OpenRaiser/Intern-Atlas huggingface dataset:
Evaluating LLMs in the Context of a Functional Programming Course: A Comprehensive Study
Venue: arXiv cs.PL 2026
Year: 2026

Large-Language Models (LLMs) are changing the way learners acquire knowledge outside the classroom setting. Previous studies have shown that LLMs seem effective in generating to short and simple questions in introductory CS courses using high-resource programming languages such as Java or Python. In this paper, we evaluate the effectiveness of LLMs in the context of a low-resource programming language -- OCaml, in an educational setting. In particular, we built three benchmarks to comprehensively evaluate 9 state-of-the-art LLMs: 1) $λ$CodeGen (a benchmark containing natural-language homework programming problems); 2) $λ$Repair (a benchmark containing programs with syntax, type, and logical errors drawn from actual student submissions); 3) $λ$Explain (a benchmark containing natural language questions regarding theoretical programming concepts). We grade each LLMs responses with respect to correctness using the OCaml compiler and an autograder. And our evaluation goes beyond common evaluation methodology by using manual grading to assess the quality of the responses. Our study shows that the top three LLMs are effective on all tasks within a typical functional programming course, although they solve much fewer homework problems in the low-resource setting compared to their success on introductory programming problems in Python and Java. The strength of LLMs lies in correcting syntax and type errors as well as generating answers to basic conceptual questions. While LLMs may not yet match dedicated language-specific tools in some areas, their convenience as a one-stop tool for multiple programming languages can outweigh the benefits of more specialized systems. We hope our benchmarks can serve multiple purposes: to assess the evolving capabilities of LLMs, to help instructors raise awareness among students about the limitations of LLM-generated solutions, and to inform programming language researchers about opportunities to integrate domain-specific reasoning into LLMs and develop more powerful code synthesis and repair tools for low-resource languages.
When Rubrics Fail: Error Enumeration as Reward in Reference-Free RL Post-Training for Virtual Try-On
Venue: arXiv cs.CV 2026
Year: 2026

Reinforcement learning with verifiable rewards (RLVR) and Rubrics as Rewards (RaR) have driven strong gains in domains with clear correctness signals and even in subjective domains by synthesizing evaluation criteria from ideal reference answers. But many real-world tasks admit multiple valid outputs and lack the single ideal answer that rubric generation depends on. We identify this reference-free setting as a gap in current post-training methods and propose Implicit Error Counting (IEC) to fill it. Instead of checking what a response gets right against a rubric, IEC enumerates what it gets wrong, applying severity-weighted scores across task-relevant axes and converting them into calibrated per-aspect rewards. We show that naïve explicit enumeration is too noisy for stable optimization, and that two design choices: implicit score emission and group calibration are necessary to make error counting a reliable reward. As a case study, we validate IEC on virtual try-on (VTO), a domain that is simultaneously too constrained for holistic scoring and too permissive for rubric-based evaluation: subtle garment errors are unacceptable, yet many output variations are correct. We introduce Cascaded Error Counting (CEC) as an evaluation metric, which tracks human preferences well (60% top-1 vs. 30% others), and curate Mismatch-DressCode (MDressBench), a benchmark with maximal attribute mismatch to stress-test reward designs. On MDressBench, IEC outperforms RaR across all metrics (CEC: 5.31 vs. 5.60 on flat references; 5.20 vs. 5.53 on non-flat). On VITON-HD and DressCode, IEC matches or surpasses six baselines on 6 of 8 perceptual metrics. These results suggest that when ideal answers are unavailable, counting errors provide a stronger signal than constructing rubrics.
TransMASK: Masked State Representation through Learned Transformation
Venue: arXiv cs.RO 2026
Year: 2026

Humans train robots to complete tasks in one environment, and expect robots to perform those same tasks in new environments. As humans, we know which aspects of the environment (i.e., the state) are relevant to the task. But there are also things that do not matter; e.g., the color of the table or the presence of clutter in the background. Ideally, the robot's policy learns to ignore these irrelevant state components. Achieving this invariance improves generalization: the robot knows not to factor irrelevant variables into its control decisions, making the policy more robust to environment changes. In this paper we therefore propose a self-supervised method to learn a mask which, when multiplied by the observed state, transforms that state into a latent representation that is biased towards relevant elements. Our method -- which we call TransMASK -- can be combined with a variety of imitation learning frameworks (such as diffusion policies) without any additional labels or alterations to the loss function. To achieve this, we recognize that the learned policy updates to better match the human's true policy. This true policy only depends on the relevant parts of the state; hence, as the gradients pass back through the learned policy and our proposed mask, they increase the value for elements that cause the robot to better imitate the human. We can therefore train TransMASK at the same time as we learn the policy. By normalizing the magnitude of each row in TransMASK, we force the mask to align with the Jacobian of the expert policy: columns that correspond to relevant states have large magnitudes, while columns for irrelevant states approach zero magnitude. We compare our approach to other methods that extract relevant states for downstream imitation learning. See our project website: https://collab.me.vt.edu/TransMASK/
Structured Multidimensional Representation Learning for Large Language Models
Venue: arXiv cs.CL 2026
Year: 2026

Transformer architectures achieve state-of-the-art performance across a wide range of pattern recognition and natural language processing tasks, but their scaling is accompanied by substantial parameter growth and redundancy in the embedding dimension. In this work, we introduce a structured spectral factorization of the embedding space based on the L-product for third-order tensors. By reshaping token representations into spectral tensor slices and performing attention and feed-forward operations in the transform domain, we obtain a Tensor Transformer architecture that decomposes the encoder into p independent spectral sub-transformers while preserving standard Transformer semantics. We prove that the proposed L-Transformer is spectrally equivalent to p parallel Transformers operating on reduceddimensional embeddings, which yields approximately 1/p reduction (up to lower-order terms such as biases and normalization parameters) in encoder parameters under fixed total embedding size. When instantiated with a real-valued Discrete Cosine Transform (DCT), the method remains fully differentiable and compatible with existing training pipelines. Beyond compression, the spectral decomposition introduces an inductive bias over embedding frequencies, enabling slice-dependent frequency scaling that improves generalization. Experiments on IMDB and AG~News show that the proposed model can substantially reduce encoder parameters (up to 75\% for p=4) while maintaining competitive accuracy. On IMDB, the tensorized encoder matches or improves upon the standard baseline under compression, whereas on AG~News at moderate width we observe a small accuracy decrease in exchange for a 4 times encoder reduction; at BERT-base width (d=768), performance returns to parity.
Let's Talk, Not Type: An Oral-First Multi-Agent Architecture for Guaraní
Venue: arXiv cs.CL 2026
Year: 2026

Although artificial intelligence (AI) and Human-Computer Interaction (HCI) systems are often presented as universal solutions, their design remains predominantly text-first, underserving primarily oral languages and indigenous communities. This position paper uses Guaraní, an official and widely spoken language of Paraguay, as a case study to argue that language support in AI remains insufficient unless it aligns with lived oral practices. We propose an alternative to the standard "text-to-speech" pipeline, proposing instead an oral-first multi-agent architecture. By decoupling Guaraní natural language understanding from dedicated agents for conversation state and community-led governance, we demonstrate a technical framework that respects indigenous data sovereignty and diglossia. Our work moves beyond mere recognition to focus on turn-taking, repair, and shared context as the primary locus of interaction. We conclude that for AI to be truly culturally grounded, it must shift from adapting oral languages to text-centric systems to treating spoken conversation as a first-class design requirement, ensuring digital ecosystems empower rather than overlook diverse linguistic practices.
Full Dynamic Range Sky-Modelling For Image Based Lighting
Venue: arXiv cs.CV 2026
Year: 2026

Accurate environment maps are a key component to modelling real-world outdoor scenes. They enable captivating visual arts, immersive virtual reality and a wide range of scientific and engineering applications. To alleviate the burden of physical-capture, physically-simulation and volumetric rendering, sky-models have been proposed as fast, flexible, and cost-saving alternatives. In recent years, sky-models have been extended through deep learning to be more comprehensive and inclusive of cloud formations, but recent work has demonstrated these models fall short in faithfully recreating accurate and photorealistic natural skies. Particularly at higher resolutions, DNN sky-models struggle to accurately model the 14EV+ class-imbalanced solar region, resulting in poor visual quality and scenes illuminated with skewed light transmission, shadows and tones. In this work, we propose Icarus, an all-weather sky-model capable of learning the exposure range of Full Dynamic Range (FDR) physically captured outdoor imagery. Our model allows conditional generation of environment maps with intuitive user-positioning of solar and cloud formations, and extends on current state-of-the-art to enable user-controlled texturing of atmospheric formations. Through our evaluation, we demonstrate Icarus is interchangeable with FDR physically captured outdoor imagery or parametric sky-models, and illuminates scenes with unprecedented accuracy, photorealism, lighting directionality (shadows), and tones in Image Based Lightning (IBL).
First-Order Softmax Weighted Switching Gradient Method for Distributed Stochastic Minimax Optimization with Stochastic Constraints
Venue: arXiv cs.LG 2026
Year: 2026

This paper addresses the distributed stochastic minimax optimization problem subject to stochastic constraints. We propose a novel first-order Softmax-Weighted Switching Gradient method tailored for federated learning. Under full client participation, our algorithm achieves the standard $\mathcal{O}(ε^{-4})$ oracle complexity to satisfy a unified bound $ε$ for both the optimality gap and feasibility tolerance. We extend our theoretical analysis to the practical partial participation regime by quantifying client sampling noise through a stochastic superiority assumption. Furthermore, by relaxing standard boundedness assumptions on the objective functions, we establish a strictly tighter lower bound for the softmax hyperparameter. We provide a unified error decomposition and establish a sharp $\mathcal{O}(\log\frac{1}δ)$ high-probability convergence guarantee. Ultimately, our framework demonstrates that a single-loop primal-only switching mechanism provides a stable alternative for optimizing worst-case client performance, effectively bypassing the hyperparameter sensitivity and convergence oscillations often encountered in traditional primal-dual or penalty-based approaches. We verify the efficacy of our algorithm via experiment on the Neyman-Pearson (NP) classification and fair classification tasks.
PVminerLLM: Structured Extraction of Patient Voice from Patient-Generated Text using Large Language Models
Venue: arXiv cs.CL 2026
Year: 2026

Motivation: Patient-generated text contains critical information about patients' lived experiences, social circumstances, and engagement in care, including factors that strongly influence adherence, care coordination, and health equity. However, these patient voice signals are rarely available in structured form, limiting their use in patient-centered outcomes research and clinical quality improvement. Reliable extraction of such information is therefore essential for understanding and addressing non-clinical drivers of health outcomes at scale. Results: We introduce PVminer, a benchmark for structured extraction of patient voice, and propose PVminerLLM, a supervised fine-tuned large language model tailored to this task. Across multiple datasets and model sizes, PVminerLLM substantially outperforms prompt-based baselines, achieving up to 83.82% F1 for Code prediction, 80.74% F1 for Sub-code prediction, and 87.03% F1 for evidence Span extraction. Notably, strong performance is achieved even with smaller models, demonstrating that reliable patient voice extraction is feasible without extreme model scale. These results enable scalable analysis of social and experiential signals embedded in patient-generated text. Availability and Implementation: Code, evaluation scripts, and trained LLMs will be released publicly. Annotated datasets will be made available upon request for research use. Keywords: Large Language Models, Supervised Fine-Tuning, Medical Annotation, Patient-Generated Text, Clinical NLP
Tutor Move Taxonomy: A Theory-Aligned Framework for Analyzing Instructional Moves in Tutoring
Venue: arXiv cs.CL 2026
Year: 2026

Understanding what makes tutoring effective requires methods for systematically analyzing tutors' instructional actions during learning interactions. This paper presents a tutor move taxonomy designed to support large-scale analysis of tutoring dialogue within the National Tutoring Observatory. The taxonomy provides a structured annotation framework for labeling tutors' instructional moves during one-on-one tutoring sessions. We developed the taxonomy through a hybrid deductive-inductive process. First, we synthesized research from cognitive science, the learning sciences, classroom discourse analysis, and intelligent tutoring systems to construct a preliminary framework of tutoring moves. We then refined the taxonomy through iterative coding of authentic tutoring transcripts conducted by expert annotators with extensive instructional and qualitative research experience. The resulting taxonomy organizes tutoring behaviors into four categories: tutoring support, learning support, social-emotional and motivational support, and logistical support. Learning support moves are further organized along a spectrum of student engagement, distinguishing between moves that elicit student reasoning and those that provide direct explanation or answers. By defining tutoring dialogue in terms of discrete instructional actions, the taxonomy enables scalable annotation using AI, computational modeling of tutoring strategies, and empirical analysis of how tutoring behaviors relate to learning outcomes.
Gathering Autonomous Mobile Robots Under the Adversarial Defected View Model
Venue: arXiv cs.DC 2026
Year: 2026

This paper studies the gathering problem for a set of $N \ge 2$ autonomous mobile robots operating in the Euclidean plane under the distributed Look-Compute-Move model. We consider oblivious robots executing under the adversarial defected view model, in which an activated robot may observe only a restricted subset of robots due to adversarial visibility faults. Consequently, the information obtained during each Look phase may be incomplete and dynamically altered. The objective is to guarantee deterministic finite-time gathering at a location not known a priori despite such sensing restrictions. We present two distributed algorithms under distinct scheduling assumptions. In the fully synchronous (FSYNC) model, we prove finite-time gathering in the adversarial (4, 2) defected view setting, resolving a previously open case without requiring additional capabilities or coordinate agreement. In the asynchronous (ASYNC) model, we establish finite-time gathering under the general adversarial (N, K) defected view model, where an activated robot observes at most K of the other $N - 1$ robots for any $1 \le K < N - 1$. Both results hold under non-rigid motion. The proposed algorithm for the ASYNC model assumes agreement in the direction and orientation of one coordinate axis.
CrossCheck: Input Validation for WAN Control Systems
Venue: arXiv cs.NI 2026
Year: 2026

We present CrossCheck, a system that validates inputs to the Software-Defined Networking (SDN) controller in a Wide Area Network (WAN). By detecting incorrect inputs - often stemming from bugs in the SDN control infrastructure - CrossCheck alerts operators before they trigger network outages. Our analysis at a large-scale WAN operator identifies invalid inputs as a leading cause of major outages, and we show how CrossCheck would have prevented those incidents. We deployed CrossCheck as a shadow validation system for four weeks in a production WAN, during which it accurately detected the single incident of invalid inputs that occurred while sustaining a 0% false positive rate under normal operation, hence imposing little additional burden on operators. In addition, we show through simulation that CrossCheck reliably detects a wide range of invalid inputs (e.g., detecting demand perturbations as small as 5% with 100% accuracy) and maintains a near-zero false positive rate for realistic levels of noisy, missing, or buggy telemetry data (e.g., sustaining zero false positives with up to 30% of corrupted telemetry data).
Evaluating LLM Alignment With Human Trust Models
Venue: arXiv cs.MA 2026
Year: 2026

Trust plays a pivotal role in enabling effective cooperation, reducing uncertainty, and guiding decision-making in both human interactions and multi-agent systems. Although it is significant, there is limited understanding of how large language models (LLMs) internally conceptualize and reason about trust. This work presents a white-box analysis of trust representation in EleutherAI/gpt-j-6B, using contrastive prompting to generate embedding vectors within the activation space of the LLM for diadic trust and related interpersonal relationship attributes. We first identified trust-related concepts from five established human trust models. We then determined a threshold for significant conceptual alignment by computing pairwise cosine similarities across 60 general emotional concepts. Then we measured the cosine similarities between the LLM's internal representation of trust and the derived trust-related concepts. Our results show that the internal trust representation of EleutherAI/gpt-j-6B aligns most closely with the Castelfranchi socio-cognitive model, followed by the Marsh Model. These findings indicate that LLMs encode socio-cognitive constructs in their activation space in ways that support meaningful comparative analyses, inform theories of social cognition, and support the design of human-AI collaborative systems.
How Well Do Current Speech Deepfake Detection Methods Generalize to the Real World?
Venue: arXiv cs.SD 2026
Year: 2026

Recent advances in speech synthesis and voice conversion have greatly improved the naturalness and authenticity of generated audio. Meanwhile, evolving encoding, compression, and transmission mechanisms on social media platforms further obscure deepfake artifacts. These factors complicate reliable detection in real-world environments, underscoring the need for representative evaluation benchmarks. To this end, we introduce ML-ITW (Multilingual In-The-Wild), a multilingual dataset covering 14 languages, seven major platforms, and 180 public figures, totaling 28.39 hours of audio. We evaluate three detection paradigms: end-to-end neural models, self-supervised feature-based (SSL) methods, and audio large language models (Audio LLMs). Experimental results reveal significant performance degradation across diverse languages and real-world acoustic conditions, highlighting the limited generalization ability of existing detectors in practical scenarios. The ML-ITW dataset is publicly available.
Evolving Medical Imaging Agents via Experience-driven Self-skill Discovery
Venue: arXiv cs.AI 2026
Year: 2026

Clinical image interpretation is inherently multi-step and tool-centric: clinicians iteratively combine visual evidence with patient context, quantify findings, and refine their decisions through a sequence of specialized procedures. While LLM-based agents promise to orchestrate such heterogeneous medical tools, existing systems treat tool sets and invocation strategies as static after deployment. This design is brittle under real-world domain shifts, across tasks, and evolving diagnostic requirements, where predefined tool chains frequently degrade and demand costly manual re-design. We propose MACRO, a self-evolving, experience-augmented medical agent that shifts from static tool composition to experience-driven tool discovery. From verified execution trajectories, the agent autonomously identifies recurring effective multi-step tool sequences, synthesizes them into reusable composite tools, and registers these as new high-level primitives that continuously expand its behavioral repertoire. A lightweight image-feature memory grounds tool selection in a visual-clinical context, while a GRPO-like training loop reinforces reliable invocation of discovered composites, enabling closed-loop self-improvement with minimal supervision. Extensive experiments across diverse medical imaging datasets and tasks demonstrate that autonomous composite tool discovery consistently improves multi-step orchestration accuracy and cross-domain generalization over strong baselines and recent state-of-the-art agentic methods, bridging the gap between brittle static tool use and adaptive, context-aware clinical AI assistance. Code will be available upon acceptance.