OpenRaiser/Intern-Atlas huggingface dataset:
Co-TAP: Three-Layer Agent Interaction Protocol Technical Report
Venue: arXiv cs.AI 2025
Year: 2025

This paper proposes Co-TAP (T: Triple, A: Agent, P: Protocol), a three-layer agent interaction protocol designed to address the challenges faced by multi-agent systems across the three core dimensions of Interoperability, Interaction and Collaboration, and Knowledge Sharing. We have designed and proposed a layered solution composed of three core protocols: the Human-Agent Interaction Protocol (HAI), the Unified Agent Protocol (UAP), and the Memory-Extraction-Knowledge Protocol (MEK). HAI focuses on the interaction layer, standardizing the flow of information between users, interfaces, and agents by defining a standardized, event-driven communication paradigm. This ensures the real-time performance, reliability, and synergy of interactions. As the core of the infrastructure layer, UAP is designed to break down communication barriers among heterogeneous agents through unified service discovery and protocol conversion mechanisms, thereby enabling seamless interconnection and interoperability of the underlying network. MEK, in turn, operates at the cognitive layer. By establishing a standardized ''Memory (M) - Extraction (E) - Knowledge (K)'' cognitive chain, it empowers agents with the ability to learn from individual experiences and form shareable knowledge, thereby laying the foundation for the realization of true collective intelligence. We believe this protocol framework will provide a solid engineering foundation and theoretical guidance for building the next generation of efficient, scalable, and intelligent multi-agent applications.
Evaluation of a Robust Control System in Real-World Cable-Driven Parallel Robots
Venue: arXiv cs.RO 2025
Year: 2025

This study evaluates the performance of classical and modern control methods for real-world Cable-Driven Parallel Robots (CDPRs), focusing on underconstrained systems with limited time discretization. A comparative analysis is conducted between classical PID controllers and modern reinforcement learning algorithms, including Deep Deterministic Policy Gradient (DDPG), Proximal Policy Optimization (PPO), and Trust Region Policy Optimization (TRPO). The results demonstrate that TRPO outperforms other methods, achieving the lowest root mean square (RMS) errors across various trajectories and exhibiting robustness to larger time intervals between control updates. TRPO's ability to balance exploration and exploitation enables stable control in noisy, real-world environments, reducing reliance on high-frequency sensor feedback and computational demands. These findings highlight TRPO's potential as a robust solution for complex robotic control tasks, with implications for dynamic environments and future applications in sensor fusion or hybrid control strategies.
Systematic Assessment of Cache Timing Vulnerabilities on RISC-V Processors
Venue: arXiv cs.CR 2025
Year: 2025

While interest in the open RISC-V instruction set architecture is growing, tools to assess the security of concrete processor implementations are lacking. There are dedicated tools and benchmarks for common microarchitectural side-channel vulnerabilities for popular processor families such as Intel x86-64 or ARM, but not for RISC-V. In this paper we describe our efforts in porting an Intel x86-64 benchmark suite for cache-based timing vulnerabilities to RISC-V. We then use this benchmark to evaluate the security of three commercially available RISC-V processors, the T-Head C910 and the SiFive U54 and U74 cores. We observe that the C910 processor exhibits more distinct timing types than the other processors, leading to the assumption that code running on the C910 would be exposed to more microarchitectural vulnerability sources. In addition, our evaluation reveals that $65.9\%$ of the vulnerabilities covered by the benchmark exist in all processors, while only $6.8\%$ are absent from all cores. Our work, in particular the ported benchmark, aims to support RISC-V processor designers to identify leakage sources early in their designs and to support the development of countermeasures.
Neuron-Level Analysis of Cultural Understanding in Large Language Models
Venue: arXiv cs.CL 2025
Year: 2025

As large language models (LLMs) are increasingly deployed worldwide, ensuring their fair and comprehensive cultural understanding is important. However, LLMs exhibit cultural bias and limited awareness of underrepresented cultures, while the mechanisms underlying their cultural understanding remain underexplored. To fill this gap, we conduct a neuron-level analysis to identify neurons that drive cultural behavior, introducing a gradient-based scoring method with additional filtering for precise refinement. We identify both culture-general neurons contributing to cultural understanding regardless of cultures, and culture-specific neurons tied to an individual culture. These neurons account for less than 1% of all neurons and are concentrated in shallow to middle MLP layers. We validate their role by showing that suppressing them substantially degrades performance on cultural benchmarks (by up to 30%), while performance on general natural language understanding (NLU) benchmarks remains largely unaffected. Moreover, we show that culture-specific neurons support knowledge of not only the target culture, but also related cultures. Finally, we demonstrate that training on NLU benchmarks can diminish models' cultural understanding when we update modules containing many culture-general neurons. These findings provide insights into the internal mechanisms of LLMs and offer practical guidance for model training and engineering. Our code is available at https://github.com/ynklab/CULNIG
Beyond Pass@k: Breadth-Depth Metrics for Reasoning Boundaries
Venue: arXiv cs.AI 2025
Year: 2025

Reinforcement Learning with Verifiable Rewards (RLVR) has emerged as a powerful paradigm to improve Large Language Models on reasoning tasks such as coding, math or logic. To assess the reasoning boundary (the fraction of problems a model can solve) researchers often report Pass@k at large sampling budgets. Recent results reveal a crossover phenomenon: while RLVR models outperform the base model at small k values, the base model usually outperforms them when sampling a very large number of completions. This has been interpreted as evidence that base models have a larger reasoning boundary. We argue that on tasks with discrete answer spaces, such as math with numeric outputs, Pass@k at large k reflects the increasingly higher chance of success in the limit of the number of trials rather than genuine reasoning, and can therefore be misleading. We propose Cover@tau, which measures the fraction of problems that a model can solve for which at least a tau proportion of completions are correct. Unlike Pass@k, Cover@tau captures reasoning under an explicit reliability threshold: models that rely on random guessing degrade rapidly as tau increases. We evaluate several RLVR models using Cover@tau-based metrics and illustrate how the relative rankings of popular algorithms change compared to Pass@1, offering a different perspective on reasoning boundaries.
Motion Exploration of Articulated Product Concepts in Interactive Sketching Environment
Venue: arXiv cs.HC 2025
Year: 2025

In the early stages of engineering design, it is essential to know how a product behaves, especially how it moves. As designers must keep adjusting the motion until it meets the intended requirements, this process is often repetitive and time-consuming. Although the physics behind these motions is usually based on simple equations, manually working through them can be tedious and inefficient. To ease this burden, some tasks are now handled by computers. One common method involves converting hand-drawn sketches into models using CAD or CAE software. However, this approach can be time- and resource-intensive. Additionally, product sketches are usually best understood only by the designers who created them. Others may struggle to interpret them correctly, relying heavily on intuition and prior experience. Since sketches are static, they fail to show how a product moves, limiting their usefulness. This paper presents a new approach that addresses these issues by digitising the natural act of sketching. It allows designers to create, simulate, and test the motion of mechanical concepts in a more interactive way. An application was developed to evaluate this method, focusing on user satisfaction and mental workload during a design task. The results showed a 77% reduction in cognitive effort compared to traditional methods, with users reporting high satisfaction. Future work will focus on expanding this approach from 2D (planar) to full 3D (spatial) design environments, enabling more complex product concept development.
LLMs Reproduce Human Purchase Intent via Semantic Similarity Elicitation of Likert Ratings
Venue: arXiv cs.AI 2025
Year: 2025

Consumer research costs companies billions annually yet suffers from panel biases and limited scale. Large language models (LLMs) offer an alternative by simulating synthetic consumers, but produce unrealistic response distributions when asked directly for numerical ratings. We present semantic similarity rating (SSR), a method that elicits textual responses from LLMs and maps these to Likert distributions using embedding similarity to reference statements. Testing on an extensive dataset comprising 57 personal care product surveys conducted by a leading corporation in that market (9,300 human responses), SSR achieves 90% of human test-retest reliability while maintaining realistic response distributions (KS similarity > 0.85). Additionally, these synthetic respondents provide rich qualitative feedback explaining their ratings. This framework enables scalable consumer research simulations while preserving traditional survey metrics and interpretability.
DeepEN: A Deep Reinforcement Learning Framework for Personalized Enteral Nutrition in Critical Care
Venue: arXiv cs.LG 2025
Year: 2025

ICU enteral feeding remains sub-optimal due to limited personalization and uncertainty about appropriate calorie, protein, and fluid targets, particularly under rapidly changing metabolic demands and heterogeneous patient responses. This study introduces DeepEN, a reinforcement learning (RL)-based framework that personalizes enteral nutrition (EN) dosing for critically ill patients using electronic health record data. DeepEN was trained on over 11,000 ICU patients from the MIMIC-IV database to generate 4-hourly, patient-specific targets for caloric, protein, and fluid intake. The model's state space integrates demographics, comorbidities, vital signs, laboratory results, and prior interventions relevant to nutritional management, while its reward function balances short-term physiological and nutrition-related goals with long-term survival. A dueling double deep Q-network with Conservative Q-Learning regularization is used to ensure safe and reliable policy learning from retrospective data. DeepEN achieved a 3.7 $\pm$ 0.17 percentage-point absolute reduction in estimated mortality compared with the clinician policy (18.8% vs 22.5%) and higher expected returns compared with guideline-based dosing (11.89 vs 8.11), with improvements in key nutritional biomarkers. U-shaped associations between deviations from clinician dosing and mortality suggest that the learned policy aligns with high-value clinician actions while diverging from suboptimal ones. These findings demonstrate the feasibility of conservative offline RL for individualized EN therapy and suggest that data-driven personalization may improve outcomes beyond guideline- or heuristic-based approaches.
Guided Star-Shaped Masked Diffusion
Venue: arXiv cs.LG 2025
Year: 2025

The performance of pre-trained masked diffusion models is often constrained by their sampling procedure, which makes decisions irreversible and struggles in low-step generation regimes. We introduce a novel sampling algorithm that works with pre-trained models and, after a lightweight fine-tuning of a single layer, significantly improves sample quality and efficiency. Our method reformulates the generation process using a star-shaped paradigm, which inherently allows for error correction. To make this process effective, we augment it with a learnable re-masking scheduler that intelligently identifies and revises likely errors. This approach yields a substantial quality boost, particularly when using a small number of sampling steps. We extensively ablate key components of our approach and show its usability in different scenarios. In comprehensive experiments on text, and code generation, our sampling algorithm outperforms or matches existing methods.
Contrastive Self-Supervised Learning at the Edge: An Energy Perspective
Venue: arXiv cs.LG 2025
Year: 2025

While contrastive learning (CL) shows considerable promise in self-supervised representation learning, its deployment on resource-constrained devices remains largely underexplored. The substantial computational demands required for training conventional CL frameworks pose a set of challenges, particularly in terms of energy consumption, data availability, and memory usage. We conduct an evaluation of four widely used CL frameworks: SimCLR, MoCo, SimSiam, and Barlow Twins. We focus on the practical feasibility of these CL frameworks for edge and fog deployment, and introduce a systematic benchmarking strategy that includes energy profiling and reduced training data conditions. Our findings reveal that SimCLR, contrary to its perceived computational cost, demonstrates the lowest energy consumption across various data regimes. Finally, we also extend our analysis by evaluating lightweight neural architectures when paired with CL frameworks. Our study aims to provide insights into the resource implications of deploying CL in edge/fog environments with limited processing capabilities and opens several research directions for its future optimization.
Human-Centered Development of Indicators for Self-Service Learning Analytics: A Transparency through Exploration Approach
Venue: arXiv cs.CY 2025
Year: 2025

The aim of learning analytics is to turn educational data into insights, decisions, and actions to improve learning and teaching. The reasoning of the provided insights, decisions, and actions is often not transparent to the end-user, and this can lead to trust and acceptance issues when interventions, feedback, and recommendations fail. In this paper, we shed light on achieving transparent learning analytics by following a transparency through exploration approach. To this end, we present the design, implementation, and evaluation details of the Indicator Editor, which aims to support self-service learning analytics (SSLA) by empowering end-users to take control of the indicator implementation process. We systematically designed and implemented the Indicator Editor through an iterative human-centered design (HCD) approach. Further, we conducted a qualitative user study (n=15) to investigate the impact of following an SSLA approach on the users' perception of and interaction with the Indicator Editor. Our study showed qualitative evidence that supporting user interaction and providing user control in the indicator implementation process can have positive effects on different crucial aspects of learning analytics, namely transparency, trust, satisfaction, and acceptance.
ClauseLens: Clause-Grounded, CVaR-Constrained Reinforcement Learning for Trustworthy Reinsurance Pricing
Venue: arXiv cs.LG 2025
Year: 2025

Reinsurance treaty pricing must satisfy stringent regulatory standards, yet current quoting practices remain opaque and difficult to audit. We introduce ClauseLens, a clause-grounded reinforcement learning framework that produces transparent, regulation-compliant, and risk-aware treaty quotes. ClauseLens models the quoting task as a Risk-Aware Constrained Markov Decision Process (RA-CMDP). Statutory and policy clauses are retrieved from legal and underwriting corpora, embedded into the agent's observations, and used both to constrain feasible actions and to generate clause-grounded natural language justifications. Evaluated in a multi-agent treaty simulator calibrated to industry data, ClauseLens reduces solvency violations by 51%, improves tail-risk performance by 27.9% (CVaR_0.10), and achieves 88.2% accuracy in clause-grounded explanations with retrieval precision of 87.4% and recall of 91.1%. These findings demonstrate that embedding legal context into both decision and explanation pathways yields interpretable, auditable, and regulation-aligned quoting behavior consistent with Solvency II, NAIC RBC, and the EU AI Act.
ARES: Multimodal Adaptive Reasoning via Difficulty-Aware Token-Level Entropy Shaping
Venue: arXiv cs.CL 2025
Year: 2025

Recent advances in multimodal large reasoning models (MLRMs) have substantially improved their ability to solve complex textual and visual tasks. However, these models tend to overthink on simple problems, producing unnecessarily lengthy reasoning traces, while under-exploring on challenging ones, leading to missed solutions. To address this imbalance, we propose ARES, a unified open-source framework for adaptive reasoning that dynamically allocates exploration effort based on task difficulty. Our approach is motivated by two key empirical findings: (i) while single-token entropy is noisy, high window-entropy (HWE) tokens (token-level entropies averaged under a sliding window) can reliably capture reasoning-critical moments; and (ii) reducing HWE usage benefits easy problems, while increasing it is essential for solving hard ones. Building on these insights, ARES introduces a two-stage training pipeline. In the Adaptive Cold-Start stage, we curate multimodal and textual data paired with reasoning traces of length proportional to problem difficulty, equipping the model with initial difficulty awareness. In the second stage, we develop Adaptive Entropy Policy Optimization (AEPO), which uses HWE tokens as exploration triggers to decide when to explore, and a hierarchical entropy reward with dynamic KL control to decide how much to explore. Extensive experiments demonstrate that ARES achieves superior performance and reasoning efficiency across diverse mathematical, logical, and multimodal benchmarks, while closing the gap to leading commercial systems under significantly lower inference costs.
The Visual Iconicity Challenge: Evaluating Vision-Language Models on Sign Language Form-Meaning Mapping
Venue: arXiv cs.CV 2025
Year: 2025

Iconicity, the resemblance between linguistic form and meaning, is pervasive in signed languages, offering a natural testbed for visual grounding. For vision-language models (VLMs), the challenge is to recover such essential mappings from dynamic human motion rather than static context. We introduce the Visual Iconicity Challenge, a novel video-based benchmark that adapts psycholinguistic measures to evaluate VLMs on three tasks: (i) phonological sign-form prediction (e.g., handshape, location), (ii) transparency (inferring meaning from visual form), and (iii) graded iconicity ratings. We assess 13 state-of-the-art VLMs in zero- and few-shot settings on Sign Language of the Netherlands and compare them to human baselines. On phonological form prediction, VLMs recover some handshape and location detail but remain below human performance; on transparency, they are far from human baselines; and only top models correlate moderately with human iconicity ratings. Interestingly, models with stronger phonological form prediction correlate better with human iconicity judgment, indicating shared sensitivity to visually grounded structure. Our findings validate these diagnostic tasks and motivate human-centric signals and embodied learning methods for modelling iconicity and improving visual grounding in multimodal models.
DeepPrune: Parallel Scaling without Inter-trace Redundancy
Venue: arXiv cs.CL 2025
Year: 2025

Parallel scaling has emerged as a powerful paradigm to enhance reasoning capabilities in large language models (LLMs) by generating multiple Chain-of-Thought (CoT) traces simultaneously. However, this approach introduces significant computational inefficiency due to inter-trace redundancy -- our analysis reveals that over 80% of parallel reasoning traces yield identical final answers, representing substantial wasted computation. To address this critical efficiency bottleneck, we propose DeepPrune, a novel framework that enables efficient parallel scaling through dynamic pruning. Our method features a specialized judge model trained with focal loss and oversampling techniques to accurately predict answer equivalence from partial reasoning traces which realizes 0.87 AUROC on equivalence prediction, combined with an online greedy clustering algorithm that dynamically prunes redundant paths while preserving answer diversity. Comprehensive evaluations across three challenging benchmarks (AIME 2024, AIME 2025, and GPQA) and multiple reasoning models demonstrate that DeepPrune achieves remarkable token reduction by over 80% compared to conventional consensus sampling on most cases, while maintaining competitive accuracy within 3 percentage points. Our work establishes a new standard for efficient parallel reasoning, making high-performance reasoning more efficient. Our code and data are here: https://deepprune.github.io/
Implementing Semantic Join Operators Efficiently
Venue: arXiv cs.DB 2025
Year: 2025

Semantic query processing engines often support semantic joins, enabling users to match rows that satisfy conditions specified in natural language. Such join conditions can be evaluated using large language models (LLMs) that solve novel tasks without task-specific training. Currently, many semantic query processing engines implement semantic joins via nested loops, invoking the LLM to evaluate the join condition on row pairs. Instead, this paper proposes a novel algorithm, inspired by the block nested loops join operator implementation in traditional database systems. The proposed algorithm integrates batches of rows from both input tables into a single prompt. The goal of the LLM invocation is to identify all matching row pairs in the current input. The paper introduces formulas that can be used to optimize the size of the row batches, taking into account constraints on the size of the LLM context window (limiting both input and output size). An adaptive variant of the proposed algorithm refers to cases in which the size of the output is difficult to estimate. A formal analysis of asymptotic processing costs, as well as empirical results, demonstrates that the proposed approach reduces costs significantly and performs well compared to join implementations used by recent semantic query processing engines.