OpenRaiser/Intern-Atlas huggingface dataset:
Thucy: An LLM-based Multi-Agent System for Claim Verification across Relational Databases
Venue: arXiv cs.DB 2026
Year: 2026

In today's age, it is becoming increasingly difficult to decipher truth from lies. Every day, politicians, media outlets, and public figures make conflicting claims -- often about topics that can, in principle, be verified against structured data. For instance, statements about crime rates, economic growth or healthcare can all be verified against official public records and structured datasets. Building a system that can automatically do that would have sounded like science fiction just a few years ago. Yet, with the extraordinary progress in LLMs and agentic AI, this is now within reach. Still, there remains a striking gap between what is technically possible and what is being demonstrated by recent work. Most existing verification systems operate only on small, single-table databases -- typically a few hundred rows -- that conveniently fit within an LLM's context window. In this paper we report our progress on Thucy, the first cross-database, cross-table multi-agent claim verification system that also provides concrete evidence for each verification verdict. Thucy remains completely agnostic to the underlying data sources before deployment and must therefore autonomously discover, inspect, and reason over all available relational databases to verify claims. Importantly, Thucy also reports the exact SQL queries that support its verdict (whether the claim is accurate or not) offering full transparency to expert users familiar with SQL. When evaluated on the TabFact dataset -- the standard benchmark for fact verification over structured data -- Thucy surpasses the previous state of the art by 5.6 percentage points in accuracy (94.3% vs. 88.7%).
Adaptive Regime-Switching Forecasts with Distribution-Free Uncertainty: Deep Switching State-Space Models Meet Conformal Prediction
Venue: arXiv cs.LG 2026
Year: 2026

Regime transitions routinely break stationarity in time series, making calibrated uncertainty as important as point accuracy. We study distribution-free uncertainty for regime-switching forecasting by coupling Deep Switching State Space Models with Adaptive Conformal Inference (ACI) and its aggregated variant (AgACI). We also introduce a unified conformal wrapper that sits atop strong sequence baselines including S4, MC-Dropout GRU, sparse Gaussian processes, and a change-point local model to produce online predictive bands with finite-sample marginal guarantees under nonstationarity and model misspecification. Across synthetic and real datasets, conformalized forecasters achieve near-nominal coverage with competitive accuracy and generally improved band efficiency.
AI Chatbots or Human Therapists? Belief-Based Predictors of Mental Health Help-Seeking Intentions in the Age of Generative AI
Venue: arXiv cs.HC 2026
Year: 2026

As generative artificial intelligence (GAI) enters the mental health landscape, questions arise about how individuals weigh AI tools against human therapists. This study examined belief-based predictors of intention to use GAI and therapists across two populations: a university sample (N = 1,155) and a nationally representative adult sample (N = 651). Using paired-sample t-tests following a MANOVA, we found that human therapists were viewed as providing greater emotional support and coping, relationship, and educational skills as well as being able to personalize treatment than GAI chatbots. In turn, GAI support was viewed as being more affordable and accessible. No differences between modalities were found with concerns about privacy, reliability, stigma, mental health literacy or help-seeking norms. Using LASSO regression, we examined how beliefs about each modality jointly shape help-seeking intentions. Across both samples, intentions to use either GAI or human therapists were most strongly associated with perceptions of interpersonal support, including emotional support, relational guidance, and personalization. Barriers differed across modalities: concerns about privacy and reliability were more strongly associated with reduced intention to use GAI, whereas structural constraints, particularly affordability, were more closely linked to human therapy use. These findings extend the Health Belief Model to a dual-modality context, demonstrating that help-seeking decisions reflect a comparative push-pull process in which barriers to one modality redirect users toward the other. Design implications are discussed for developing trustworthy, emotionally resonant GAI tools that complement rather than replace human care.
Tuning for TraceTarnish: Techniques, Trends, and Testing Tangible Traits
Venue: arXiv cs.CR 2026
Year: 2026

In this study, we more rigorously evaluated our attack script $\textit{TraceTarnish}$, which leverages adversarial stylometry principles to anonymize the authorship of text-based messages. To ensure the efficacy and utility of our attack, we sourced, processed, and analyzed Reddit comments -- comments that were later alchemized into $\textit{TraceTarnish}$ data -- to gain valuable insights. The transformed $\textit{TraceTarnish}$ data was then further augmented by $\textit{StyloMetrix}$ to manufacture stylometric features -- features that were culled using the Information Gain criterion, leaving only the most informative, predictive, and discriminative ones. Our results found that function words and function word types ($L\_FUNC\_A$ $\&$ $L\_FUNC\_T$); content words and content word types ($L\_CONT\_A$ $\&$ $L\_CONT\_T$); and the Type-Token Ratio ($ST\_TYPE\_TOKEN\_RATIO\_LEMMAS$) yielded significant Information-Gain readings. The identified stylometric cues -- function-word frequencies, content-word distributions, and the Type-Token Ratio -- serve as reliable indicators of compromise (IoCs), revealing when a text has been deliberately altered to mask its true author. Similarly, these features could function as forensic beacons, alerting defenders to the presence of an adversarial stylometry attack; granted, in the absence of the original message, this signal may go largely unnoticed, as it appears to depend on a pre- and post-transformation comparison. "In trying to erase a trace, you often imprint a larger one." Armed with this understanding, we framed $\textit{TraceTarnish}$'s operations and outputs around these five isolated features, using them to conceptualize and implement enhancements that further strengthen the attack.
From Reliability to Security: How RIS-Assisted Adaptive SM and SSK Enhances Wireless Systems
Venue: arXiv cs.IT 2026
Year: 2026

This paper proposes two novel wireless transmission schemes, namely reconfigurable intelligent surface (RIS)-assisted received adaptive spatial modulation (RASM) scheme and RIS-assisted received adaptive space shift keying (RASSK) scheme, designed to enhance spectral efficiency (SE) and physical layer security (PLS). In both proposed schemes, transmitting bits are dynamically mapped at receive antennas by leveraging the characteristics of the RIS in each time slot, which enables the enhancement of signal-to-noise ratio (SNR) at specific selected antennas with nearly few power, thus leading to a reliable and green wireless communication. Unlike conventional fixed-antenna RIS-RSM/GSSK, the term ``adaptive'' indicates the number of active antennas dynamically changes per symbol, conveying extra spatial information to break existing spectral efficiency bottlenecks.This adaptive approach facilitates the conveyance of extra bits to the receiver, which means it needs less cost of radio-frequency chains at transmitter while improving SE. Besides, the proposed schemes offer an inherent PLS security advantage, as the eavesdropper is unable to completely detect signals reflected from the RIS. To comprehensively evaluate the performance of the proposed RASM and RASSK schemes, this paper presents a detailed analytical performance of their spectral efficiency, detection complexity, bit error rate, and secrecy rate, which are accompanied by insightful findings and conclusions. Simulation and analytical results demonstrate the superiority of the proposed schemes, showcasing their improved error performance and robustness against wiretapping, while also highlighting the potential of the RASM and RASSK schemes for future wireless applications.
Cross-embodied Co-design for Dexterous Hands
Venue: arXiv cs.RO 2026
Year: 2026

Dexterous manipulation is limited by both control and design, without consensus as to what makes manipulators best for performing dexterous tasks. This raises a fundamental challenge: how should we design and control robot manipulators that are optimized for dexterity? We present a co-design framework that learns task-specific hand morphology and complementary dexterous control policies. The framework supports 1) an expansive morphology search space including joint, finger, and palm generation, 2) scalable evaluation across the wide design space via morphology-conditioned cross-embodied control, and 3) real-world fabrication with accessible components. We evaluate the approach across multiple dexterous tasks, including in-hand rotation with simulation and real deployment. Our framework enables an end-to-end pipeline that can design, train, fabricate, and deploy a new robotic hand in under 24 hours. The full framework will be open-sourced and available on our website: https://an-axolotl.github.io/HouseofDextra/ .
Human-controllable AI: Meaningful Human Control
Venue: arXiv cs.HC 2026
Year: 2026

Developing human-controllable artificial intelligence (AI) and achieving meaningful human control (MHC) has become a vital principle to address these challenges, ensuring ethical alignment and effective governance in AI. MHC is also a critical focus in human-centered AI (HCAI) research and application. This chapter systematically examines MHC in AI, articulating its foundational principles and future trajectory. MHC is not simply the right to operate, but the unity of human understanding, intervention, and the traceablity of responsibility in AI decision-making, which requires technological design, AI governance, and humans to play a role together. MHC ensures AI autonomy serves humans without constraining technological progress. The mode of human control needs to match the levels of technology, and human supervision should balance the trust and doubt of AI. For future AI systems, MHC mandates human controllability as a prerequisite, requiring: (1) technical architectures with embedded mechanisms for human control; (2) human-AI interactions optimized for better access to human understanding; and (3) the evolution of AI systems harmonizing intelligence and human controllability. Governance must prioritize HCAI strategies: policies balancing innovation and risk mitigation, human-centered participatory frameworks transcending technical elite dominance, and global promotion of MHC as a universal governance paradigm to safeguard HCAI development. Looking ahead, there is a need to strengthen interdisciplinary research on the controllability of AI systems, enhance ethical and legal awareness among stakeholders, moving beyond simplistic technology design perspectives, focus on the knowledge construction, complexity interpretation, and influencing factors surrounding human control. By fostering MHC, the development of human-controllable AI can be further advanced, delivering HCAI systems.
UserSimCRS v2: Simulation-Based Evaluation for Conversational Recommender Systems
Venue: arXiv cs.IR 2026
Year: 2026

Resources for simulation-based evaluation of conversational recommender systems (CRSs) are scarce. The UserSimCRS toolkit was introduced to address this gap. In this work, we present UserSimCRS v2, a significant upgrade aligning the toolkit with state-of-the-art research. Key extensions include an enhanced agenda-based user simulator, introduction of large language model-based simulators, integration for a wider range of CRSs and datasets, and new LLM-as-a-judge evaluation utilities. We demonstrate these extensions in a case study.
Topology Matters: Measuring Memory Leakage in Multi-Agent LLMs
Venue: arXiv cs.CR 2026
Year: 2026

Graph topology is a fundamental determinant of memory leakage in multi-agent LLM systems, yet its effects remain poorly quantified. We introduce MAMA (Multi-Agent Memory Attack), a framework that measures how network structure shapes leakage. MAMA operates on synthetic documents containing labeled Personally Identifiable Information (PII) entities, from which we generate sanitized task instructions. We execute a two-phase protocol: Engram (seeding private information into a target agent's memory) and Resonance (multi-round interaction where an attacker attempts extraction). Over 10 rounds, we measure leakage as exact-match recovery of ground-truth PII from attacker outputs. We evaluate six canonical topologies (complete, ring, chain, tree, star, star-ring) across $n\in\{4,5,6\}$, attacker-target placements, and base models. Results are consistent: denser connectivity, shorter attacker-target distance, and higher target centrality increase leakage; most leakage occurs in early rounds and then plateaus; model choice shifts absolute rates but preserves topology ordering; spatiotemporal/location attributes leak more readily than identity credentials or regulated identifiers. We distill practical guidance for system design: favor sparse or hierarchical connectivity, maximize attacker-target separation, and restrict hub/shortcut pathways via topology-aware access control.
Computing Supported Models via Transformation to Stable Models
Venue: arXiv cs.LO 2026
Year: 2026

Answer Set Programming (ASP) with stable model semantics has proven highly effective for knowledge representation and reasoning. However, the minimality requirement of stable models can be restrictive for applications requiring exploration of non-minimal but logically consistent solution spaces. Supported models, introduced by Apt, Blair, and Walker in 1988, relax this minimality constraint while maintaining a support condition ensuring every true atom is justified by some rule. Despite their theoretical significance, supported models lack practical computational tools integrated with modern ASP solvers. We present a novel transformation-based method enabling computation of supported models using standard ASP infrastructure. Our approach transforms any ground logic program into an equivalent program whose stable models correspond exactly to the supported models of the original program. We implement this transformation for Clingo, providing the first practical tool for computing supported models with state-of-the-art ASP solvers. We demonstrate applications in software verification, medical diagnosis, and planning where supported models enable valuable exploratory reasoning capabilities beyond those provided by stable models. We also provide an empirical evaluation to justify the practical utility of our approach compared to established methods. Our implementation is publicly available and compatible with standard ASP syntax.
When Scaffolding Breaks: Investigating Student Interaction with LLM-Based Writing Support in Real-Time K-12 EFL Classrooms
Venue: arXiv cs.HC 2026
Year: 2026

Large language models (LLMs) are promising tools for scaffolding students' English writing skills, but their effectiveness in real-time K-12 classrooms remains underexplored. Addressing this gap, our study examines the benefits and limitations of using LLMs as real-time learning support, considering how classroom constraints, such as diverse proficiency levels and limited time, affect their effectiveness. We conducted a deployment study with 157 eighth-grade students in a South Korean middle school English class over six weeks. Our findings reveal that while scaffolding improved students' ability to compose grammatically correct sentences, this step-by-step approach demotivated lower-proficiency students and increased their system reliance. We also observed challenges to classroom dynamics, where extroverted students often dominated the teacher's attention, and the system's assistance made it difficult for teachers to identify struggling students. Based on these findings, we discuss design guidelines for integrating LLMs into real-time writing classes as inclusive educational tools.
The T12 System for AudioMOS Challenge 2025: Audio Aesthetics Score Prediction System Using KAN- and VERSA-based Models
Venue: arXiv cs.SD 2026
Year: 2026

We propose an audio aesthetics score (AES) prediction system by CyberAgent (AESCA) for AudioMOS Challenge 2025 (AMC25) Track 2. The AESCA comprises a Kolmogorov--Arnold Network (KAN)-based audiobox aesthetics and a predictor from the metric scores using the VERSA toolkit. In the KAN-based predictor, we replaced each multi-layer perceptron layer in the baseline model with a group-rational KAN and trained the model with labeled and pseudo-labeled audio samples. The VERSA-based predictor was designed as a regression model using extreme gradient boosting, incorporating outputs from existing metrics. Both the KAN- and VERSA-based models predicted the AES, including the four evaluation axes. The final AES values were calculated using an ensemble model that combined four KAN-based models and a VERSA-based model. Our proposed T12 system yielded the best correlations among the submitted systems, in three axes at the utterance level, two axes at the system level, and the overall average. We also released the inference model of the proposed KAN-based predictor (KAN #1-#4).
Internal Deployment in the EU AI Act
Venue: arXiv cs.CY 2026
Year: 2026

This memorandum analyzes and stress-tests arguments in favor and against the inclusion of internal deployment within the scope of the European Union Artificial Intelligence Act (EU AI Act). In doing so, it aims to offer several possible interpretative pathways to the European Commission, AI providers and deployers, courts, and the legal and policy community at large based on Articles 2(1), 2(6), 2(8) of the EU AI Act. Specifically, this memorandum first analyzes four interpretative pathways based on Article 2(1)(a)-(c) supporting the application of the EU AI Act to internally deployed AI models and systems. Then, it examines possible objections and exceptions based on Articles 2(1)(a), 2(6), and 2(8), with particular attention to the complexity of the scientific R&D exception under Article 2(6). Finally, it illustrates how Articles 2(1), 2(6), and 2(8) can be viewed as complementary to each other, once broken down to their most plausible meaning and interpreted in conjunction with Articles 3(1), 3(3), 3(4), 3(9), 3(10), 3(11), 3(12), 3(63), and Recitals 12, 13, 21, 25, 97, 109, and 110.
Whatever Remains Must Be True: Filtering Drives Reasoning in LLMs, Shaping Diversity
Venue: arXiv cs.LG 2026
Year: 2026

Reinforcement Learning (RL) has become the de facto standard for tuning LLMs to solve tasks involving reasoning. However, growing evidence shows that models trained in such way often suffer from a significant loss in diversity. We argue that this arises because RL implicitly optimizes the "mode-seeking" or "zero-forcing" Reverse KL to a target distribution causing the model to concentrate mass on certain high-probability regions of the target while neglecting others. In this work, we instead begin from an explicit target distribution, obtained by filtering out incorrect answers while preserving the relative probabilities of correct ones. Starting from a pre-trained LLM, we approximate this target distribution using the $α$-divergence family, which unifies prior approaches and enables direct control of the precision-diversity trade-off by interpolating between mode-seeking and mass-covering divergences. On a Lean theorem-proving benchmark, our method achieves state-of-the-art performance along the coverage-precision Pareto frontier, outperforming all prior methods on the coverage axis.
K2-V2: A 360-Open, Reasoning-Enhanced LLM
Venue: arXiv cs.LG 2026
Year: 2026

We introduce K2-V2, a 360-open LLM built from scratch as a superior base for reasoning adaptation, in addition to functions such as conversation and knowledge retrieval from general LLMs. It stands as the strongest fully open model, rivals open-weight leaders in its size class, outperforms Qwen2.5-72B and approaches the performance of Qwen3-235B. We actively infuse domain knowledge, reasoning, long-context, and tool use throughout the training process. This explicitly prepares the model for complex reasoning tasks. We demonstrate this potential using simple supervised fine-tuning, establishing a strong baseline that indicates significant headroom for advanced alignment. By releasing the full training history and data composition, we maximize the effectiveness of continuous training, a key open source production scenario. We release the model weights and signature LLM360 artifacts, such as complete training data, to empower the community with a capable, reasoning-centric foundation.
DUET: Agentic Design Understanding via Experimentation and Testing
Venue: arXiv cs.SE 2026
Year: 2026

AI agents powered by large language models (LLMs) are being used to solve increasingly complex software engineering challenges, but struggle with hardware design tasks. Register Transfer Level (RTL) code presents a unique challenge for LLMs, as it encodes complex, dynamic, time-evolving behaviors using the low-level language features of SystemVerilog. LLMs struggle to infer these complex behaviors from the syntax of RTL alone, which limits their ability to complete all downstream tasks like code completion, documentation, or verification. In response to this issue, we present DUET: a general methodology for developing Design Understanding via Experimentation and Testing. DUET mimics how hardware design experts develop an understanding of complex designs: not just via a one-off readthrough of the RTL, but via iterative experimentation using a number of tools. DUET iteratively generates hypotheses, tests them with EDA tools (e.g., simulation, waveform inspection, and formal verification), and integrates the results to build a bottom-up understanding of the design. In our evaluations, we show that DUET improves AI agent performance on formal verification, when compared to a baseline flow without experimentation.
JEEVHITAA -- An End-to-End HCAI System to Support Collective Care
Venue: arXiv cs.HC 2026
Year: 2026

Current mobile health platforms are predominantly individual-centric and lack the support for coordinated, auditable multi-actor workflows. However, in many settings worldwide, health decisions are enacted by multi-actor care networks rather than single users. We present JEEVHITAA, a cross-platform mobile system enabling role-aware sharing and verifiable information flows within permissioned care circles. JEEVHITAA ingests platform and device data (Health-Connect, BLE), builds layered profiles from sensors and tiered onboarding, and enforces fine-grained, time-bounded access control across care graphs. Data are end-to-end encrypted both locally and during peer synchronization; documents can be captured or uploaded as PDFs. An integrated retrieval-augmented LLM produces structured, role-targeted summaries and action plans, offers evidence-grounded verification with provenance and confidence scores, and supports advanced insights on reports. We describe the architecture, connector abstractions, security primitives, and report robustness evaluations using synthetic ontology-driven data, as well as a feasibility study with a real-life care circle. We outline plans for longitudinal in-the-wild evaluation of access control correctness and credibility support.