OpenRaiser/Intern-Atlas huggingface dataset:
A Review on Edge Large Language Models: Design, Execution, and Applications
Venue: arXiv cs.DC 2025
Year: 2025

Large language models (LLMs) have revolutionized natural language processing with their exceptional understanding, synthesizing, and reasoning capabilities. However, deploying LLMs on resource-constrained edge devices presents significant challenges due to computational limitations, memory constraints, and edge hardware heterogeneity. This survey provides a comprehensive overview of recent advancements in edge LLMs, covering the entire lifecycle: from resource-efficient model design and pre-deployment strategies to runtime inference optimizations. It also explores on-device applications across various domains. By synthesizing state-of-the-art techniques and identifying future research directions, this survey bridges the gap between the immense potential of LLMs and the constraints of edge computing.
MLPerf Power: Benchmarking the Energy Efficiency of Machine Learning Systems from Microwatts to Megawatts for Sustainable AI
Venue: arXiv cs.AR 2025
Year: 2025

Rapid adoption of machine learning (ML) technologies has led to a surge in power consumption across diverse systems, from tiny IoT devices to massive datacenter clusters. Benchmarking the energy efficiency of these systems is crucial for optimization, but presents novel challenges due to the variety of hardware platforms, workload characteristics, and system-level interactions. This paper introduces MLPerf Power, a comprehensive benchmarking methodology with capabilities to evaluate the energy efficiency of ML systems at power levels ranging from microwatts to megawatts. Developed by a consortium of industry professionals from more than 20 organizations, MLPerf Power establishes rules and best practices to ensure comparability across diverse architectures. We use representative workloads from the MLPerf benchmark suite to collect 1,841 reproducible measurements from 60 systems across the entire range of ML deployment scales. Our analysis reveals trade-offs between performance, complexity, and energy efficiency across this wide range of systems, providing actionable insights for designing optimized ML solutions from the smallest edge devices to the largest cloud infrastructures. This work emphasizes the importance of energy efficiency as a key metric in the evaluation and comparison of the ML system, laying the foundation for future research in this critical area. We discuss the implications for developing sustainable AI solutions and standardizing energy efficiency benchmarking for ML systems.
Embedded Model Form Uncertainty Quantification with Measurement Noise for Bayesian Model Calibration
Venue: arXiv cs.CE 2025
Year: 2025

A key factor in ensuring the accuracy of computer simulations that model physical systems is the proper calibration of their parameters based on real-world observations or experimental data. Inevitably, uncertainties arise, and Bayesian methods provide a robust framework for quantifying and propagating these uncertainties to model predictions. Nevertheless, Bayesian methods paired with inexact models usually produce predictions unable to represent the observed datapoints. Additionally, the quantified uncertainties of these overconfident models cannot be propagated to other Quantities of Interest (QoIs) reliably. A promising solution involves embedding a model inadequacy term in the inference parameters, allowing the quantified model form uncertainty to influence non-observed QoIs. This paper introduces a more interpretable framework for embedding the model inadequacy compared to existing methods. To overcome the limitations of current approaches, we adapt the existing likelihood models to properly account for noise in the measurements and propose two new formulations designed to address their shortcomings. Moreover, we evaluate the performance of this inadequacy-embedding approach in the presence of discrepancies between measurements and model predictions, including noise and outliers. Particular attention is given to how the uncertainty associated with the model inadequacy term propagates to the QoIs, enabling a more comprehensive statistical analysis of prediction's reliability. Finally, the proposed approach is applied to estimate the uncertainty in the predicted heat flux from a transient thermal simulation using temperature observations.
Comparative Performance of Collaborative Bandit Algorithms: Effect of Sparsity and Exploration Intensity
Venue: arXiv cs.LG 2025
Year: 2025

This paper offers a comprehensive analysis of collaborative bandit algorithms and provides a thorough comparison of their performance. Collaborative bandits aim to improve the performance of contextual bandits by introducing relationships between arms (or items), allowing effective propagation of information. Collaboration among arms allows the feedback obtained through a single user (item) to be shared across related users (items). Introducing collaboration also alleviates the cold user (item) problem, i.e., lack of historical information when a new user (item) arriving to the platform with no prior record of interactions. In the context of modeling the relationships between arms (items), there are two main approaches: Hard and soft clustering. We call approaches that model the relationship between arms in an \textit{absolute} manner as hard clustering, i.e., the relationship is binary. Soft clustering relaxes membership constraints, allowing \textit{fuzzy} assignment. Focusing on the latter, we provide extensive experiments on the state-of-the-art collaborative contextual bandit algorithms and investigate the effect of sparsity and how the exploration intensity acts as a correction mechanism. Our numerical experiments demonstrate that controlling for sparsity in collaboration improves data efficiency and performance as it better informs learning. Meanwhile, increasing the exploration intensity acts as a correction because it effectively reduces variance due to potentially misspecified relationships among users. We observe that this misspecification is further remedied by introducing latent factors, and thus, increasing the dimensionality of the bandit parameters.
DocETL: Agentic Query Rewriting and Evaluation for Complex Document Processing
Venue: arXiv cs.DB 2025
Year: 2025

Analyzing unstructured data has been a persistent challenge in data processing. Large Language Models (LLMs) have shown promise in this regard, leading to recent proposals for declarative frameworks for LLM-powered processing of unstructured data. However, these frameworks focus on reducing cost when executing user-specified operations using LLMs, rather than improving accuracy, executing most operations as-is (in a single LLM call). This is problematic for complex tasks and data, where LLM outputs for user-defined operations are often inaccurate, even with optimized prompts. For example, an LLM may struggle to identify {\em all} instances of specific clauses, like force majeure or indemnification, in lengthy legal documents, requiring decomposition of the data, the task, or both. We present DocETL, a system that optimizes complex document processing pipelines, while accounting for LLM shortcomings. DocETL offers a declarative interface for users to define such pipelines and uses an agent-based approach to automatically optimize them, leveraging novel agent-based rewrites (that we call rewrite directives), as well as an optimization and evaluation framework. We introduce (i) logical rewriting of pipelines, tailored for LLM-based tasks, (ii) an agent-guided plan evaluation mechanism that synthesizes and orchestrates task-specific validation prompts, and (iii) an optimization algorithm that efficiently finds promising plans, considering the latencies of agent-based plan generation and evaluation. Our evaluation on four different unstructured document analysis tasks demonstrates that DocETL finds plans with outputs that are 25 to 80% more accurate than well-engineered baselines, addressing a critical gap in unstructured data analysis. DocETL is open-source at docetl.org, and as of March 2025, has amassed over 1.7k GitHub Stars, with users spanning a variety of domains.
Causally-Aware Unsupervised Feature Selection Learning
Venue: arXiv cs.LG 2025
Year: 2025

Unsupervised feature selection (UFS) has recently gained attention for its effectiveness in processing unlabeled high-dimensional data. However, existing methods overlook the intrinsic causal mechanisms within the data, resulting in the selection of irrelevant features and poor interpretability. Additionally, previous graph-based methods fail to account for the differing impacts of non-causal and causal features in constructing the similarity graph, which leads to false links in the generated graph. To address these issues, a novel UFS method, called Causally-Aware UnSupErvised Feature Selection learning (CAUSE-FS), is proposed. CAUSE-FS introduces a novel causal regularizer that reweights samples to balance the confounding distribution of each treatment feature. This regularizer is subsequently integrated into a generalized unsupervised spectral regression model to mitigate spurious associations between features and clustering labels, thus achieving causal feature selection. Furthermore, CAUSE-FS employs causality-guided hierarchical clustering to partition features with varying causal contributions into multiple granularities. By integrating similarity graphs learned adaptively at different granularities, CAUSE-FS increases the importance of causal features when constructing the fused similarity graph to capture the reliable local structure of data. Extensive experimental results demonstrate the superiority of CAUSE-FS over state-of-the-art methods, with its interpretability further validated through feature visualization.
UTF:Undertrained Tokens as Fingerprints A Novel Approach to LLM Identification
Venue: arXiv cs.CR 2025
Year: 2025

Fingerprinting large language models (LLMs) is essential for verifying model ownership, ensuring authenticity, and preventing misuse. Traditional fingerprinting methods often require significant computational overhead or white-box verification access. In this paper, we introduce UTF, a novel and efficient approach to fingerprinting LLMs by leveraging under-trained tokens. Under-trained tokens are tokens that the model has not fully learned during its training phase. By utilizing these tokens, we perform supervised fine-tuning to embed specific input-output pairs into the model. This process allows the LLM to produce predetermined outputs when presented with certain inputs, effectively embedding a unique fingerprint. Our method has minimal overhead and impact on model's performance, and does not require white-box access to target model's ownership identification. Compared to existing fingerprinting methods, UTF is also more effective and robust to fine-tuning and random guess.
Learning by Surprise: Surplexity for Mitigating Model Collapse in Generative AI
Venue: arXiv cs.CL 2025
Year: 2025

As synthetic content increasingly infiltrates the web, generative AI models may be retrained on their own outputs: a process termed "autophagy". This leads to model collapse: a progressive loss of performance and diversity across generations. Recent studies have examined the emergence of model collapse across various generative AI models and data types, and have proposed mitigation strategies that rely on incorporating human-authored content. However, current characterizations of model collapse remain limited, and existing mitigation methods assume reliable knowledge of whether training data is human-authored or AI-generated. In this paper, we address these gaps by introducing new measures that characterise collapse directly from a model's next-token probability distributions, rather than from properties of AI-generated text. Using these measures, we show that the degree of collapse depends on the complexity of the initial training set, as well as on the extent of autophagy. Our experiments prompt a new suggestion: that model collapse occurs when a model trains on data that does not "surprise" it. We express this hypothesis in terms of the well-known Free Energy Principle in cognitive science. Building on this insight, we propose a practical mitigation strategy: filtering training items by high surplexity, maximising the surprise of the model. Unlike existing methods, this approach does not require distinguishing between human- and AI-generated data. Experiments across datasets and models demonstrate that our strategy is at least as effective as human-data baselines, and even more effective in reducing distributional skewedness. Our results provide a richer understanding of model collapse and point toward more resilient approaches for training generative AI systems in environments increasingly saturated with synthetic data.
HumanEval-V: Benchmarking High-Level Visual Reasoning with Complex Diagrams in Coding Tasks
Venue: arXiv cs.CV 2025
Year: 2025

Understanding and reasoning over diagrams is a fundamental aspect of human intelligence. While Large Multimodal Models (LMMs) have demonstrated impressive capabilities across various tasks, existing benchmarks lack comprehensive evaluation of their diagram interpretation and reasoning abilities, particularly in coding contexts. We present HumanEval-V, a rigorous benchmark of human-annotated coding tasks that spans six task types and evaluates diverse visual reasoning capabilities. Each task features carefully crafted diagrams paired with function signatures and test cases, employing novel code generation tasks to thoroughly assess models' diagram comprehension. Through extensive experiments with 22 LMMs, we find that even top-performing models achieve modest success rates, with Claude 3.5 Sonnet reaching only 36.8% pass@1, highlighting substantial room for improvement. Our analysis reveals that current LMMs struggle with spatial transformations, topological relationships, and dynamic patterns that humans find intuitive. These findings provide valuable insights for advancing LMMs' visual reasoning abilities. We have open-sourced our code and benchmark at https://github.com/HumanEval-V/HumanEval-V-Benchmark.
Training Neural Samplers with Reverse Diffusive KL Divergence
Venue: arXiv cs.LG 2025
Year: 2025

Training generative models to sample from unnormalized density functions is an important and challenging task in machine learning. Traditional training methods often rely on the reverse Kullback-Leibler (KL) divergence due to its tractability. However, the mode-seeking behavior of reverse KL hinders effective approximation of multi-modal target distributions. To address this, we propose to minimize the reverse KL along diffusion trajectories of both model and target densities. We refer to this objective as the reverse diffusive KL divergence, which allows the model to capture multiple modes. Leveraging this objective, we train neural samplers that can efficiently generate samples from the target distribution in one step. We demonstrate that our method enhances sampling performance across various Boltzmann distributions, including both synthetic multi-modal densities and n-body particle systems.
Stable Object Placement Planning From Contact Point Robustness
Venue: arXiv cs.RO 2025
Year: 2025

We introduce a planner designed to guide robot manipulators in stably placing objects within intricate scenes. Our proposed method reverses the traditional approach to object placement: our planner selects contact points first and then determines a placement pose that solicits the selected points. This is instead of sampling poses, identifying contact points, and evaluating pose quality. Our algorithm facilitates stability-aware object placement planning, imposing no restrictions on object shape, convexity, or mass density homogeneity, while avoiding combinatorial computational complexity. Our proposed stability heuristic enables our planner to find a solution about 20 times faster when compared to the same algorithm not making use of the heuristic and eight times faster than a state-of-the-art method that uses the traditional sample-and-evaluate approach. Our proposed planner is also more successful in finding stable placements than the five other benchmarked algorithms. Derived from first principles and validated in ten real robot experiments, our planner offers a general and scalable method to tackle the problem of object placement planning with rigid objects.
Kamp Theorem for Pomset Languages of Higher Dimensional Automata
Venue: arXiv cs.FL 2025
Year: 2025

Temporal logics are a powerful tool to specify properties of computational systems. For concurrent programs, Higher Dimensional Automata (HDA) are a very expressive model of non-interleaving concurrency. HDA recognize languages of partially ordered multisets, or pomsets. Recent work has shown that Monadic Second Order (MSO) logic is as expressive as HDA for pomset languages. In the case of words, Kamp's theorem states that First Order (FO) logic is as expressive as Linear Temporal Logic (LTL). In this paper, we extend this result to pomsets. To do so, we first investigate the class of pomset languages that are definable in FO. As expected, this is a strict subclass of MSO-definable languages. Then, we define a Linear Temporal Logic for pomsets, and show that it is equivalent to FO.
REST API Testing in DevOps: A Study on an Evolving Healthcare IoT Application
Venue: arXiv cs.SE 2025
Year: 2025

Healthcare Internet of Things (IoT) applications often integrate various third-party healthcare applications and medical devices through REST APIs, resulting in complex and interdependent networks of REST APIs. Oslo City's healthcare department collaborates with various industry partners to develop such healthcare IoT applications enriched with a diverse set of REST APIs. Following the DevOps process, these REST APIs continuously evolve to accommodate evolving needs such as new features, services, and devices. Oslo City's primary goal is to utilize automated solutions for continuous testing of these REST APIs at each evolution stage, thereby ensuring their dependability. Although the literature offers various automated REST API testing tools, their effectiveness in regression testing of the evolving REST APIs of healthcare IoT applications within a DevOps context remains undetermined. This paper evaluates state-of-the-art and well-established REST API testing tools, specifically, RESTest, EvoMaster, Schemathesis, RESTler, and RestTestGen, for the regression testing of a real-world healthcare IoT application, considering failures, faults, coverage, regressions, and cost. We conducted experiments using all accessible REST APIs (17 APIs with 120 endpoints), and 14 releases evolved during DevOps. Overall, all tools generated tests leading to several failures, 18 potential faults, up to 84% coverage, and 23 regressions. Over 70% of tests generated by all tools fail to detect failures, resulting in significant overhead.
MambaBEV: An efficient 3D detection model with Mamba2
Venue: arXiv cs.CV 2025
Year: 2025

Accurate 3D object detection in autonomous driving relies on Bird's Eye View (BEV) perception and effective temporal fusion.However, existing fusion strategies based on convolutional layers or deformable self attention struggle with global context modeling in BEV space,leading to lower accuracy for large objects. To address this, we introduce MambaBEV, a novel BEV based 3D object detection model that leverages Mamba2, an advanced state space model (SSM) optimized for long sequence processing.Our key contribution is TemporalMamba, a temporal fusion module that enhances global awareness by introducing a BEV feature discrete rearrangement mechanism tailored for Mamba's sequential processing. Additionally, we propose Mamba based DETR as the detection head to improve multi object representation.Evaluations on the nuScenes dataset demonstrate that MambaBEV base achieves an NDS of 51.7\% and an mAP of 42.7\%.Furthermore, an end to end autonomous driving paradigm validates its effectiveness in motion forecasting and planning.Our results highlight the potential of SSMs in autonomous driving perception, particularly in enhancing global context understanding and large object detection.
Toward Optimal-Complexity Hash-Based Asynchronous MVBA with Optimal Resilience
Venue: arXiv cs.DC 2025
Year: 2025

Multi-valued validated Byzantine agreement (MVBA), a fundamental primitive of distributed computing, allows $n$ processes to agree on a valid $\ell$-bit value, despite $t$ faulty processes behaving maliciously. Among hash-based solutions for the asynchronous setting with adaptive faults, the state-of-the-art HMVBA protocol achieves optimal $O(n^2)$ message complexity, (near-)optimal $O(n\ell+n^2 λ\log n)$ bit complexity, and optimal $O(1)$ time complexity. However, it only tolerates up to $t < \frac15 n$ adaptive failures. In contrast, the best known optimally resilient protocol, FIN-MVBA, exchanges $O(n^3)$ messages and $O(n^2\ell + n^3λ)$ bits. This highlights a fundamental question: can a hash-based protocol be designed for the asynchronous setting with adaptive faults that simultaneously achieves both optimal complexity and optimal resilience? In this paper, we take a significant step toward answering the question. Namely, we introduce Reducer, an MVBA protocol that retains HMVBA's complexity while improving its resilience to $t<\frac14 n$. Like HMVBA and FIN-MVBA, Reducer relies exclusively on collision-resistant hash functions. A key innovation in Reducer's design is its internal use of strong multi-valued Byzantine agreement (SMBA), a variant of strong consensus we introduce and construct, which ensures agreement on a correct process's proposal. To further advance resilience toward the optimal one-third bound, we then propose Reducer++, an MVBA protocol that tolerates up to $t < (\frac13-ε)n$ adaptive failures, for any fixed constant $ε> 0$. Unlike Reducer, Reducer++ does not rely on SMBA. Instead, it employs a novel approach involving hash functions modeled as random oracles to ensure termination. Reducer++ maintains constant time complexity, quadratic message complexity, and quasi-quadratic bit complexity, with constants dependent on $ε$.
Diversity of Thought Elicits Stronger Reasoning Capabilities in Multi-Agent Debate Frameworks
Venue: arXiv cs.CL 2025
Year: 2025

Large language models (LLMs) excel in natural language generation but often confidently produce incorrect responses, especially in tasks like mathematical reasoning. Chain-of-thought prompting, self-verification, and multi-agent debate are among the strategies proposed to improve the reasoning and factual accuracy of LLMs. Building on Du et al.'s multi-agent debate framework, we find that multi-agent debate helps at any model scale, and that diversity of thought elicits stronger reasoning in debating LLMs. Across various model sizes, performance on mathematical reasoning tasks benefits most when diverse trained models are used. Remarkably, after 4 rounds of debate, a diverse set of medium-capacity models (Gemini-Pro, Mixtral 7BX8, and PaLM 2-M) outperforms GPT-4 on the GSM-8K benchmark, scoring 91% accuracy. By comparison, when 3 instances of Gemini-Pro are used, performance only reaches 82%. Finally, this diverse set of medium-capacity models sets a new state-of-the-art performance on the ASDiv benchmark (94%). These results underscore the idea that the future of AI is agentic, with diverse cooperating agents yielding emergent capabilities beyond even the most powerful individual models.