OpenRaiser/Intern-Atlas huggingface dataset:
FreeRet: MLLMs as Training-Free Retrievers
Venue: arXiv cs.CV 2025
Year: 2025

Multimodal large language models (MLLMs) are emerging as versatile foundations for mixed-modality retrieval. Yet, they often require heavy post-hoc training to convert them into contrastive encoders for retrieval. This work asks: Can off-the-shelf MLLMs serve as powerful retrievers without additional training? We present FreeRet, a plug-and-play framework that turns any MLLM into a two-stage retriever. FreeRet first derives semantically grounded embeddings directly from the model for fast candidate search, and then exploits its reasoning ability for precise reranking. The framework contributes three advances: bypassing lexical alignment layers to obtain semantically faithful embeddings, conditioning representation generation with explicit priors, and mitigating framing effect in reranking via neutral choice framing. On the MMEB and MMEB-V2 benchmarks spanning 46 datasets, FreeRet substantially outperforms models trained on millions of pairs. Beyond benchmarks, FreeRet is model-agnostic and scales seamlessly across MLLM families and sizes, preserves their generative abilities, supports arbitrary modality combinations, and unifies retrieval, reranking, and generation into end-to-end RAG within a single model. Our findings demonstrate that pretrained MLLMs, when carefully harnessed, can serve as strong retrieval engines without training, closing a critical gap in their role as generalists.
PRIVMARK: Private Large Language Models Watermarking with MPC
Venue: arXiv cs.CR 2025
Year: 2025

The rapid growth of Large Language Models (LLMs) has highlighted the pressing need for reliable mechanisms to verify content ownership and ensure traceability. Watermarking offers a promising path forward, but it remains limited by privacy concerns in sensitive scenarios, as traditional approaches often require direct access to a model's parameters or its training data. In this work, we propose a secure multi-party computation (MPC)-based private LLMs watermarking framework, PRIVMARK, to address the concerns. Concretely, we investigate PostMark (EMNLP'2024), one of the state-of-the-art LLMs Watermarking methods, and formulate its basic operations. Then, we construct efficient protocols for these operations using the MPC primitives in a black-box manner. In this way, PRIVMARK enables multiple parties to collaboratively watermark an LLM's output without exposing the model's weights to any single computing party. We implement PRIVMARK using SecretFlow-SPU (USENIX ATC'2023) and evaluate its performance using the ABY3 (CCS'2018) backend. The experimental results show that PRIVMARK achieves semantically identical results compared to the plaintext baseline without MPC and is resistant against paraphrasing and removing attacks with reasonable efficiency.
Bridging Developer Instructions and Code Completion Through Instruction-Aware Fill-in-the-Middle Paradigm
Venue: arXiv cs.SE 2025
Year: 2025

Large Language Models (LLMs) have significantly advanced code completion, yet they often fail when the developer's intent is underspecified in the code context. To address this, developers usually add natural language instructions (e.g., comments) into the code context to clarify their intent. However, existing code LLMs applied for code completion systems merely undergo a fill-in-the-middle (FIM) pre-training, which struggles to leverage this information effectively due to the lack of instruction-like training data. Existing instruction-tuning techniques, improving instruction-following in general code generation, paradoxically degrades FIM performance, forcing a trade-off between instruction-following and infilling capabilities. To address this gap, we introduce Instruction-aware Fill-in-the-Middle (IFIM), an instruction-tuning method specifically designed to enhance FIM code completion models. IFIM extends the conventional FIM training objective by incorporating an explicit instruction section into the input, enabling the model to learn from (prefix, instruction, suffix) triplets. This approach allows the model to effectively leverage developer-provided directives while preserving its core completion abilities when no instructions are present. To facilitate this, we constructed a large-scale dataset by using GPT-4o to generate concise, intent-focused instructions for code infilling examples. We evaluated IFIM by applying it to two popular base models, Deepseek-Coder and Qwen2.5-Coder, on the benchmarks derived from HumanEval-infilling and RepoMasterEval. The results demonstrate that IFIM significantly improves instruction-following capabilities, boosting the Pass@1 score from 84.6% to 93.6%. Moreover, this enhancement does not compromise the models' original performance on FIM code completion tasks with no instructions provided.
RIFLE: Removal of Image Flicker-Banding via Latent Diffusion Enhancement
Venue: arXiv cs.CV 2025
Year: 2025

Capturing screens is now routine in our everyday lives. But the photographs of emissive displays are often influenced by the flicker-banding (FB), which is alternating bright%u2013dark stripes that arise from temporal aliasing between a camera's rolling-shutter readout and the display's brightness modulation. Unlike moire degradation, which has been extensively studied, the FB remains underexplored despite its frequent and severe impact on readability and perceived quality. We formulate FB removal as a dedicated restoration task and introduce Removal of Image Flicker-Banding via Latent Diffusion Enhancement, RIFLE, a diffusion-based framework designed to remove FB while preserving fine details. We propose the flicker-banding prior estimator (FPE) that predicts key banding attributes and injects it into the restoration network. Additionally, Masked Loss (ML) is proposed to concentrate supervision on banded regions without sacrificing global fidelity. To overcome data scarcity, we provide a simulation pipeline that synthesizes FB in the luminance domain with stochastic jitter in banding angle, banding spacing, and banding width. Feathered boundaries and sensor noise are also applied for a more realistic simulation. For evaluation, we collect a paired real-world FB dataset with pixel-aligned banding-free references captured via long exposure. Across quantitative metrics and visual comparisons on our real-world dataset, RIFLE consistently outperforms recent image reconstruction baselines from mild to severe flicker-banding. To the best of our knowledge, it is the first work to research the simulation and removal of FB. Our work establishes a great foundation for subsequent research in both the dataset construction and the removal model design. Our dataset and code will be released soon.
InfLLM-V2: Dense-Sparse Switchable Attention for Seamless Short-to-Long Adaptation
Venue: arXiv cs.CL 2025
Year: 2025

Long-sequence processing is a critical capability for modern large language models. However, the self-attention mechanism in the standard Transformer architecture faces severe computational and memory bottlenecks when processing long sequences. While trainable sparse attention methods offer a promising solution, existing approaches such as NSA introduce excessive extra parameters and disrupt the conventional \textit{pretrain-on-short, finetune-on-long} workflow, resulting in slow convergence and difficulty in acceleration. To overcome these limitations, we introduce dense-sparse switchable attention framework, termed as InfLLM-V2. InfLLM-V2 is a trainable sparse attention that seamlessly adapts models from short to long sequences. Specifically, InfLLM-V2 reuses dense attention parameters through parameter-free architecture modification, maintaining consistency between short and long sequence processing. Additionally, InfLLM-V2 ensures computational efficiency across all sequence lengths, by using dense attention for short inputs and smoothly transitioning to sparse attention for long sequences. To achieve practical acceleration, we further introduce an efficient implementation of InfLLM-V2 that significantly reduces the computational overhead. Our experiments on long-context understanding and chain-of-thought reasoning demonstrate that InfLLM-V2 is 4$\times$ faster than dense attention while retaining 98.1% and 99.7% of the performance, respectively. Based on the InfLLM-V2 framework, we have trained and open-sourced MiniCPM4.1 (https://huggingface.co/openbmb/MiniCPM4.1-8B), a hybrid reasoning model, providing a reproducible implementation for the research community.
SANA-Video: Efficient Video Generation with Block Linear Diffusion Transformer
Venue: arXiv cs.CV 2025
Year: 2025

We introduce SANA-Video, a small diffusion model that can efficiently generate videos up to 720x1280 resolution and minute-length duration. SANA-Video synthesizes high-resolution, high-quality and long videos with strong text-video alignment at a remarkably fast speed, deployable on RTX 5090 GPU. Two core designs ensure our efficient, effective and long video generation: (1) Linear DiT: We leverage linear attention as the core operation, which is more efficient than vanilla attention given the large number of tokens processed in video generation. (2) Constant-Memory KV cache for Block Linear Attention: we design block-wise autoregressive approach for long video generation by employing a constant-memory state, derived from the cumulative properties of linear attention. This KV cache provides the Linear DiT with global context at a fixed memory cost, eliminating the need for a traditional KV cache and enabling efficient, minute-long video generation. In addition, we explore effective data filters and model training strategies, narrowing the training cost to 12 days on 64 H100 GPUs, which is only 1% of the cost of MovieGen. Given its low cost, SANA-Video achieves competitive performance compared to modern state-of-the-art small diffusion models (e.g., Wan 2.1-1.3B and SkyReel-V2-1.3B) while being 16x faster in measured latency. Moreover, SANA-Video can be deployed on RTX 5090 GPUs with NVFP4 precision, accelerating the inference speed of generating a 5-second 720p video from 71s to 29s (2.4x speedup). In summary, SANA-Video enables low-cost, high-quality video generation.
Diamonds in the rough: Transforming SPARCs of imagination into a game concept by leveraging medium sized LLMs
Venue: arXiv cs.HC 2025
Year: 2025

Recent research has demonstrated that large language models (LLMs) can support experts across various domains, including game design. In this study, we examine the utility of medium-sized LLMs, models that operate on consumer-grade hardware typically available in small studios or home environments. We began by identifying ten key aspects that contribute to a strong game concept and used ChatGPT to generate thirty sample game ideas. Three medium-sized LLMs, LLaMA 3.1, Qwen 2.5, and DeepSeek-R1, were then prompted to evaluate these ideas according to the previously identified aspects. A qualitative assessment by two researchers compared the models' outputs, revealing that DeepSeek-R1 produced the most consistently useful feedback, despite some variability in quality. To explore real-world applicability, we ran a pilot study with ten students enrolled in a storytelling course for game development. At the early stages of their own projects, students used our prompt and DeepSeek-R1 to refine their game concepts. The results indicate a positive reception: most participants rated the output as high quality and expressed interest in using such tools in their workflows. These findings suggest that current medium-sized LLMs can provide valuable feedback in early game design, though further refinement of prompting methods could improve consistency and overall effectiveness.
Evaluation of Polarimetric Fusion for Semantic Segmentation in Aquatic Environments
Venue: arXiv cs.CV 2025
Year: 2025

Accurate segmentation of floating debris on water is often compromised by surface glare and changing outdoor illumination. Polarimetric imaging offers a single-sensor route to mitigate water-surface glare that disrupts semantic segmentation of floating objects. We benchmark state-of-the-art fusion networks on PoTATO, a public dataset of polarimetric images of plastic bottles in inland waterways, and compare their performance with single-image baselines using traditional models. Our results indicate that polarimetric cues help recover low-contrast objects and suppress reflection-induced false positives, raising mean IoU and lowering contour error relative to RGB inputs. These sharper masks come at a cost: the additional channels enlarge the models increasing the computational load and introducing the risk of new false positives. By providing a reproducible, diagnostic benchmark and publicly available code, we hope to help researchers choose if polarized cameras are suitable for their applications and to accelerate related research.
APREBot: Active Perception System for Reflexive Evasion Robot
Venue: arXiv cs.RO 2025
Year: 2025

Reliable onboard perception is critical for quadruped robots navigating dynamic environments, where obstacles can emerge from any direction under strict reaction-time constraints. Single-sensor systems face inherent limitations: LiDAR provides omnidirectional coverage but lacks rich texture information, while cameras capture high-resolution detail but suffer from restricted field of view. We introduce APREBot (Active Perception System for Reflexive Evasion Robot), a novel framework that integrates reflexive evasion with active hierarchical perception. APREBot strategically combines LiDAR-based omnidirectional scanning with camera-based active focusing, achieving comprehensive environmental awareness essential for agile obstacle avoidance in quadruped robots. We validate APREBot through extensive sim-to-real experiments on a quadruped platform, evaluating diverse obstacle types, trajectories, and approach directions. Our results demonstrate substantial improvements over state-of-the-art baselines in both safety metrics and operational efficiency, highlighting APREBot's potential for dependable autonomy in safety-critical scenarios. Videos are available at https://sites.google.com/view/aprebot/
VTPerception-R1: Enhancing Multimodal Reasoning via Explicit Visual and Textual Perceptual Grounding
Venue: arXiv cs.CV 2025
Year: 2025

Multimodal large language models (MLLMs) often struggle to ground reasoning in perceptual evidence. We present a systematic study of perception strategies-explicit, implicit, visual, and textual-across four multimodal benchmarks and two MLLMs. Our findings show that explicit perception, especially when paired with textual cues, consistently yields the best improvements, particularly for smaller models. Based on this insight, we propose VTPerception-R1, a unified two-stage framework that decouples perception from reasoning. Stage 1 introduces perception-augmented fine-tuning, and Stage 2 applies perception-aware reinforcement learning with novel visual, textual, and consistency rewards. Experiments demonstrate that VTPerception-R1 significantly improves reasoning accuracy and robustness across diverse tasks, offering a scalable and auditable solution for perception-grounded multimodal reasoning. Our code is available at: https://github.com/yizhuoDi/VTPerceprion-R1.
Assessing the risk of future Dunkelflaute events for Germany using generative deep learning
Venue: arXiv cs.LG 2025
Year: 2025

The European electricity power grid is transitioning towards renewable energy sources, characterized by an increasing share of off- and onshore wind and solar power. However, the weather dependency of these energy sources poses a challenge to grid stability, with so-called Dunkelflaute events -- periods of low wind and solar power generation -- being of particular concern due to their potential to cause electricity supply shortages. In this study, we investigate the impact of these events on the German electricity production in the years and decades to come. For this purpose, we adapt a recently developed generative deep learning framework to downscale climate simulations from the CMIP6 ensemble. We first compare their statistics to the historical record taken from ERA5 data. Next, we use these downscaled simulations to assess plausible future occurrences of Dunkelflaute events in Germany under the optimistic low (SSP2-4.5) and high (SSP5-8.5) emission scenarios. Our analysis indicates that both the frequency and duration of Dunkelflaute events in Germany in the ensemble mean are projected to remain largely unchanged compared to the historical period. This suggests that, under the considered climate scenarios, the associated risk is expected to remain stable throughout the century.
DiaCDM: Cognitive Diagnosis in Teacher-Student Dialogues using the Initiation-Response-Evaluation Framework
Venue: arXiv cs.CL 2025
Year: 2025

While cognitive diagnosis (CD) effectively assesses students' knowledge mastery from structured test data, applying it to real-world teacher-student dialogues presents two fundamental challenges. Traditional CD models lack a suitable framework for handling dynamic, unstructured dialogues, and it's difficult to accurately extract diagnostic semantics from lengthy dialogues. To overcome these hurdles, we propose DiaCDM, an innovative model. We've adapted the initiation-response-evaluation (IRE) framework from educational theory to design a diagnostic framework tailored for dialogue. We also developed a unique graph-based encoding method that integrates teacher questions with relevant knowledge components to capture key information more precisely. To our knowledge, this is the first exploration of cognitive diagnosis in a dialogue setting. Experiments on three real-world dialogue datasets confirm that DiaCDM not only significantly improves diagnostic accuracy but also enhances the results' interpretability, providing teachers with a powerful tool for assessing students' cognitive states. The code is available at https://github.com/Mind-Lab-ECNU/DiaCDM/tree/main.
Evaluating SAP Joule for Code Generation
Venue: arXiv cs.SE 2025
Year: 2025

SAP has released its own proprietary generative model SAP Joule, intended for various generative tasks, including serving as a code assistant for software engineers. While Joule is yet not focused on SAP-specific ABAP code generation, it can be used for other common languages, including Javascript. This paper compares SAP Joules Javascript coding capabilities against a total of 29 other models using the HumanEval-X Javascript benchmark. SAP Joule achieves a strict accuracy of 80.49% as the fifth best model in our evaluation. To the best of our knowledge, this is the first comparative evaluation of SAP Joule code generation capabilities.
Interface Design to Support Legal Reading and Writing: Insights from Interviews with Legal Experts
Venue: arXiv cs.HC 2025
Year: 2025

Legal professionals spend significant time reading, writing, and interpreting complex documents, yet research has not fully captured how they approach these tasks or what they expect from skimming and writing-support tools. To examine practices and views on emerging tools, we interviewed 22 legal professionals about workflows, challenges, and technology use. In each session, we leveraged prior HCI-based skimming and writing prototypes that surface emergent cross-document relationships and support AI-resilient interaction (noticing, judging, and recovering from model errors or unexpected behavior); participants completed a contextual fit evaluation to assess whether and how they would use the tools, which document types, and at what stages in their work. Our analysis details limitations and challenges in workflows, domain-specific feedback on AI-resilient interfaces, and expert insights on legal tech design. These findings offer actionable guidance for technology designers developing reading and writing-support for legal professionals, and for legal professionals seeking peer-informed tool integration strategies.
Vision At Night: Exploring Biologically Inspired Preprocessing For Improved Robustness Via Color And Contrast Transformations
Venue: arXiv cs.CV 2025
Year: 2025

Inspired by the human visual system's mechanisms for contrast enhancement and color-opponency, we explore biologically motivated input preprocessing for robust semantic segmentation. By applying Difference-of-Gaussians (DoG) filtering to RGB, grayscale, and opponent-color channels, we enhance local contrast without modifying model architecture or training. Evaluations on Cityscapes, ACDC, and Dark Zurich show that such preprocessing maintains in-distribution performance while improving robustness to adverse conditions like night, fog, and snow. As this processing is model-agnostic and lightweight, it holds potential for integration into imaging pipelines, enabling imaging systems to deliver task-ready, robust inputs for downstream vision models in safety-critical environments.
Towards Modular and Accessible AUV Systems
Venue: arXiv cs.RO 2025
Year: 2025

This paper reports the development of a new open-access modular framework, called Marine Vehicle Packages (MVP), for Autonomous Underwater Vehicles. The framework consists of both software and hardware designs allowing easy construction of AUV for research with increased customizability and sufficient payload capacity. This paper will present the scalable hardware system design and the modular software design architecture. New features, such as articulated thruster integration and high-level Graphic User Interface will be discussed. Both simulation and field experiments results are shown to highlight the performance and compatibility of the MVP.
Metaphor identification using large language models: A comparison of RAG, prompt engineering, and fine-tuning
Venue: arXiv cs.CL 2025
Year: 2025

Metaphor is a pervasive feature of discourse and a powerful lens for examining cognition, emotion, and ideology. Large-scale analysis, however, has been constrained by the need for manual annotation due to the context-sensitive nature of metaphor. This study investigates the potential of large language models (LLMs) to automate metaphor identification in full texts. We compare three methods: (i) retrieval-augmented generation (RAG), where the model is provided with a codebook and instructed to annotate texts based on its rules and examples; (ii) prompt engineering, where we design task-specific verbal instructions; and (iii) fine-tuning, where the model is trained on hand-coded texts to optimize performance. Within prompt engineering, we test zero-shot, few-shot, and chain-of-thought strategies. Our results show that state-of-the-art closed-source LLMs can achieve high accuracy, with fine-tuning yielding a median F1 score of 0.79. A comparison of human and LLM outputs reveals that most discrepancies are systematic, reflecting well-known grey areas and conceptual challenges in metaphor theory. We propose that LLMs can be used to at least partly automate metaphor identification and can serve as a testbed for developing and refining metaphor identification protocols and the theory that underpins them.