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Deep Learning · Large Language Models

Jiaqi Xue, Qian Lou, Jiarong Xing, Heng Huang

As LLMs proliferate with diverse capabilities and costs, LLM routing has emerged by learning to predict each LLM's quality and cost for a given query, then selecting the one with high quality and low cost. However, existing routers implicitly assume a single fixed quality and cost per LLM for each query, ignoring that the same LLM's quality varies with its output length. This causes routers to exclude powerful LLMs when their estimated cost exceeds the budget, missing the opportunity that these LLMs could still deliver high quality at reduced cost with shorter outputs. To address this, we introduce R2-Router, which treats output length budget as a controllable variable and jointly selects the best LLM and length budget, enforcing the budget via length-constrained instructions. This enables R2-Router to discover that a powerful LLM with constrained output can outperform a weaker LLM at comparable cost—efficient configurations invisible to prior methods. Together with the router framework, we construct R2-Bench, the first routing dataset capturing LLM behavior across diverse output length budgets. Experiments show that R2-Router achieves state-of-the-art performance at $4-5\times$ lower cost compared with existing routers. This work opens a new direction: \textit{routing as reasoning}, where routers evolve from reactive selectors to deliberate reasoners that explore which LLM to use and at what cost budget. Source code is available at https://anonymous.4open.science/r/router-763C/README.md, with an interactive demo at https://r2-router.org.

Reinforcement Learning · Deep RL

Paulius Sasnauskas, Yiğit Yalın, Goran Radanovic

We study the corruption-robustness of in-context reinforcement learning (ICRL), focusing on the Decision-Pretrained Transformer (DPT, Lee et al., 2023). To address the challenge of reward poisoning attacks targeting the DPT, we propose a novel adversarial training framework, called Adversarially Trained DPT (AT-DPT). Our method simultaneously trains a population of attackers to minimize the true reward of the DPT by poisoning environment rewards, and a DPT model to infer optimal actions from the poisoned data. We evaluate the effectiveness of our approach against standard bandit algorithms, including robust baselines designed to handle reward contamination. Our results show that AT-DPT significantly outperforms them in bandit settings under a learned attacker, and generalizes to more complex environments such as adaptive attackers and MDPs. It shows promise in ICRL as a meta-RL approach to learning effective corruption-robust algorithms.

Deep Learning · Other Representation Learning

Stefanos Pertigkiozoglou, Mircea Petrache, Shubhendu Trivedi, Kostas Daniilidis

Equivariant neural networks exploit underlying task symmetries to improve generalization, but strict equivariance constraints can induce more complex optimization dynamics that can hinder learning. Prior work addresses these limitations by relaxing strict equivariance during training, but typically relies on prespecified, explicit, or implicit target levels of relaxation for each network layer, which are task-dependent and costly to tune. We propose Recurrent Equivariant Constraint Modulation (RECM), a layer-wise constraint modulation mechanism that learns appropriate relaxation levels solely from the training signal and the symmetry properties of each layer's input-target distribution, without requiring any prior knowledge about the task-dependent target relaxation level. We demonstrate that under the proposed RECM update, the relaxation level of each layer provably converges to a value upper-bounded by its symmetry gap, namely the degree to which its input-target distribution deviates from exact symmetry. Consequently, layers processing symmetric distributions recover full equivariance, while those with approximate symmetries retain sufficient flexibility to learn non-symmetric solutions when warranted by the data. Empirically, RECM outperforms prior methods across diverse exact and approximate equivariant tasks, including the challenging molecular conformer generation on the GEOM-Drugs dataset.

Deep Learning · Large Language Models

Felix Parker, Nimeesha Chan, Chi Zhang, Kimia Ghobadi

Time series data is fundamental to decision-making across many domains including healthcare, finance, power systems, and logistics. However, analyzing this data correctly often requires incorporating unstructured contextual information, answering domain-specific questions, and generating natural language explanations – capabilities that traditional time series models lack. While Large Language Models (LLMs) excel at contextual reasoning and knowledge integration, they struggle with numerical time series due to inefficient text-based representations and limited exposure to numerical data during pretraining. We address this gap by augmenting an LLM with specialized time series perception through a patch-based encoder-decoder architecture. We train this Time Series-augmented LLM (TsLLM) on a large corpus of over 20 billion tokens of interleaved time series and text spanning diverse tasks: forecasting with contextual information, question-answering, anomaly detection, classification, report generation, and more, all unified as next token prediction. This training enables TsLLM to leverage both its language understanding and newly acquired temporal reasoning capabilities. While not designed to surpass specialized models on traditional benchmarks, TsLLM demonstrates strong performance on tasks requiring the integration of time series analysis with natural language – capabilities that existing approaches cannot provide. It also exhibits strong zero-shot and few-shot performance, showing it can adapt to new data without additional training.

Stella Biderman, Mohammad Aflah Khan, Niloofar Mireshghallah, Catherine Arnett, Fazl Barez, Naomi Saphra

What would it mean to have a *scientific* understanding of AI? Language models are not static objects—they are snapshots of time-evolving processes shaped by data, objectives, and optimization dynamics. Yet the field predominantly treats models as fixed artifacts, analyzing behaviors after training rather than asking *why* they emerge. **This position paper argues that AI research should move beyond *post hoc* fixes and study the learning dynamics of models.** We envision a hierarchy of scientific maturity: first *predict* outcomes from early training signals, then *intervene* when trajectories go wrong, ultimately *design* training procedures that guarantee desired properties. Scaling laws have reached the first level for loss; the challenge is extending all three levels to general capabilities, biases, and safety. We articulate requirements for such theories, survey progress across mechanistic interpretability, fairness, memorization, and learning dynamics, and identify concrete open problems. The path forward requires treating models as processes to be understood, not just artifacts to be patched.

Applications · Chemistry, Physics, and Earth Sciences

Aleix Segui, Wesley Armour

In AI for Science, physics-informed losses are becoming popular to train learned compressors, but their rate-distortion consequences are poorly understood. We formalise this problem via a geometric framework, showing that physics-aware compression is governed by the interaction of two Riemannian structures in latent space: a Hessian-based physics sensitivity geometry induced by the physical observable, and a rate geometry induced by the entropy model. This theoretical view yields an explicit mechanism for error allocation: the codec concentrates precision along spectrally stiff and rate-expensive directions, while pushing uncertainty into directions that are weakly sensed by the physical observable. We prove fundamental limits from this alignment: (i) rate-efficient preservation is theoretically possible only when physical sensitivity is strongly anisotropic, and (ii) when physics and fidelity are not spectrally aligned, improving physical observables at fixed rate provably worsens standard distortion. We validate these predictions across chaotic fluid dynamics simulations, and introduce simple geometric alignment diagnostics that anticipate when physics-aligned training will succeed.

Applications · Computer Vision

Shuo Cao, Jiayang Li, Xiaohui Li, Yuandong Pu, Kaiwen Zhu, Yuanting Gao, Siqi Luo, Yi Xin, Qi Qin, Yu Zhou 等

Multimodal large language models (MLLMs) have achieved remarkable progress in visual understanding tasks such as visual grounding, segmentation, and captioning. However, their ability to perceive perceptual-level image features remains limited. In this work, we present UniPercept-Bench, a unified framework for perceptual-level image understanding across three key domains: Aesthetics, Quality, Structure and Texture. We establish a hierarchical definition system and construct large-scale datasets to evaluate perceptual-level image understanding. Based on this foundation, we develop a strong baseline UniPercept trained via Domain-Adaptive Pre-Training and Task-Aligned RL, enabling robust generalization across both Visual Rating (VR) and Visual Question Answering (VQA) tasks. UniPercept outperforms existing MLLMs on perceptual-level image understanding and can serve as a plug-and-play reward model for text-to-image generation. This work defines perceptual-level image understanding in the era of MLLMs and, through the introduction of a comprehensive benchmark together with a strong baseline, provides a solid foundation for advancing perceptual-level multimodal image understanding.

Dennis Frauen, Athiya Deviyani, Mihaela van der Schaar, Stefan Feuerriegel

Evaluating the performance of large language models (LLMs) from human preference data is crucial for obtaining LLM leaderboards. However, many existing approaches either rely on restrictive parametric assumptions or lack valid uncertainty quantification when flexible machine learning methods are used. In this paper, we propose a nonparametric statistical framework, called DMLRank, for comparing and ranking LLMs from preference data using debiased machine learning (DML). For this, we introduce generalized average ranking scores (GARS), which generalize commonly used ranking models, including the Bradley-Terry model or PageRank/ Rank centrality with complex human responses such as ties. \framework comes with the following advantages: (i)~It produces statistically efficient estimates of GARS ranking scores. (ii) It naturally allows to incorporate black-box machine learning methods for estimation. (iii) It can be combined with pre-trained LLM evaluators (e.g., using LLM-as-a-judge). (iv) It suggests optimal policies for collecting preference data under budget constraints. We demonstrate these advantages both theoretically and empirically using both synthetic and real-world preference datasets. In summary, our framework provides practitioners with powerful, state-of-the-art methods for comparing or ranking LLMs for leaderboards.

Deep Learning · Large Language Models

Nikhil Chandak, Shashwat Goel, Ameya Pandurang Prabhu, Moritz Hardt, Jonas Geiping

High-stakes decision making involves reasoning under uncertainty about the future. In this work, we train language models to make predictions on open-ended forecasting questions. To scale up training data, we synthesize novel forecasting questions from global events reported in daily news. While directly training on this data leads to performance drops, carefully curating questions creates a valuable training resource. We use the resulting dataset, OpenForesight, to post-train Qwen3 thinking models. To prevent leakage of future information during training and evaluation, we use an offline news corpus, both for data generation and retrieval in our forecasting system. Guided by a small validation set, we show the benefits of retrieval, and an improved reward function for reinforcement learning (RL). Once we obtain our final forecasting system, we perform held-out testing between May to August 2025. Our specialized model, OpenForecaster-8B, matches much larger proprietary models, with our training improving the accuracy, calibration, and consistency of predictions. We find calibration improvements from forecasting training generalize across popular benchmarks. We will open-source our models, code, and data to make LLM based forecasting research broadly accessible.

Deep Learning · Large Language Models

Yihong Huang, KE QIN, Rongzheng Wang, Muquan Li, Jiakai Li, Xiurui Xie, Shuang Liang

The Plan-then-Code paradigm effectively enhances Large Language Models (LLMs) in complex code generation by decomposing reasoning into explicit, interpretable steps. However, introducing the plan and verification report substantially enlarges the context, which in turn misdirects the model’s attention toward irrelevant tokens and the most recently generated code. This effect leads the model to overlook critical constraints and to generate incorrect code, especially for small-scale LLMs (less than 8B). To address this issue, we propose \textbf{P}erturbation-Verified \textbf{A}ttention \textbf{D}istillation and Dynamic \textbf{A}lignment (PADA). PADA identifies the key tokens most critical to the student model and constructs the optimal attention target matrix, dynamically aligning the student’s focus with key tokens for each plan step. We evaluate PADA with two teacher models and three student models across seven benchmarks, and the results show that PADA improves Pass@1 by up to 16.7\% and outperforms SOTA methods in all settings. Our code is available at https://anonymous.4open.science/r/PADA-coder

Applications · Chemistry, Physics, and Earth Sciences

Yue Yu, Weiqi Chen, Binqing Wu, Dongliang Cui, Wanyi Jiang, Zongjiang Shang, Bo Wu, Liang Sun, Ling Chen

Accurate seasonal‑to‑interannual climate forecasting provides critical support for decision-making in agriculture, energy, and disaster preparedness. Current deterministic models often fail to capture climate uncertainty, while existing generative approaches oversimplify the system by neglecting key spatiotemporal dependencies and cross-scale interactions. To address these limitations, we introduce ClimateAR, an AutoRegressive generative model for probabilistic seasonal-to-interannual Climate forecasting. The framework incorporates two novel components: (1) an aligned tokenizer that bridges and aligns heterogeneous simulation and real-world data to improve transferability across domains, and (2) a mixed-scale conditioning mechanism that captures multi-scale climate interactions for robust probabilistic forecasting. Extensive evaluations on the ERA5 reanalysis dataset show that ClimateAR achieves state-of-the-art performance, improving anomaly correlation skill by 37.56\% on average compared to leading baselines. The Code is available at https://anonymous.4open.science/r/ClimateAR-956D.

Deep Learning · Large Language Models

Jiaming Li, Haoran Ye, Yukun Chen, Xinyue Li, Lei Zhang, Hamid Alinejad-Rokny, Jimmy Chih-Hsien Peng, Min Yang

Sparse Autoencoders (SAEs) have become a cornerstone in mechanistic interpretability. However, current training methods inherit the Block Training paradigm from LLM pre-training. We identify this as a critical methodological oversight when applied to instruct models. Theoretically, utilizing GSNR analysis, we prove that attention leakage from unrelated contexts introduces destructive gradient noise. To rectify this paradigm, we propose $\underline{\textbf{F}}$inetuning-$\underline{\textbf{a}}$ligned $\underline{\textbf{S}}$equential $\underline{\textbf{T}}$raining ($\textit{FAST}$), a novel training method specifically tailored for instruct models.$\textit{FAST}$ aligns the training process with the data distribution and activation patterns of instruct models, achieving substantial improvements in both reconstruction and feature interpretability. Experimental results validate the efficacy of$\textit{FAST}$. GSNR analysis confirms improved training performance, with $\textit{FAST}$ demonstrating higher GSNR. This translates into superior reconstruction fidelity: $\textit{FAST}$ achieves an MSE of 0.6468 (significantly outperforming the baseline’s 5.1985) and maintains a near-zero Delta Loss (-0.51% to 0.37%). Consequently, feature quality is markedly enhanced; on Llama-3.2-3B-it, $\textit{FAST}$ yields 21.1% high-quality features, surpassing the 7.0% and 10.2% achieved by baselines. Surprisingly, we discover that intervening on special token activations via SAEs improves output quality, suggesting new opportunities for fine-grained control. Code, data, and all 240 trained SAEs will be publicly released.

Stella Biderman, Mohammad Aflah Khan, Niloofar Mireshghallah, Catherine Arnett, Fazl Barez, Naomi Saphra

What would it mean to have a *scientific* understanding of AI? Language models are not static objects—they are snapshots of time-evolving processes shaped by data, objectives, and optimization dynamics. Yet the field predominantly treats models as fixed artifacts, analyzing behaviors after training rather than asking *why* they emerge. **This position paper argues that AI research should move beyond *post hoc* fixes and study the learning dynamics of models.** We envision a hierarchy of scientific maturity: first *predict* outcomes from early training signals, then *intervene* when trajectories go wrong, ultimately *design* training procedures that guarantee desired properties. Scaling laws have reached the first level for loss; the challenge is extending all three levels to general capabilities, biases, and safety. We articulate requirements for such theories, survey progress across mechanistic interpretability, fairness, memorization, and learning dynamics, and identify concrete open problems. The path forward requires treating models as processes to be understood, not just artifacts to be patched.

Deep Learning · Large Language Models

Jiakai Li, KE QIN, Rongzheng Wang, Yizhuo Ma, Qizhi Chen, Muquan Li, Shuang Liang

By incorporating test-time compute scaling, large reasoning models (LRMs) are able to solve complex problems by generating explicit chain-of-thought (CoT) reasoning processes. However, they often suffer from overthinking during generation, resulting in redundant token outputs and degraded accuracy. Existing methods to mitigate this issue remain limited: training-based approaches incur substantial training costs, while training-free methods often rely on well-crafted prompting or unreliable confidence signals. In this work, we study early stopping through attention distributions and propose a simple method, ASAG, that infers the model's reasoning state and adaptively adjusts the generation strategy. The proposed method is training-free and plug-and-play, enabling seamless integration into existing LRMs. Extensive experiments on nine benchmarks demonstrate consistent improvements across mainstream LRMs with varying parameter scales, including the Deepseek-R1-Distill and Qwen3 series. In particular, ASAG achieves a 4.4% relative improvement in accuracy while reducing the number of generated tokens by over 40% across all reasoning tasks on Qwen3-8B.

Theory · Everything Else

John Cooper, Mingchen Ma, Ilias Diakonikolas, Frederic Sala

Hybrid sequence models—combining Transformer and state-space model layers—seek to gain the expressive versatility of attention as well as the computational efficiency of state-space model layers. Despite burgeoning interest in hybrid models, we lack a basic understanding of the settings where—and underlying mechanisms through which—they offer benefits over their constituent models. In this paper, we study this question, focusing on a broad family of core synthetic tasks. For this family of tasks, we prove the existence of fundamental limitations for non-hybrid models. Specifically, any Transformer or state-space model that solves the underlying task requires either a large number of parameters or a large working memory. On the other hand, for two prototypical tasks within this family—namely selective copying and associative recall—we construct hybrid models of small size and working memory that provably solve these tasks, thus achieving the best of both worlds. Our experimental evaluation empirically validates our theoretical findings. Importantly, going beyond the settings in our theoretical analysis, we empirically show that learned—rather than constructed—hybrids outperform non-hybrid models with up to $6 \times$ as many parameters. We additionally demonstrate that hybrid models exhibit stronger length generalization and out-of-distribution robustness than non-hybrids.

Applications · Computer Vision

Jiayang Li, Shuo Cao, Xiaohui Li, Zhizhen Zhang, Kaiwen Zhu, Yule Duan, Yu Qiao, Jian Zhang, Yihao Liu

In most real-world image-to-image (I2I) scenarios, existing evaluations primarily focus on instruction following and the perceptual quality or aesthetics of the generated images. However, they largely fail to assess whether the output image preserves the semantic correspondence and spatial structure of the input image. To address this limitation, we propose StableI2I, a unified and dynamic evaluation framework that explicitly measures content fidelity and pre--post consistency across a wide range of I2I tasks without requiring reference images, including image editing and image restoration. In addition, we construct StableI2I-Bench, a benchmark designed to systematically evaluate the accuracy of MLLMs on such fidelity and consistency assessment tasks. Extensive experimental results demonstrate that StableI2I provides accurate, fine-grained, and interpretable evaluations of content fidelity and consistency, with strong correlations to human subjective judgments. Our framework serves as a practical and reliable evaluation tool for diagnosing content consistency and benchmarking model performance in real-world I2I systems.

Applications · Computer Vision

Shenghao Fu, Yukun Su, Fengyun Rao, Jing LYU, Xiaohua Xie, Wei-Shi Zheng

Aligning objects with corresponding textual descriptions is a fundamental challenge and a realistic requirement in vision-language understanding. While recent multimodal embedding models excel at global image-text alignment, they often struggle with fine-grained alignment between image regions and specific phrases. In this work, we present ObjEmbed, a novel MLLM embedding model that decomposes the input image into multiple regional embeddings, each corresponding to an individual object, along with global embeddings. It supports a wide range of visual understanding tasks like visual grounding, local image retrieval, and global image retrieval. ObjEmbed enjoys three key properties: (1) Object-Oriented Representation: It captures both semantic and spatial aspects of objects by generating two complementary embeddings for each region: an object embedding for semantic matching and an IoU embedding that predicts localization quality. The final object matching score combines semantic similarity with the predicted IoU, enabling more accurate retrieval. (2) Versatility: It seamlessly handles both region-level and image-level tasks. (3) Efficient Encoding: All objects in an image, along with the full image, are encoded in a single forward pass for high efficiency. Superior performance on 18 diverse benchmarks demonstrates its strong semantic discrimination. We will release the code, models, and data for future research.

Social Aspects · Privacy

Tom Segal, Asaf Shabtai, Yuval Elovici

Fine-tuning large language models (LLMs) on sensitive datasets raises privacy concerns, as training data extraction (TDE) attacks can expose highly confidential information. Existing defenses against such attacks either lack formal privacy guarantees or incur substantial utility degradation. We observe that fine-tuning induces widespread probability shifts, yet preserving only a small subset of influential token-level deviations is sufficient; the remaining shifts can be aggressively smoothed with minimal impact on utility. Motivated by this insight, we propose SCP-$\Delta_r$, a Near Access Freeness (NAF)-based algorithm that operates on relative probabilities and explicitly smooths low-impact tokens using a base model. SCP-$\Delta_r$ achieves orders-of-magnitude better theoretical bounds than existing NAF based methods and provides strong empirical protection against TDE attacks with minimal performance loss.

Optimization · Everything Else

Abhishek Gupta, Manoj Kumar, Sarthak Singh, Ujjwal Yadav, Yifan Sun, Sandeep Kumar

Graph coarsening is a fundamental dimensionality reduction technique for scaling large graphs while preserving structural and feature information. However, most existing coarsening methods are designed for static graphs and do not extend well to dynamic settings where nodes, edges, and connectivity patterns evolve over time. Recomputing a coarsened graph from scratch after every update is often infeasible, which limits scalability and real-time applicability. To address this, we propose a unified framework for coarsening discrete-time dynamic graphs by incrementally updating the coarsening mapping matrix. The framework initializes from any static coarsening technique and then efficiently incorporates real-world graph events, including node additions, node deletions, and edge modifications. We instantiate this framework with two optimization based incremental update algorithms tailored to different dynamic regimes, one focusing on efficiently integrating growth related changes and another handling broader topology evolution with adaptive reassignment. We derive fast and scalable solvers with convergence guarantees, and provide theoretical guarantee via $\epsilon$-similarity bounds that quantify and control quality degradation in the coarsened graph. Extensive experiments under realistic dynamic scenarios show substantial improvements in runtime and memory, delivering significant speedups while maintaining or improving downstream task performance, including graph neural network accuracy.

Probabilistic Methods · Monte Carlo and Sampling Methods

Jennifer R. Andersson, Zheng Zhao

This paper is concerned with differentiable resampling in the context of sequential Monte Carlo (e.g., particle filtering). We propose a new informative resampling method that is instantly differentiable, based on an ensemble score diffusion model. We theoretically prove that our diffusion resampling method provides a consistent resampling distribution, and we show empirically that it outperforms the state-of-the-art differentiable resampling methods on multiple filtering and parameter estimation benchmarks. Finally, we show that it achieves competitive end-to-end performance when used in learning a complex dynamics-decoder model with high-dimensional image observations.