverage. Second, we introduce a shared-sketch protocol in which each agent sends a $k$-dimensional linear sketch of its prediction vector instead of the full $m$-dimensional message, where $k\ll m$. We analyze this protocol through a reduction to least squares on the compressed sample together with an oblivious subspace embedding argument. The resulting bound separates statistics from communication: the statistical term is still governed by the original sample size $m$, while the additional error due to compression is controlled by the sketch dimension $k$. For Gaussian sketches, we obtain an explicit high-probability population-risk bound, showing that row subsampling is not the only possible communication-statistics tradeoff for networked information aggregation.

cs.LG · cs.AI · cs.GT

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arXiv ↗PDF ↗
新论文 · v1arXiv:2611.11031

Hybrid Product Search Without the Copy: Lexical, Semantic and Filtered Retrieval Served Directly from a Data Lake

Raghvendra Yadav, Xiang Fu, Chinmay Soman

原文摘要

E-commerce catalogs increasingly live in lakehouse tables: Apache Iceberg metadata over Parquet files in object storage. Product search, however, still runs on copies of that catalog, in a lexical engine and a vector database, each with its own ingestion pipeline, schema, staleness window and bill. We show that search applications can be hosted directly on the data lake, with the lakehouse table as the single system of record. We extend Apache Pinot, a real-time OLAP engine that already serves Iceberg tables in place, with BM25 relevance scoring over its Lucene text index, corpus-wide term statistics so that scores are comparable across files, and reciprocal rank fusion in plain SQL; a real-time table fed by a stream can sit behind the same table, merged by the broker. On the Amazon ESCI benchmark (1.2M US products, 22,458 test queries), hybrid search served from the lake matches exact retrieval (nDCG@10 .361). The lake table is queryable 149 s after its files are registered, and a lake commit is searchable in 3.8 s, close to indexing directly into a search engine. On a brand-sorted lake of 277M products (0.8 TB), one 16-vCPU node answers brand-scoped vector, BM25 and client-fused hybrid searches in 6.5-9 ms (p50), sustains 1,100 vector and 635 BM25 searches per second at under 180 ms p99, and makes a commit searchable in 4.3 s. Search adds only its indexes: no copy of the catalog, no sync pipeline and no second schema, which cuts storage, rebuild time and the operational work of keeping copies consistent.

cs.LG · cs.DB · cs.IR

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arXiv ↗PDF ↗
新论文 · v1arXiv:2611.11027

MoTo: Mixture of Tokenizers Towards Fair Multilingual Language Modeling

Gül Sena Altıntaş, Colin Raffel

原文摘要

Large language models rely on a single tokenizer that is chosen at training time and fixed thereafter. Most tokenizers are trained on English-dominant corpora, making them under-serve morphologically rich and non-Latin-script languages, where they can produce fragmented representations, longer sequence lengths, and reduced information density during training. We propose Mixture of Tokenizers MoTo, a modular tokenization framework that trains dedicated per-language BPE tokenizers and composes them into a unified superset vocabulary, where tokens shared across languages are deduplicated into a common ID space. Each tokenizer maintains its own normalization policy, pre-tokenization rules, and vocabulary budget. Despite allocating significantly fewer entries to English, MoTo retains English performance while delivering consistent gains across 18 out of 21 typologically diverse languages spanning six scripts, with improvements most pronounced on morphologically complex and non-Latin-script languages that are systematically underserved by English-dominant tokenizer design. Our results suggest that modular subword tokenization is a practical and extensible alternative to monolithic tokenization for multilingual parity.

cs.CL · cs.LG

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arXiv ↗PDF ↗
新论文 · v1arXiv:2611.11024

Phase-Conditioned Reward Shaping for Token-Efficient LLM Reasoning

Zhe Chen, Xiaolin Zhou, Xiyang Hu

原文摘要

Extended reasoning can help language models solve complex problems, but long responses often contain both useful computations and redundant checks. Conventional length penalties encourage shorter responses without distinguishing the functions of intermediate steps. We propose Phase-Conditioned Reward Shaping (PCRS), a framework that combines phase identification with training-time reward allocation. We investigate two classification routes: PCRS-H with a hidden-state classifier and PCRS-T with a frozen text classifier. Our main text classifier implementation assigns overlapping reasoning, execution, and revision labels to native chunks, then uses these labels and exact repetition to adjust existing token penalties. We compare the completed configurations on six mathematical and scientific reasoning benchmarks and examine four reward-weight settings with the text classifier. Compared with a baseline using token-level length penalties, the default text classifier configuration reduces mean response length by 3.13%; sampled Pass@1 is approximately 62.07% for both methods, while oracle Pass@16 rises from 86.08% to 87.67%. These results show how phase-informed penalty allocation can reduce output volume and improve multi-attempt coverage without inference-time classification.

cs.LG · cs.AI

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arXiv ↗PDF ↗
新论文 · v1arXiv:2611.11023

Where Should Models Run? A Collaborative Inference Framework for Embodied Intelligence in 6G

Wanlu Sun, Zhenhui Yuan, Xiang Ji, Yi Han, Qing Li, Gan Zheng

原文摘要

Embodied intelligence systems rely on fast and reliable inference to enable real-time perception, reasoning, and control. Recent advances in large-scale artificial intelligence (AI) models, including Large Language Models (LLMs) and Vision-Language-Action (VLA) models, have significantly enhanced the capabilities of embodied agents in complex environments. However, deploying these models remains challenging due to the limited computational resources and energy budgets of robotic platforms. A fundamental question is where inference should be executed. Collaborative inference, which partitions models across end devices, edge servers, and the cloud, has emerged as a promising solution. Yet determining the optimal partitioning point remains an open problem, as it depends on wireless network conditions, model structure, and privacy requirements. This article reviews the state of the art in collaborative inference for embodied intelligence and proposes an edge-centric framework in which the edge coordinates model partitioning and execution. Through both simulation and real-world experiments over WiFi and 5G networks, we demonstrate that the optimal partitioning strategy varies with network conditions and model structure. We further identify latency-minimizing partition points for ResNet-101, Qwen2.5-VL-7B, and OpenVLA-7B in collaborative inference between NVIDIA Jetson Nano or AGX Xavier devices and an AGX Thor edge server under varying wireless conditions. Finally, we discuss the future directions for collaborative inference in 6G-empowered embodied intelligence systems.

cs.LG · cs.AI

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arXiv ↗PDF ↗
新论文 · v1arXiv:2611.11020

Unified Factored Representations Resist Catastrophic Forgetting

Joel Lehman, Kenneth O. Stanley

原文摘要

This paper explores an unexpected connection between the quality of a neural network's (NN's) internal representation and its vulnerability to catastrophic forgetting. The representational forgetting hypothesis is that networks with unified, factored representation (UFR) can resist catastrophic forgetting without any modification to the underlying fine-tuning algorithm or network architecture. The key insight is that catastrophic forgetting may largely result from fractured entangled representation (FER): fine-tuning on only part of the input domain results in disruptive changes for other input patterns, because of how individual features are globally entangled. In contrast, if a gradient update is compatible with how a UFR network factors its representation, such an update may result in coordinated change across the input domain, alleviating catastrophic forgetting. Experiments in two domains provide evidence for this hypothesis: (1) an illustrative 1D regression task, in which UFR networks constructed by hand are compared against networks trained from scratch with SGD, and (2) an image-regression task with NNs previously shown to exhibit UFR. In both settings, UFR networks forget substantially less than their FER counterparts when repeatedly fine-tuned on UFR-compatible variations. Further experiments show that a training loss that distills UFR representations into fresh NNs reduces forgetting; along with several additional controls, these results help isolate representation as the critical factor. Together these results suggest that representational quality is an underappreciated contributor to catastrophic forgetting, and that the encouragement and maintenance of such quality may require attending to what patterns a particular NN is ready to learn.

cs.LG · cs.AI

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arXiv ↗PDF ↗
新论文 · v1arXiv:2611.11019

Toward universal excited-state machine-learned potentials: a local excitation Hamiltonian for size consistency and transferability

Pascal Friederich

原文摘要

Universal machine-learned interatomic potentials could make excited-state simulations across chemical space routine, but excitation energies, unlike ground-state energies, are not sums of atomic contributions. The excited states of noninteracting molecules are the union of their states, which pooled multistate readouts reproduce at best for the lowest state. We introduce LEH-MACE, whose excitation energies are the eigenvalues of a local excitation Hamiltonian assembled from atom-centred blocks, so this union holds exactly. We generated TD-DFT energies and forces for about 300,000 equilibrium, thermal, and excited-state geometries of over 100,000 molecules. LEH-MACE's advantage over direct readouts grew with the distance from the training data and was largest above $S_1$, with about half their $S_2$ and $S_3$ errors for long conjugated oligomers. For molecule pairs, only LEH-MACE kept its single-molecule accuracy, while a softmin readout remained superior for $S_1$ dynamics of larger chromophores. Exact size consistency is thus one step toward such potentials.

physics.chem-ph · cond-mat.mtrl-sci · cs.LG

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arXiv ↗PDF ↗
新论文 · v1arXiv:2611.11017

ProtLink: Sequence and Multispecies Graph Context for Protein Interaction Prediction

Xingyuan Zhang

原文摘要

Predicting protein-protein interactions (PPIs) from sequence alone is difficult, while known interaction networks may provide informative biological context. We introduce ProtLink, a sequence-conditioned graph-learning framework that combines frozen ESM-2 representations with protein interaction topology and orthogroup-mediated connections between species. We compare sequence-only models, GraphSAGE, a homogeneous graph transformer, and a heterogeneous graph transformer (HGT) for human PPI prediction. In an initial edge-level comparison, three-layer multispecies HGT with ESM-2 650M features achieves an average precision (AP) of 0.980 under random negative sampling, compared with 0.951 for the homogeneous transformer; its AP decreases to 0.878 with two-hop-preferred negatives. In controlled three-seed experiments, three-layer full-graph HGT obtains an AP of $0.9701\pm0.0022$, exceeding an orthogroup-only model ($0.9435\pm0.0094$). The corresponding two-layer comparison shows little additional benefit from non-human PPI edges, consistent with the greater message-passing depth needed to transmit their information. For human proteins excluded from training interactions, multispecies HGT achieves $0.8335\pm0.0094$ AP versus $0.6482\pm0.0052$ for a sequence-only pair model. These results identify the value, depth dependence, and limits of evolutionary graph context while distinguishing prediction of held-out edges from generalization to held-out proteins.

cs.LG · q-bio.MN

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arXiv ↗PDF ↗
新论文 · v1arXiv:2611.11016

RoutingTrie: Metadata Routing for Constrained Generative Retrieval

Darshan Fofadiya, Yang Liu, Shuying Sun, Kyumin Park, Juhana Kangaspunta

原文摘要

Advances in large language models and semantic IDs (SIDs) have brought generative retrieval (GR) into industrial applications. These systems must return items that satisfy request metadata constraints as well as relevance requirements. A catalog trie ensures SID validity, but filtering metadata only after generation wastes computation on rejected candidates and may leave too few eligible results. We propose RoutingTrie to incorporate metadata constraints before beam selection. RoutingTrie maintains one shared SID trie and builds a compact index for each metadata field using Dense Bitset or Sparse List. We derive a storage cost formula to guide the choice between these index representations for different field distributions. During decoding, field probes determine whether requested values occur below candidate edges and produce a combined mask that guides beam selection. Experiments on ShopTrie, our configurable synthetic benchmark, demonstrate compact storage and efficient CPU and GPU probing across diverse metadata distributions. In a production replay, RoutingTrie raises Hit@100 from 67.5\% to 96.1\%, a gain of 28.6 percentage points, with only 1.1 ms latency increase. We will release the ShopTrie generator, benchmark scripts, and RoutingTrie CPU and Triton GPU implementations to support reproducibility.

cs.IR · cs.LG

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arXiv ↗PDF ↗
版本更新 · v2arXiv:2510.00569

Guaranteed Noisy CP Tensor Recovery via Riemannian Optimization on the Segre Manifold

Ke Xu, Yuefeng Han

原文摘要

Recovering a low-CP-rank tensor from noisy linear measurements is a central challenge in high-dimensional data analysis, with applications spanning tensor PCA, scalar-on-tensor regression, tensor-on-tensor regression, and beyond. We exploit the intrinsic geometry of rank-one tensors by casting the recovery task as an optimization problem over the Segre manifold, the smooth Riemannian manifold of rank-one tensors. This geometric viewpoint yields two powerful algorithms: Riemannian Gradient Descent (RGD) and Riemannian Gauss-Newton (RGN), each of which preserves feasibility at every iteration. Under mild noise assumptions, we prove that RGD converges at a local linear rate, while RGN exhibits an initial local quadratic phase followed by linear refinement to the statistical error. Extensive synthetic experiments validate these convergence guarantees and demonstrate the practical effectiveness of our methods.

stat.ML · cs.LG · math.OC · math.ST · stat.ME

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arXiv ↗PDF ↗
新论文 · v1arXiv:2611.11011

Learning Actionable Information Landscapes for Multimodal Active Sensing in Hawkmoths

Abdelrahman Sharafeldin, Yaqing Wang, Simon Sponberg, Hannah Choi

原文摘要

Animals integrate information across sensory modalities to guide exploration, suggesting that they maintain internal estimates of where actions are expected to reduce uncertainty. However, how such action-conditioned information maps are learned from multimodal sensory experience remains unclear, as previous studies have been largely confined to unimodal sensing frameworks. We introduce a multimodal predictive-coding framework for learning uncertainty-reduction maps from visual and mechanosensory observations in hawkmoth flower interaction tasks. A variational generative model trained on simulated environments recovers modality-specific information landscapes over the action space, capturing how visual patterns, surface geometry, and mechanosensory cues shape the value of exploratory actions. These learned maps are consistent with several features of hawkmoth behavior, including nonzero tracking offsets, probing along visual patterns, and geometry-dependent exploration. Applying the model to behavioral data from flower tracking and nectary search, we find that natural trajectories tend to occupy regions of high uncertainty reduction in these learned maps. Building on these observations, we develop an uncertainty-gated lexicographic algorithm that switches between information gathering and reward exploitation based on perceptual uncertainty. In nectary search, uncertainty gating improves learning relative to exploit-only, $ε$-greedy, fixed-time, and uncertainty-calibrated non-lexicographic controls, with better generalization and robustness to sensory and curvature shifts. These results link information landscapes learned through generative modeling to biological sensing and embodied exploration.

cs.LG · q-bio.NC

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arXiv ↗PDF ↗
版本更新 · v2arXiv:2505.20536

Covariate-Adjusted Deep Causal Learning for Heterogeneous Panel Data Models

Guanhao Zhou, Yuefeng Han, Xiufan Yu

原文摘要

This paper studies the task of estimating heterogeneous treatment effects in causal panel data models with covariate effects. We propose a novel Covariate-Adjusted Deep Causal Learning (CoDEAL) framework that cohesively deals with the underlying heterogeneity and nonlinearity of both panel units and covariate effects. CoDEAL integrates nonlinear covariate effect components (parameterized by a feed-forward neural network) with nonlinear factor structures (modeled by a multi-output autoencoder) to form a heterogeneous causal panel model. The nonlinear covariate component flexibly captures complex covariate influences on outcomes, and the nonlinear factor decomposition enables CoDEAL to effectively capture both cross-sectional and temporal dependencies inherent in the data panel. This latent structural information is subsequently integrated into a customized matrix completion algorithm, thereby facilitating more accurate counterfactual imputation. Moreover, the use of a multi-output autoencoder explicitly accounts for heterogeneity across units and enhances the model interpretability of the latent factors. We establish theoretical guarantees on the convergence of the estimated counterfactuals and demonstrate the compelling performance of the proposed method using extensive simulation studies and real-world data applications.

stat.ML · cs.LG · econ.EM · stat.ME

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arXiv ↗PDF ↗
版本更新 · v2arXiv:2608.25005

The Imperfective Paradox Is Not Necessarily in Large Language Models: A Benchmark Failure Before a Model Failure

Kaiqiao Han, Katarzyna Pruś, Mark Steedman, Yizhou Sun

原文摘要

Recent work uses an NLI benchmark of the imperfective paradox to argue that large language models exhibit a Teleological Bias and Calibration Crisis. We identify three validity concerns in benchmark construction, including aspectual reduction, semantic bias, and relation bias. Under a strict NLI standard, 76% of the Group A instances do not explicitly rule out culmination; in native-speaker annotation, 38% of Group A and 29% of Group C examples permit an alternative interpretation. To better isolate the semantic contrast, we construct Lexically Matched Minimal Pairs. Moreover, by formulating event-semantic NLI as a multi-step reasoning problem, we evaluate intermediate semantic judgments and use oracle-guided settings. We find that incorrect NLI entailment does not fully support teleological bias. Prompting often changes final NLI decisions without reliably improving the underlying semantic judgments, offering a possible explanation for the Calibration Crisis.

cs.CL · cs.LG

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arXiv ↗PDF ↗
新论文 · v1arXiv:2611.11009

PIA: Agentic Physics-Informed Runtime Anomaly Detection

Ali Mohammadi Ruzbahani, Abbas Yazdinejad, Hadis Karimipour

原文摘要

Runtime anomaly detectors for cyber-physical systems are either learned as black boxes from normal data, which miss stealthy manipulations that keep every signal inside its normal range, or derived by hand from physical models, which requires expert effort for every plant. We introduce physicsinformed agents (PIA), in which a large language model agent writes the detector itself as a set of physical residuals, such as pump characteristics, tank mass balances, and controller logic, and refines every candidate against normal operating data through an evaluation workbench before deployment. The result is a compact, human-readable detector that runs in milliseconds and names the component behind each alarm. On the BATADAL water distribution attack benchmark, PIA with Claude Opus 5.5 reaches an F1 of 0.878 on the test attacks against 0.504 for the strongest of six statistical and deep baselines, keeps detecting attacks concealed by replayed and offset sensor readings, and attributes every detected test attack to the attacked component. Ablations show that testing hypotheses against data is decisive: agents that write residuals from the plant description alone lose up to 0.28 F1, whereas agents that see only anonymized signals recover most of the physical structure.

cs.LG · cs.AI

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arXiv ↗PDF ↗
版本更新 · v2arXiv:2605.24405

Generative OOD-regularized Model-based Policy Optimization

Aysin Tumay, Jiahe Huang, Elise Jortberg, Rose Yu

原文摘要

We study sequential decision-making with offline reinforcement learning (RL). Traditional offline RL policies may result in out-of-distribution (OOD) actions when training relies only on sparse offline representations. To ensure safe offline policies in a sparse state-action space, we explore how density estimation models can be integrated into model-based RL methods to avoid OOD regions. Generative models are capable of explicitly modeling the density in sparse state-action spaces. Building on this, we introduce Generative OOD-regularized Model-based Policy Optimization (GORMPO), a density-regularized offline RL algorithm that uses generative density modeling to restrict policy updates to high-density areas of the dataset. Furthermore, we examine whether better OOD detection corresponds to better model-based offline policies. We compare (1) the OOD detection capabilities of various density estimators and (2) their performance within the GORMPO framework on a real-world medical dataset and sparse offline RL datasets. We provide theoretical guarantees bounding GORMPO's performance gap in terms of density estimation error, ensuring near-optimal policies under imperfect density estimates. Empirically, GORMPO remains competitive in reward and improves clinical metrics compared to state-of-the-art clinical RL models, and outperforms state-of-the-art offline RL models in offline RL benchmarks. Our empirical findings show that better OOD detection generally results in improved policies in environments with stable dynamics, while conservative penalties with poor density estimation are favored when dynamics are uncertain. Code, checkpoints, sparse, and OOD D4RL datasets are available at https://github.com/Rose-STL-Lab/GORMPO.

cs.LG · cs.AI

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arXiv ↗PDF ↗
新论文 · v1arXiv:2611.11005

GradReason: LLMs as Meta-Learners that Replace Training Epochs with Training-Data-Free Weight Updates

Ali Mohammadi Ruzbahani, Abbas Yazdinejad, Hadis Karimipour

原文摘要

Standard training recipes make a fixed number of passes over the training data, even when an epoch mostly adds mini-batch noise. We study whether a large language model (LLM) that reads the weight and gradient history of a run can replace some of these epochs with updates that compute no gradient and read no training image. In GradReason, an LLM agent is called every ten epochs. In a restricted sandbox, it analyses recent weight snapshots, epoch-mean gradients, and validation metrics, then submits a Python program that computes new weights and a plan marking each of the next ten epochs as data or virtual. A virtual epoch is kept only if it passes a forward-only check on 5,000 held-out validation images. In 120 runs on CIFAR-10 and CIFAR-100 with three architectures, Claude Opus 5.5 used 32.8% fewer data epochs than the 80-epoch recipe at 0.31 percentage points lower test accuracy (95% CI -0.72 to +0.09; 0.94 points on ResNet-18), on the recipe's own frontier of data against accuracy. Unprompted, Opus averaged recent weight snapshots in every run. This choice produced the savings: a fixed rule with the same acceptance check but extrapolations from prior work saved 5.4% of the data epochs, and the same rule with averaging added saved 25.0%. The LLM's contribution is to find a working update within a single run; once found, a fixed rule implements it equally well. An Opus run cost 0.68 USD, but at this scale the agent's latency exceeded the GPU time it saved.

cs.LG · cs.AI

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arXiv ↗PDF ↗
版本更新 · v2arXiv:2609.11894

3D Point Splatting for mmWave Radar Novel View Synthesis

Adnan Armouti, Yixuan Gao, Rajalakshmi Nandakumar

原文摘要

Solving novel view synthesis (NVS) for millimeter-wave (mmWave) radar requires a renderer that is physically faithful, complex-valued, and multi-viewpoint-tractable. No prior method achieves these three properties simultaneously. Differentiable Monte Carlo (MC) ray tracers implement the radar forward model directly with explicit material modeling and complex outputs, but a joint fit across views takes over an hour per scene. Optical-NVS ports of NeRF, hash grids, and 3D Gaussians train fast but discard phase and replace explicit material modeling with opaque learned features, restricting them to power-only range-azimuth (RA) magnitudes. We propose 3D Point Splatting (3DPS), the first differentiable point splatting renderer for radar, derived directly from the standard solid-angle form of the radar equation. Each oriented 3D point carries a material model built on ITU-R P.2040 permittivity and slab reflection, evaluated in closed form, with the resulting complex phasor splatted into range bins through a precomputed point spread function (PSF). The complex-valued output makes the renderer product-agnostic. The same optimized scene yields analog-to-digital converter (ADC), complex range profile (CRP), and RA outputs through standard fast Fourier transform (FFT) pipelines without retraining for each format. On six outdoor ColoRadar scenes, 3DPS reaches 0.587 mean Pearson correlation on held-out RA images. This is between 1.7x and 5.2x the three optical-NVS baselines (RadarSplat, Radar Fields, DART) and 1.3x the mmIR ray tracer at 1/25 of its wall-clock time. Training takes approximately 3 minutes per scene on a single RTX 4090. Project page: https://3d-point-splatting.github.io/

cs.CV · cs.GR · cs.LG · eess.SP

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新论文 · v1arXiv:2611.11001

Elastic Representations via Hyperbolic Geometry

Arjun Ramesh Kaushik, Rudrasis Chakraborty, Nalini Ratha, Venu Govindaraju

原文摘要

Learned representations (or embeddings) are the foundation of modern machine learning systems, yet they are typically trained as fixed-size embeddings, without accounting for varying downstream resource or task constraints. Matryoshka Representation Learning (MRL) addresses this limitation by learning nested representations, but requires costly end-to-end retraining and does not support post hoc expansion, while Contrastive Sparse Representations (CSR) reduce training overhead via lightweight adaptors but operate at a 4x larger dimensionality. Additionally, all prior works restrict their framework towards compression only. In this work, we introduce Hyperbolic Elastic Representation Learning (HERL), a framework for learning dimension-adaptive embeddings (compression and expansion) through a lightweight adaptor without retraining the base encoder. Our key idea is to exploit the exponential capacity of hyperbolic geometry by learning a non-linear mapping from the encoder space to hyperbolic space. We train a simple MLP to downsample the representations before projecting them onto a Lorentzian and learn adaptive representations that respect the Lorentzian geometry. Our approach is model-agnostic and effective in both supervised and unsupervised settings. Through extensive experiments across image and text modalities, we show that HERL preserves downstream performance across a wide range of embedding sizes, exhibits smooth interpolation between dimensions, and achieves these benefits at a fraction of the computational cost. Code: https://github.com/arjunrkaushik/herl.

cs.LG · cs.CV

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arXiv ↗PDF ↗
新论文 · v1arXiv:2611.10999

Differentiable Agent-Based Modeling for Multi-Echelon Supply Chains: Gradient-Based Optimization of Heterogeneous Policies at Scale

Bulent Soykan, Murtadha Alsayegh, Sean Mondesire, Ghaith Rabadi

原文摘要

Global supply chains rely on the interactions of thousands of heterogeneous agents, posing significant challenges for optimization. While traditional optimization often assumes homogeneity to ensure tractability, Reinforcement Learning struggles with sample inefficiency when scaling to high-dimensional state spaces. We propose a Differentiable Agent-Based Model for multi-echelon inventory management. By implementing stochastic dynamics via differentiable tensor operations, we enable direct gradient propagation through the simulation. We validate the framework by recovering theoretical Newsvendor optima without supervision. Crucially, we demonstrate ``Mass Personalization'' by optimizing 10,000 distinct agent policies simultaneously in seconds, achieving a 53% cost reduction compared to scalar baselines. Furthermore, we utilize gradient sensitivity analysis to generate causal heatmaps, accurately recovering inherent lead-time delays from the computation graph. This approach bridges the gap between simulation and optimization, enabling the efficient training of self-correcting Digital Twins.

cs.LG · cs.AI

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arXiv ↗PDF ↗
版本更新 · v2arXiv:2605.05755

Transformers Provably Implement In-Context Reinforcement Learning with Policy Improvement

Haodong Liang, Lifeng Lai

原文摘要

We investigate the ability of transformers to perform in-context reinforcement learning (ICRL), where a model must infer and execute learning algorithms from trajectory data without parameter updates. We show that a linear self-attention transformer block can provably implement policy-improvement methods, including semi-gradient SARSA and actor-critic, via explicit parameter constructions. Beyond existence, we design a teacher-mimicking training procedure, analyze its gradient-flow dynamics, and establish the first convergence guarantee in the ICRL literature: under suitable richness conditions on the training MDP distribution, gradient flow converges locally and exponentially to an optimal parameter manifold corresponding to the desired RL update. Empirically, training transformers on randomly generated tabular MDPs confirms these predictions: the learned models recover the parameter structure of our explicit constructions and, when deployed on unseen MDPs, deliver strong in-context control performance. Together, these results illuminate how transformer architectures internalize and execute classical reinforcement learning algorithms in context, bridging mechanistic understanding and training dynamics in ICRL.

stat.ML · cs.AI · cs.LG

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arXiv ↗PDF ↗
新论文 · v1arXiv:2611.10996

Toward a theory of Evaluability

Kunal Talwar

原文摘要

Machine learning models, particularly large foundation models, are increasingly evaluated along multiple axes that are relevant for their usage. In addition to accuracy measures appropriate for the specific task of interest, measures capturing safety, calibration and fairness properties are often of interest. The classical approach to these problems assumes that the test set has been held out from the training process, an assumption that is increasingly harder to justify for large foundation models. In this work, we formalize a notion of evaluability without this assumption, and ask if it is feasible to evaluate black-box models in settings where our test samples may have been used during training. We study this problem under different settings, capturing different levels of access to the model. We first study the natural setting, where the evaluator only looks at the output of the model on the test set. We fully characterize the class of distributions where accurate evaluation is possible, and show tight upper and lower bounds on the sample complexity of evaluation. Our results show that black-box evaluation in this setup is feasible if and only if the distribution is close to being small support. We then consider evaluation algorithms that can query the model on additional inputs and demonstrate connections to self-correctors studied in program testing. Using these, we show that a small amount of query access can increase the power of the evaluator for natural distributions and concept classes.

cs.LG · stat.ML

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arXiv ↗PDF ↗
版本更新 · v2arXiv:2605.05973

Towards Reliable LLM Evaluation: Correcting the Winner's Curse in Adaptive Benchmarking

Jiefu Zhang, Yang Xu, Haixiang Sun, Zihan Zhou, Tianyu Cao, Vaneet Aggarwal

原文摘要

Adaptive prompt and program search makes LLM evaluation selection-sensitive. Once benchmark items are reused inside tuning, the observed winner's score need not estimate the fresh-data performance of the full tune-then-deploy procedure. We study inference for this procedure-level target under explicit tuning budgets. We propose SIREN, a selection-aware repeated-split reporting protocol that freezes the post-search shortlist, separates splitwise selection from held-out evaluation, and uses an item-level Gaussian multiplier bootstrap for uncertainty quantification. In a fixed-shortlist regime with smooth stabilized selection, the estimator admits a first-order item-level representation, and the bootstrap yields valid simultaneous inference on a finite budget grid. This supports confidence intervals for procedure-performance curves and pre-specified equal-budget and cross-budget comparisons. Controlled simulations and MMLU-Pro tuning experiments show that winner-based reporting can be optimistic and can change deployment conclusions, while SIREN remains close to the finite-sample reporting target.

stat.ML · cs.AI · cs.LG · stat.AP

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arXiv ↗PDF ↗