OpenRaiser/Intern-Atlas huggingface dataset:
ExPairT-LLM: Exact Learning for LLM Code Selection by Pairwise Queries
Venue: arXiv cs.LG 2026
Year: 2026

Despite recent advances in LLMs, the task of code generation is still challenging. To cope, code selection algorithms select the best program from multiple programs generated by an LLM. However, existing algorithms can fail to identify the correct program, either because they can misidentify nonequivalent programs or because they rely on an LLM and assume it always correctly determines the output for every input. We present ExPairT-LLM, an exact learning algorithm for code selection that selects a program by posing to an LLM oracle two new types of queries: pairwise membership and pairwise equivalence. These queries are simpler for LLMs and enable ExPairT-LLM to identify the correct program through a tournament, which is robust to some LLM mistakes. We evaluate ExPairT-LLM on four popular code datasets. Its pass@1 (success rate) outperforms the state-of-the-art code selection algorithm on average by +13.0% and up to +27.1%. It also improves the pass@1 of LLMs performing complex reasoning by +24.0%.
Private Zeroth-Order Optimization with Public Data
Venue: arXiv cs.LG 2026
Year: 2026

One of the major bottlenecks for deploying popular first-order differentially private (DP) machine learning algorithms (e.g., DP-SGD) lies in their high computation and memory cost, despite the existence of optimized implementations. Zeroth-order methods have promise in mitigating the overhead, as they leverage function evaluations to approximate the gradients, hence significantly easier to privatize. While recent works have explored zeroth-order approaches in both private and non-private settings, they still suffer from relatively low utilities compared with DP-SGD, and have only been evaluated in limited application domains. In this work, we propose to leverage public information to guide and improve gradient approximation of private zeroth-order algorithms. We explore a suite of public-data-assisted zeroth-order optimizers (PAZO) with minimal overhead. We provide theoretical analyses of the PAZO framework under an assumption of the similarity between public and private data. Empirically, we demonstrate that PAZO achieves superior privacy/utility tradeoffs across vision and text tasks in both pre-training and fine-tuning settings, outperforming the best first-order baselines (with public data) especially in highly private regimes, while offering up to $16\times$ runtime speedup.
GraphToxin: Reconstructing Full Unlearned Graphs from Graph Unlearning
Venue: arXiv cs.LG 2026
Year: 2026

Graph unlearning has emerged as a promising solution to comply with "the right to be forgotten" regulations by enabling the removal of sensitive information upon request. However, this solution is not foolproof. The involvement of multiple parties creates new attack surfaces, and residual traces of deleted data can still remain in the unlearned graph neural networks (GNNs). These vulnerabilities can be exploited by attackers to recover the supposedly erased samples, thereby undermining the intended functionality of graph unlearning. In this work, we propose GraphToxin, the first full graph reconstruction attack against graph unlearning. Specifically, we introduce a novel curvature matching module to provide fine-grained guidance for unlearned graph recovery. We demonstrate that GraphToxin can successfully subvert the regulatory guarantees expected from graph unlearning, it can recover not only a deleted individual's information and personal links but also sensitive content from their connections, thereby posing substantially more detrimental threats. Furthermore, we extend GraphToxin to multiple-node removal under both white-box and black-box settings, showcasing its practical feasibility and potential to cause considerable harm. We highlight the necessity of worst-case analysis and propose a systematic evaluation framework to assess attack performance under both random and worst-case node removal scenarios. Our extensive experiments demonstrate the effectiveness and flexibility of GraphToxin. Notably, existing defense mechanisms are largely ineffective against this attack or even amplify its performance in some cases. Given the severe privacy risks posed by GraphToxin, our work underscores the urgent need for more effective and robust defenses.
SCL Decoding of Non-Binary Linear Block Codes
Venue: arXiv cs.IT 2026
Year: 2026

Non-binary linear block codes (NB-LBCs) are an important class of error-correcting codes that are especially competent in correcting burst errors. They have broad applications in modern communications and storage systems. However, efficient soft-decision decoding of these codes remains to be further developed. This paper proposes successive cancellation list (SCL) decoding for NB-LBCs that are defined over a finite field of characteristic two, i.e., F_{2^r}, where r is the extension degree. By establishing a one-to-r mapping between the binary composition of each non-binary codeword and $r$ binary polar codewords, SCL decoding of the r polar codes can be performed with a complexity that is sub-quadratic in the codeword length. A simplified path sorting is further proposed to facilitate the decoding. Simulation results on short-length extended Reed-Solomon (eRS) and non-binary extended BCH (NB-eBCH) codes show that SCL decoding can outperform their state-of-the-art soft-decision decoding with fewer finite field arithmetic operations. For length-16 eRS codes, their maximum-likelihood (ML) decoding performances can be approached with a moderate list size.
Robust inverse material design with physical guarantees using the Voigt-Reuss Net
Venue: arXiv cs.LG 2026
Year: 2026

We propose a spectrally normalized surrogate for forward and inverse mechanical homogenization with hard physical guarantees. Leveraging the Voigt-Reuss bounds, we factor their difference via a Cholesky-like operator and learn a dimensionless, symmetric positive semi-definite representation with eigenvalues in $[0,1]$; the inverse map returns symmetric positive-definite predictions that lie between the bounds in the Löwner sense. In 3D linear elasticity on an open dataset of stochastic biphasic microstructures, a fully connected Voigt-Reuss net trained on $>\!7.5\times 10^{5}$ FFT-based labels with 236 isotropy-invariant descriptors and three contrast parameters recovers the isotropic projection with near-perfect fidelity (isotropy-related entries: $R^2 \ge 0.998$), while anisotropy-revealing couplings are unidentifiable from $SO(3)$-invariant inputs. Tensor-level relative Frobenius errors have median $\approx 1.7\%$ and mean $\approx 3.4\%$ across splits. For 2D plane strain on thresholded trigonometric microstructures, coupling spectral normalization with a differentiable renderer and a CNN yields $R^2>0.99$ on all components, subpercent normalized losses, accurate tracking of percolation-induced eigenvalue jumps, and robust generalization to out-of-distribution images. Treating the parametric microstructure as design variables, batched first-order optimization with a single surrogate matches target tensors within a few percent and returns diverse near-optimal designs. Overall, the Voigt-Reuss net unifies accurate, physically admissible forward prediction with large-batch, constraint-consistent inverse design, and is generic to elliptic operators and coupled-physics settings.
ImAgent: A Unified Multimodal Agent Framework for Test-Time Scalable Image Generation
Venue: arXiv cs.CV 2026
Year: 2026

Recent text-to-image (T2I) models have made remarkable progress in generating visually realistic and semantically coherent images. However, they still suffer from randomness and inconsistency with the given prompts, particularly when textual descriptions are vague or underspecified. Existing approaches, such as prompt rewriting, best-of-N sampling, and self-refinement, can mitigate these issues but usually require additional modules and operate independently, hindering test-time scaling efficiency and increasing computational overhead. In this paper, we introduce ImAgent, a training-free unified multimodal agent that integrates reasoning, generation, and self-evaluation within a single framework for efficient test-time scaling. Guided by a policy controller, multiple generation actions dynamically interact and self-organize to enhance image fidelity and semantic alignment without relying on external models. Extensive experiments on image generation and editing tasks demonstrate that ImAgent consistently improves over the backbone and even surpasses other strong baselines where the backbone model fails, highlighting the potential of unified multimodal agents for adaptive and efficient image generation under test-time scaling.
Parameterized complexity of the f-Critical Set problem
Venue: arXiv cs.CC 2026
Year: 2026

Given a graph $G=(V,E)$, and a function $f:V(G) \rightarrow \mathbb{N}$, an $f$-reversible process on $G$ is a dynamical system such that, given an initial vertex labeling $c_0 : V(G) \rightarrow \{0,1\}$, every vertex $v$ changes its label if and only if it has at least $f(v)$ neighbors with the opposite label. The updates occur synchronously in discrete time steps $t=0,1,2,\ldots$. An $f$-critical set of $G$ is a subset of vertices of $G$ whose initial label is $1$ such that, in an $f$-reversible process on $G$, all vertices reach label $1$ within one time step and then remain unchanged. The critical set number $r^c_f(G)$ is the minimum size of an $f$-critical set of $G$. Given a graph $G$, a threshold function $f$, and an integer $k$, the $f$-Critical Set problem asks whether $r^c_f(G) \leq k$. We prove that this problem is NP-complete for planar subcubic bipartite graphs with maximum threshold $m(f) = 2$ and W[1]-hard when parameterized by the treewidth $tw(G)$ of $G$. Additionally, we show that the problem is FPT when parameterized by $tw(G)+m(f)$, $tw(G)+Δ(G)$, and $k$, where $Δ(G)$ denotes the maximum degree of $G$. Finally, we present two kernels of sizes $O(k \cdot m(f))$ and $O(k \cdot Δ(G))$.
OSGym: Scalable Distributed Data Engine for Generalizable Computer Agents
Venue: arXiv cs.DC 2026
Year: 2026

We introduce OSGym, a scalable distributed Data Engine for training agents across diverse computer use tasks. OSGym efficiently scales to more than a thousand operating system (OS) replicas under academia-affordable cost budget, to serve as agent runtime environments. OSGym has three advantages: 1) Scalability: Despite intensive resource consumption for running OS replicas, OSGym can parallelize a thousand OS replicas while maintaining the operation efficiency under limited resources. Its scalable parallelization enables generating a vast amount of data (1420 multi-turn trajectories per minute). 2) Generality and Customizability: OSGym supports a wide variety of tasks as long as they run on operating systems, including functional tool-use, browser interactions, software engineering, office applications, etc. It also enables easy and flexible customization of model training algorithms. 3) Economic Viability for Academia Use: Only costs 0.2 to 0.3 USD per day per OS replica on easily accessible on-demand compute providers. Our experiments demonstrate the effectiveness of OSGym for implementing comprehensive pipelines for data collection, supervised fine-tuning, and reinforcement learning for computer use agents. We believe OSGym will push the scalability and universality in future agent research.
Generative AI Purpose-built for Social and Mental Health: A Real-World Pilot
Venue: arXiv cs.CY 2026
Year: 2026

Generative artificial intelligence (GAI) chatbots built for mental health could deliver safe, personalized, and scalable mental health support. We evaluate a foundation model designed for mental health. Adults completed mental health measures while engaging with the chatbot between May 15, 2025 and September 15, 2025. Users completed an opt-in consent, demographic information, mental health symptoms, social connection, and self-identified goals. Measures were repeated every two weeks up to 6 weeks, and a final follow-up at 10 weeks. Analyses included effect sizes, and growth mixture models to identify participant groups and their characteristic engagement, severity, and demographic factors. Users demonstrated significant reductions in PHQ-9 and GAD-7 that were sustained at follow-up. Significant improvements in Hope, Behavioral Activation, Social Interaction, Loneliness, and Perceived Social Support were observed throughout and maintained at 10 week follow-up. Engagement was high and predicted outcomes. Working alliance was comparable to traditional care and predicted outcomes. Automated safety guardrails functioned as designed, with 76 sessions flagged for risk and all handled according to escalation policies. This single arm naturalistic observational study provides initial evidence that a GAI foundation model for mental health can deliver accessible, engaging, effective, and safe mental health support. These results lend support to findings from early randomized designs and offer promise for future study of mental health GAI in real world settings.
Better LLM Reasoning via Dual-Play
Venue: arXiv cs.LG 2026
Year: 2026

Large Language Models (LLMs) have achieved remarkable progress through Reinforcement Learning with Verifiable Rewards (RLVR), yet still rely heavily on external supervision (e.g., curated labels). Adversarial learning, particularly through self-play, offers a promising alternative that enables models to iteratively learn from themselves - thus reducing reliance on external supervision. Dual-play extends adversarial learning by assigning specialized roles to two models and training them against each other, fostering sustained competition and mutual evolution. Despite its promise, adapting dual-play training to LLMs remains limited, largely due to their susceptibility to reward hacking and training instability. In this paper, we introduce PasoDoble, a novel LLM dual-play framework. PasoDoble adversarially trains two models initialized from the same base model: a Proposer, which generates challenging questions with ground-truth answers, and a Solver, which attempts to solve them. We enrich the Proposer with knowledge from a pre-training dataset to ensure the questions' quality and diversity. To avoid reward hacking, the Proposer is rewarded for producing only valid questions that push the Solver's limit, while the Solver is rewarded for solving them correctly, and both are updated jointly. To further enhance training stability, we introduce an optional offline paradigm that decouples Proposer and Solver updates, alternately updating each for several steps while holding the other fixed. Notably, PasoDoble operates without supervision during training. Experimental results show that PasoDoble can improve the reasoning performance of LLMs. Our project page is available at https://hcy123902.github.io/PasoDoble.
Context-Emotion Aware Therapeutic Dialogue Generation: A Multi-component Reinforcement Learning Approach to Language Models for Mental Health Support
Venue: arXiv cs.CL 2026
Year: 2026

Mental health disorders impose a substantial global socioeconomic burden. While large language models (LLMs) offer 24/7, non-judgmental interactions to address this gap, pretrained models lack contextual coherence and emotional alignment for appropriate therapeutic dialogue. Existing methods suffer from three critical methodological gaps: 1) Supervised Fine-Tuning (SFT) produces repetitive, context-insensitive outputs that fail to balance clinical accuracy with genuine empathy; 2) Reinforcement Learning (RL)-based therapeutic systems rely on generic reward functions (e.g., BLEU, ROUGE) that prioritise lexical similarity over clinical-specific emotional appropriateness and contextual relevance; 3) LLMs are resource-intensive and pose data privacy risks, making local deployment in clinical settings infeasible. To address these gaps, this study investigates the application of SFT and RL techniques to enhance GPT-2's capacity for therapeutic dialogue generation. The methodology restructured input formats to enable simultaneous processing of contextual information and emotional states alongside user input, employing a novel multi-component reward function that explicitly aligns model outputs with professional therapeutic logic (not just lexical overlap) and annotated emotions. Results demonstrated substantial improvements through RLs over baseline GPT-2 across multiple evaluation metrics: BLEU (0.0111), ROUGE-1 (0.1397), ROUGE-2 (0.0213), ROUGE-L (0.1317), and METEOR (0.0581). LLM evaluation confirmed high contextual relevance and professionalism, while RL achieved 99.34% emotion accuracy compared to 66.96% for baseline GPT-2. These findings demonstrate RL's effectiveness in developing therapeutic dialogue systems that can serve as valuable assistive tools for therapists, while maintaining essential human clinical oversight.
A Systematic Analysis of Out-of-Distribution Detection Under Representation and Training Paradigm Shifts
Venue: arXiv cs.LG 2026
Year: 2026

We present the largest systematic comparison to date of out-of-distribution (OOD) detection methods using AURC and AUGRC as primary metrics. Our comparison explores different regimes of distribution shift (stratified by CLIP embeddings of the out-of-distribution image datasets) with varying numbers of classes and uses a representation-centric view of OOD detection, including neural collapse metrics, for subsequent analysis. Together the empirical results and representation analysis provides novel insights and statistically grounded guidance for method selection under distribution shift. Experiments cover two representation paradigms: CNNs trained from scratch and a fine-tuned Vision Transformer (ViT), evaluated on CIFAR-10/100, SuperCIFAR-100, and TinyImageNet. Using a multiple-comparison-controlled, rank-based pipeline (Friedman test with Conover-Holm post-hoc) and Bron-Kerbosch cliques, we find that the learned feature space largely determines OOD efficacy. For both CNNs and ViTs, probabilistic scores (e.g., MSR, GEN) dominate misclassification (ID) detection. Under stronger shifts, geometry-aware scores (e.g., NNGuide, fDBD, CTM) prevail on CNNs, whereas on ViTs GradNorm and KPCA Reconstruction Error remain consistently competitive. We further show a class-count-dependent trade-off for Monte-Carlo Dropout (MCD) and that a simple PCA projection improves several detectors. The neural-collapse-based geometric analysis explains when prototype and boundary-based scores become optimal under strong shifts.
On the Entropy Calibration of Language Models
Venue: arXiv cs.CL 2026
Year: 2026

We study the problem of entropy calibration, which asks whether a language model's entropy over generations matches its log loss on human text. Past work found that models are miscalibrated, with entropy per step increasing as generations grow longer, due to error accumulation. To calibrate the model and improve text quality, it has become standard practice to truncate the distribution, but this approach reduces output diversity, which we would like to avoid. Therefore, in this paper, we ask: does miscalibration improve automatically with scale, and if not, is it theoretically possible to calibrate without tradeoffs? To build intuition, we first study a simplified theoretical setting to characterize the scaling behavior of miscalibration with respect to dataset size. We find that the rate of scaling depends on the power law exponent of the data distribution -- in particular, for a power law exponent close to 1, the scaling exponent is close to 0, meaning that miscalibration improves very slowly with scale. Next, we measure miscalibration empirically in language models ranging from 0.5B to 70B parameters. We find that the observed scaling behavior is similar to what is predicted theoretically: our fitted scaling exponents for text are close to 0, meaning that larger models accumulate error at a similar rate as smaller ones. This scaling (or, lack thereof) provides one explanation for why we sample from larger models with similar amounts of truncation as smaller models, even though the larger models are of higher quality. However, truncation is not a satisfying solution because it comes at the cost of increased log loss. In theory, is it even possible to reduce entropy while preserving log loss? We prove that it is possible, if we assume access to a black box which can fit models to predict the future entropy of text.
CITADEL: A Semi-Supervised Active Learning Framework for Malware Detection Under Continuous Distribution Drift
Venue: arXiv cs.CR 2026
Year: 2026

Android malware detection systems suffer severe performance degradation over time due to concept drift caused by evolving malicious and benign app behaviors. Although recent methods leverage active learning and hierarchical contrastive loss to address drift, they remain fully supervised, computationally expensive, and ineffective on long-term real-world benchmark. Moreover, expert labeling does not scale to the monthly emergence of nearly 300K new Android malware samples, leaving most data unlabeled and underutilized. To address these challenges, we propose CITADEL, a semi-supervised active learning framework for Android malware detection. Existing semi-supervised methods assume continuous and semantically meaningful input transformations, and fail to generalize well to high-dimensional binary malware features. We bridge this gap with malware-specific augmentations, Bernoulli bit flips and feature masking, that stochastically perturb feature to regularize learning under evolving malware distributions. \system further incorporates supervised contrastive loss to improve boundary sample discrimination and combines it with a multi-criteria active learning strategy based on prediction confidence, $L_p$-norm distance, and boundary uncertainty, enabling effective adaptation under constrained labeling budgets. Extensive evaluation on four large-scale Android malware benchmarks -- APIGraph, Chen-AZ, MaMaDroid, and LAMDA, demonstrates that \system outperforms prior work, achieving F1 score of over 1\%, 3\%, 7\%, and 14\% respectively, using only 40\% labeled samples. Furthermore, \system shows significant efficiency over prior work incurring $24\times$ faster training and $13\times$ fewer operations. \paragraph{Availability} The code is available at https://github.com/IQSeC-Lab/CITADEL.git.