OpenRaiser/Intern-Atlas huggingface dataset:
Secure and Green RSMA-Assisted Heterogeneous ISAC
Venue: arXiv cs.IT 2026
Year: 2026

This paper investigates sensing, communication, security, and energy efficiency of the heterogeneous integrated sensing and communication networks under challenging operational conditions. We focus on scenarios in which communication performance, security, and sensing accuracy are degraded by interference, eavesdropping, and imperfect channel state information. To this end, we analyze communication and sensing signals within ISAC framework as well as the communication signals of a multicast network based on \emph{rate-splitting multiple access} (RSMA). Then, sensing signal-to-cluster-plus-noise ratio, communication rate, security rate, and \emph{security energy efficiency} (SEE) are evaluated. To simultaneously enhance these system performances, we propose a targeted optimization framework aimed at maximizing SEE. This framework characterizes the sensing-security trade-off by jointly optimizing the transmit \emph{beamforming} (BF) vectors and the echo BF vector to construct green interference using the echo signal, as well as common and private streams generated by RSMA. Particularly, the joint design improves the security rate and reduces power consumption, thereby enabling a higher SEE. Given the non-convex nature of the optimization problem, we present an alternative approach that leverages Taylor series expansion, majorization-minimization, semi-definite programming, and successive convex approximation techniques. Specifically, we decompose the original non-convex and intractable optimization problem into three simplified sub-optimization problems, which are iteratively solved using an alternating optimization strategy. Simulations provide comparisons with state-of-the-art schemes, highlighting the superior efficiency, robustness, and scalability of the proposed joint multi-BF optimization scheme based on RSMA and green interference in improving system performances.
Optimistically Optimistic Exploration for Provably Efficient Infinite-Horizon Reinforcement and Imitation Learning
Venue: arXiv cs.LG 2026
Year: 2026

We study the problem of reinforcement learning in infinite-horizon discounted linear Markov decision processes (MDPs), and propose the first computationally efficient algorithm achieving rate-optimal regret guarantees in this setting. Our main idea is to combine two classic techniques for optimistic exploration: additive exploration bonuses applied to the reward function, and artificial transitions made to an absorbing state with maximal return. We show that, combined with a regularized approximate dynamic-programming scheme, the resulting algorithm achieves a regret of order $\tilde{\mathcal{O}} (\sqrt{d^3 (1 - γ)^{- 7 / 2} T})$, where $T$ is the total number of sample transitions, $γ\in (0,1)$ is the discount factor, and $d$ is the feature dimensionality. The results continue to hold against adversarial reward sequences, enabling application of our method to the problem of imitation learning in linear MDPs, where we achieve state-of-the-art results.
Glycemic-Aware and Architecture-Agnostic Training Framework for Blood Glucose Forecasting in Type 1 Diabetes
Venue: arXiv cs.LG 2026
Year: 2026

Managing Type 1 Diabetes (T1D) demands constant vigilance as individuals strive to regulate their blood glucose levels and avoid dysglycemia, including hyperglycemia and hypoglycemia. Despite advances in automated insulin delivery (AID) systems, achieving optimal glycemic control remains challenging. These systems integrate data from wearable devices such as insulin pumps and continuous glucose monitors (CGMs), helping reduce variability and improve time in range. However, they often fail to prevent dysglycemia due to limitations in prediction algorithms that cannot accurately anticipate glycemic excursions. This limitation highlights the need for more advanced glucose forecasting methods. To address this need, we introduce GLIMMER (Glucose Level Indicator Model with Modified Error Rate), a modular and architecture-agnostic training framework for glucose forecasting. GLIMMER combines structured preprocessing, a region-aware loss formulation, and genetic algorithm-based weight optimization to emphasize prediction accuracy in dysglycemic regions. We evaluate GLIMMER using two datasets: the publicly available OhioT1DM dataset and a newly collected AZT1D dataset consisting of data from 25 individuals with T1D. Our analyses demonstrate that GLIMMER consistently improves forecasting performance across baseline architectures, reducing RMSE and MAE by up to 24.6% and 29.6%, respectively. Additionally, GLIMMER achieves a recall of 98.4% and an F1-score of 86.8% for dysglycemia prediction, highlighting strong performance in clinically high-risk regions. Compared with state-of-the-art models containing millions of parameters-such as TimesNet (18.7M), BG-BERT (2.1M), and Gluformer (11.2M)-GLIMMER attains comparable accuracy while using only 10K parameters, demonstrating its efficiency as a lightweight and architecture-agnostic solution for glycemic forecasting.
LIFT: A Novel Framework for Enhancing Long-Context Understanding of LLMs via Long Input Fine-Tuning
Venue: arXiv cs.CL 2026
Year: 2026

Long context understanding remains challenging for large language models due to their limited context windows. This paper introduces Long Input Fine-Tuning (LIFT), a novel framework for long-context modeling that can enhance the long-context performance of arbitrary short-context LLMs by dynamically adapting their parameters to the given long input. Importantly, rather than endlessly extending the context window size to accommodate increasingly longer inputs in context, LIFT stores and absorbs the long input in parameters. By fine-tuning the long input into model parameters, LIFT allows short-context LLMs to answer questions even when the required information is not provided in the context during inference, avoiding the quadratic complexity w.r.t. input length of a normal long context model. Furthermore, LIFT does not simply perform continued pretraining on new, long contexts, but leverages carefully designed LLM-generated synthetic tasks to enhance the comprehension of long contexts, moving beyond mere memorization. To accommodate the additional cost of fine-tuning, we design a highly optimized pipeline that reduces the Time to First Token (TTFT) to less than 10 seconds for 8k context. We further provide a comprehensive analysis of LIFT's strengths and limitations in long-context understanding, discuss its feasibility for large-scale real-world deployment, and highlight valuable directions for future research.
Variance Reduction Methods Do Not Need to Compute Full Gradients: Improved Efficiency through Shuffling
Venue: arXiv cs.LG 2026
Year: 2026

Stochastic optimization algorithms are widely used for machine learning with large-scale data. However, their convergence often suffers from non-vanishing variance. Variance Reduction (VR) methods, such as SVRG and SARAH, address this issue but introduce a bottleneck by requiring periodic full gradient computations. In this paper, we explore popular VR techniques and propose an approach that eliminates the necessity for expensive full gradient calculations. To avoid these computations and make our approach memory-efficient, we employ two key techniques: the shuffling heuristic and the concept of SAG/SAGA methods. For non-convex objectives, our convergence rates match those of standard shuffling methods, while under strong convexity, they demonstrate an improvement. We empirically validate the efficiency of our approach and demonstrate its scalability on large-scale machine learning tasks including image classification problem on CIFAR-10 and CIFAR-100 datasets.
I-MCTS: Enhancing Agentic AutoML via Introspective Monte Carlo Tree Search
Venue: arXiv cs.CL 2026
Year: 2026

Recent advancements in large language models (LLMs) have shown remarkable potential in automating machine learning tasks. However, existing LLM-based agents often struggle with low-diversity and suboptimal code generation. While recent work has introduced Monte Carlo Tree Search (MCTS) to address these issues, limitations persist in the quality and diversity of thoughts generated, as well as in the scalar value feedback mechanisms used for node selection. In this study, we introduce Introspective Monte Carlo Tree Search (I-MCTS), a novel approach that iteratively expands tree nodes through an introspective process that meticulously analyzes solutions and results from parent and sibling nodes. This facilitates a continuous refinement of the node in the search tree, thereby enhancing the overall decision-making process. Furthermore, we integrate a Large Language Model (LLM)-based value model to facilitate direct evaluation of each node's solution prior to conducting comprehensive computational rollouts. A hybrid rewarding mechanism is implemented to seamlessly transition the Q-value from LLM-estimated scores to actual performance scores. This allows higher-quality nodes to be traversed earlier. Applied to the various ML tasks, our approach demonstrates a 4% absolute improvement in performance compared to the strong open-source AutoML agents, showcasing its effectiveness in enhancing agentic AutoML systems. Resource available at https://github.com/jokieleung/I-MCTS
ReVision: A Dataset and Baseline VLM for Privacy-Preserving Task-Oriented Visual Instruction Rewriting
Venue: arXiv cs.CL 2026
Year: 2026

Efficient and privacy-preserving multimodal interaction is essential as AR, VR, and modern smartphones with powerful cameras become primary interfaces for human-computer communication. Existing powerful large vision-language models (VLMs) enabling multimodal interaction often rely on cloud-based processing, raising significant concerns about (1) visual privacy by transmitting sensitive vision data to servers, and (2) their limited real-time, on-device usability. This paper explores Visual Instruction Rewriting, a novel approach that transforms multimodal instructions into text-only commands, allowing seamless integration of lightweight on-device instruction rewriter VLMs (250M parameters) with existing conversational AI systems, enhancing vision data privacy. To achieve this, we present a dataset of over 39,000 examples across 14 domains and develop a compact VLM, pretrained on image captioning datasets and fine-tuned for instruction rewriting. Experimental results, evaluated through NLG metrics such as BLEU, METEOR, and ROUGE, along with semantic parsing analysis, demonstrate that even a quantized version of the model (<500MB storage footprint) can achieve effective instruction rewriting, thus enabling privacy-focused, multimodal AI applications.
Learning to Solve and Verify: A Self-Play Framework for Code and Test Generation
Venue: arXiv cs.SE 2026
Year: 2026

Recent advances in large language models (LLMs) have improved their performance on coding benchmarks. However, improvement is plateauing due to the exhaustion of readily available high-quality data. Prior work has shown the potential of synthetic self-instruct data, but naively training on a model's own outputs can cause error accumulation, especially in coding tasks, where generalization may collapse due to overly simple or erroneous training data, highlighting the need for rigorous quality checks on synthetic data. In this work, we explore an effective approach whereby the model itself verifies the correctness of its own data. We thus propose Sol-Ver, a self-play solver-verifier framework that jointly improves a single model's code and test generation capacity. By iteratively refining code (LLM-as-a-solver) and tests (LLM-as-a-verifier) together, we boost both capabilities without relying on human annotations or larger teacher models. Experiments with the Llama 3.1 8B model demonstrate substantial performance enhancements, achieving average relative improvements of 19.63% in code generation and 17.49% in test generation on MBPP and LiveCodeBench.
Investigating a Model-Agnostic and Imputation-Free Approach for Irregularly-Sampled Multivariate Time-Series Modeling
Venue: arXiv cs.LG 2026
Year: 2026

Modeling Irregularly-sampled and Multivariate Time Series (IMTS) is crucial across a variety of applications where different sets of variates may be missing at different time-steps due to sensor malfunctions or high data acquisition costs. Existing approaches for IMTS either consider a two-stage impute-then-model framework or involve specialized architectures specific to a particular model and task. We perform a series of experiments to derive novel insights about the performance of IMTS methods on a variety of semi-synthetic and real-world datasets for both classification and forecasting. We also introduce Missing Feature-aware Time Series Modeling (MissTSM) or MissTSM, a novel model-agnostic and imputation-free approach for IMTS modeling. We show that MissTSM shows competitive performance compared to other IMTS approaches, especially when the amount of missing values is large and the data lacks simplistic periodic structures - conditions common to real-world IMTS applications.
MetaSym: A Symplectic Meta-learning Framework for Physical Intelligence
Venue: arXiv cs.LG 2026
Year: 2026

Scalable and generalizable physics-aware deep learning has long been considered a significant challenge with various applications across diverse domains ranging from robotics to molecular dynamics. Central to almost all physical systems are symplectic forms, the geometric backbone that underpins fundamental invariants like energy and momentum. In this work, we introduce a novel deep learning framework, MetaSym. In particular, MetaSym combines a strong symplectic inductive bias obtained from a symplectic encoder, and an autoregressive decoder with meta-attention. This principled design ensures that core physical invariants remain intact, while allowing flexible, data efficient adaptation to system heterogeneities. We benchmark MetaSym with highly varied and realistic datasets, such as a high-dimensional spring-mesh system Otness et al. (2021), an open quantum system with dissipation and measurement backaction, and robotics-inspired quadrotor dynamics. Crucially, we fine-tune and deploy MetaSym on real-world quadrotor data, demonstrating robustness to sensor noise and real-world uncertainty. Across all tasks, MetaSym achieves superior few-shot adaptation and outperforms larger state-of-the-art (SOTA) models.
Can Explanations Improve Recommendations? A Joint Optimization with LLM Reasoning
Venue: arXiv cs.IR 2026
Year: 2026

Modern recommender systems rely on large-scale ML models that are data-hungry and black-box. Recent advances in LLMs suggest that explicit reasoning can improve learning efficiency, yet it remains unclear how generative LLMs can systematically improve recommendation tasks that are discriminative in nature. Moreover, in personalized settings, LLMs tend to hallucinate. Existing explainable recommender systems either generate explanations independently of predictions or provide post-hoc rationales; in both cases, explanations do not improve accuracy over black-box recommenders. We argue that when properly calibrated to prediction outcomes, natural-language explanations can in fact improve recommendations. We propose RecPIE (Recommendation with Prediction-Informed Explanations), a framework that jointly optimizes prediction-informed explanations and explanation-informed predictions. In RecPIE, the recommendation task guides the learning of consumer representations, which are used by a trainable LLM to generate explanations for why a consumer may or may not like a product; these explanations are then fed back into a neural recommender to improve predictions. The two components are trained alternately, allowing explanations to be progressively refined based on how much they improve recommendation accuracy. Empirically, on next point-of-interest recommendation using Google Maps data, RecPIE improves accuracy by 3-4% over state-of-the-art baselines and matches the best baseline using only 12% of the training data. Human evaluations show that RecPIE's explanations are preferred 61.5% of the time among five competing methods. To our knowledge, this work is among the first to demonstrate that generative explanation and discriminative recommendation tasks can be jointly learned to outperform standalone approaches on either task.
Learning to Add, Multiply, and Execute Algorithmic Instructions Exactly with Neural Networks
Venue: arXiv cs.LG 2026
Year: 2026

Neural networks are known for their ability to approximate smooth functions, yet they fail to generalize perfectly to unseen inputs when trained on discrete operations. Such operations lie at the heart of algorithmic tasks such as arithmetic, which is often used as a test bed for algorithmic execution in neural networks. In this work, we ask: can neural networks learn to execute binary-encoded algorithmic instructions exactly? We use the Neural Tangent Kernel (NTK) framework to study the training dynamics of two-layer fully connected networks in the infinite-width limit and show how a sufficiently large ensemble of such models can be trained to execute exactly, with high probability, four fundamental tasks: binary permutations, binary addition, binary multiplication, and Subtract and Branch if Negative (SBN) instructions. Since SBN is Turing-complete, our framework extends to computable functions. We show how this can be efficiently achieved using only logarithmically many training data. Our approach relies on two techniques: structuring the training data to isolate bit-level rules, and controlling correlations in the NTK regime to align model predictions with the target algorithmic executions.
Unveiling Downstream Performance Scaling of LLMs: A Clustering-Based Perspective
Venue: arXiv cs.CL 2026
Year: 2026

The escalating scale and cost of Large Language Models (LLMs) training necessitate accurate pre-training prediction of downstream task performance for comprehensive understanding of scaling properties. This is challenged by: 1) the emergence phenomenon, where unpredictable capabilities appearing suddenly at critical model scales; and 2) uneven task difficulty and inconsistent performance scaling patterns, leading to high metric variability. Current prediction methods lack accuracy and reliability. We propose a Clustering-On-Difficulty (COD) framework for downstream performance prediction. The COD framework clusters tasks by their difficulty scaling features, thereby constructing a more stable and predictable task subset that exhibits well-behaved scaling characteristics with the increase of compute budget. We adopt a performance scaling law to predict cluster-wise performance with theoretical support. Predictable subset performance acts as an intermediate predictor for the full evaluation set. We further derive a mapping function to accurately extrapolate the performance of the subset to the full set. Applied to an LLM with 70B parameters, COD achieved a 1.55\% average prediction error across eight key LLM benchmarks, thus providing actionable insights for scaling properties and training monitoring during LLM pre-training.
Enriching physical-virtual interaction in AR gaming by tracking identical objects via an egocentric partial observation frame
Venue: arXiv cs.HC 2026
Year: 2026

Augmented reality (AR) games, particularly those designed for head-mounted displays, have grown increasingly prevalent. However, most existing systems depend on pre-scanned, static environments and rely heavily on continuous tracking or marker-based solutions, which limit adaptability in dynamic physical spaces. This is particularly problematic for AR headsets and glasses, which typically follow the user's head movement and cannot maintain a fixed, stationary view of the scene. Moreover, continuous scene observation is neither power-efficient nor practical for wearable devices, given their limited battery and processing capabilities. A persistent challenge arises when multiple identical objects are present in the environment-standard object tracking pipelines often fail to maintain consistent identities without uninterrupted observation or external sensors. These limitations hinder fluid physical-virtual interactions, especially in dynamic or occluded scenes where continuous tracking is infeasible. To address this, we introduce a novel optimization-based framework for re-identifying identical objects in AR scenes using only one partial egocentric observation frame captured by a headset. We formulate the problem as a label assignment task solved via integer programming, augmented with a Voronoi diagram-based pruning strategy to improve computational efficiency. This method reduces computation time by 50% while preserving 91% accuracy in simulated experiments. Moreover, we evaluated our approach in quantitative synthetic and quantitative real-world experiments. We also conducted three qualitative real-world experiments to demonstrate the practical utility and generalizability for enabling dynamic, markerless object interaction in AR environments. Our video demo is available at https://youtu.be/RwptEfLtW1U.
Improving Value-based Process Verifier via Structural Prior Injection
Venue: arXiv cs.LG 2026
Year: 2026

In the Large Language Model(LLM) reasoning scenario, people often estimate state value via Monte Carlo sampling. Though Monte Carlo estimation is an elegant method with less inductive bias, noise and errors are inevitably introduced due to the limited sampling. To handle the problem, we inject the structural prior into the value representation and transfer the scalar value into the expectation of a pre-defined categorical distribution, representing the noise and errors from a distribution perspective. Specifically, by treating the result of Monte Carlo sampling as a single sample from the prior ground-truth Binomial distribution, we quantify the sampling error as the mismatch between posterior estimated distribution and ground-truth distribution, which is thus optimized via distribution selection optimization. We test the performance of value-based process verifiers on Best-of-N task and Beam search task. Compared with the scalar value representation, we show that reasonable structural prior injection induced by different objective functions or optimization methods can improve the performance of value-based process verifiers for about 1$\sim$2 points at little-to-no cost. We also show that under different structural prior, the verifiers' performances vary greatly despite having the same optimal solution, indicating the importance of reasonable structural prior injection.