OpenRaiser/Intern-Atlas huggingface dataset:
Batch-of-Thought: Cross-Instance Learning for Enhanced LLM Reasoning
Venue: arXiv cs.AI 2026
Year: 2026

Current Large Language Model reasoning systems process queries independently, discarding valuable cross-instance signals such as shared reasoning patterns and consistency constraints. We introduce Batch-of-Thought (BoT), a training-free method that processes related queries jointly to enable cross-instance learning. By performing comparative analysis across batches, BoT identifies high-quality reasoning templates, detects errors through consistency checks, and amortizes computational costs. We instantiate BoT within a multi-agent reflection architecture (BoT-R), where a Reflector performs joint evaluation to unlock mutual information gain unavailable in isolated processing. Experiments across three model families and six benchmarks demonstrate that BoT-R consistently improves accuracy and confidence calibration while reducing inference costs by up to 61%. Our theoretical and experimental analysis reveals when and why batch-aware reasoning benefits LLM systems. Our code is available at https://github.com/xuanyang19/BoT
The World is Not Mono: Enabling Spatial Understanding in Large Audio-Language Models
Venue: arXiv cs.SD 2026
Year: 2026

Existing large audio-language models perceive the world as "mono"-a single stream of audio that ignores the critical spatial dimension ("where") required for universal audio scene analysis (ASA). To bridge this gap, we first introduce a hierarchical framework for audio scene analysis. Guided by this framework, we introduce a system that enables large audio-language models (LALMs) to understand and reason about the complex acoustic world. Our system endows LALMs with universal spatial understanding through four key innovations: (1) A scalable simulation pipeline that synthesizes high-quality First-Order-Ambisonics(FOA) data; (2) A unified model framework that integrates universal spatial encoding with a dense hybrid projection mechanism to bridge the modality gap; (3) A progressive training curriculum that evolves from representation alignment to reinforcement learning-based reasoning; and (4) A comprehensive benchmark for audio scene analysis (ASA) designed to rigorously evaluate atomic perception, relational integration, and cognitive reasoning capabilities, on which our model demonstrates comparatively strong capability for spatial understanding. Our work provides a clear pathway for leveraging the powerful reasoning abilities of LALMs towards holistic ASA, advancing from "mono" semantic recognition to spatial intelligence.
Correct, Concise and Complete: Multi-stage Training For Adaptive Reasoning
Venue: arXiv cs.CL 2026
Year: 2026

The reasoning capabilities of large language models (LLMs) have improved substantially through increased test-time computation, typically in the form of intermediate tokens known as chain-of-thought (CoT). However, CoT often becomes unnecessarily long, increasing computation cost without actual accuracy gains or sometimes even degrading performance, a phenomenon known as ``overthinking''. We propose a multi-stage efficient reasoning method that combines supervised fine-tuning -- via rejection sampling or reasoning trace reformatting -- with reinforcement learning using an adaptive length penalty. We introduce a lightweight reward function that penalizes tokens generated after the first correct answer but encouraging self-verification only when beneficial. We conduct a holistic evaluation across seven diverse reasoning tasks, analyzing the accuracy-response length trade-off. Our approach reduces response length by an average of 28\% for 8B models and 40\% for 32B models, while incurring only minor performance drops of 1.6 and 2.5 points, respectively. Despite its conceptual simplicity, it achieves a superior trade-off compared to more complex state-of-the-art efficient reasoning methods, scoring 76.6, in terms of the area under the Overthinking-Adjusted Accuracy curve ($\text{AUC}_{\text{OAA}}$) -- 5 points above the base model and 2.5 points above the second-best approach.
P-Check: Advancing Personalized Reward Model via Learning to Generate Dynamic Checklist
Venue: arXiv cs.CL 2026
Year: 2026

Recent approaches in personalized reward modeling have primarily focused on leveraging user interaction history to align model judgments with individual preferences. However, existing approaches largely treat user context as a static or implicit conditioning signal, failing to capture the dynamic and multi-faceted nature of human judgment. In this paper, we propose P-Check, a novel personalized reward modeling framework, designed to train a plug-and-play checklist generator that synthesizes dynamic evaluation criteria for guiding the reward prediction. To better align these checklists with personalized nuances, we introduce Preference-Contrastive Criterion Weighting, a training strategy that assigns saliency scores to criteria based on their discriminative power for personalized judgment. We conduct extensive experiments and demonstrate that P-Check not only improves reward accuracy but also enhances downstream personalized generation, and remains robust in OOD scenarios.
LAMS-Edit: Latent and Attention Mixing with Schedulers for Improved Content Preservation in Diffusion-Based Image and Style Editing
Venue: arXiv cs.CV 2026
Year: 2026

Text-to-Image editing using diffusion models faces challenges in balancing content preservation with edit application and handling real-image editing. To address these, we propose LAMS-Edit, leveraging intermediate states from the inversion process--an essential step in real-image editing--during edited image generation. Specifically, latent representations and attention maps from both processes are combined at each step using weighted interpolation, controlled by a scheduler. This technique, Latent and Attention Mixing with Schedulers (LAMS), integrates with Prompt-to-Prompt (P2P) to form LAMS-Edit--an extensible framework that supports precise editing with region masks and enables style transfer via LoRA. Extensive experiments demonstrate that LAMS-Edit effectively balances content preservation and edit application.
LLMs, You Can Evaluate It! Design of Multi-perspective Report Evaluation for Security Operation Centers
Venue: arXiv cs.CR 2026
Year: 2026

Security operation centers (SOCs) often produce analysis reports on security incidents, and large language models (LLMs) will likely be used for this task in the near future. We postulate that a better understanding of how veteran analysts evaluate reports, including their feedback, can help produce analysis reports in SOCs. In this paper, we aim to leverage LLMs for analysis reports. To this end, we first construct a Analyst-wise checklist to reflect SOC practitioners' opinions for analysis report evaluation through literature review and user study with SOC practitioners. Next, we design a novel LLM-based conceptual framework, named MESSALA, by further introducing two new techniques, granularization guideline and multi-perspective evaluation. MESSALA can maximize report evaluation and provide feedback on veteran SOC practitioners' perceptions. When we conduct extensive experiments with MESSALA, the evaluation results by MESSALA are the closest to those of veteran SOC practitioners compared with the existing LLM-based methods. We then show two key insights. We also conduct qualitative analysis with MESSALA, and then identify that MESSALA can provide actionable items that are necessary for improving analysis reports.
Dementia-R1: Reinforced Pretraining and Reasoning from Unstructured Clinical Notes for Real-World Dementia Prognosis
Venue: arXiv cs.CL 2026
Year: 2026

While Large Language Models (LLMs) have shown strong performance on clinical text understanding, they struggle with longitudinal prediction tasks such as dementia prognosis, which require reasoning over complex, non-monotonic symptom trajectories across multiple visits. Standard supervised training lacks explicit annotations for symptom evolution, while direct Reinforcement Learning (RL) is hindered by sparse binary rewards. To address this challenge, we introduce Dementia-R1, an RL-based framework for longitudinal dementia prognosis from unstructured clinical notes. Our approach adopts a Cold-Start RL strategy that pre-trains the model to predict verifiable clinical indices extracted from patient histories, enhancing the capability to reason about disease progression before determining the final clinical status. Extensive experiments demonstrate that Dementia-R1 achieves an F1 score of 77.03% on real-world unstructured clinical datasets. Notably, on the ADNI benchmark, our 7B model rivals GPT-4o, effectively capturing fluctuating cognitive trajectories. Code is available at https://anonymous.4open.science/r/dementiar1-CDB5
Hardness of Regular Expression Matching with Extensions
Venue: arXiv cs.CC 2026
Year: 2026

The regular expression matching problem asks whether a given regular expression of length $m$ matches a given string of length $n$. As is well known, the problem can be solved in $O(nm)$ time using Thompson's algorithm. Moreover, recent studies have shown that the matching problem for regular expressions extended with a practical extension called lookaround can be solved in the same time complexity. In this work, we consider four well-known extensions to regular expressions called backreference, squaring, intersection and complement, and we show that, unlike in the case of lookaround, the matching problem for regular expressions extended with any of the four (for backreference, even when restricted to one capturing group) cannot be solved in $O(n^{2-\varepsilon} \mathrm{poly}(m))$ time for any constant $\varepsilon > 0$ under the Orthogonal Vectors Conjecture. Moreover, we study the matching problem for regular expressions extended with complement in more detail, which is also known as extended regular expression (ERE) matching. We show that there is no ERE matching algorithm that runs in $O(n^{ω-\varepsilon} \mathrm{poly}(m))$ time ($2 \le ω< 2.3714$ is the exponent of square matrix multiplication) for any constant $\varepsilon > 0$ under the $k$-Clique Hypothesis, and there is no combinatorial ERE matching algorithm that runs in $O(n^{3-\varepsilon} \mathrm{poly}(m))$ time for any constant $\varepsilon > 0$ under the Combinatorial $k$-Clique Hypothesis. This shows that the $O(n^3 m)$-time algorithm introduced by Hopcroft and Ullman in 1979 and recently improved by Bille et al. to run in $O(n^ωm)$ time using fast matrix multiplication was already optimal in a sense, and sheds light on why the theoretical computer science community has struggled to improve the time complexity of ERE matching with respect to $n$ and $m$ for more than 45 years.
Reducing Hallucinations in LLMs via Factuality-Aware Preference Learning
Venue: arXiv cs.CL 2026
Year: 2026

Preference alignment methods such as RLHF and Direct Preference Optimization (DPO) improve instruction following, but they can also reinforce hallucinations when preference judgments reward fluency and confidence over factual correctness. We introduce F-DPO (Factuality-aware Direct Preference Optimization), a simple extension of DPO that uses only binary factuality labels. F-DPO (i) applies a label-flipping transformation that corrects misordered preference pairs so the chosen response is never less factual than the rejected one, and (ii) adds a factuality-aware margin that emphasizes pairs with clear correctness differences, while reducing to standard DPO when both responses share the same factuality. We construct factuality-aware preference data by augmenting DPO pairs with binary factuality indicators and synthetic hallucinated variants. Across seven open-weight LLMs (1B-14B), F-DPO consistently improves factuality and reduces hallucination rates relative to both base models and standard DPO. On Qwen3-8B, F-DPO reduces hallucination rates by five times (from 0.424 to 0.084) while improving factuality scores by 50 percent (from 5.26 to 7.90). F-DPO also generalizes to out-of-distribution benchmarks: on TruthfulQA, Qwen2.5-14B achieves plus 17 percent MC1 accuracy (0.500 to 0.585) and plus 49 percent MC2 accuracy (0.357 to 0.531). F-DPO requires no auxiliary reward model, token-level annotations, or multi-stage training.
FlexProofs: A Vector Commitment with Flexible Linear Time for Computing All Proofs
Venue: arXiv cs.CR 2026
Year: 2026

In this paper, we introduce FlexProofs, a new vector commitment (VC) scheme that achieves two key properties: (1) the prover can generate all individual opening proofs for a vector of size $N$ in optimal time ${\cal O}(N)$, and there is a flexible batch size parameter $b$ that can be increased to further reduce the time to generate all proofs; and (2) the scheme is directly compatible with a family of zkSNARKs that encode their input as a multi-linear polynomial. As a critical building block, we propose the first functional commitment (FC) scheme for multi-exponentiations with batch opening. Compared with HydraProofs, the only existing VC scheme that computes all proofs in optimal time ${\cal O}(N)$ and is directly compatible with zkSNARKs, FlexProofs may speed up the process of generating all proofs, if the parameter $b$ is properly chosen. Our experiments show that for $N=2^{16}$ and $b=\log^2 N$, FlexProofs can be $6\times$ faster than HydraProofs. Moreover, when combined with suitable zkSNARKs, FlexProofs enable practical applications such as verifiable secret sharing and verifiable robust aggregation.
On the Intrinsic Limits of Transformer Image Embeddings in Non-Solvable Spatial Reasoning
Venue: arXiv cs.CV 2026
Year: 2026

Vision Transformers (ViTs) excel in semantic recognition but exhibit systematic failures in spatial reasoning tasks such as mental rotation. While often attributed to data scale, we propose that this limitation arises from the intrinsic circuit complexity of the architecture. We formalize spatial understanding as learning a Group Homomorphism: mapping image sequences to a latent space that preserves the algebraic structure of the underlying transformation group. We demonstrate that for non-solvable groups (e.g., the 3D rotation group $\mathrm{SO}(3)$), maintaining such a structure-preserving embedding is computationally lower-bounded by the Word Problem, which is $\mathsf{NC^1}$-complete. In contrast, we prove that constant-depth ViTs with polynomial precision are strictly bounded by $\mathsf{TC^0}$. Under the conjecture $\mathsf{TC^0} \subsetneq \mathsf{NC^1}$, we establish a complexity boundary: constant-depth ViTs fundamentally lack the logical depth to efficiently capture non-solvable spatial structures. We validate this complexity gap via latent-space probing, demonstrating that ViT representations suffer a structural collapse on non-solvable tasks as compositional depth increases.
Exploring the Relationship Between Local Election Results and Online Public Opinion in Taiwan: A Case Study of Taitung County
Venue: arXiv cs.SI 2026
Year: 2026

This study examines the relationship between online buzz and local election outcomes in Taiwan, with a focus on Taitung County. As social media becomes a major channel for public discourse, online buzz is increasingly seen as a factor influencing elections. However, its impact on local elections in Taiwan remains underexplored. This research addresses that gap through a comparative analysis of social media data and actual vote shares during the election period. A review of existing literature establishes the study's framework and highlights the need for empirical investigation in this area. The findings aim to reveal whether online discussions align with electoral results and to what extent digital sentiment reflects voter behavior. The study also discusses methodological and data limitations that may affect interpretation. Beyond its academic value, the research offers practical insights into how online buzz can inform campaign strategies and enhance election predictions. By analyzing the Taitung County case, this study contributes to a deeper understanding of the role of online discourse in Taiwan's local elections and offers a foundation for future research in the field.
Unified Thinker: A General Reasoning Modular Core for Image Generation
Venue: arXiv cs.CV 2026
Year: 2026

Despite impressive progress in high-fidelity image synthesis, generative models still struggle with logic-intensive instruction following, exposing a persistent reasoning--execution gap. Meanwhile, closed-source systems (e.g., Nano Banana) have demonstrated strong reasoning-driven image generation, highlighting a substantial gap to current open-source models. We argue that closing this gap requires not merely better visual generators, but executable reasoning: decomposing high-level intents into grounded, verifiable plans that directly steer the generative process. To this end, we propose Unified Thinker, a task-agnostic reasoning architecture for general image generation, designed as a unified planning core that can plug into diverse generators and workflows. Unified Thinker decouples a dedicated Thinker from the image Generator, enabling modular upgrades of reasoning without retraining the entire generative model. We further introduce a two-stage training paradigm: we first build a structured planning interface for the Thinker, then apply reinforcement learning to ground its policy in pixel-level feedback, encouraging plans that optimize visual correctness over textual plausibility. Extensive experiments on text-to-image generation and image editing show that Unified Thinker substantially improves image reasoning and generation quality.
PersonaLedger: Generating Realistic Financial Transactions with Persona Conditioned LLMs and Rule Grounded Feedback
Venue: arXiv cs.LG 2026
Year: 2026

Strict privacy regulations limit access to real transaction data, slowing open research in financial AI. Synthetic data can bridge this gap, but existing generators do not jointly achieve behavioral diversity and logical groundedness. Rule-driven simulators rely on hand-crafted workflows and shallow stochasticity, which miss the richness of human behavior. Learning-based generators such as GANs capture correlations yet often violate hard financial constraints and still require training on private data. We introduce PersonaLedger, a generation engine that uses a large language model conditioned on rich user personas to produce diverse transaction streams, coupled with an expert configurable programmatic engine that maintains correctness. The LLM and engine interact in a closed loop: after each event, the engine updates the user state, enforces financial rules, and returns a context aware "nextprompt" that guides the LLM toward feasible next actions. With this engine, we create a public dataset of 30 million transactions from 23,000 users and a benchmark suite with two tasks, illiquidity classification and identity theft segmentation. PersonaLedger offers a realistic, privacy preserving resource that supports rigorous evaluation of forecasting and anomaly detection models. PersonaLedger offers the community a rich, realistic, and privacy preserving resource -- complete with code, rules, and generation logs -- to accelerate innovation in financial AI and enable rigorous, reproducible evaluation.
Predicting Time Pressure of Powered Two-Wheeler Riders for Proactive Safety Interventions
Venue: arXiv cs.LG 2026
Year: 2026

Time pressure critically influences risky maneuvers and crash proneness among powered two-wheeler riders, yet its prediction remains underexplored in intelligent transportation systems. We present a large-scale dataset of 129,000+ labeled multivariate time-series sequences from 153 rides by 51 participants under No, Low, and High Time Pressure conditions. Each sequence captures 63 features spanning vehicle kinematics, control inputs, behavioral violations, and environmental context. Our empirical analysis shows High Time Pressure induces 48% higher speeds, 36.4% greater speed variability, 58% more risky turns at intersections, 36% more sudden braking, and 50% higher rear brake forces versus No Time Pressure. To benchmark this dataset, we propose MotoTimePressure, a deep learning model combining convolutional preprocessing, dual-stage temporal attention, and Squeeze-and-Excitation feature recalibration, achieving 91.53% accuracy and 98.93% ROC AUC, outperforming eight baselines. Since time pressure cannot be directly measured in real time, we demonstrate its utility in collision prediction and threshold determination. Using MTPS-predicted time pressure as features, improves Informer-based collision risk accuracy from 91.25% to 93.51%, approaching oracle performance (93.72%). Thresholded time pressure states capture rider cognitive stress and enable proactive ITS interventions, including adaptive alerts, haptic feedback, V2I signaling, and speed guidance, supporting safer two-wheeler mobility under the Safe System Approach.
AnatomiX, an Anatomy-Aware Grounded Multimodal Large Language Model for Chest X-Ray Interpretation
Venue: arXiv cs.CV 2026
Year: 2026

Multimodal medical large language models have shown substantial progress in chest X-ray interpretation but continue to face challenges in spatial reasoning and anatomical understanding. Although existing grounding techniques improve overall performance, they often fail to establish a true anatomical correspondence, resulting in incorrect anatomical understanding in the medical domain. To address this gap, we introduce AnatomiX, a multitask multimodal large language model for anatomically grounded chest X-ray interpretation. Inspired by the radiological workflow, AnatomiX adopts a two stage approach: first, it identifies anatomical structures and extracts their features, and then leverages a large language model to perform diverse downstream tasks such as phrase grounding, report generation, visual question answering, and image understanding. Extensive experiments across multiple benchmarks demonstrate that AnatomiX achieves superior anatomical reasoning and delivers over 25% improvement in performance on anatomy grounding, phrase grounding, grounded diagnosis and grounded captioning tasks compared to existing approaches. Code and pretrained model are available at github.com/aneesurhashmi/anatomix.
Sparse Knowledge Distillation: A Mathematical Framework for Probability-Domain Temperature Scaling and Multi-Stage Compression
Venue: arXiv cs.LG 2026
Year: 2026

We develop a unified theoretical framework for sparse knowledge distillation based on probability-domain softening operators. While the equivalence $p^{1/T} \propto \mathrm{softmax}(z/T)$ is well known, our contribution is an operator-level analytical framework built on this foundation rather than the equivalence itself. The framework comprises four core components: (i) operator-agnostic bias--variance decompositions that characterize when sparse students outperform dense teachers, (ii) a homotopy path formalization of multi-stage pruning in function space explaining why iterative compression succeeds where one-shot pruning fails, (iii) convergence guarantees establishing $O(1/n)$ rates for $n$-stage distillation with explicit parameter dependence, and (iv) equivalence class characterizations identifying distinct probability-domain operators that yield identical student models under capacity constraints. We introduce an axiomatic definition of probability-domain softening operators based on ranking preservation, continuity, entropy monotonicity, identity, and boundary behavior, and show that multiple non-equivalent operator families satisfy these axioms. All learning-theoretic guarantees are shown to hold uniformly across this operator class, independent of implementation details. These results provide theoretical grounding for black-box teacher distillation, partial-access settings such as top-$k$ truncation and text-only outputs, and privacy-preserving model compression.