OpenRaiser/Intern-Atlas huggingface dataset:
Deciding not to Decide: Sound and Complete Effect Inference in the Presence of Higher-Rank Polymorphism
Venue: arXiv cs.PL 2025
Year: 2025

Type-and-effect systems help the programmer to organize data and computational effects in a program. While for traditional type systems expressive variants with sophisticated inference algorithms have been developed and widely used in programming languages, type-and-effect systems did not yet gain widespread adoption. One reason for this is that type-and-effect systems are more complex and the existing inference algorithms make compromises between expressiveness, intuitiveness, and decidability. In this work, we present an effect inference algorithm for a type-and-effect system with subtyping, expressive higher-rank polymorphism, and intuitive set-like semantics of effects. In order to deal with scoping issues of higher-rank polymorphism, we delay solving of effect constraints by transforming them into formulae of propositional logic. We prove soundness and completeness of our algorithm with respect to a declarative type-and-effect system. All the presented results have been formalized in the Rocq proof assistant, and the algorithm has been successfully implemented in a realistic programming language.
ARC-Encoder: learning compressed text representations for large language models
Venue: arXiv cs.CL 2025
Year: 2025

Recent techniques such as retrieval-augmented generation or chain-of-thought reasoning have led to longer contexts and increased inference costs. Context compression techniques can reduce these costs, but the most effective approaches require fine-tuning the target model or even modifying its architecture. This can degrade its general abilities when not used for this specific purpose. Here we explore an alternative approach: an encoder that compresses the context into continuous representations which replace token embeddings in decoder LLMs. First, we perform a systematic study of training strategies and architecture choices for the encoder. Our findings led to the design of an Adaptable text Representations Compressor, named ARC-Encoder, which outputs $x$-times fewer continuous representations (typically $x\!\in\!\{4,8\}$) than text tokens. We evaluate ARC-Encoder across a variety of LLM usage scenarios, ranging from in-context learning to context window extension, on both instruct and base decoders. Results show that ARC-Encoder achieves state-of-the-art performance on several benchmarks while improving computational efficiency at inference. Finally, we demonstrate that our models can be adapted to multiple decoders simultaneously, allowing a single encoder to generalize across different decoder LLMs. This makes ARC-Encoder a flexible and efficient solution for portable encoders that work seamlessly with multiple LLMs. We release a training code at https://github.com/kyutai-labs/ARC-Encoder , fine-tuning dataset and pretrained models are available at https://huggingface.co/collections/kyutai/arc-encoders-68ee18787301407d60a57047 .
Blur2seq: Blind Deblurring and Camera Trajectory Estimation from a Single Camera Motion-blurred Image
Venue: arXiv cs.CV 2025
Year: 2025

Motion blur caused by camera shake, particularly under large or rotational movements, remains a major challenge in image restoration. We propose a deep learning framework that jointly estimates the latent sharp image and the underlying camera motion trajectory from a single blurry image. Our method leverages the Projective Motion Blur Model (PMBM), implemented efficiently using a differentiable blur creation module compatible with modern networks. A neural network predicts a full 3D rotation trajectory, which guides a model-based restoration network trained end-to-end. This modular architecture provides interpretability by revealing the camera motion that produced the blur. Moreover, this trajectory enables the reconstruction of the sequence of sharp images that generated the observed blurry image. To further refine results, we optimize the trajectory post-inference via a reblur loss, improving consistency between the blurry input and the restored output. Extensive experiments show that our method achieves state-of-the-art performance on both synthetic and real datasets, particularly in cases with severe or spatially variant blur, where end-to-end deblurring networks struggle. Code and trained models are available at https://github.com/GuillermoCarbajal/Blur2Seq/
Compiling the Mimosa programming language to RTOS tasks
Venue: arXiv cs.PL 2025
Year: 2025

This paper introduces a compilation scheme for programs written in the Mimosa programming language, which builds upon the MIMOS model of computation. Mimosa describes embedded systems software as a collection of time-triggered processes which communicate through FIFO queues. We formally describe an adaptation of the Lustre compilation scheme to the semantics of Mimosa and show how the coordination layer can be mapped to real-time operating system primitives.
Provably Small Portfolios for Multiobjective Optimization with Application to Subsidized Facility Location
Venue: arXiv cs.DS 2025
Year: 2025

Many multiobjective real-world problems, such as facility location and bus routing, become more complex when optimizing the priorities of multiple stakeholders. These are often modeled using infinite classes of objectives, e.g., $L_p$ norms over group distances induced by feasible solutions in a fixed domain. Traditionally, the literature has considered explicitly balancing `equity' (or min-max) and `efficiency' (or min-sum) objectives to capture this trade-off. However, the structure of solutions obtained by such modeling choices can be very different. Taking a solution-centric approach, we introduce the concept of provably small set of solutions $P$, called a {\it portfolio}, such that for every objective function $h(\cdot)$ in the given class $\mathbf{C}$, there exists some solution in $P$ which is an $α$-approximation for $h(\cdot)$. Constructing such portfolios can help decision-makers understand the impact of balancing across multiple objectives. Given a finite set of base objectives $h_1, \ldots, h_N$, we give provable algorithms for constructing portfolios for (1) the class of conic combinations $\mathbf{C} = \{\sum_{j \in [N]}λ_j h_j: λ\ge 0\}$ and for (2) any class $\mathbf{C}$ of functions that interpolates monotonically between the min-sum efficiency objective (i.e., $h_1 + \ldots + h_N$) and the min-max equity objective (i.e., $\max_{j \in [N]} h_j$). Examples of the latter are $L_p$ norms and top-$\ell$ norms. As an application, we study the Fair Subsidized Facility Location (FSFL) problem, motivated by the crisis of medical deserts caused due to pharmacy closures. FSFL allows subsidizing facilities in underserved areas using revenue from profitable locations. We develop a novel bicriteria approximation algorithm and show a significant reduction of medical deserts across states in the U.S.
On Optimal Hyperparameters for Differentially Private Deep Transfer Learning
Venue: arXiv cs.LG 2025
Year: 2025

Differentially private (DP) transfer learning, i.e., fine-tuning a pretrained model on private data, is the current state-of-the-art approach for training large models under privacy constraints. We focus on two key hyperparameters in this setting: the clipping bound $C$ and batch size $B$. We show a clear mismatch between the current theoretical understanding of how to choose an optimal $C$ (stronger privacy requires smaller $C$) and empirical outcomes (larger $C$ performs better under strong privacy), caused by changes in the gradient distributions. Assuming a limited compute budget (fixed epochs), we demonstrate that the existing heuristics for tuning $B$ do not work, while cumulative DP noise better explains whether smaller or larger batches perform better. We also highlight how the common practice of using a single $(C,B)$ setting across tasks can lead to suboptimal performance. We find that performance drops especially when moving between loose and tight privacy and between plentiful and limited compute, which we explain by analyzing clipping as a form of gradient re-weighting and examining cumulative DP noise.
Decentralized Exchange that Mitigate a Bribery Attack
Venue: arXiv cs.CR 2025
Year: 2025

Despite the popularity of Hashed Time-Locked Contracts (HTLCs) because of their use in wide areas of applications such as payment channels, atomic swaps, etc, their use in exchange is still questionable. This is because of its incentive incompatibility and susceptibility to bribery attacks. State-of-the-art solutions such as MAD-HTLC (Oakland'21) and He-HTLC (NDSS'23) address this by leveraging miners' profit-driven behaviour to mitigate such attacks. The former is the mitigation against passive miners; however, the latter works against both active and passive miners. However, they consider only two bribing scenarios where either of the parties involved in the transfer collude with the miner. In this paper, we expose vulnerabilities in state-of-the-art solutions by presenting a miner-collusion bribery attack with implementation and game-theoretic analysis. Additionally, we propose a stronger attack on MAD-HTLC than He-HTLC, allowing the attacker to earn profits equivalent to attacking naive HTLC. Leveraging our insights, we propose \prot, a game-theoretically secure HTLC protocol resistant to all bribery scenarios. \prot\ employs a two-phase approach, preventing unauthorized token confiscation by third parties, such as miners. In Phase 1, parties commit to the transfer; in Phase 2, the transfer is executed without manipulation. We demonstrate \prot's efficiency in transaction cost and latency via implementations on Bitcoin and Ethereum.
HybridSOMSpikeNet: A Deep Model with Differentiable Soft Self-Organizing Maps and Spiking Dynamics for Waste Classification
Venue: arXiv cs.CV 2025
Year: 2025

Accurate waste classification is vital for achieving sustainable waste management and reducing the environmental footprint of urbanization. Misclassification of recyclable materials contributes to landfill accumulation, inefficient recycling, and increased greenhouse gas emissions. To address these issues, this study introduces HybridSOMSpikeNet, a hybrid deep learning framework that integrates convolutional feature extraction, differentiable self-organization, and spiking-inspired temporal processing to enable intelligent and energy-efficient waste classification. The proposed model employs a pre-trained ResNet-152 backbone to extract deep spatial representations, followed by a Differentiable Soft Self-Organizing Map (Soft-SOM) that enhances topological clustering and interpretability. A spiking neural head accumulates temporal activations over discrete time steps, improving robustness and generalization. Trained on a ten-class waste dataset, HybridSOMSpikeNet achieved a test accuracy of 97.39%, outperforming several state-of-the-art architectures while maintaining a lightweight computational profile suitable for real-world deployment. Beyond its technical innovations, the framework provides tangible environmental benefits. By enabling precise and automated waste segregation, it supports higher recycling efficiency, reduces contamination in recyclable streams, and minimizes the ecological and operational costs of waste processing. The approach aligns with global sustainability priorities, particularly the United Nations Sustainable Development Goals (SDG 11 and SDG 12), by contributing to cleaner cities, circular economy initiatives, and intelligent environmental management systems.
A Soundness and Precision Benchmark for Java Debloating Tools
Venue: arXiv cs.SE 2025
Year: 2025

Modern software development reuses code by importing libraries as dependencies. Software projects typically include an average of 36 dependencies, with 80% being transitive, meaning they are dependencies of dependencies. Recent research indicates that only 24.9% of these dependencies are required at runtime, and even within those, many program constructs remain unused, adding unnecessary code to the project. This has led to the development of debloating tools that remove unnecessary dependencies and program constructs while balancing precision by eliminating unused constructs and soundness by preserving all required constructs. To systematically evaluate this trade-off, we developed Deblometer, a micro-benchmark consisting of 59 test cases designed to assess support for various Java language features in debloating tools. Each test case includes a manually curated ground truth specifying necessary and bloated classes, methods, and fields, enabling precise measurement of soundness and precision. Using Deblometer, we evaluated three popular Java debloating tools: Deptrim, JShrink, and ProGuard. Our evaluation reveals that all tools remove required program constructs, which results in changed semantics or execution crashes. In particular, the dynamic class loading feature introduces unsoundness in all evaluated tools. Our comparison shows that Deptrim retains more bloated constructs, while ProGuard removes more required constructs. JShrink's soundness is significantly affected by limited support for annotations, which leads to corrupted debloated artifacts. These soundness issues highlight the need to improve debloating tools to ensure stable and reliable debloated software.
Downsizing Diffusion Models for Cardinality Estimation
Venue: arXiv cs.DB 2025
Year: 2025

Learned cardinality estimation requires accurate model designs to capture the local characteristics of probability distributions. However, existing models may fail to accurately capture complex, multilateral dependencies between attributes. Diffusion models, meanwhile, can succeed in estimating image distributions with thousands of dimensions, making them promising candidates, but their heavy weight and high latency prohibit effective implementation. We seek to make diffusion models more lightweight by introducing Accelerated Diffusion Cardest (ADC), the first "downsized" diffusion model framework for efficient, high-precision cardinality estimation. ADC utilizes a hybrid architecture that integrates a Gaussian Mixture-Bayesnet selectivity estimator with a score-based density estimator to perform precise Monte Carlo integration. Addressing the issue of prohibitive inference latencies common in large generative models, we provide theoretical advancements concerning the asymptotic behavior of score functions as time $t$ approaches zero and convergence rate estimates as $t$ increases, enabling the adaptation of score-based diffusion models to the moderate dimensionalities and stringent latency requirements of database systems. Through experiments conducted against five learned estimators, including the state-of-the-art Naru, we demonstrate that ADC offer superior robustness when handling datasets with multilateral dependencies, which cannot be effectively summarized using pairwise or triple-wise correlations. In fact, ADC is 10 times more accurate than Naru on such datasets. Additionally, ADC achieves competitive accuracy comparable to Naru across all tested datasets while maintaining latency half that of Naru's and requiring minimal storage (<350KB) on most datasets.
Trust, But Verify: An Empirical Evaluation of AI-Generated Code for SDN Controllers
Venue: arXiv cs.NI 2025
Year: 2025

Generative Artificial Intelligence (AI) tools have been used to generate human-like content across multiple domains (e.g., sound, image, text, and programming). However, their reliability in terms of correctness and functionality in novel contexts such as programmable networks remains unclear. Hence, this paper presents an empirical evaluation of the source code of a POX controller generated by different AI tools, namely ChatGPT, Copilot, DeepSeek, and BlackBox.ai. To evaluate such a code, three networking tasks of increasing complexity were defined and for each task, zero-shot and few-shot prompting techniques were input to the tools. Next, the output code was tested in emulated network topologies with Mininet and analyzed according to functionality, correctness, and the need for manual fixes. Results show that all evaluated models can produce functional controllers. However, ChatGPT and DeepSeek exhibited higher consistency and code quality, while Copilot and BlackBox.ai required more adjustments.
Super-Linear Growth of the Capacity-Achieving Input Support for the Amplitude-Constrained AWGN Channel
Venue: arXiv cs.IT 2025
Year: 2025

We study the growth of the support size of the capacity-achieving input distribution for the amplitude-constrained additive white Gaussian noise (AWGN) channel. While it is known since Smith (1971) that the optimal input is discrete with finitely many mass points, tight bounds on the number of support points $K_A$ as the amplitude constraint $A$ increases remain open. Not much is known until recently, when Dytso et al. (2019) proved that $K_A$ grows at least linearly and at most quadratically in $A$. Here, we provide a novel method, building on Ma et al. (2024); Zhang (1994), to derive the first non-trivial lower bound showing that KA grows super-linearly in A.
FieldGen: From Teleoperated Pre-Manipulation Trajectories to Field-Guided Data Generation
Venue: arXiv cs.RO 2025
Year: 2025

Large-scale and diverse datasets are vital for training robust robotic manipulation policies, yet existing data collection methods struggle to balance scale, diversity, and quality. Simulation offers scalability but suffers from sim-to-real gaps, while teleoperation yields high-quality demonstrations with limited diversity and high labor cost. We introduce FieldGen, a field-guided data generation framework that enables scalable, diverse, and high-quality real-world data collection with minimal human supervision. FieldGen decomposes manipulation into two stages: a pre-manipulation phase, allowing trajectory diversity, and a fine manipulation phase requiring expert precision. Human demonstrations capture key contact and pose information, after which an attraction field automatically generates diverse trajectories converging to successful configurations. This decoupled design combines scalable trajectory diversity with precise supervision. Moreover, FieldGen-Reward augments generated data with reward annotations to further enhance policy learning. Experiments demonstrate that policies trained with FieldGen achieve higher success rates and improved stability compared to teleoperation-based baselines, while significantly reducing human effort in long-term real-world data collection. Webpage is available at https://fieldgen.github.io/.
Simple Context Compression: Mean-Pooling and Multi-Ratio Training
Venue: arXiv cs.CL 2025
Year: 2025

A common strategy to reduce the computational costs of using long contexts in retrieval-augmented generation (RAG) with large language models (LLMs) is soft context compression, where the input sequence is transformed into a shorter continuous representation. We develop a lightweight and simple mean-pooling approach that consistently outperforms the widely used compression-tokens architecture, and study training the same compressor to output multiple compression ratios. We conduct extensive experiments across in-domain and out-of-domain QA datasets, as well as across model families, scales, and compression ratios. Overall, our simple mean-pooling approach achieves the strongest performance, with a relatively small drop when training for multiple compression ratios. More broadly though, across architectures and training regimes the trade-offs are more nuanced, illustrating the complex landscape of compression methods.
Generative Reasoning Recommendation via LLMs
Venue: arXiv cs.IR 2025
Year: 2025

Despite their remarkable reasoning capabilities across diverse domains, large language models (LLMs) face fundamental challenges in natively functioning as generative reasoning recommendation models (GRRMs), where the intrinsic modeling gap between textual semantics and collaborative filtering signals, combined with the sparsity and stochasticity of user feedback, presents significant obstacles. This work explores how to build GRRMs by adapting pre-trained LLMs, which achieves a unified understanding-reasoning-prediction manner for recommendation tasks. We propose GREAM, an end-to-end framework that integrates three components: (i) Collaborative-Semantic Alignment, which fuses heterogeneous textual evidence to construct semantically consistent, discrete item indices and auxiliary alignment tasks that ground linguistic representations in interaction semantics; (ii) Reasoning Curriculum Activation, which builds a synthetic dataset with explicit Chain-of-Thought supervision and a curriculum that progresses through behavioral evidence extraction, latent preference modeling, intent inference, recommendation formulation, and denoised sequence rewriting; and (iii) Sparse-Regularized Group Policy Optimization (SRPO), which stabilizes post-training via Residual-Sensitive Verifiable Reward and Bonus-Calibrated Group Advantage Estimation, enabling end-to-end optimization under verifiable signals despite sparse successes. GREAM natively supports two complementary inference modes: Direct Sequence Recommendation for high-throughput, low-latency deployment, and Sequential Reasoning Recommendation that first emits an interpretable reasoning chain for causal transparency. Experiments on three datasets demonstrate consistent gains over strong baselines, providing a practical path toward verifiable-RL-driven LLM recommenders.
Incentivizing Consistent, Effective and Scalable Reasoning Capability in Audio LLMs via Reasoning Process Rewards
Venue: arXiv cs.LG 2025
Year: 2025

The role of reasoning in Audio Large Language Models remains widely underexplored, as introducing a reasoning process often degrades rather than improves performance during inference, a phenomenon we term test-time inverse scaling, where longer reasoning chains yield progressively worse results. We demonstrate that this stems not from fundamental limitations of reasoning itself, but from inadequate training: models without proper guidance for the reasoning process produce hallucinatory, inconsistent reasoning that accumulates errors over longer chains. To address these challenges, we introduce CESAR (Consistent, Effective, and Scalable Audio Reasoners), shifting from outcome verification to rewarding the reasoning process. Our online reinforcement learning framework employs Group Relative Policy Optimization with a multi-faceted reward suite that incentivizes not only correctness and format but also consistency, structured analytical patterns, causal reasoning, domain-knowledge integration, and calibrated reasoning depth. CESAR resolves test-time inverse scaling, transforming reasoning from detriments into gains while revealing model-specific ``reasoning sweet spots", where performance peaks during test-time scaling. We achieve state-of-the-art results on MMAU Test-mini, substantially outperforming Gemini 2.5 Pro and GPT-4o Audio, and near-human-level performance on MMSU reasoning tasks. Through AI-as-judge evaluations and qualitative comparisons, we provide both quantitative and qualitative validation of our improved reasoning quality. Importantly, enhanced reasoning creates synergistic effects, simultaneously improving multimodal reasoning and perception capabilities. Overall, CESAR establishes a principled method for developing robust and scalable reasoning in Audio LLMs.