arxiv_id float64 1.5k 2.51k | title stringlengths 9 178 ⌀ | authors stringlengths 2 22.8k | categories stringlengths 4 146 | summary stringlengths 103 1.92k ⌀ | published stringdate 2015-02-06 10:44:00 2025-07-10 17:59:58 ⌀ | comments stringlengths 2 417 ⌀ | journal_ref stringclasses 321
values | doi stringclasses 398
values | ss_title stringlengths 8 159 ⌀ | ss_authors stringlengths 11 8.38k ⌀ | ss_year float64 2.02k 2.03k ⌀ | ss_venue stringclasses 281
values | ss_citationCount float64 0 134k ⌀ | ss_referenceCount float64 0 429 ⌀ | ss_fieldsOfStudy stringclasses 47
values |
|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|
2,506.06281 | TerraFM: A Scalable Foundation Model for Unified Multisensor Earth
Observation | ['Muhammad Sohail Danish', 'Muhammad Akhtar Munir', 'Syed Roshaan Ali Shah', 'Muhammad Haris Khan', 'Rao Muhammad Anwer', 'Jorma Laaksonen', 'Fahad Shahbaz Khan', 'Salman Khan'] | ['cs.CV'] | Modern Earth observation (EO) increasingly leverages deep learning to harness
the scale and diversity of satellite imagery across sensors and regions. While
recent foundation models have demonstrated promising generalization across EO
tasks, many remain limited by the scale, geographical coverage, and spectral
diversity of their training data, factors critical for learning globally
transferable representations. In this work, we introduce TerraFM, a scalable
self-supervised learning model that leverages globally distributed Sentinel-1
and Sentinel-2 imagery, combined with large spatial tiles and land-cover aware
sampling to enrich spatial and semantic coverage. By treating sensing
modalities as natural augmentations in our self-supervised approach, we unify
radar and optical inputs via modality-specific patch embeddings and adaptive
cross-attention fusion. Our training strategy integrates local-global
contrastive learning and introduces a dual-centering mechanism that
incorporates class-frequency-aware regularization to address long-tailed
distributions in land cover.TerraFM achieves strong generalization on both
classification and segmentation tasks, outperforming prior models on GEO-Bench
and Copernicus-Bench. Our code and pretrained models are publicly available at:
https://github.com/mbzuai-oryx/TerraFM . | 2025-06-06T17:59:50Z | null | null | null | null | null | null | null | null | null | null |
2,506.06962 | AR-RAG: Autoregressive Retrieval Augmentation for Image Generation | ['Jingyuan Qi', 'Zhiyang Xu', 'Qifan Wang', 'Lifu Huang'] | ['cs.CV'] | We introduce Autoregressive Retrieval Augmentation (AR-RAG), a novel paradigm
that enhances image generation by autoregressively incorporating knearest
neighbor retrievals at the patch level. Unlike prior methods that perform a
single, static retrieval before generation and condition the entire generation
on fixed reference images, AR-RAG performs context-aware retrievals at each
generation step, using prior-generated patches as queries to retrieve and
incorporate the most relevant patch-level visual references, enabling the model
to respond to evolving generation needs while avoiding limitations (e.g.,
over-copying, stylistic bias, etc.) prevalent in existing methods. To realize
AR-RAG, we propose two parallel frameworks: (1) Distribution-Augmentation in
Decoding (DAiD), a training-free plug-and-use decoding strategy that directly
merges the distribution of model-predicted patches with the distribution of
retrieved patches, and (2) Feature-Augmentation in Decoding (FAiD), a
parameter-efficient fine-tuning method that progressively smooths the features
of retrieved patches via multi-scale convolution operations and leverages them
to augment the image generation process. We validate the effectiveness of
AR-RAG on widely adopted benchmarks, including Midjourney-30K, GenEval and
DPG-Bench, demonstrating significant performance gains over state-of-the-art
image generation models. | 2025-06-08T01:33:05Z | Image Generation, Retrieval Augmented Generation | null | null | null | null | null | null | null | null | null |
2,506.07032 | A Culturally-diverse Multilingual Multimodal Video Benchmark & Model | ['Bhuiyan Sanjid Shafique', 'Ashmal Vayani', 'Muhammad Maaz', 'Hanoona Abdul Rasheed', 'Dinura Dissanayake', 'Mohammed Irfan Kurpath', 'Yahya Hmaiti', 'Go Inoue', 'Jean Lahoud', 'Md. Safirur Rashid', 'Shadid Intisar Quasem', 'Maheen Fatima', 'Franco Vidal', 'Mykola Maslych', 'Ketan Pravin More', 'Sanoojan Baliah', 'Hasindri Watawana', 'Yuhao Li', 'Fabian Farestam', 'Leon Schaller', 'Roman Tymtsiv', 'Simon Weber', 'Hisham Cholakkal', 'Ivan Laptev', "Shin'ichi Satoh", 'Michael Felsberg', 'Mubarak Shah', 'Salman Khan', 'Fahad Shahbaz Khan'] | ['cs.CL', 'cs.CV'] | Large multimodal models (LMMs) have recently gained attention due to their
effectiveness to understand and generate descriptions of visual content. Most
existing LMMs are in English language. While few recent works explore
multilingual image LMMs, to the best of our knowledge, moving beyond the
English language for cultural and linguistic inclusivity is yet to be
investigated in the context of video LMMs. In pursuit of more inclusive video
LMMs, we introduce a multilingual Video LMM benchmark, named ViMUL-Bench, to
evaluate Video LMMs across 14 languages, including both low- and high-resource
languages: English, Chinese, Spanish, French, German, Hindi, Arabic, Russian,
Bengali, Urdu, Sinhala, Tamil, Swedish, and Japanese. Our ViMUL-Bench is
designed to rigorously test video LMMs across 15 categories including eight
culturally diverse categories, ranging from lifestyles and festivals to foods
and rituals and from local landmarks to prominent cultural personalities.
ViMUL-Bench comprises both open-ended (short and long-form) and multiple-choice
questions spanning various video durations (short, medium, and long) with 8k
samples that are manually verified by native language speakers. In addition, we
also introduce a machine translated multilingual video training set comprising
1.2 million samples and develop a simple multilingual video LMM, named ViMUL,
that is shown to provide a better tradeoff between high-and low-resource
languages for video understanding. We hope our ViMUL-Bench and multilingual
video LMM along with a large-scale multilingual video training set will help
ease future research in developing cultural and linguistic inclusive
multilingual video LMMs. Our proposed benchmark, video LMM and training data
will be publicly released at https://mbzuai-oryx.github.io/ViMUL/. | 2025-06-08T07:52:20Z | null | null | null | null | null | null | null | null | null | null |
2,506.07044 | Lingshu: A Generalist Foundation Model for Unified Multimodal Medical
Understanding and Reasoning | ['LASA Team', 'Weiwen Xu', 'Hou Pong Chan', 'Long Li', 'Mahani Aljunied', 'Ruifeng Yuan', 'Jianyu Wang', 'Chenghao Xiao', 'Guizhen Chen', 'Chaoqun Liu', 'Zhaodonghui Li', 'Yu Sun', 'Junao Shen', 'Chaojun Wang', 'Jie Tan', 'Deli Zhao', 'Tingyang Xu', 'Hao Zhang', 'Yu Rong'] | ['cs.CL', 'cs.AI', 'cs.CV'] | Multimodal Large Language Models (MLLMs) have demonstrated impressive
capabilities in understanding common visual elements, largely due to their
large-scale datasets and advanced training strategies. However, their
effectiveness in medical applications remains limited due to the inherent
discrepancies between data and tasks in medical scenarios and those in the
general domain. Concretely, existing medical MLLMs face the following critical
limitations: (1) limited coverage of medical knowledge beyond imaging, (2)
heightened susceptibility to hallucinations due to suboptimal data curation
processes, (3) lack of reasoning capabilities tailored for complex medical
scenarios. To address these challenges, we first propose a comprehensive data
curation procedure that (1) efficiently acquires rich medical knowledge data
not only from medical imaging but also from extensive medical texts and
general-domain data; and (2) synthesizes accurate medical captions, visual
question answering (VQA), and reasoning samples. As a result, we build a
multimodal dataset enriched with extensive medical knowledge. Building on the
curated data, we introduce our medical-specialized MLLM: Lingshu. Lingshu
undergoes multi-stage training to embed medical expertise and enhance its
task-solving capabilities progressively. Besides, we preliminarily explore the
potential of applying reinforcement learning with verifiable rewards paradigm
to enhance Lingshu's medical reasoning ability. Additionally, we develop
MedEvalKit, a unified evaluation framework that consolidates leading multimodal
and textual medical benchmarks for standardized, fair, and efficient model
assessment. We evaluate the performance of Lingshu on three fundamental medical
tasks, multimodal QA, text-based QA, and medical report generation. The results
show that Lingshu consistently outperforms the existing open-source multimodal
models on most tasks ... | 2025-06-08T08:47:30Z | Technical Report, 53 pages, 25 tables, and 16 figures. Our webpage is
https://alibaba-damo-academy.github.io/lingshu/ | null | null | null | null | null | null | null | null | null |
2,506.0708 | FLAIR-HUB: Large-scale Multimodal Dataset for Land Cover and Crop
Mapping | ['Anatol Garioud', 'Sébastien Giordano', 'Nicolas David', 'Nicolas Gonthier'] | ['cs.CV'] | The growing availability of high-quality Earth Observation (EO) data enables
accurate global land cover and crop type monitoring. However, the volume and
heterogeneity of these datasets pose major processing and annotation
challenges. To address this, the French National Institute of Geographical and
Forest Information (IGN) is actively exploring innovative strategies to exploit
diverse EO data, which require large annotated datasets. IGN introduces
FLAIR-HUB, the largest multi-sensor land cover dataset with
very-high-resolution (20 cm) annotations, covering 2528 km2 of France. It
combines six aligned modalities: aerial imagery, Sentinel-1/2 time series, SPOT
imagery, topographic data, and historical aerial images. Extensive benchmarks
evaluate multimodal fusion and deep learning models (CNNs, transformers) for
land cover or crop mapping and also explore multi-task learning. Results
underscore the complexity of multimodal fusion and fine-grained classification,
with best land cover performance (78.2% accuracy, 65.8% mIoU) achieved using
nearly all modalities. FLAIR-HUB supports supervised and multimodal
pretraining, with data and code available at
https://ignf.github.io/FLAIR/flairhub. | 2025-06-08T10:48:51Z | null | null | null | null | null | null | null | null | null | null |
2,506.0731 | AllTracker: Efficient Dense Point Tracking at High Resolution | ['Adam W. Harley', 'Yang You', 'Xinglong Sun', 'Yang Zheng', 'Nikhil Raghuraman', 'Yunqi Gu', 'Sheldon Liang', 'Wen-Hsuan Chu', 'Achal Dave', 'Pavel Tokmakov', 'Suya You', 'Rares Ambrus', 'Katerina Fragkiadaki', 'Leonidas J. Guibas'] | ['cs.CV'] | We introduce AllTracker: a model that estimates long-range point tracks by
way of estimating the flow field between a query frame and every other frame of
a video. Unlike existing point tracking methods, our approach delivers
high-resolution and dense (all-pixel) correspondence fields, which can be
visualized as flow maps. Unlike existing optical flow methods, our approach
corresponds one frame to hundreds of subsequent frames, rather than just the
next frame. We develop a new architecture for this task, blending techniques
from existing work in optical flow and point tracking: the model performs
iterative inference on low-resolution grids of correspondence estimates,
propagating information spatially via 2D convolution layers, and propagating
information temporally via pixel-aligned attention layers. The model is fast
and parameter-efficient (16 million parameters), and delivers state-of-the-art
point tracking accuracy at high resolution (i.e., tracking 768x1024 pixels, on
a 40G GPU). A benefit of our design is that we can train on a wider set of
datasets, and we find that doing so is crucial for top performance. We provide
an extensive ablation study on our architecture details and training recipe,
making it clear which details matter most. Our code and model weights are
available at https://alltracker.github.io . | 2025-06-08T22:55:06Z | null | null | null | null | null | null | null | null | null | null |
2,506.07434 | Well Begun is Half Done: Low-resource Preference Alignment by
Weak-to-Strong Decoding | ['Feifan Song', 'Shaohang Wei', 'Wen Luo', 'Yuxuan Fan', 'Tianyu Liu', 'Guoyin Wang', 'Houfeng Wang'] | ['cs.CL', 'cs.AI'] | Large Language Models (LLMs) require alignment with human preferences to
avoid generating offensive, false, or meaningless content. Recently,
low-resource methods for LLM alignment have been popular, while still facing
challenges in obtaining both high-quality and aligned content. Motivated by the
observation that the difficulty of generating aligned responses is concentrated
at the beginning of decoding, we propose a novel framework, Weak-to-Strong
Decoding (WSD), to enhance the alignment ability of base models by the guidance
of a small aligned model. The small model first drafts well-aligned beginnings,
followed by the large base model to continue the rest, controlled by a
well-designed auto-switch mechanism. We also collect a new dataset, GenerAlign,
to fine-tune a small-sized Pilot-3B as the draft model, which effectively
enhances different base models under the WSD framework to outperform all
baseline methods, while avoiding degradation on downstream tasks, termed as the
alignment tax. Extensive experiments are further conducted to examine the
impact of different settings and time efficiency, as well as analyses on the
intrinsic mechanisms of WSD in depth. | 2025-06-09T05:21:22Z | Accepted by ACL 2025 Findings | null | null | null | null | null | null | null | null | null |
2,506.07438 | LGAI-EMBEDDING-Preview Technical Report | ['Jooyoung Choi', 'Hyun Kim', 'Hansol Jang', 'Changwook Jun', 'Kyunghoon Bae', 'Hyewon Choi', 'Stanley Jungkyu Choi', 'Honglak Lee', 'Chulmin Yun'] | ['cs.CL'] | This report presents a unified instruction-based framework for learning
generalized text embeddings optimized for both information retrieval (IR) and
non-IR tasks. Built upon a decoder-only large language model (Mistral-7B), our
approach combines in-context learning, soft supervision, and adaptive
hard-negative mining to generate context-aware embeddings without task-specific
fine-tuning. Structured instructions and few-shot examples are used to guide
the model across diverse tasks, enabling strong performance on classification,
semantic similarity, clustering, and reranking benchmarks. To improve semantic
discrimination, we employ a soft labeling framework where continuous relevance
scores, distilled from a high-performance dense retriever and reranker, serve
as fine-grained supervision signals. In addition, we introduce adaptive
margin-based hard-negative mining, which filters out semantically ambiguous
negatives based on their similarity to positive examples, thereby enhancing
training stability and retrieval robustness. Our model is evaluated on the
newly introduced MTEB (English, v2) benchmark, covering 41 tasks across seven
categories. Results show that our method achieves strong generalization and
ranks among the top-performing models by Borda score, outperforming several
larger or fully fine-tuned baselines. These findings highlight the
effectiveness of combining in-context prompting, soft supervision, and adaptive
sampling for scalable, high-quality embedding generation. | 2025-06-09T05:30:35Z | 10 pages | null | null | LG-ANNA-Embedding technical report | ['Jooyoung Choi', 'Hyun Kim', 'Hansol Jang', 'Changwook Jun', 'Kyunghoon Bae', 'Hyewon Choi', 'Stanley Jungkyu Choi', 'Honglak Lee', 'Chulmin Yun'] | 2,025 | null | 0 | 35 | ['Computer Science'] |
2,506.07491 | SpatialLM: Training Large Language Models for Structured Indoor Modeling | ['Yongsen Mao', 'Junhao Zhong', 'Chuan Fang', 'Jia Zheng', 'Rui Tang', 'Hao Zhu', 'Ping Tan', 'Zihan Zhou'] | ['cs.CV'] | SpatialLM is a large language model designed to process 3D point cloud data
and generate structured 3D scene understanding outputs. These outputs include
architectural elements like walls, doors, windows, and oriented object boxes
with their semantic categories. Unlike previous methods which exploit
task-specific network designs, our model adheres to the standard multimodal LLM
architecture and is fine-tuned directly from open-source LLMs.
To train SpatialLM, we collect a large-scale, high-quality synthetic dataset
consisting of the point clouds of 12,328 indoor scenes (54,778 rooms) with
ground-truth 3D annotations, and conduct a careful study on various modeling
and training decisions. On public benchmarks, our model gives state-of-the-art
performance in layout estimation and competitive results in 3D object
detection. With that, we show a feasible path for enhancing the spatial
understanding capabilities of modern LLMs for applications in augmented
reality, embodied robotics, and more. | 2025-06-09T07:10:58Z | null | null | null | SpatialLM: Training Large Language Models for Structured Indoor Modeling | ['Yongsen Mao', 'Junhao Zhong', 'Chuan Fang', 'Jia Zheng', 'Rui Tang', 'Hao Zhu', 'Ping Tan', 'Zihan Zhou'] | 2,025 | arXiv.org | 1 | 68 | ['Computer Science'] |
2,506.0752 | LeVo: High-Quality Song Generation with Multi-Preference Alignment | ['Shun Lei', 'Yaoxun Xu', 'Zhiwei Lin', 'Huaicheng Zhang', 'Wei Tan', 'Hangting Chen', 'Jianwei Yu', 'Yixuan Zhang', 'Chenyu Yang', 'Haina Zhu', 'Shuai Wang', 'Zhiyong Wu', 'Dong Yu'] | ['cs.SD', 'cs.AI', 'eess.AS'] | Recent advances in large language models (LLMs) and audio language models
have significantly improved music generation, particularly in lyrics-to-song
generation. However, existing approaches still struggle with the complex
composition of songs and the scarcity of high-quality data, leading to
limitations in sound quality, musicality, instruction following, and
vocal-instrument harmony. To address these challenges, we introduce LeVo, an
LM-based framework consisting of LeLM and a music codec. LeLM is capable of
parallelly modeling two types of tokens: mixed tokens, which represent the
combined audio of vocals and accompaniment to achieve vocal-instrument harmony,
and dual-track tokens, which separately encode vocals and accompaniment for
high-quality song generation. It employs two decoder-only transformers and a
modular extension training strategy to prevent interference between different
token types. To further enhance musicality and instruction following, we
introduce a multi-preference alignment method based on Direct Preference
Optimization (DPO). This method handles diverse human preferences through a
semi-automatic data construction process and DPO post-training. Experimental
results demonstrate that LeVo consistently outperforms existing methods on both
objective and subjective metrics. Ablation studies further justify the
effectiveness of our designs. Audio examples are available at
https://levo-demo.github.io/. Code is released at
https://github.com/tencent-ailab/songgeneration. | 2025-06-09T07:57:24Z | null | null | null | null | null | null | null | null | null | null |
2,506.07527 | Learning What Reinforcement Learning Can't: Interleaved Online
Fine-Tuning for Hardest Questions | ['Lu Ma', 'Hao Liang', 'Meiyi Qiang', 'Lexiang Tang', 'Xiaochen Ma', 'Zhen Hao Wong', 'Junbo Niu', 'Chengyu Shen', 'Runming He', 'Bin Cui', 'Wentao Zhang'] | ['cs.AI', 'cs.LG'] | Recent advances in large language model (LLM) reasoning have shown that
sophisticated behaviors such as planning and self-reflection can emerge through
reinforcement learning (RL). However, despite these successes, RL in its
current form remains insufficient to induce capabilities that exceed the
limitations of the base model, as it is primarily optimized based on existing
knowledge of the model rather than facilitating the acquisition of new
information. To address this limitation, we employ supervised fine-tuning (SFT)
to learn what RL cannot, which enables the incorporation of new knowledge and
reasoning patterns by leveraging high-quality demonstration data. We analyze
the training dynamics of RL and SFT for LLM reasoning and find that RL excels
at maintaining and improving performance on questions within the model's
original capabilities, while SFT is more effective at enabling progress on
questions beyond the current scope of the model. Motivated by the complementary
strengths of RL and SFT, we introduce a novel training approach,
\textbf{ReLIFT} (\textbf{Re}inforcement \textbf{L}earning \textbf{I}nterleaved
with Online \textbf{F}ine-\textbf{T}uning). In ReLIFT, the model is primarily
trained using RL, but when it encounters challenging questions, high-quality
solutions are collected for fine-tuning, and the training process alternates
between RL and fine-tuning to enhance the model's reasoning abilities. ReLIFT
achieves an average improvement of over +5.2 points across five
competition-level benchmarks and one out-of-distribution benchmark compared to
other zero-RL models. Furthermore, we demonstrate that ReLIFT outperforms both
RL and SFT while using only 13\% of the detailed demonstration data,
highlighting its scalability. These results provide compelling evidence that
ReLIFT overcomes the fundamental limitations of RL and underscores the
significant potential. | 2025-06-09T08:11:20Z | 12 pages, 5 figures | null | null | Learning What Reinforcement Learning Can't: Interleaved Online Fine-Tuning for Hardest Questions | ['Lu Ma', 'Hao Liang', 'Meiyi Qiang', 'Lexiang Tang', 'Xiaochen Ma', 'Zhen Hao Wong', 'Junbo Niu', 'Chengyu Shen', 'Runming He', 'Bin Cui', 'Wentao Zhang'] | 2,025 | arXiv.org | 0 | 31 | ['Computer Science'] |
2,506.0753 | BitVLA: 1-bit Vision-Language-Action Models for Robotics Manipulation | ['Hongyu Wang', 'Chuyan Xiong', 'Ruiping Wang', 'Xilin Chen'] | ['cs.RO', 'cs.CV'] | Vision-Language-Action (VLA) models have shown impressive capabilities across
a wide range of robotics manipulation tasks. However, their growing model size
poses significant challenges for deployment on resource-constrained robotic
systems. While 1-bit pretraining has proven effective for enhancing the
inference efficiency of large language models with minimal performance loss,
its application to VLA models remains underexplored. In this work, we present
BitVLA, the first 1-bit VLA model for robotics manipulation, in which every
parameter is ternary, i.e., {-1, 0, 1}. To further reduce the memory footprint
of the vision encoder, we propose the distillation-aware training strategy that
compresses the full-precision encoder to 1.58-bit weights. During this process,
a full-precision encoder serves as a teacher model to better align latent
representations. Despite the lack of large-scale robotics pretraining, BitVLA
achieves performance comparable to the state-of-the-art model OpenVLA-OFT with
4-bit post-training quantization on the LIBERO benchmark, while consuming only
29.8% of the memory. These results highlight BitVLA's promise for deployment on
memory-constrained edge devices. We release the code and model weights in
https://github.com/ustcwhy/BitVLA. | 2025-06-09T08:15:11Z | Work in progress | null | null | BitVLA: 1-bit Vision-Language-Action Models for Robotics Manipulation | ['Hongyu Wang', 'Chuyan Xiong', 'Ruiping Wang', 'Xilin Chen'] | 2,025 | arXiv.org | 0 | 46 | ['Computer Science'] |
2,506.07597 | Instructing Large Language Models for Low-Resource Languages: A
Systematic Study for Basque | ['Oscar Sainz', 'Naiara Perez', 'Julen Etxaniz', 'Joseba Fernandez de Landa', 'Itziar Aldabe', 'Iker García-Ferrero', 'Aimar Zabala', 'Ekhi Azurmendi', 'German Rigau', 'Eneko Agirre', 'Mikel Artetxe', 'Aitor Soroa'] | ['cs.CL'] | Instructing language models with user intent requires large instruction
datasets, which are only available for a limited set of languages. In this
paper, we explore alternatives to conventional instruction adaptation pipelines
in low-resource scenarios. We assume a realistic scenario for low-resource
languages, where only the following are available: corpora in the target
language, existing open-weight multilingual base and instructed backbone LLMs,
and synthetically generated instructions sampled from the instructed backbone.
We present a comprehensive set of experiments for Basque that systematically
study different combinations of these components evaluated on benchmarks and
human preferences from 1,680 participants. Our conclusions show that target
language corpora are essential, with synthetic instructions yielding robust
models, and, most importantly, that using as backbone an instruction-tuned
model outperforms using a base non-instructed model, and improved results when
scaling up. Using Llama 3.1 instruct 70B as backbone our model comes near
frontier models of much larger sizes for Basque, without using any Basque data
apart from the 1.2B word corpora. We release code, models, instruction
datasets, and human preferences to support full reproducibility in future
research on low-resource language adaptation. | 2025-06-09T09:54:47Z | Under review | null | null | null | null | null | null | null | null | null |
2,506.07621 | LoRMA: Low-Rank Multiplicative Adaptation for LLMs | ['Harsh Bihany', 'Shubham Patel', 'Ashutosh Modi'] | ['cs.CL', 'cs.AI', 'cs.LG'] | Large Language Models have shown remarkable capabilities in the NLP domain.
Their effectiveness can mainly be attributed to their ability to adapt to an
array of downstream tasks. However, generally, full fine-tuning is a
computationally expensive job. To mitigate this, many techniques have been
developed that prime efficiency, a prominent one being Low-Rank Adaptation
(LoRA). However, LoRA and its variants employ re-parametrized additive updates.
In this paper, we propose Low-Rank Multiplicative Adaptation (LoRMA), which
shifts the paradigm of additive updates to a richer space of matrix
multiplicative transformations. We tackle challenges such as computational
complexity and rank bottleneck of matrix multiplication by effectively
re-ordering operations and introducing rank inflation strategies. We conduct
extensive experiments to demonstrate the effectiveness of our approach in terms
of various evaluation metrics. | 2025-06-09T10:36:46Z | Accepted at ACL Findings 2025; 21 pages (9 main paper + 5 pages
references + 7 pages appendix) | null | null | null | null | null | null | null | null | null |
2,506.07634 | SongBloom: Coherent Song Generation via Interleaved Autoregressive
Sketching and Diffusion Refinement | ['Chenyu Yang', 'Shuai Wang', 'Hangting Chen', 'Wei Tan', 'Jianwei Yu', 'Haizhou Li'] | ['eess.AS', 'cs.MM'] | Generating music with coherent structure, harmonious instrumental and vocal
elements remains a significant challenge in song generation. Existing language
models and diffusion-based methods often struggle to balance global coherence
with local fidelity, resulting in outputs that lack musicality or suffer from
incoherent progression and mismatched lyrics. This paper introduces
$\textbf{SongBloom}$, a novel framework for full-length song generation that
leverages an interleaved paradigm of autoregressive sketching and
diffusion-based refinement. SongBloom employs an autoregressive diffusion model
that combines the high fidelity of diffusion models with the scalability of
language models. Specifically, it gradually extends a musical sketch from short
to long and refines the details from coarse to fine-grained. The interleaved
generation paradigm effectively integrates prior semantic and acoustic context
to guide the generation process. Experimental results demonstrate that
SongBloom outperforms existing methods across both subjective and objective
metrics and achieves performance comparable to the state-of-the-art commercial
music generation platforms. Audio samples are available on our demo page:
https://cypress-yang.github.io/SongBloom_demo. The code and model weights have
been released on https://github.com/Cypress-Yang/SongBloom . | 2025-06-09T11:01:01Z | Submitted to NeurIPS2025 | null | null | null | null | null | null | null | null | null |
2,506.07636 | SWE-Dev: Building Software Engineering Agents with Training and
Inference Scaling | ['Haoran Wang', 'Zhenyu Hou', 'Yao Wei', 'Jie Tang', 'Yuxiao Dong'] | ['cs.AI'] | Large language models (LLMs) have advanced rapidly from conversational
problem solving to addressing real-world tasks involving tool use, such as
software engineering (SWE). Recent LLM-powered toolkits, such as OpenAI Codex
and Cursor, have offered end-to-end automation of the software development
process. However, building effective SWE agents remains challenging due to the
lack of high-quality training data and effective test cases. To address this
issue, we present SWE-Dev, an SWE agent built upon open-source LLMs. First, we
develop a robust pipeline to synthesize test cases for patch evaluation.
Second, we scale up agent trajectories to construct the training data for
building SWE-Dev. Experiments on the SWE-bench-Verified benchmark show that the
SWE-Dev models can achieve top performance among all open SWE agents.
Specifically, the success rates of the SWE-Dev 7B and 32B parameter models
reach 23.4% and 36.6%, respectively, outperforming state-of-the-art open-source
models. All code, models, and datasets are publicly available at
https://github.com/THUDM/SWE-Dev. | 2025-06-09T11:03:16Z | Accepted to Findings of ACL'25 | null | null | null | null | null | null | null | null | null |
2,506.07643 | Synthetic Visual Genome | ['Jae Sung Park', 'Zixian Ma', 'Linjie Li', 'Chenhao Zheng', 'Cheng-Yu Hsieh', 'Ximing Lu', 'Khyathi Chandu', 'Quan Kong', 'Norimasa Kobori', 'Ali Farhadi', 'Yejin Choi', 'Ranjay Krishna'] | ['cs.CV'] | Reasoning over visual relationships-spatial, functional, interactional,
social, etc.-is considered to be a fundamental component of human cognition.
Yet, despite the major advances in visual comprehension in multimodal language
models (MLMs), precise reasoning over relationships and their generations
remains a challenge. We introduce ROBIN: an MLM instruction-tuned with densely
annotated relationships capable of constructing high-quality dense scene graphs
at scale. To train ROBIN, we curate SVG, a synthetic scene graph dataset by
completing the missing relations of selected objects in existing scene graphs
using a teacher MLM and a carefully designed filtering process to ensure
high-quality. To generate more accurate and rich scene graphs at scale for any
image, we introduce SG-EDIT: a self-distillation framework where GPT-4o further
refines ROBIN's predicted scene graphs by removing unlikely relations and/or
suggesting relevant ones. In total, our dataset contains 146K images and 5.6M
relationships for 2.6M objects. Results show that our ROBIN-3B model, despite
being trained on less than 3 million instances, outperforms similar-size models
trained on over 300 million instances on relationship understanding benchmarks,
and even surpasses larger models up to 13B parameters. Notably, it achieves
state-of-the-art performance in referring expression comprehension with a score
of 88.9, surpassing the previous best of 87.4. Our results suggest that
training on the refined scene graph data is crucial to maintaining high
performance across diverse visual reasoning task. | 2025-06-09T11:09:10Z | CVPR 2025 | null | null | null | null | null | null | null | null | null |
2,506.07833 | Improving Large Language Models with Concept-Aware Fine-Tuning | ['Michael K. Chen', 'Xikun Zhang', 'Jiaxing Huang', 'Dacheng Tao'] | ['cs.LG', 'cs.AI', 'cs.CL'] | Large language models (LLMs) have become the cornerstone of modern AI.
However, the existing paradigm of next-token prediction fundamentally limits
their ability to form coherent, high-level concepts, making it a critical
barrier to human-like understanding and reasoning. Take the phrase "ribonucleic
acid" as an example: an LLM will first decompose it into tokens, i.e.,
artificial text fragments ("rib", "on", ...), then learn each token
sequentially, rather than grasping the phrase as a unified, coherent semantic
entity. This fragmented representation hinders deeper conceptual understanding
and, ultimately, the development of truly intelligent systems. In response, we
introduce Concept-Aware Fine-Tuning (CAFT), a novel multi-token training method
that redefines how LLMs are fine-tuned. By enabling the learning of sequences
that span multiple tokens, this method fosters stronger concept-aware learning.
Our experiments demonstrate significant improvements compared to conventional
next-token finetuning methods across diverse tasks, including traditional
applications like text summarization and domain-specific ones like de novo
protein design. Multi-token prediction was previously only possible in the
prohibitively expensive pretraining phase; CAFT, to our knowledge, is the first
to bring the multi-token setting to the post-training phase, thus effectively
democratizing its benefits for the broader community of practitioners and
researchers. Finally, the unexpected effectiveness of our proposed method
suggests wider implications for the machine learning research community. All
code and data are available at https://github.com/michaelchen-lab/caft-llm | 2025-06-09T14:55:00Z | null | null | null | Improving Large Language Models with Concept-Aware Fine-Tuning | ['Michael Chen', 'Xikun Zhang', 'Jiaxing Huang', 'Dacheng Tao'] | 2,025 | arXiv.org | 0 | 63 | ['Computer Science'] |
2,506.07837 | HAIBU-ReMUD: Reasoning Multimodal Ultrasound Dataset and Model Bridging
to General Specific Domains | ['Shijie Wang', 'Yilun Zhang', 'Zeyu Lai', 'Dexing Kong'] | ['cs.AI'] | Multimodal large language models (MLLMs) have shown great potential in
general domains but perform poorly in some specific domains due to a lack of
domain-specific data, such as image-text data or vedio-text data. In some
specific domains, there is abundant graphic and textual data scattered around,
but lacks standardized arrangement. In the field of medical ultrasound, there
are ultrasonic diagnostic books, ultrasonic clinical guidelines, ultrasonic
diagnostic reports, and so on. However, these ultrasonic materials are often
saved in the forms of PDF, images, etc., and cannot be directly used for the
training of MLLMs. This paper proposes a novel image-text reasoning supervised
fine-tuning data generation pipeline to create specific domain quadruplets
(image, question, thinking trace, and answer) from domain-specific materials. A
medical ultrasound domain dataset ReMUD is established, containing over 45,000
reasoning and non-reasoning supervised fine-tuning Question Answering (QA) and
Visual Question Answering (VQA) data. The ReMUD-7B model, fine-tuned on
Qwen2.5-VL-7B-Instruct, outperforms general-domain MLLMs in medical ultrasound
field. To facilitate research, the ReMUD dataset, data generation codebase, and
ReMUD-7B parameters will be released at https://github.com/ShiDaizi/ReMUD,
addressing the data shortage issue in specific domain MLLMs. | 2025-06-09T15:01:38Z | null | null | null | null | null | null | null | null | null | null |
2,506.079 | MiniCPM4: Ultra-Efficient LLMs on End Devices | ['MiniCPM Team', 'Chaojun Xiao', 'Yuxuan Li', 'Xu Han', 'Yuzhuo Bai', 'Jie Cai', 'Haotian Chen', 'Wentong Chen', 'Xin Cong', 'Ganqu Cui', 'Ning Ding', 'Shengdan Fan', 'Yewei Fang', 'Zixuan Fu', 'Wenyu Guan', 'Yitong Guan', 'Junshao Guo', 'Yufeng Han', 'Bingxiang He', 'Yuxiang Huang', 'Cunliang Kong', 'Qiuzuo Li', 'Siyuan Li', 'Wenhao Li', 'Yanghao Li', 'Yishan Li', 'Zhen Li', 'Dan Liu', 'Biyuan Lin', 'Yankai Lin', 'Xiang Long', 'Quanyu Lu', 'Yaxi Lu', 'Peiyan Luo', 'Hongya Lyu', 'Litu Ou', 'Yinxu Pan', 'Zekai Qu', 'Qundong Shi', 'Zijun Song', 'Jiayuan Su', 'Zhou Su', 'Ao Sun', 'Xianghui Sun', 'Peijun Tang', 'Fangzheng Wang', 'Feng Wang', 'Shuo Wang', 'Yudong Wang', 'Yesai Wu', 'Zhenyu Xiao', 'Jie Xie', 'Zihao Xie', 'Yukun Yan', 'Jiarui Yuan', 'Kaihuo Zhang', 'Lei Zhang', 'Linyue Zhang', 'Xueren Zhang', 'Yudi Zhang', 'Hengyu Zhao', 'Weilin Zhao', 'Weilun Zhao', 'Yuanqian Zhao', 'Zhi Zheng', 'Ge Zhou', 'Jie Zhou', 'Wei Zhou', 'Zihan Zhou', 'Zixuan Zhou', 'Zhiyuan Liu', 'Guoyang Zeng', 'Chao Jia', 'Dahai Li', 'Maosong Sun'] | ['cs.CL', 'cs.AI'] | This paper introduces MiniCPM4, a highly efficient large language model (LLM)
designed explicitly for end-side devices. We achieve this efficiency through
systematic innovation in four key dimensions: model architecture, training
data, training algorithms, and inference systems. Specifically, in terms of
model architecture, we propose InfLLM v2, a trainable sparse attention
mechanism that accelerates both prefilling and decoding phases for long-context
processing. Regarding training data, we propose UltraClean, an efficient and
accurate pre-training data filtering and generation strategy, and UltraChat v2,
a comprehensive supervised fine-tuning dataset. These datasets enable
satisfactory model performance to be achieved using just 8 trillion training
tokens. Regarding training algorithms, we propose ModelTunnel v2 for efficient
pre-training strategy search, and improve existing post-training methods by
introducing chunk-wise rollout for load-balanced reinforcement learning and
data-efficient tenary LLM, BitCPM. Regarding inference systems, we propose
CPM.cu that integrates sparse attention, model quantization, and speculative
sampling to achieve efficient prefilling and decoding. To meet diverse
on-device requirements, MiniCPM4 is available in two versions, with 0.5B and 8B
parameters, respectively. Sufficient evaluation results show that MiniCPM4
outperforms open-source models of similar size across multiple benchmarks,
highlighting both its efficiency and effectiveness. Notably, MiniCPM4-8B
demonstrates significant speed improvements over Qwen3-8B when processing long
sequences. Through further adaptation, MiniCPM4 successfully powers diverse
applications, including trustworthy survey generation and tool use with model
context protocol, clearly showcasing its broad usability. | 2025-06-09T16:16:50Z | MiniCPM4 Technical Report | null | null | null | null | null | null | null | null | null |
2,506.07905 | WeThink: Toward General-purpose Vision-Language Reasoning via
Reinforcement Learning | ['Jie Yang', 'Feipeng Ma', 'Zitian Wang', 'Dacheng Yin', 'Kang Rong', 'Fengyun Rao', 'Ruimao Zhang'] | ['cs.CV'] | Building on the success of text-based reasoning models like DeepSeek-R1,
extending these capabilities to multimodal reasoning holds great promise. While
recent works have attempted to adapt DeepSeek-R1-style reinforcement learning
(RL) training paradigms to multimodal large language models (MLLM), focusing on
domain-specific tasks like math and visual perception, a critical question
remains: How can we achieve the general-purpose visual-language reasoning
through RL? To address this challenge, we make three key efforts: (1) A novel
Scalable Multimodal QA Synthesis pipeline that autonomously generates
context-aware, reasoning-centric question-answer (QA) pairs directly from the
given images. (2) The open-source WeThink dataset containing over 120K
multimodal QA pairs with annotated reasoning paths, curated from 18 diverse
dataset sources and covering various question domains. (3) A comprehensive
exploration of RL on our dataset, incorporating a hybrid reward mechanism that
combines rule-based verification with model-based assessment to optimize RL
training efficiency across various task domains. Across 14 diverse MLLM
benchmarks, we demonstrate that our WeThink dataset significantly enhances
performance, from mathematical reasoning to diverse general multimodal tasks.
Moreover, we show that our automated data pipeline can continuously increase
data diversity to further improve model performance. | 2025-06-09T16:20:54Z | null | null | null | WeThink: Toward General-purpose Vision-Language Reasoning via Reinforcement Learning | ['Jie Yang', 'Feipeng Ma', 'Zitian Wang', 'Dacheng Yin', 'Kang Rong', 'Fengyun Rao', 'Ruimao Zhang'] | 2,025 | arXiv.org | 0 | 96 | ['Computer Science'] |
2,506.07918 | CausalPFN: Amortized Causal Effect Estimation via In-Context Learning | ['Vahid Balazadeh', 'Hamidreza Kamkari', 'Valentin Thomas', 'Benson Li', 'Junwei Ma', 'Jesse C. Cresswell', 'Rahul G. Krishnan'] | ['cs.LG', 'stat.ML'] | Causal effect estimation from observational data is fundamental across
various applications. However, selecting an appropriate estimator from dozens
of specialized methods demands substantial manual effort and domain expertise.
We present CausalPFN, a single transformer that amortizes this workflow:
trained once on a large library of simulated data-generating processes that
satisfy ignorability, it infers causal effects for new observational datasets
out-of-the-box. CausalPFN combines ideas from Bayesian causal inference with
the large-scale training protocol of prior-fitted networks (PFNs), learning to
map raw observations directly to causal effects without any task-specific
adjustment. Our approach achieves superior average performance on heterogeneous
and average treatment effect estimation benchmarks (IHDP, Lalonde, ACIC).
Moreover, it shows competitive performance for real-world policy making on
uplift modeling tasks. CausalPFN provides calibrated uncertainty estimates to
support reliable decision-making based on Bayesian principles. This
ready-to-use model does not require any further training or tuning and takes a
step toward automated causal inference (https://github.com/vdblm/CausalPFN). | 2025-06-09T16:31:06Z | null | null | null | CausalPFN: Amortized Causal Effect Estimation via In-Context Learning | ['Vahid Balazadeh', 'Hamidreza Kamkari', 'Valentin Thomas', 'Benson Li', 'Junwei Ma', 'Jesse C. Cresswell', 'Rahul G. Krishnan'] | 2,025 | arXiv.org | 0 | 107 | ['Computer Science', 'Mathematics'] |
2,506.07932 | Squeeze3D: Your 3D Generation Model is Secretly an Extreme Neural
Compressor | ['Rishit Dagli', 'Yushi Guan', 'Sankeerth Durvasula', 'Mohammadreza Mofayezi', 'Nandita Vijaykumar'] | ['cs.GR', 'cs.CV', 'cs.LG'] | We propose Squeeze3D, a novel framework that leverages implicit prior
knowledge learnt by existing pre-trained 3D generative models to compress 3D
data at extremely high compression ratios. Our approach bridges the latent
spaces between a pre-trained encoder and a pre-trained generation model through
trainable mapping networks. Any 3D model represented as a mesh, point cloud, or
a radiance field is first encoded by the pre-trained encoder and then
transformed (i.e. compressed) into a highly compact latent code. This latent
code can effectively be used as an extremely compressed representation of the
mesh or point cloud. A mapping network transforms the compressed latent code
into the latent space of a powerful generative model, which is then conditioned
to recreate the original 3D model (i.e. decompression). Squeeze3D is trained
entirely on generated synthetic data and does not require any 3D datasets. The
Squeeze3D architecture can be flexibly used with existing pre-trained 3D
encoders and existing generative models. It can flexibly support different
formats, including meshes, point clouds, and radiance fields. Our experiments
demonstrate that Squeeze3D achieves compression ratios of up to 2187x for
textured meshes, 55x for point clouds, and 619x for radiance fields while
maintaining visual quality comparable to many existing methods. Squeeze3D only
incurs a small compression and decompression latency since it does not involve
training object-specific networks to compress an object. | 2025-06-09T16:52:10Z | null | null | null | null | null | null | null | null | null | null |
2,506.07966 | SpaCE-10: A Comprehensive Benchmark for Multimodal Large Language Models
in Compositional Spatial Intelligence | ['Ziyang Gong', 'Wenhao Li', 'Oliver Ma', 'Songyuan Li', 'Jiayi Ji', 'Xue Yang', 'Gen Luo', 'Junchi Yan', 'Rongrong Ji'] | ['cs.CV'] | Multimodal Large Language Models (MLLMs) have achieved remarkable progress in
various multimodal tasks. To pursue higher intelligence in space, MLLMs require
integrating multiple atomic spatial capabilities to handle complex and dynamic
tasks. However, existing benchmarks struggle to comprehensively evaluate the
spatial intelligence of common MLLMs from the atomic level to the compositional
level. To fill this gap, we present SpaCE-10, a comprehensive benchmark for
compositional spatial evaluations. In SpaCE-10, we define 10 atomic spatial
capabilities, which are combined to form 8 compositional capabilities. Based on
these definitions, we propose a novel hierarchical annotation pipeline to
generate high-quality and diverse question-answer (QA) pairs. With over 150+
hours of human expert effort, we obtain over 5k QA pairs for 811 real indoor
scenes in SpaCE-10, which covers various evaluation settings like point cloud
input and multi-choice QA. We conduct an extensive evaluation of common MLLMs
on SpaCE-10 and find that even the most advanced MLLM still lags behind humans
by large margins. Through our careful study, we also draw several significant
findings that benefit the MLLM community. For example, we reveal that the
shortcoming of counting capability greatly limits the compositional spatial
capabilities of existing MLLMs. The evaluation code and benchmark datasets are
available at https://github.com/Cuzyoung/SpaCE-10. | 2025-06-09T17:41:36Z | null | null | null | null | null | null | null | null | null | null |
2,506.07986 | Rethinking Cross-Modal Interaction in Multimodal Diffusion Transformers | ['Zhengyao Lv', 'Tianlin Pan', 'Chenyang Si', 'Zhaoxi Chen', 'Wangmeng Zuo', 'Ziwei Liu', 'Kwan-Yee K. Wong'] | ['cs.CV'] | Multimodal Diffusion Transformers (MM-DiTs) have achieved remarkable progress
in text-driven visual generation. However, even state-of-the-art MM-DiT models
like FLUX struggle with achieving precise alignment between text prompts and
generated content. We identify two key issues in the attention mechanism of
MM-DiT, namely 1) the suppression of cross-modal attention due to token
imbalance between visual and textual modalities and 2) the lack of
timestep-aware attention weighting, which hinder the alignment. To address
these issues, we propose \textbf{Temperature-Adjusted Cross-modal Attention
(TACA)}, a parameter-efficient method that dynamically rebalances multimodal
interactions through temperature scaling and timestep-dependent adjustment.
When combined with LoRA fine-tuning, TACA significantly enhances text-image
alignment on the T2I-CompBench benchmark with minimal computational overhead.
We tested TACA on state-of-the-art models like FLUX and SD3.5, demonstrating
its ability to improve image-text alignment in terms of object appearance,
attribute binding, and spatial relationships. Our findings highlight the
importance of balancing cross-modal attention in improving semantic fidelity in
text-to-image diffusion models. Our codes are publicly available at
\href{https://github.com/Vchitect/TACA} | 2025-06-09T17:54:04Z | Project Page: https://vchitect.github.io/TACA/ | null | null | null | null | null | null | null | null | null |
2,506.07999 | MADFormer: Mixed Autoregressive and Diffusion Transformers for
Continuous Image Generation | ['Junhao Chen', 'Yulia Tsvetkov', 'Xiaochuang Han'] | ['cs.CV', 'cs.LG'] | Recent progress in multimodal generation has increasingly combined
autoregressive (AR) and diffusion-based approaches, leveraging their
complementary strengths: AR models capture long-range dependencies and produce
fluent, context-aware outputs, while diffusion models operate in continuous
latent spaces to refine high-fidelity visual details. However, existing hybrids
often lack systematic guidance on how and why to allocate model capacity
between these paradigms. In this work, we introduce MADFormer, a Mixed
Autoregressive and Diffusion Transformer that serves as a testbed for analyzing
AR-diffusion trade-offs. MADFormer partitions image generation into spatial
blocks, using AR layers for one-pass global conditioning across blocks and
diffusion layers for iterative local refinement within each block. Through
controlled experiments on FFHQ-1024 and ImageNet, we identify two key insights:
(1) block-wise partitioning significantly improves performance on
high-resolution images, and (2) vertically mixing AR and diffusion layers
yields better quality-efficiency balances--improving FID by up to 75% under
constrained inference compute. Our findings offer practical design principles
for future hybrid generative models. | 2025-06-09T17:59:01Z | null | null | null | null | null | null | null | null | null | null |
2,506.08003 | Audio-Sync Video Generation with Multi-Stream Temporal Control | ['Shuchen Weng', 'Haojie Zheng', 'Zheng Chang', 'Si Li', 'Boxin Shi', 'Xinlong Wang'] | ['cs.CV', 'cs.AI'] | Audio is inherently temporal and closely synchronized with the visual world,
making it a naturally aligned and expressive control signal for controllable
video generation (e.g., movies). Beyond control, directly translating audio
into video is essential for understanding and visualizing rich audio narratives
(e.g., Podcasts or historical recordings). However, existing approaches fall
short in generating high-quality videos with precise audio-visual
synchronization, especially across diverse and complex audio types. In this
work, we introduce MTV, a versatile framework for audio-sync video generation.
MTV explicitly separates audios into speech, effects, and music tracks,
enabling disentangled control over lip motion, event timing, and visual mood,
respectively -- resulting in fine-grained and semantically aligned video
generation. To support the framework, we additionally present DEMIX, a dataset
comprising high-quality cinematic videos and demixed audio tracks. DEMIX is
structured into five overlapped subsets, enabling scalable multi-stage training
for diverse generation scenarios. Extensive experiments demonstrate that MTV
achieves state-of-the-art performance across six standard metrics spanning
video quality, text-video consistency, and audio-video alignment. Project page:
https://hjzheng.net/projects/MTV/. | 2025-06-09T17:59:42Z | null | null | null | null | null | null | null | null | null | null |
2,506.08007 | Reinforcement Pre-Training | ['Qingxiu Dong', 'Li Dong', 'Yao Tang', 'Tianzhu Ye', 'Yutao Sun', 'Zhifang Sui', 'Furu Wei'] | ['cs.CL'] | In this work, we introduce Reinforcement Pre-Training (RPT) as a new scaling
paradigm for large language models and reinforcement learning (RL).
Specifically, we reframe next-token prediction as a reasoning task trained
using RL, where it receives verifiable rewards for correctly predicting the
next token for a given context. RPT offers a scalable method to leverage vast
amounts of text data for general-purpose RL, rather than relying on
domain-specific annotated answers. By incentivizing the capability of
next-token reasoning, RPT significantly improves the language modeling accuracy
of predicting the next tokens. Moreover, RPT provides a strong pre-trained
foundation for further reinforcement fine-tuning. The scaling curves show that
increased training compute consistently improves the next-token prediction
accuracy. The results position RPT as an effective and promising scaling
paradigm to advance language model pre-training. | 2025-06-09T17:59:53Z | null | null | null | null | null | null | null | null | null | null |
2,506.08009 | Self Forcing: Bridging the Train-Test Gap in Autoregressive Video
Diffusion | ['Xun Huang', 'Zhengqi Li', 'Guande He', 'Mingyuan Zhou', 'Eli Shechtman'] | ['cs.CV', 'cs.AI', 'cs.LG'] | We introduce Self Forcing, a novel training paradigm for autoregressive video
diffusion models. It addresses the longstanding issue of exposure bias, where
models trained on ground-truth context must generate sequences conditioned on
their own imperfect outputs during inference. Unlike prior methods that denoise
future frames based on ground-truth context frames, Self Forcing conditions
each frame's generation on previously self-generated outputs by performing
autoregressive rollout with key-value (KV) caching during training. This
strategy enables supervision through a holistic loss at the video level that
directly evaluates the quality of the entire generated sequence, rather than
relying solely on traditional frame-wise objectives. To ensure training
efficiency, we employ a few-step diffusion model along with a stochastic
gradient truncation strategy, effectively balancing computational cost and
performance. We further introduce a rolling KV cache mechanism that enables
efficient autoregressive video extrapolation. Extensive experiments demonstrate
that our approach achieves real-time streaming video generation with sub-second
latency on a single GPU, while matching or even surpassing the generation
quality of significantly slower and non-causal diffusion models. Project
website: http://self-forcing.github.io/ | 2025-06-09T17:59:55Z | Project website: http://self-forcing.github.io/ | null | null | null | null | null | null | null | null | null |
2,506.0801 | Vision Transformers Don't Need Trained Registers | ['Nick Jiang', 'Amil Dravid', 'Alexei Efros', 'Yossi Gandelsman'] | ['cs.CV', 'cs.AI'] | We investigate the mechanism underlying a previously identified phenomenon in
Vision Transformers -- the emergence of high-norm tokens that lead to noisy
attention maps. We observe that in multiple models (e.g., CLIP, DINOv2), a
sparse set of neurons is responsible for concentrating high-norm activations on
outlier tokens, leading to irregular attention patterns and degrading
downstream visual processing. While the existing solution for removing these
outliers involves retraining models from scratch with additional learned
register tokens, we use our findings to create a training-free approach to
mitigate these artifacts. By shifting the high-norm activations from our
discovered register neurons into an additional untrained token, we can mimic
the effect of register tokens on a model already trained without registers. We
demonstrate that our method produces cleaner attention and feature maps,
enhances performance over base models across multiple downstream visual tasks,
and achieves results comparable to models explicitly trained with register
tokens. We then extend test-time registers to off-the-shelf vision-language
models to improve their interpretability. Our results suggest that test-time
registers effectively take on the role of register tokens at test-time,
offering a training-free solution for any pre-trained model released without
them. | 2025-06-09T17:59:57Z | Project page and code: https://avdravid.github.io/test-time-registers | null | null | null | null | null | null | null | null | null |
2,506.08011 | Play to Generalize: Learning to Reason Through Game Play | ['Yunfei Xie', 'Yinsong Ma', 'Shiyi Lan', 'Alan Yuille', 'Junfei Xiao', 'Chen Wei'] | ['cs.CV', 'cs.CL'] | Developing generalizable reasoning capabilities in multimodal large language
models (MLLMs) remains challenging. Motivated by cognitive science literature
suggesting that gameplay promotes transferable cognitive skills, we propose a
novel post-training paradigm, Visual Game Learning, or ViGaL, where MLLMs
develop out-of-domain generalization of multimodal reasoning through playing
arcade-like games. Specifically, we show that post-training a 7B-parameter MLLM
via reinforcement learning (RL) on simple arcade-like games, e.g. Snake,
significantly enhances its downstream performance on multimodal math benchmarks
like MathVista, and on multi-discipline questions like MMMU, without seeing any
worked solutions, equations, or diagrams during RL, suggesting the capture of
transferable reasoning skills. Remarkably, our model outperforms specialist
models tuned on multimodal reasoning data in multimodal reasoning benchmarks,
while preserving the base model's performance on general visual benchmarks, a
challenge where specialist models often fall short. Our findings suggest a new
post-training paradigm: synthetic, rule-based games can serve as controllable
and scalable pre-text tasks that unlock generalizable multimodal reasoning
abilities in MLLMs. | 2025-06-09T17:59:57Z | Project Page: https://yunfeixie233.github.io/ViGaL/ | null | null | Play to Generalize: Learning to Reason Through Game Play | ['Yunfei Xie', 'Yinsong Ma', 'Shiyi Lan', 'Alan L. Yuille', 'Junfei Xiao', 'Chen Wei'] | 2,025 | arXiv.org | 0 | 74 | ['Computer Science'] |
2,506.08293 | Diffusion Sequence Models for Enhanced Protein Representation and
Generation | ['Logan Hallee', 'Nikolaos Rafailidis', 'David B. Bichara', 'Jason P. Gleghorn'] | ['q-bio.BM'] | Proteins are fundamental to biology, executing diverse functions through
complex physicochemical interactions, and they hold transformative potential
across medicine, materials science, and environmental applications. Protein
Language Models (pLMs) aim to unlock insights from the vast space of unlabeled
protein sequences by learning rich, semantic representations from primary
sequences via masked language modeling. However, these models typically exhibit
limited generative capacity. In this work, we introduce the Diffusion Sequence
Model (DSM), a novel pLM trained with masked diffusion to enable both
high-quality representation learning and generative protein design. DSM builds
upon the ESM2 architecture by incorporating a masked forward diffusion process
inspired by the LLaDA framework. After training, DSM is capable of generating
diverse, biomimetic sequences that align with expected amino acid compositions,
secondary structures, and predicted functions, even with 90\% token corruption.
Furthermore, DSM's learned representations match or exceed those of similarly
sized pLMs on downstream tasks. We also introduce DSM(ppi), a variant
fine-tuned to generate protein binders by attending to target sequences. We
demonstrate DSM(ppi)'s effectiveness on the challenging Bench-tested Binder
Benchmark (BenchBB), where both DSM and DSM(ppi) produce candidates with
superior predicted binding affinity compared to known binders. Our results
establish masked diffusion as a powerful paradigm for unifying protein
representation and generation in a single framework. | 2025-06-09T23:50:11Z | 20 pages, 15 figures | null | null | null | null | null | null | null | null | null |
2,506.083 | Institutional Books 1.0: A 242B token dataset from Harvard Library's
collections, refined for accuracy and usability | ['Matteo Cargnelutti', 'Catherine Brobston', 'John Hess', 'Jack Cushman', 'Kristi Mukk', 'Aristana Scourtas', 'Kyle Courtney', 'Greg Leppert', 'Amanda Watson', 'Martha Whitehead', 'Jonathan Zittrain'] | ['cs.CL', 'cs.DL'] | Large language models (LLMs) use data to learn about the world in order to
produce meaningful correlations and predictions. As such, the nature, scale,
quality, and diversity of the datasets used to train these models, or to
support their work at inference time, have a direct impact on their quality.
The rapid development and adoption of LLMs of varying quality has brought into
focus the scarcity of publicly available, high-quality training data and
revealed an urgent need to ground the stewardship of these datasets in
sustainable practices with clear provenance chains. To that end, this technical
report introduces Institutional Books 1.0, a large collection of public domain
books originally digitized through Harvard Library's participation in the
Google Books project, beginning in 2006. Working with Harvard Library, we
extracted, analyzed, and processed these volumes into an extensively-documented
dataset of historic texts. This analysis covers the entirety of Harvard
Library's collection scanned as part of that project, originally spanning
1,075,899 volumes written in over 250 different languages for a total of
approximately 250 billion tokens. As part of this initial release, the
OCR-extracted text (original and post-processed) as well as the metadata
(bibliographic, source, and generated) of the 983,004 volumes, or 242B tokens,
identified as being in the public domain have been made available. This report
describes this project's goals and methods as well as the results of the
analyses we performed, all in service of making this historical collection more
accessible and easier for humans and machines alike to filter, read and use. | 2025-06-10T00:11:30Z | null | null | null | null | null | null | null | null | null | null |
2,506.08388 | Reinforcement Learning Teachers of Test Time Scaling | ['Edoardo Cetin', 'Tianyu Zhao', 'Yujin Tang'] | ['cs.LG', 'cs.AI', 'cs.CL'] | Training reasoning language models (LMs) with reinforcement learning (RL) for
one-hot correctness inherently relies on the LM being able to explore and solve
its task with some chance at initialization. Furthermore, a key use case of
reasoning LMs is to act as teachers for distilling new students and
cold-starting future RL iterations rather than being deployed themselves. From
these considerations, we introduce a new framework that avoids RL's exploration
challenge by training a new class of Reinforcement-Learned Teachers (RLTs)
focused on yielding the most effective downstream distillation. RLTs are
prompted with both the question and solution to each problem, and tasked to
simply "connect-the-dots" with detailed explanations tailored for their
students. We train RLTs with dense rewards obtained by feeding each explanation
to the student and testing its understanding of the problem's solution. In
practice, the raw outputs of a 7B RLT provide higher final performance on
competition and graduate-level tasks than existing distillation and
cold-starting pipelines that collect and postprocess the reasoning traces of
orders of magnitude larger LMs. Furthermore, RLTs maintain their effectiveness
when training larger students and when applied zero-shot to out-of-distribution
tasks, unlocking new levels of efficiency and re-usability for the RL reasoning
framework. | 2025-06-10T02:53:24Z | Code available at: https://github.com/SakanaAI/RLT | null | null | Reinforcement Learning Teachers of Test Time Scaling | ['Edoardo Cetin', 'Tianyu Zhao', 'Yujin Tang'] | 2,025 | arXiv.org | 0 | 45 | ['Computer Science'] |
2,506.0864 | Orientation Matters: Making 3D Generative Models Orientation-Aligned | ['Yichong Lu', 'Yuzhuo Tian', 'Zijin Jiang', 'Yikun Zhao', 'Yuanbo Yang', 'Hao Ouyang', 'Haoji Hu', 'Huimin Yu', 'Yujun Shen', 'Yiyi Liao'] | ['cs.CV'] | Humans intuitively perceive object shape and orientation from a single image,
guided by strong priors about canonical poses. However, existing 3D generative
models often produce misaligned results due to inconsistent training data,
limiting their usability in downstream tasks. To address this gap, we introduce
the task of orientation-aligned 3D object generation: producing 3D objects from
single images with consistent orientations across categories. To facilitate
this, we construct Objaverse-OA, a dataset of 14,832 orientation-aligned 3D
models spanning 1,008 categories. Leveraging Objaverse-OA, we fine-tune two
representative 3D generative models based on multi-view diffusion and 3D
variational autoencoder frameworks to produce aligned objects that generalize
well to unseen objects across various categories. Experimental results
demonstrate the superiority of our method over post-hoc alignment approaches.
Furthermore, we showcase downstream applications enabled by our aligned object
generation, including zero-shot object orientation estimation via
analysis-by-synthesis and efficient arrow-based object rotation manipulation. | 2025-06-10T09:54:37Z | Project Page: https://xdimlab.github.io/Orientation_Matters | null | null | null | null | null | null | null | null | null |
2,506.08672 | RuleReasoner: Reinforced Rule-based Reasoning via Domain-aware Dynamic
Sampling | ['Yang Liu', 'Jiaqi Li', 'Zilong Zheng'] | ['cs.CL', 'cs.AI', 'cs.LG'] | Rule-based reasoning has been acknowledged as one of the fundamental problems
in reasoning, while deviations in rule formats, types, and complexity in
real-world applications pose severe challenges. Recent studies have shown that
large reasoning models (LRMs) have remarkable reasoning capabilities, and their
performance is substantially enhanced by reinforcement learning (RL). However,
it remains an open question whether small reasoning models (SRMs) can learn
rule-based reasoning effectively with robust generalization across diverse
tasks and domains. To address this, we introduce Reinforced Rule-based
Reasoning, a.k.a. RuleReasoner, a simple yet effective method to conduct
rule-based reasoning via a wide collection of curated tasks and a novel
domain-aware dynamic sampling approach. Specifically, RuleReasoner resamples
each training batch by updating the sampling weights of different domains based
on historical rewards. This facilitates domain augmentation and flexible online
learning schedules for RL, obviating the need for pre-hoc human-engineered
mix-training recipes used in existing methods. Empirical evaluations on
in-distribution (ID) and out-of-distribution (OOD) benchmarks reveal that
RuleReasoner outperforms frontier LRMs by a significant margin ($\Delta$4.1%
average points on eight ID tasks and $\Delta$10.4% average points on three OOD
tasks over OpenAI-o1). Notably, our approach also exhibits higher computational
efficiency compared to prior dynamic sampling methods for RL. | 2025-06-10T10:31:21Z | 22 pages, 10 figures, 8 tables | null | null | null | null | null | null | null | null | null |
2,506.08897 | PlantDeBERTa: An Open Source Language Model for Plant Science | ['Hiba Khey', 'Amine Lakhder', 'Salma Rouichi', 'Imane El Ghabi', 'Kamal Hejjaoui', 'Younes En-nahli', 'Fahd Kalloubi', 'Moez Amri'] | ['cs.CL', 'cs.AI'] | The rapid advancement of transformer-based language models has catalyzed
breakthroughs in biomedical and clinical natural language processing; however,
plant science remains markedly underserved by such domain-adapted tools. In
this work, we present PlantDeBERTa, a high-performance, open-source language
model specifically tailored for extracting structured knowledge from plant
stress-response literature. Built upon the DeBERTa architecture-known for its
disentangled attention and robust contextual encoding-PlantDeBERTa is
fine-tuned on a meticulously curated corpus of expert-annotated abstracts, with
a primary focus on lentil (Lens culinaris) responses to diverse abiotic and
biotic stressors. Our methodology combines transformer-based modeling with
rule-enhanced linguistic post-processing and ontology-grounded entity
normalization, enabling PlantDeBERTa to capture biologically meaningful
relationships with precision and semantic fidelity. The underlying corpus is
annotated using a hierarchical schema aligned with the Crop Ontology,
encompassing molecular, physiological, biochemical, and agronomic dimensions of
plant adaptation. PlantDeBERTa exhibits strong generalization capabilities
across entity types and demonstrates the feasibility of robust domain
adaptation in low-resource scientific fields.By providing a scalable and
reproducible framework for high-resolution entity recognition, PlantDeBERTa
bridges a critical gap in agricultural NLP and paves the way for intelligent,
data-driven systems in plant genomics, phenomics, and agronomic knowledge
discovery. Our model is publicly released to promote transparency and
accelerate cross-disciplinary innovation in computational plant science. | 2025-06-10T15:24:03Z | null | null | null | null | null | null | null | null | null | null |
2,506.089 | MIRAGE: Multimodal foundation model and benchmark for comprehensive
retinal OCT image analysis | ['José Morano', 'Botond Fazekas', 'Emese Sükei', 'Ronald Fecso', 'Taha Emre', 'Markus Gumpinger', 'Georg Faustmann', 'Marzieh Oghbaie', 'Ursula Schmidt-Erfurth', 'Hrvoje Bogunović'] | ['cs.CV'] | Artificial intelligence (AI) has become a fundamental tool for assisting
clinicians in analyzing ophthalmic images, such as optical coherence tomography
(OCT). However, developing AI models often requires extensive annotation, and
existing models tend to underperform on independent, unseen data. Foundation
models (FMs), large AI models trained on vast unlabeled datasets, have shown
promise in overcoming these challenges. Nonetheless, available FMs for
ophthalmology lack extensive validation, especially for segmentation tasks, and
focus on a single imaging modality. In this context, we propose MIRAGE, a novel
multimodal FM for the analysis of OCT and scanning laser ophthalmoscopy (SLO)
images. Additionally, we propose a new evaluation benchmark with OCT/SLO
classification and segmentation tasks. The comparison with general and
specialized FMs and segmentation methods shows the superiority of MIRAGE in
both types of tasks, highlighting its suitability as a basis for the
development of robust AI systems for retinal OCT image analysis. Both MIRAGE
and the evaluation benchmark are publicly available:
https://github.com/j-morano/MIRAGE. | 2025-06-10T15:25:55Z | null | null | null | MIRAGE: Multimodal foundation model and benchmark for comprehensive retinal OCT image analysis | ['José Morano', 'Botond Fazekas', 'Emese Sukei', 'Ronald Fecso', 'T. Emre', 'Markus Gumpinger', 'Georg Faustmann', 'Marzieh Oghbaie', 'U. Schmidt-Erfurth', "Hrvoje Bogunovi'c"] | 2,025 | arXiv.org | 0 | 0 | ['Computer Science'] |
2,506.08967 | Step-Audio-AQAA: a Fully End-to-End Expressive Large Audio Language
Model | ['Ailin Huang', 'Bingxin Li', 'Bruce Wang', 'Boyong Wu', 'Chao Yan', 'Chengli Feng', 'Heng Wang', 'Hongyu Zhou', 'Hongyuan Wang', 'Jingbei Li', 'Jianjian Sun', 'Joanna Wang', 'Mingrui Chen', 'Peng Liu', 'Ruihang Miao', 'Shilei Jiang', 'Tian Fei', 'Wang You', 'Xi Chen', 'Xuerui Yang', 'Yechang Huang', 'Yuxiang Zhang', 'Zheng Ge', 'Zheng Gong', 'Zhewei Huang', 'Zixin Zhang', 'Bin Wang', 'Bo Li', 'Buyun Ma', 'Changxin Miao', 'Changyi Wan', 'Chen Xu', 'Dapeng Shi', 'Dingyuan Hu', 'Enle Liu', 'Guanzhe Huang', 'Gulin Yan', 'Hanpeng Hu', 'Haonan Jia', 'Jiahao Gong', 'Jiaoren Wu', 'Jie Wu', 'Jie Yang', 'Junzhe Lin', 'Kaixiang Li', 'Lei Xia', 'Longlong Gu', 'Ming Li', 'Nie Hao', 'Ranchen Ming', 'Shaoliang Pang', 'Siqi Liu', 'Song Yuan', 'Tiancheng Cao', 'Wen Li', 'Wenqing He', 'Xu Zhao', 'Xuelin Zhang', 'Yanbo Yu', 'Yinmin Zhong', 'Yu Zhou', 'Yuanwei Liang', 'Yuanwei Lu', 'Yuxiang Yang', 'Zidong Yang', 'Zili Zhang', 'Binxing Jiao', 'Heung-Yeung Shum', 'Jiansheng Chen', 'Jing Li', 'Xiangyu Zhang', 'Xinhao Zhang', 'Yibo Zhu', 'Daxin Jiang', 'Shuchang Zhou', 'Chen Hu'] | ['cs.SD', 'cs.CL', 'eess.AS'] | Large Audio-Language Models (LALMs) have significantly advanced intelligent
human-computer interaction, yet their reliance on text-based outputs limits
their ability to generate natural speech responses directly, hindering seamless
audio interactions. To address this, we introduce Step-Audio-AQAA, a fully
end-to-end LALM designed for Audio Query-Audio Answer (AQAA) tasks. The model
integrates a dual-codebook audio tokenizer for linguistic and semantic feature
extraction, a 130-billion-parameter backbone LLM and a neural vocoder for
high-fidelity speech synthesis. Our post-training approach employs interleaved
token-output of text and audio to enhance semantic coherence and combines
Direct Preference Optimization (DPO) with model merge to improve performance.
Evaluations on the StepEval-Audio-360 benchmark demonstrate that
Step-Audio-AQAA excels especially in speech control, outperforming the
state-of-art LALMs in key areas. This work contributes a promising solution for
end-to-end LALMs and highlights the critical role of token-based vocoder in
enhancing overall performance for AQAA tasks. | 2025-06-10T16:37:39Z | 12 pages, 3 figures | null | null | null | null | null | null | null | null | null |
2,506.09007 | Branched Schrödinger Bridge Matching | ['Sophia Tang', 'Yinuo Zhang', 'Alexander Tong', 'Pranam Chatterjee'] | ['cs.LG', 'q-bio.QM'] | Predicting the intermediate trajectories between an initial and target
distribution is a central problem in generative modeling. Existing approaches,
such as flow matching and Schr\"odinger Bridge Matching, effectively learn
mappings between two distributions by modeling a single stochastic path.
However, these methods are inherently limited to unimodal transitions and
cannot capture branched or divergent evolution from a common origin to multiple
distinct outcomes. To address this, we introduce Branched Schr\"odinger Bridge
Matching (BranchSBM), a novel framework that learns branched Schr\"odinger
bridges. BranchSBM parameterizes multiple time-dependent velocity fields and
growth processes, enabling the representation of population-level divergence
into multiple terminal distributions. We show that BranchSBM is not only more
expressive but also essential for tasks involving multi-path surface
navigation, modeling cell fate bifurcations from homogeneous progenitor states,
and simulating diverging cellular responses to perturbations. | 2025-06-10T17:29:48Z | null | null | null | null | null | null | null | null | null | null |
2,506.09278 | UFM: A Simple Path towards Unified Dense Correspondence with Flow | ['Yuchen Zhang', 'Nikhil Keetha', 'Chenwei Lyu', 'Bhuvan Jhamb', 'Yutian Chen', 'Yuheng Qiu', 'Jay Karhade', 'Shreyas Jha', 'Yaoyu Hu', 'Deva Ramanan', 'Sebastian Scherer', 'Wenshan Wang'] | ['cs.CV', 'cs.LG', 'cs.RO'] | Dense image correspondence is central to many applications, such as visual
odometry, 3D reconstruction, object association, and re-identification.
Historically, dense correspondence has been tackled separately for
wide-baseline scenarios and optical flow estimation, despite the common goal of
matching content between two images. In this paper, we develop a Unified Flow &
Matching model (UFM), which is trained on unified data for pixels that are
co-visible in both source and target images. UFM uses a simple, generic
transformer architecture that directly regresses the (u,v) flow. It is easier
to train and more accurate for large flows compared to the typical
coarse-to-fine cost volumes in prior work. UFM is 28% more accurate than
state-of-the-art flow methods (Unimatch), while also having 62% less error and
6.7x faster than dense wide-baseline matchers (RoMa). UFM is the first to
demonstrate that unified training can outperform specialized approaches across
both domains. This result enables fast, general-purpose correspondence and
opens new directions for multi-modal, long-range, and real-time correspondence
tasks. | 2025-06-10T22:32:13Z | Project Page: https://uniflowmatch.github.io/ | null | null | null | null | null | null | null | null | null |
2,506.09344 | Ming-Omni: A Unified Multimodal Model for Perception and Generation | ['Inclusion AI', 'Biao Gong', 'Cheng Zou', 'Chuanyang Zheng', 'Chunluan Zhou', 'Canxiang Yan', 'Chunxiang Jin', 'Chunjie Shen', 'Dandan Zheng', 'Fudong Wang', 'Furong Xu', 'GuangMing Yao', 'Jun Zhou', 'Jingdong Chen', 'Jianxin Sun', 'Jiajia Liu', 'Jianjiang Zhu', 'Jun Peng', 'Kaixiang Ji', 'Kaiyou Song', 'Kaimeng Ren', 'Libin Wang', 'Lixiang Ru', 'Lele Xie', 'Longhua Tan', 'Lyuxin Xue', 'Lan Wang', 'Mochen Bai', 'Ning Gao', 'Pei Chen', 'Qingpei Guo', 'Qinglong Zhang', 'Qiang Xu', 'Rui Liu', 'Ruijie Xiong', 'Sirui Gao', 'Tinghao Liu', 'Taisong Li', 'Weilong Chai', 'Xinyu Xiao', 'Xiaomei Wang', 'Xiaoxue Chen', 'Xiao Lu', 'Xiaoyu Li', 'Xingning Dong', 'Xuzheng Yu', 'Yi Yuan', 'Yuting Gao', 'Yunxiao Sun', 'Yipeng Chen', 'Yifei Wu', 'Yongjie Lyu', 'Ziping Ma', 'Zipeng Feng', 'Zhijiang Fang', 'Zhihao Qiu', 'Ziyuan Huang', 'Zhengyu He'] | ['cs.AI', 'cs.CL', 'cs.CV', 'cs.LG', 'cs.SD', 'eess.AS'] | We propose Ming-Omni, a unified multimodal model capable of processing
images, text, audio, and video, while demonstrating strong proficiency in both
speech and image generation. Ming-Omni employs dedicated encoders to extract
tokens from different modalities, which are then processed by Ling, an MoE
architecture equipped with newly proposed modality-specific routers. This
design enables a single model to efficiently process and fuse multimodal inputs
within a unified framework, thereby facilitating diverse tasks without
requiring separate models, task-specific fine-tuning, or structural redesign.
Importantly, Ming-Omni extends beyond conventional multimodal models by
supporting audio and image generation. This is achieved through the integration
of an advanced audio decoder for natural-sounding speech and Ming-Lite-Uni for
high-quality image generation, which also allow the model to engage in
context-aware chatting, perform text-to-speech conversion, and conduct
versatile image editing. Our experimental results showcase Ming-Omni offers a
powerful solution for unified perception and generation across all modalities.
Notably, our proposed Ming-Omni is the first open-source model we are aware of
to match GPT-4o in modality support, and we release all code and model weights
to encourage further research and development in the community. | 2025-06-11T02:50:49Z | 18 pages,8 figures | null | null | null | null | null | null | null | null | null |
2,506.09366 | SkillBlender: Towards Versatile Humanoid Whole-Body Loco-Manipulation
via Skill Blending | ['Yuxuan Kuang', 'Haoran Geng', 'Amine Elhafsi', 'Tan-Dzung Do', 'Pieter Abbeel', 'Jitendra Malik', 'Marco Pavone', 'Yue Wang'] | ['cs.RO', 'cs.LG'] | Humanoid robots hold significant potential in accomplishing daily tasks
across diverse environments thanks to their flexibility and human-like
morphology. Recent works have made significant progress in humanoid whole-body
control and loco-manipulation leveraging optimal control or reinforcement
learning. However, these methods require tedious task-specific tuning for each
task to achieve satisfactory behaviors, limiting their versatility and
scalability to diverse tasks in daily scenarios. To that end, we introduce
SkillBlender, a novel hierarchical reinforcement learning framework for
versatile humanoid loco-manipulation. SkillBlender first pretrains
goal-conditioned task-agnostic primitive skills, and then dynamically blends
these skills to accomplish complex loco-manipulation tasks with minimal
task-specific reward engineering. We also introduce SkillBench, a parallel,
cross-embodiment, and diverse simulated benchmark containing three embodiments,
four primitive skills, and eight challenging loco-manipulation tasks,
accompanied by a set of scientific evaluation metrics balancing accuracy and
feasibility. Extensive simulated experiments show that our method significantly
outperforms all baselines, while naturally regularizing behaviors to avoid
reward hacking, resulting in more accurate and feasible movements for diverse
loco-manipulation tasks in our daily scenarios. Our code and benchmark will be
open-sourced to the community to facilitate future research. Project page:
https://usc-gvl.github.io/SkillBlender-web/. | 2025-06-11T03:24:26Z | null | null | null | SkillBlender: Towards Versatile Humanoid Whole-Body Loco-Manipulation via Skill Blending | ['Yuxuan Kuang', 'Haoran Geng', 'Amine Elhafsi', 'Tan-Dzung Do', 'Pieter Abbeel', 'Jitendra Malik', 'Marco Pavone', 'Yue Wang'] | 2,025 | arXiv.org | 1 | 54 | ['Computer Science'] |
2,506.09369 | ScaleLSD: Scalable Deep Line Segment Detection Streamlined | ['Zeran Ke', 'Bin Tan', 'Xianwei Zheng', 'Yujun Shen', 'Tianfu Wu', 'Nan Xue'] | ['cs.CV'] | This paper studies the problem of Line Segment Detection (LSD) for the
characterization of line geometry in images, with the aim of learning a
domain-agnostic robust LSD model that works well for any natural images. With
the focus of scalable self-supervised learning of LSD, we revisit and
streamline the fundamental designs of (deep and non-deep) LSD approaches to
have a high-performing and efficient LSD learner, dubbed as ScaleLSD, for the
curation of line geometry at scale from over 10M unlabeled real-world images.
Our ScaleLSD works very well to detect much more number of line segments from
any natural images even than the pioneered non-deep LSD approach, having a more
complete and accurate geometric characterization of images using line segments.
Experimentally, our proposed ScaleLSD is comprehensively testified under
zero-shot protocols in detection performance, single-view 3D geometry
estimation, two-view line segment matching, and multiview 3D line mapping, all
with excellent performance obtained. Based on the thorough evaluation, our
ScaleLSD is observed to be the first deep approach that outperforms the
pioneered non-deep LSD in all aspects we have tested, significantly expanding
and reinforcing the versatility of the line geometry of images. Code and Models
are available at https://github.com/ant-research/scalelsd | 2025-06-11T03:34:21Z | accepted to CVPR 2025; 17 pages, appendices included | null | null | null | null | null | null | null | null | null |
2,506.0944 | GigaChat Family: Efficient Russian Language Modeling Through Mixture of
Experts Architecture | ['GigaChat team', 'Mamedov Valentin', 'Evgenii Kosarev', 'Gregory Leleytner', 'Ilya Shchuckin', 'Valeriy Berezovskiy', 'Daniil Smirnov', 'Dmitry Kozlov', 'Sergei Averkiev', 'Lukyanenko Ivan', 'Aleksandr Proshunin', 'Ainur Israfilova', 'Ivan Baskov', 'Artem Chervyakov', 'Emil Shakirov', 'Mikhail Kolesov', 'Daria Khomich', 'Darya Latortseva', 'Sergei Porkhun', 'Yury Fedorov', 'Oleg Kutuzov', 'Polina Kudriavtseva', 'Sofiia Soldatova', 'Kolodin Egor', 'Stanislav Pyatkin', 'Dzmitry Menshykh', 'Grafov Sergei', 'Eldar Damirov', 'Karlov Vladimir', 'Ruslan Gaitukiev', 'Arkadiy Shatenov', 'Alena Fenogenova', 'Nikita Savushkin', 'Fedor Minkin'] | ['cs.CL', 'cs.AI'] | Generative large language models (LLMs) have become crucial for modern NLP
research and applications across various languages. However, the development of
foundational models specifically tailored to the Russian language has been
limited, primarily due to the significant computational resources required.
This paper introduces the GigaChat family of Russian LLMs, available in various
sizes, including base models and instruction-tuned versions. We provide a
detailed report on the model architecture, pre-training process, and
experiments to guide design choices. In addition, we evaluate their performance
on Russian and English benchmarks and compare GigaChat with multilingual
analogs. The paper presents a system demonstration of the top-performing models
accessible via an API, a Telegram bot, and a Web interface. Furthermore, we
have released three open GigaChat models in open-source
(https://huggingface.co/ai-sage), aiming to expand NLP research opportunities
and support the development of industrial solutions for the Russian language. | 2025-06-11T06:46:49Z | ACL-2025 System Demo | null | null | null | null | null | null | null | null | null |
2,506.09482 | Marrying Autoregressive Transformer and Diffusion with Multi-Reference
Autoregression | ['Dingcheng Zhen', 'Qian Qiao', 'Tan Yu', 'Kangxi Wu', 'Ziwei Zhang', 'Siyuan Liu', 'Shunshun Yin', 'Ming Tao'] | ['cs.CV'] | We introduce TransDiff, the first image generation model that marries
Autoregressive (AR) Transformer with diffusion models. In this joint modeling
framework, TransDiff encodes labels and images into high-level semantic
features and employs a diffusion model to estimate the distribution of image
samples. On the ImageNet 256x256 benchmark, TransDiff significantly outperforms
other image generation models based on standalone AR Transformer or diffusion
models. Specifically, TransDiff achieves a Frechet Inception Distance (FID) of
1.61 and an Inception Score (IS) of 293.4, and further provides x2 faster
inference latency compared to state-of-the-art methods based on AR Transformer
and x112 faster inference compared to diffusion-only models. Furthermore,
building on the TransDiff model, we introduce a novel image generation paradigm
called Multi-Reference Autoregression (MRAR), which performs autoregressive
generation by predicting the next image. MRAR enables the model to reference
multiple previously generated images, thereby facilitating the learning of more
diverse representations and improving the quality of generated images in
subsequent iterations. By applying MRAR, the performance of TransDiff is
improved, with the FID reduced from 1.61 to 1.42. We expect TransDiff to open
up a new frontier in the field of image generation. | 2025-06-11T07:50:31Z | null | null | null | null | null | null | null | null | null | null |
2,506.09513 | ReasonMed: A 370K Multi-Agent Generated Dataset for Advancing Medical
Reasoning | ['Yu Sun', 'Xingyu Qian', 'Weiwen Xu', 'Hao Zhang', 'Chenghao Xiao', 'Long Li', 'Yu Rong', 'Wenbing Huang', 'Qifeng Bai', 'Tingyang Xu'] | ['cs.CL', 'cs.AI', 'cs.MA'] | Though reasoning-based large language models (LLMs) have excelled in
mathematics and programming, their capabilities in knowledge-intensive medical
question answering remain underexplored. To address this, we introduce
ReasonMed, the largest medical reasoning dataset, comprising 370k high-quality
examples distilled from 1.7 million initial reasoning paths generated by
various LLMs. ReasonMed is constructed through a \textit{multi-agent
verification and refinement process}, where we design an \textit{Error Refiner}
to enhance the reasoning paths by identifying and correcting error-prone steps
flagged by a verifier. Leveraging ReasonMed, we systematically investigate best
practices for training medical reasoning models and find that combining
detailed Chain-of-Thought (CoT) reasoning with concise answer summaries yields
the most effective fine-tuning strategy. Based on this strategy, we train
ReasonMed-7B, which sets a new benchmark for sub-10B models, outperforming the
prior best by 4.17\% and even exceeding LLaMA3.1-70B on PubMedQA by 4.60\%. | 2025-06-11T08:36:55Z | 24 pages, 6 figures, 7 tables | null | null | null | null | null | null | null | null | null |
2,506.0956 | Towards Open Foundation Language Model and Corpus for Macedonian: A
Low-Resource Language | ['Stefan Krsteski', 'Matea Tashkovska', 'Borjan Sazdov', 'Hristijan Gjoreski', 'Branislav Gerazov'] | ['cs.CL'] | The increase in technological adoption worldwide comes with demands for novel
tools to be used by the general population. Large Language Models (LLMs)
provide a great opportunity in this respect, but their capabilities remain
limited for low-resource languages, restricting applications in countries where
such languages are spoken. We create several resources to facilitate the
adoption of LLMs and to support research advancements for Macedonian. We
collect the largest Macedonian corpus to date, consisting of 40GB of textual
data and totaling 3.5B words. To support conversational applications, we
collect a 106k-instance instruction dataset, carefully built to be culturally
grounded. For evaluation, we construct a Macedonian evaluation suite covering
seven benchmarks. Finally, we train domestic-yak, a state-of-the-art
8B-parameter model, on our curated datasets and evaluate it against eight
baseline models using the newly constructed benchmark suite. Our model
outperforms all existing models in the 8B parameter range across all
benchmarks, and achieves performance comparable to models up to 10x larger.
Furthermore, a qualitative analysis with native speakers reveals that our model
is preferred over larger counterparts, receiving higher ratings for grammatical
correctness and cultural appropriateness. All datasets, code, and model weights
are openly released, setting a foundation for advancing LLMs in similarly
underrepresented languages. These resources are publicly available at
github.com/LVSTCK for source code, and at huggingface.co/LVSTCK for pretrained
model weights and data. | 2025-06-11T09:46:58Z | Camera-ready version accepted at SlavNLP-2025@ACL | null | null | null | null | null | null | null | null | null |
2,506.09645 | Learning Efficient and Generalizable Graph Retriever for Knowledge-Graph
Question Answering | ['Tianjun Yao', 'Haoxuan Li', 'Zhiqiang Shen', 'Pan Li', 'Tongliang Liu', 'Kun Zhang'] | ['cs.CL', 'cs.IR', 'cs.LG', 'I.2.6'] | Large Language Models (LLMs) have shown strong inductive reasoning ability
across various domains, but their reliability is hindered by the outdated
knowledge and hallucinations. Retrieval-Augmented Generation mitigates these
issues by grounding LLMs with external knowledge; however, most existing RAG
pipelines rely on unstructured text, limiting interpretability and structured
reasoning. Knowledge graphs, which represent facts as relational triples, offer
a more structured and compact alternative. Recent studies have explored
integrating knowledge graphs with LLMs for knowledge graph question answering
(KGQA), with a significant proportion adopting the retrieve-then-reasoning
paradigm. In this framework, graph-based retrievers have demonstrated strong
empirical performance, yet they still face challenges in generalization
ability. In this work, we propose RAPL, a novel framework for efficient and
effective graph retrieval in KGQA. RAPL addresses these limitations through
three aspects: (1) a two-stage labeling strategy that combines heuristic
signals with parametric models to provide causally grounded supervision; (2) a
model-agnostic graph transformation approach to capture both intra- and
inter-triple interactions, thereby enhancing representational capacity; and (3)
a path-based reasoning strategy that facilitates learning from the injected
rational knowledge, and supports downstream reasoner through structured inputs.
Empirically, RAPL outperforms state-of-the-art methods by $2.66\%-20.34\%$, and
significantly reduces the performance gap between smaller and more powerful
LLM-based reasoners, as well as the gap under cross-dataset settings,
highlighting its superior retrieval capability and generalizability. Codes are
available at: https://github.com/tianyao-aka/RAPL. | 2025-06-11T12:03:52Z | 32 pages, 28 figures | null | null | Learning Efficient and Generalizable Graph Retriever for Knowledge-Graph Question Answering | ['Tianjun Yao', 'Haoxuan Li', 'Zhiqiang Shen', 'Pan Li', 'Tongliang Liu', 'Kun Zhang'] | 2,025 | arXiv.org | 0 | 66 | ['Computer Science'] |
2,506.09736 | Vision Matters: Simple Visual Perturbations Can Boost Multimodal Math
Reasoning | ['Yuting Li', 'Lai Wei', 'Kaipeng Zheng', 'Jingyuan Huang', 'Linghe Kong', 'Lichao Sun', 'Weiran Huang'] | ['cs.CV', 'cs.AI'] | Despite the rapid progress of multimodal large language models (MLLMs), they
have largely overlooked the importance of visual processing. In a simple yet
revealing experiment, we interestingly find that language-only models, when
provided with image captions, can achieve comparable or even better performance
than MLLMs that consume raw visual inputs. This suggests that current MLLMs may
generate accurate visual descriptions but fail to effectively integrate them
during reasoning. Motivated by this, we propose a simple visual perturbation
framework that enhances perceptual robustness without requiring algorithmic
modifications or additional training data. Our approach introduces three
targeted perturbations: distractor concatenation, dominance-preserving mixup,
and random rotation, that can be easily integrated into existing post-training
pipelines including SFT, DPO, and GRPO. Through extensive experiments across
multiple datasets, we demonstrate consistent improvements in mathematical
reasoning performance, with gains comparable to those achieved through
algorithmic changes. Additionally, we achieve competitive performance among
open-source 7B RL-tuned models by training Qwen2.5-VL-7B with visual
perturbation. Through comprehensive ablation studies, we analyze the
effectiveness of different perturbation strategies, revealing that each
perturbation type contributes uniquely to different aspects of visual
reasoning. Our findings highlight the critical role of visual perturbation in
multimodal mathematical reasoning: better reasoning begins with better seeing.
Our code is available at https://github.com/YutingLi0606/Vision-Matters. | 2025-06-11T13:39:46Z | Technical Report | null | null | null | null | null | null | null | null | null |
2,506.0982 | CoRT: Code-integrated Reasoning within Thinking | ['Chengpeng Li', 'Zhengyang Tang', 'Ziniu Li', 'Mingfeng Xue', 'Keqin Bao', 'Tian Ding', 'Ruoyu Sun', 'Benyou Wang', 'Xiang Wang', 'Junyang Lin', 'Dayiheng Liu'] | ['cs.CL', 'cs.AI', 'cs.LG'] | Large Reasoning Models (LRMs) like o1 and DeepSeek-R1 have shown remarkable
progress in natural language reasoning with long chain-of-thought (CoT), yet
they remain inefficient or inaccurate when handling complex mathematical
operations. Addressing these limitations through computational tools (e.g.,
computation libraries and symbolic solvers) is promising, but it introduces a
technical challenge: Code Interpreter (CI) brings external knowledge beyond the
model's internal text representations, thus the direct combination is not
efficient. This paper introduces CoRT, a post-training framework for teaching
LRMs to leverage CI effectively and efficiently. As a first step, we address
the data scarcity issue by synthesizing code-integrated reasoning data through
Hint-Engineering, which strategically inserts different hints at appropriate
positions to optimize LRM-CI interaction. We manually create 30 high-quality
samples, upon which we post-train models ranging from 1.5B to 32B parameters,
with supervised fine-tuning, rejection fine-tuning and reinforcement learning.
Our experimental results demonstrate that Hint-Engineering models achieve 4\%
and 8\% absolute improvements on DeepSeek-R1-Distill-Qwen-32B and
DeepSeek-R1-Distill-Qwen-1.5B respectively, across five challenging
mathematical reasoning datasets. Furthermore, Hint-Engineering models use about
30\% fewer tokens for the 32B model and 50\% fewer tokens for the 1.5B model
compared with the natural language models. The models and code are available at
https://github.com/ChengpengLi1003/CoRT. | 2025-06-11T14:59:02Z | work in progress | null | null | null | null | null | null | null | null | null |
2,506.0993 | From Intention to Execution: Probing the Generalization Boundaries of
Vision-Language-Action Models | ['Irving Fang', 'Juexiao Zhang', 'Shengbang Tong', 'Chen Feng'] | ['cs.RO', 'cs.CV'] | One promise that Vision-Language-Action (VLA) models hold over traditional
imitation learning for robotics is to leverage the broad generalization
capabilities of large Vision-Language Models (VLMs) to produce versatile,
"generalist" robot policies. However, current evaluations of VLAs remain
insufficient. Traditional imitation learning benchmarks are unsuitable due to
the lack of language instructions. Emerging benchmarks for VLAs that
incorporate language often come with limited evaluation tasks and do not intend
to investigate how much VLM pretraining truly contributes to the generalization
capabilities of the downstream robotic policy. Meanwhile, much research relies
on real-world robot setups designed in isolation by different institutions,
which creates a barrier for reproducibility and accessibility. To address this
gap, we introduce a unified probing suite of 50 simulation-based tasks across
10 subcategories spanning language instruction, vision, and objects. We
systematically evaluate several state-of-the-art VLA architectures on this
suite to understand their generalization capability. Our results show that
while VLM backbones endow VLAs with robust perceptual understanding and high
level planning, which we refer to as good intentions, this does not reliably
translate into precise motor execution: when faced with out-of-distribution
observations, policies often exhibit coherent intentions, but falter in action
execution. Moreover, finetuning on action data can erode the original VLM's
generalist reasoning abilities. We release our task suite and evaluation code
to serve as a standardized benchmark for future VLAs and to drive research on
closing the perception-to-action gap. More information, including the source
code, can be found at https://ai4ce.github.io/INT-ACT/ | 2025-06-11T16:52:18Z | Under review | null | null | From Intention to Execution: Probing the Generalization Boundaries of Vision-Language-Action Models | ['Irving Fang', 'Juexiao Zhang', 'Shengbang Tong', 'Chen Feng'] | 2,025 | arXiv.org | 1 | 38 | ['Computer Science'] |
2,506.09942 | VerIF: Verification Engineering for Reinforcement Learning in
Instruction Following | ['Hao Peng', 'Yunjia Qi', 'Xiaozhi Wang', 'Bin Xu', 'Lei Hou', 'Juanzi Li'] | ['cs.CL', 'cs.AI'] | Reinforcement learning with verifiable rewards (RLVR) has become a key
technique for enhancing large language models (LLMs), with verification
engineering playing a central role. However, best practices for RL in
instruction following remain underexplored. In this work, we explore the
verification challenge in RL for instruction following and propose VerIF, a
verification method that combines rule-based code verification with LLM-based
verification from a large reasoning model (e.g., QwQ-32B). To support this
approach, we construct a high-quality instruction-following dataset,
VerInstruct, containing approximately 22,000 instances with associated
verification signals. We apply RL training with VerIF to two models, achieving
significant improvements across several representative instruction-following
benchmarks. The trained models reach state-of-the-art performance among models
of comparable size and generalize well to unseen constraints. We further
observe that their general capabilities remain unaffected, suggesting that RL
with VerIF can be integrated into existing RL recipes to enhance overall model
performance. We have released our datasets, codes, and models to facilitate
future research at https://github.com/THU-KEG/VerIF. | 2025-06-11T17:10:36Z | 16 pages, 8 figures | null | null | null | null | null | null | null | null | null |
2,506.09965 | Reinforcing Spatial Reasoning in Vision-Language Models with Interwoven
Thinking and Visual Drawing | ['Junfei Wu', 'Jian Guan', 'Kaituo Feng', 'Qiang Liu', 'Shu Wu', 'Liang Wang', 'Wei Wu', 'Tieniu Tan'] | ['cs.CV', 'cs.AI'] | As textual reasoning with large language models (LLMs) has advanced
significantly, there has been growing interest in enhancing the multimodal
reasoning capabilities of large vision-language models (LVLMs). However,
existing methods primarily approach multimodal reasoning in a straightforward,
text-centric manner, where both reasoning and answer derivation are conducted
purely through text, with the only difference being the presence of multimodal
input. As a result, these methods often encounter fundamental limitations in
spatial reasoning tasks that demand precise geometric understanding and
continuous spatial tracking-capabilities that humans achieve through mental
visualization and manipulation. To address the limitations, we propose drawing
to reason in space, a novel paradigm that enables LVLMs to reason through
elementary drawing operations in the visual space. By equipping models with
basic drawing operations, including annotating bounding boxes and drawing
auxiliary lines, we empower them to express and analyze spatial relationships
through direct visual manipulation, meanwhile avoiding the performance ceiling
imposed by specialized perception tools in previous tool-integrated reasoning
approaches. To cultivate this capability, we develop a three-stage training
framework: cold-start training with synthetic data to establish basic drawing
abilities, reflective rejection sampling to enhance self-reflection behaviors,
and reinforcement learning to directly optimize for target rewards. Extensive
experiments demonstrate that our model, named VILASR, consistently outperforms
existing methods across diverse spatial reasoning benchmarks, involving maze
navigation, static spatial reasoning, video-based reasoning, and
multi-view-based reasoning tasks, with an average improvement of 18.4%. | 2025-06-11T17:41:50Z | null | null | null | null | null | null | null | null | null | null |
2,506.0998 | Efficient Part-level 3D Object Generation via Dual Volume Packing | ['Jiaxiang Tang', 'Ruijie Lu', 'Zhaoshuo Li', 'Zekun Hao', 'Xuan Li', 'Fangyin Wei', 'Shuran Song', 'Gang Zeng', 'Ming-Yu Liu', 'Tsung-Yi Lin'] | ['cs.CV'] | Recent progress in 3D object generation has greatly improved both the quality
and efficiency. However, most existing methods generate a single mesh with all
parts fused together, which limits the ability to edit or manipulate individual
parts. A key challenge is that different objects may have a varying number of
parts. To address this, we propose a new end-to-end framework for part-level 3D
object generation. Given a single input image, our method generates
high-quality 3D objects with an arbitrary number of complete and semantically
meaningful parts. We introduce a dual volume packing strategy that organizes
all parts into two complementary volumes, allowing for the creation of complete
and interleaved parts that assemble into the final object. Experiments show
that our model achieves better quality, diversity, and generalization than
previous image-based part-level generation methods. | 2025-06-11T17:55:03Z | Code: https://github.com/NVlabs/PartPacker Project Page:
https://research.nvidia.com/labs/dir/partpacker/ | null | null | null | null | null | null | null | null | null |
2,506.09991 | Multiverse: Your Language Models Secretly Decide How to Parallelize and
Merge Generation | ['Xinyu Yang', 'Yuwei An', 'Hongyi Liu', 'Tianqi Chen', 'Beidi Chen'] | ['cs.LG'] | Autoregressive Large Language Models (AR-LLMs) frequently exhibit implicit
parallelism in sequential generation. Inspired by this, we introduce
Multiverse, a new generative model that enables natively parallel generation.
Multiverse internalizes a MapReduce paradigm, generating automatically through
three stages: (i) a Map stage for adaptive task decomposition, (ii) a Process
stage for parallel subtask execution, and (iii) a Reduce stage for lossless
result synthesis. Next, we build a real-world Multiverse reasoning model with
co-design of data, algorithm, and system, enabling rapid and seamless transfer
from frontier AR-LLMs. For data creation, we develop Multiverse Curator, an
automated LLM-assisted pipeline that transforms sequential reasoning chains
into structured training data, avoiding costly human annotations.
Algorithmically, we design Multiverse Attention to separate parallel reasoning
steps while keeping compatibility with causal attention for efficient training.
Systematically, we implement Multiverse Engine to support parallel inference.
It features a dedicated interpreter that dynamically switches between
sequential and parallel generation, triggered directly by the model. After a
3-hour fine-tuning with 1K examples, our Multiverse-32B stands as the only
open-sourced non-AR model achieving performance on par with leading AR-LLMs of
the same scale, evidenced by AIME24 & 25 scores of 54% and 46%, respectively.
Moreover, our budget control experiments show that Multiverse-32B exhibits
superior scaling, outperforming AR-LLMs by 1.87% on average using the same
context length. Such scaling further leads to practical efficiency gains,
achieving up to 2x speedup across varying batch sizes. We have open-sourced the
entire Multiverse ecosystem, including data, model weights, engine, as well as
complete data curation prompts and detailed training and evaluation recipes. | 2025-06-11T17:59:23Z | null | null | null | null | null | null | null | null | null | null |
2,506.10357 | Optimus-3: Towards Generalist Multimodal Minecraft Agents with Scalable
Task Experts | ['Zaijing Li', 'Yuquan Xie', 'Rui Shao', 'Gongwei Chen', 'Weili Guan', 'Dongmei Jiang', 'Liqiang Nie'] | ['cs.AI'] | Recently, agents based on multimodal large language models (MLLMs) have
achieved remarkable progress across various domains. However, building a
generalist agent with capabilities such as perception, planning, action,
grounding, and reflection in open-world environments like Minecraft remains
challenges: insufficient domain-specific data, interference among heterogeneous
tasks, and visual diversity in open-world settings. In this paper, we address
these challenges through three key contributions. 1) We propose a
knowledge-enhanced data generation pipeline to provide scalable and
high-quality training data for agent development. 2) To mitigate interference
among heterogeneous tasks, we introduce a Mixture-of-Experts (MoE) architecture
with task-level routing. 3) We develop a Multimodal Reasoning-Augmented
Reinforcement Learning approach to enhance the agent's reasoning ability for
visual diversity in Minecraft. Built upon these innovations, we present
Optimus-3, a general-purpose agent for Minecraft. Extensive experimental
results demonstrate that Optimus-3 surpasses both generalist multimodal large
language models and existing state-of-the-art agents across a wide range of
tasks in the Minecraft environment. Project page:
https://cybertronagent.github.io/Optimus-3.github.io/ | 2025-06-12T05:29:40Z | 24 pages, 10 figures | null | null | null | null | null | null | null | null | null |
2,506.10452 | Towards Robust Multimodal Emotion Recognition under Missing Modalities
and Distribution Shifts | ['Guowei Zhong', 'Ruohong Huan', 'Mingzhen Wu', 'Ronghua Liang', 'Peng Chen'] | ['cs.CV', 'cs.CL', 'cs.LG', 'cs.MM'] | Recent advancements in Multimodal Emotion Recognition (MER) face challenges
in addressing both modality missing and Out-Of-Distribution (OOD) data
simultaneously. Existing methods often rely on specific models or introduce
excessive parameters, which limits their practicality. To address these issues,
we propose a novel robust MER framework, Causal Inference Distiller (CIDer),
and introduce a new task, Random Modality Feature Missing (RMFM), to generalize
the definition of modality missing. CIDer integrates two key components: a
Model-Specific Self-Distillation (MSSD) module and a Model-Agnostic Causal
Inference (MACI) module. MSSD enhances robustness under the RMFM task through a
weight-sharing self-distillation approach applied across low-level features,
attention maps, and high-level representations. Additionally, a Word-level
Self-aligned Attention Module (WSAM) reduces computational complexity, while a
Multimodal Composite Transformer (MCT) facilitates efficient multimodal fusion.
To tackle OOD challenges, MACI employs a tailored causal graph to mitigate
label and language biases using a Multimodal Causal Module (MCM) and
fine-grained counterfactual texts. Notably, MACI can independently enhance OOD
generalization with minimal additional parameters. Furthermore, we also
introduce the new repartitioned MER OOD datasets. Experimental results
demonstrate that CIDer achieves robust performance in both RMFM and OOD
scenarios, with fewer parameters and faster training compared to
state-of-the-art methods. The implementation of this work is publicly
accessible at https://github.com/gw-zhong/CIDer. | 2025-06-12T07:58:17Z | Submitted to TAC. The code is available at
https://github.com/gw-zhong/CIDer | null | null | Towards Robust Multimodal Emotion Recognition under Missing Modalities and Distribution Shifts | ['Guowei Zhong', 'Ruohong Huan', 'Mingzhen Wu', 'Ronghua Liang', 'Peng Chen'] | 2,025 | arXiv.org | 0 | 41 | ['Computer Science'] |
2,506.10601 | Semantic-decoupled Spatial Partition Guided Point-supervised Oriented
Object Detection | ['Xinyuan Liu', 'Hang Xu', 'Yike Ma', 'Yucheng Zhang', 'Feng Dai'] | ['cs.CV'] | Recent remote sensing tech advancements drive imagery growth, making oriented
object detection rapid development, yet hindered by labor-intensive annotation
for high-density scenes. Oriented object detection with point supervision
offers a cost-effective solution for densely packed scenes in remote sensing,
yet existing methods suffer from inadequate sample assignment and instance
confusion due to rigid rule-based designs. To address this, we propose SSP
(Semantic-decoupled Spatial Partition), a unified framework that synergizes
rule-driven prior injection and data-driven label purification. Specifically,
SSP introduces two core innovations: 1) Pixel-level Spatial Partition-based
Sample Assignment, which compactly estimates the upper and lower bounds of
object scales and mines high-quality positive samples and hard negative samples
through spatial partitioning of pixel maps. 2) Semantic Spatial Partition-based
Box Extraction, which derives instances from spatial partitions modulated by
semantic maps and reliably converts them into bounding boxes to form
pseudo-labels for supervising the learning of downstream detectors. Experiments
on DOTA-v1.0 and others demonstrate SSP\' s superiority: it achieves 45.78% mAP
under point supervision, outperforming SOTA method PointOBB-v2 by 4.10%.
Furthermore, when integrated with ORCNN and ReDet architectures, the SSP
framework achieves mAP values of 47.86% and 48.50%, respectively. The code is
available at https://github.com/antxinyuan/ssp. | 2025-06-12T11:44:34Z | null | null | null | null | null | null | null | null | null | null |
2,506.10707 | ConTextTab: A Semantics-Aware Tabular In-Context Learner | ['Marco Spinaci', 'Marek Polewczyk', 'Maximilian Schambach', 'Sam Thelin'] | ['cs.LG', 'cs.AI'] | Tabular in-context learning (ICL) has recently achieved state-of-the-art
(SOTA) performance on several tabular prediction tasks. Previously restricted
to classification problems on small tables, recent advances such as TabPFN and
TabICL have extended its use to larger datasets. While being architecturally
efficient and well-adapted to tabular data structures, current table-native ICL
architectures, being trained exclusively on synthetic data, do not fully
leverage the rich semantics and world knowledge contained in real-world tabular
data. On another end of this spectrum, tabular ICL models based on pretrained
large language models such as TabuLa-8B integrate deep semantic understanding
and world knowledge but are only able to make use of a small amount of context
due to inherent architectural limitations. With the aim to combine the best of
both these worlds, we introduce ConTextTab, integrating semantic understanding
and alignment into a table-native ICL framework. By employing specialized
embeddings for different data modalities and by training on large-scale
real-world tabular data, our model is competitive with SOTA across a broad set
of benchmarks while setting a new standard on the semantically rich CARTE
benchmark. Code and checkpoints are available at
https://github.com/SAP-samples/contexttab | 2025-06-12T13:57:29Z | null | null | null | ConTextTab: A Semantics-Aware Tabular In-Context Learner | ['Marco Spinaci', 'Marek Polewczyk', 'Maximilian Schambach', 'Sam Thelin'] | 2,025 | arXiv.org | 0 | 38 | ['Computer Science'] |
2,506.10741 | PosterCraft: Rethinking High-Quality Aesthetic Poster Generation in a
Unified Framework | ['SiXiang Chen', 'Jianyu Lai', 'Jialin Gao', 'Tian Ye', 'Haoyu Chen', 'Hengyu Shi', 'Shitong Shao', 'Yunlong Lin', 'Song Fei', 'Zhaohu Xing', 'Yeying Jin', 'Junfeng Luo', 'Xiaoming Wei', 'Lei Zhu'] | ['cs.CV'] | Generating aesthetic posters is more challenging than simple design images:
it requires not only precise text rendering but also the seamless integration
of abstract artistic content, striking layouts, and overall stylistic harmony.
To address this, we propose PosterCraft, a unified framework that abandons
prior modular pipelines and rigid, predefined layouts, allowing the model to
freely explore coherent, visually compelling compositions. PosterCraft employs
a carefully designed, cascaded workflow to optimize the generation of
high-aesthetic posters: (i) large-scale text-rendering optimization on our
newly introduced Text-Render-2M dataset; (ii) region-aware supervised
fine-tuning on HQ-Poster100K; (iii) aesthetic-text-reinforcement learning via
best-of-n preference optimization; and (iv) joint vision-language feedback
refinement. Each stage is supported by a fully automated data-construction
pipeline tailored to its specific needs, enabling robust training without
complex architectural modifications. Evaluated on multiple experiments,
PosterCraft significantly outperforms open-source baselines in rendering
accuracy, layout coherence, and overall visual appeal-approaching the quality
of SOTA commercial systems. Our code, models, and datasets can be found in the
Project page: https://ephemeral182.github.io/PosterCraft | 2025-06-12T14:28:12Z | null | null | null | null | null | null | null | null | null | null |
2,506.10892 | The Diffusion Duality | ['Subham Sekhar Sahoo', 'Justin Deschenaux', 'Aaron Gokaslan', 'Guanghan Wang', 'Justin Chiu', 'Volodymyr Kuleshov'] | ['cs.LG', 'cs.AI', 'cs.CL'] | Uniform-state discrete diffusion models hold the promise of fast text
generation due to their inherent ability to self-correct. However, they are
typically outperformed by autoregressive models and masked diffusion models. In
this work, we narrow this performance gap by leveraging a key insight:
Uniform-state diffusion processes naturally emerge from an underlying Gaussian
diffusion. Our method, Duo, transfers powerful techniques from Gaussian
diffusion to improve both training and sampling. First, we introduce a
curriculum learning strategy guided by the Gaussian process, doubling training
speed by reducing variance. Models trained with curriculum learning surpass
autoregressive models in zero-shot perplexity on 3 of 7 benchmarks. Second, we
present Discrete Consistency Distillation, which adapts consistency
distillation from the continuous to the discrete setting. This algorithm
unlocks few-step generation in diffusion language models by accelerating
sampling by two orders of magnitude. We provide the code and model checkpoints
on the project page: http://s-sahoo.github.io/duo | 2025-06-12T16:55:35Z | ICML 2025. We provide the code at: https://github.com/s-sahoo/duo | null | null | null | null | null | null | null | null | null |
2,506.10896 | BioClinical ModernBERT: A State-of-the-Art Long-Context Encoder for
Biomedical and Clinical NLP | ['Thomas Sounack', 'Joshua Davis', 'Brigitte Durieux', 'Antoine Chaffin', 'Tom J. Pollard', 'Eric Lehman', 'Alistair E. W. Johnson', 'Matthew McDermott', 'Tristan Naumann', 'Charlotta Lindvall'] | ['cs.CL', 'cs.AI'] | Encoder-based transformer models are central to biomedical and clinical
Natural Language Processing (NLP), as their bidirectional self-attention makes
them well-suited for efficiently extracting structured information from
unstructured text through discriminative tasks. However, encoders have seen
slower development compared to decoder models, leading to limited domain
adaptation in biomedical and clinical settings. We introduce BioClinical
ModernBERT, a domain-adapted encoder that builds on the recent ModernBERT
release, incorporating long-context processing and substantial improvements in
speed and performance for biomedical and clinical NLP. BioClinical ModernBERT
is developed through continued pretraining on the largest biomedical and
clinical corpus to date, with over 53.5 billion tokens, and addresses a key
limitation of prior clinical encoders by leveraging 20 datasets from diverse
institutions, domains, and geographic regions, rather than relying on data from
a single source. It outperforms existing biomedical and clinical encoders on
four downstream tasks spanning a broad range of use cases. We release both base
(150M parameters) and large (396M parameters) versions of BioClinical
ModernBERT, along with training checkpoints to support further research. | 2025-06-12T17:01:11Z | null | null | null | null | null | null | null | null | null | null |
2,506.1091 | Magistral | ['Mistral-AI', ':', 'Abhinav Rastogi', 'Albert Q. Jiang', 'Andy Lo', 'Gabrielle Berrada', 'Guillaume Lample', 'Jason Rute', 'Joep Barmentlo', 'Karmesh Yadav', 'Kartik Khandelwal', 'Khyathi Raghavi Chandu', 'Léonard Blier', 'Lucile Saulnier', 'Matthieu Dinot', 'Maxime Darrin', 'Neha Gupta', 'Roman Soletskyi', 'Sagar Vaze', 'Teven Le Scao', 'Yihan Wang', 'Adam Yang', 'Alexander H. Liu', 'Alexandre Sablayrolles', 'Amélie Héliou', 'Amélie Martin', 'Andy Ehrenberg', 'Anmol Agarwal', 'Antoine Roux', 'Arthur Darcet', 'Arthur Mensch', 'Baptiste Bout', 'Baptiste Rozière', 'Baudouin De Monicault', 'Chris Bamford', 'Christian Wallenwein', 'Christophe Renaudin', 'Clémence Lanfranchi', 'Darius Dabert', 'Devon Mizelle', 'Diego de las Casas', 'Elliot Chane-Sane', 'Emilien Fugier', 'Emma Bou Hanna', 'Gauthier Delerce', 'Gauthier Guinet', 'Georgii Novikov', 'Guillaume Martin', 'Himanshu Jaju', 'Jan Ludziejewski', 'Jean-Hadrien Chabran', 'Jean-Malo Delignon', 'Joachim Studnia', 'Jonas Amar', 'Josselin Somerville Roberts', 'Julien Denize', 'Karan Saxena', 'Kush Jain', 'Lingxiao Zhao', 'Louis Martin', 'Luyu Gao', 'Lélio Renard Lavaud', 'Marie Pellat', 'Mathilde Guillaumin', 'Mathis Felardos', 'Maximilian Augustin', 'Mickaël Seznec', 'Nikhil Raghuraman', 'Olivier Duchenne', 'Patricia Wang', 'Patrick von Platen', 'Patryk Saffer', 'Paul Jacob', 'Paul Wambergue', 'Paula Kurylowicz', 'Pavankumar Reddy Muddireddy', 'Philomène Chagniot', 'Pierre Stock', 'Pravesh Agrawal', 'Romain Sauvestre', 'Rémi Delacourt', 'Sanchit Gandhi', 'Sandeep Subramanian', 'Shashwat Dalal', 'Siddharth Gandhi', 'Soham Ghosh', 'Srijan Mishra', 'Sumukh Aithal', 'Szymon Antoniak', 'Thibault Schueller', 'Thibaut Lavril', 'Thomas Robert', 'Thomas Wang', 'Timothée Lacroix', 'Valeriia Nemychnikova', 'Victor Paltz', 'Virgile Richard', 'Wen-Ding Li', 'William Marshall', 'Xuanyu Zhang', 'Yunhao Tang'] | ['cs.CL'] | We introduce Magistral, Mistral's first reasoning model and our own scalable
reinforcement learning (RL) pipeline. Instead of relying on existing
implementations and RL traces distilled from prior models, we follow a ground
up approach, relying solely on our own models and infrastructure. Notably, we
demonstrate a stack that enabled us to explore the limits of pure RL training
of LLMs, present a simple method to force the reasoning language of the model,
and show that RL on text data alone maintains most of the initial checkpoint's
capabilities. We find that RL on text maintains or improves multimodal
understanding, instruction following and function calling. We present Magistral
Medium, trained for reasoning on top of Mistral Medium 3 with RL alone, and we
open-source Magistral Small (Apache 2.0) which further includes cold-start data
from Magistral Medium. | 2025-06-12T17:22:37Z | null | null | null | null | null | null | null | null | null | null |
2,506.10941 | VINCIE: Unlocking In-context Image Editing from Video | ['Leigang Qu', 'Feng Cheng', 'Ziyan Yang', 'Qi Zhao', 'Shanchuan Lin', 'Yichun Shi', 'Yicong Li', 'Wenjie Wang', 'Tat-Seng Chua', 'Lu Jiang'] | ['cs.CV', 'cs.AI', 'cs.CL', 'cs.LG', 'cs.MM'] | In-context image editing aims to modify images based on a contextual sequence
comprising text and previously generated images. Existing methods typically
depend on task-specific pipelines and expert models (e.g., segmentation and
inpainting) to curate training data. In this work, we explore whether an
in-context image editing model can be learned directly from videos. We
introduce a scalable approach to annotate videos as interleaved multimodal
sequences. To effectively learn from this data, we design a block-causal
diffusion transformer trained on three proxy tasks: next-image prediction,
current segmentation prediction, and next-segmentation prediction.
Additionally, we propose a novel multi-turn image editing benchmark to advance
research in this area. Extensive experiments demonstrate that our model
exhibits strong in-context image editing capabilities and achieves
state-of-the-art results on two multi-turn image editing benchmarks. Despite
being trained exclusively on videos, our model also shows promising abilities
in multi-concept composition, story generation, and chain-of-editing
applications. | 2025-06-12T17:46:54Z | Project page: https://vincie2025.github.io/ | null | null | null | null | null | null | null | null | null |
2,506.1096 | ChineseHarm-Bench: A Chinese Harmful Content Detection Benchmark | ['Kangwei Liu', 'Siyuan Cheng', 'Bozhong Tian', 'Xiaozhuan Liang', 'Yuyang Yin', 'Meng Han', 'Ningyu Zhang', 'Bryan Hooi', 'Xi Chen', 'Shumin Deng'] | ['cs.CL', 'cs.AI', 'cs.CR', 'cs.IR', 'cs.LG'] | Large language models (LLMs) have been increasingly applied to automated
harmful content detection tasks, assisting moderators in identifying policy
violations and improving the overall efficiency and accuracy of content review.
However, existing resources for harmful content detection are predominantly
focused on English, with Chinese datasets remaining scarce and often limited in
scope. We present a comprehensive, professionally annotated benchmark for
Chinese content harm detection, which covers six representative categories and
is constructed entirely from real-world data. Our annotation process further
yields a knowledge rule base that provides explicit expert knowledge to assist
LLMs in Chinese harmful content detection. In addition, we propose a
knowledge-augmented baseline that integrates both human-annotated knowledge
rules and implicit knowledge from large language models, enabling smaller
models to achieve performance comparable to state-of-the-art LLMs. Code and
data are available at https://github.com/zjunlp/ChineseHarm-bench. | 2025-06-12T17:57:05Z | Work in progress | null | null | null | null | null | null | null | null | null |
2,506.11029 | Output Scaling: YingLong-Delayed Chain of Thought in a Large Pretrained
Time Series Forecasting Model | ['Xue Wang', 'Tian Zhou', 'Jinyang Gao', 'Bolin Ding', 'Jingren Zhou'] | ['cs.LG', 'cs.AI'] | We present a joint forecasting framework for time series prediction that
contrasts with traditional direct or recursive methods. This framework achieves
state-of-the-art performance for our designed foundation model, YingLong, and
reveals a novel scaling effect: longer outputs significantly enhance model
accuracy due to delayed chain-of-thought reasoning in our non-causal approach.
YingLong is a non-causal, bidirectional attention encoder-only transformer
trained through masked token recovery, aligning more effectively with language
understanding tasks than with generation tasks. Additionally, we boost
performance by tackling output variance with a multi-input ensemble. We release
four foundation models ranging from 6M to 300M parameters, demonstrating
superior results in zero-shot tasks on the ETT and Weather datasets. YingLong
achieves more than 60% best performance. To ensure generalizability, we
assessed the models using the GIFT-Eval benchmark, which comprises 23 time
series datasets across 7 domains. Yinglong significantly outperformed the best
time-series foundation models, end-to-end trained models by 14% and 44% in rank
respectively.The pretrained 300M model is available at
https://huggingface.co/qcw1314/YingLong_300m | 2025-05-20T14:31:06Z | null | null | null | Output Scaling: YingLong-Delayed Chain of Thought in a Large Pretrained Time Series Forecasting Model | ['Xue Wang', 'Tian Zhou', 'Jinyang Gao', 'Bolin Ding', 'Jingren Zhou'] | 2,025 | arXiv.org | 0 | 58 | ['Computer Science'] |
2,506.11115 | Incorporating Domain Knowledge into Materials Tokenization | ['Yerim Oh', 'Jun-Hyung Park', 'Junho Kim', 'SungHo Kim', 'SangKeun Lee'] | ['cs.CL', 'cs.AI'] | While language models are increasingly utilized in materials science, typical
models rely on frequency-centric tokenization methods originally developed for
natural language processing. However, these methods frequently produce
excessive fragmentation and semantic loss, failing to maintain the structural
and semantic integrity of material concepts. To address this issue, we propose
MATTER, a novel tokenization approach that integrates material knowledge into
tokenization. Based on MatDetector trained on our materials knowledge base and
a re-ranking method prioritizing material concepts in token merging, MATTER
maintains the structural integrity of identified material concepts and prevents
fragmentation during tokenization, ensuring their semantic meaning remains
intact. The experimental results demonstrate that MATTER outperforms existing
tokenization methods, achieving an average performance gain of $4\%$ and $2\%$
in the generation and classification tasks, respectively. These results
underscore the importance of domain knowledge for tokenization strategies in
scientific text processing. Our code is available at
https://github.com/yerimoh/MATTER | 2025-06-09T04:59:13Z | null | null | null | null | null | null | null | null | null | null |
2,506.1113 | A Self-Refining Framework for Enhancing ASR Using TTS-Synthesized Data | ['Cheng-Kang Chou', 'Chan-Jan Hsu', 'Ho-Lam Chung', 'Liang-Hsuan Tseng', 'Hsi-Chun Cheng', 'Yu-Kuan Fu', 'Kuan Po Huang', 'Hung-Yi Lee'] | ['cs.CL', 'cs.AI', 'cs.SD', 'eess.AS'] | We propose a self-refining framework that enhances ASR performance with only
unlabeled datasets. The process starts with an existing ASR model generating
pseudo-labels on unannotated speech, which are then used to train a
high-fidelity text-to-speech (TTS) system. Then, synthesized speech text pairs
are bootstrapped into the original ASR system, completing the closed-loop
self-improvement cycle. We demonstrated the effectiveness of the framework on
Taiwanese Mandarin speech. Leveraging 6,000 hours of unlabeled speech, a
moderate amount of text data, and synthetic content from the AI models, we
adapt Whisper-large-v2 into a specialized model, Twister. Twister reduces error
rates by up to 20% on Mandarin and 50% on Mandarin-English code-switching
benchmarks compared to Whisper. Results highlight the framework as a compelling
alternative to pseudo-labeling self-distillation approaches and provides a
practical pathway for improving ASR performance in low-resource or
domain-specific settings. | 2025-06-10T17:30:32Z | null | null | null | null | null | null | null | null | null | null |
2,506.11305 | Don't Pay Attention | ['Mohammad Hammoud', 'Devang Acharya'] | ['cs.CL', 'cs.AI'] | The Transformer has become the de facto standard for large language models
and a wide range of downstream tasks across various domains. Despite its
numerous advantages like inherent training parallelism, the Transformer still
faces key challenges due to its inability to effectively process sequences
beyond a fixed context window and the quadratic complexity of its attention
mechanism. These challenges have renewed interest in RNN-like architectures,
which offer linear scaling with sequence length and improved handling of
long-range dependencies, albeit with limited parallelism due to their
inherently recurrent nature. In this paper, we propose Avey, a new neural
foundational architecture that breaks away from both attention and recurrence.
Avey comprises a ranker and an autoregressive neural processor, which
collaboratively identify and contextualize only the most relevant tokens for
any given token, regardless of their positions in the sequence. Specifically,
Avey decouples sequence length from context width, thus enabling effective
processing of arbitrarily long sequences. Experimental results show that Avey
compares favorably to the Transformer across a variety of standard short-range
NLP benchmarks, while notably excelling at capturing long-range dependencies. | 2025-06-12T21:11:06Z | null | null | null | null | null | null | null | null | null | null |
2,506.1135 | GLAP: General contrastive audio-text pretraining across domains and
languages | ['Heinrich Dinkel', 'Zhiyong Yan', 'Tianzi Wang', 'Yongqing Wang', 'Xingwei Sun', 'Yadong Niu', 'Jizhong Liu', 'Gang Li', 'Junbo Zhang', 'Jian Luan'] | ['cs.SD', 'cs.CL', 'eess.AS'] | Contrastive Language Audio Pretraining (CLAP) is a widely-used method to
bridge the gap between audio and text domains. Current CLAP methods enable
sound and music retrieval in English, ignoring multilingual spoken content. To
address this, we introduce general language audio pretraining (GLAP), which
expands CLAP with multilingual and multi-domain abilities. GLAP demonstrates
its versatility by achieving competitive performance on standard audio-text
retrieval benchmarks like Clotho and AudioCaps, while significantly surpassing
existing methods in speech retrieval and classification tasks. Additionally,
GLAP achieves strong results on widely used sound-event zero-shot benchmarks,
while simultaneously outperforming previous methods on speech content
benchmarks. Further keyword spotting evaluations across 50 languages emphasize
GLAP's advanced multilingual capabilities. Finally, multilingual sound and
music understanding is evaluated across four languages. Checkpoints and Source:
https://github.com/xiaomi-research/dasheng-glap. | 2025-06-12T22:54:31Z | null | null | null | GLAP: General contrastive audio-text pretraining across domains and languages | ['Heinrich Dinkel', 'Zhiyong Yan', 'Tianzi Wang', 'Yongqing Wang', 'Xingwei Sun', 'Yadong Niu', 'Jizhong Liu', 'Gang Li', 'Junbo Zhang', 'Jian Luan'] | 2,025 | arXiv.org | 0 | 33 | ['Computer Science', 'Engineering'] |
2,506.11474 | Med-PRM: Medical Reasoning Models with Stepwise, Guideline-verified
Process Rewards | ['Jaehoon Yun', 'Jiwoong Sohn', 'Jungwoo Park', 'Hyunjae Kim', 'Xiangru Tang', 'Yanjun Shao', 'Yonghoe Koo', 'Minhyeok Ko', 'Qingyu Chen', 'Mark Gerstein', 'Michael Moor', 'Jaewoo Kang'] | ['cs.CL'] | Large language models have shown promise in clinical decision making, but
current approaches struggle to localize and correct errors at specific steps of
the reasoning process. This limitation is critical in medicine, where
identifying and addressing reasoning errors is essential for accurate diagnosis
and effective patient care. We introduce Med-PRM, a process reward modeling
framework that leverages retrieval-augmented generation to verify each
reasoning step against established medical knowledge bases. By verifying
intermediate reasoning steps with evidence retrieved from clinical guidelines
and literature, our model can precisely assess the reasoning quality in a
fine-grained manner. Evaluations on five medical QA benchmarks and two
open-ended diagnostic tasks demonstrate that Med-PRM achieves state-of-the-art
performance, with improving the performance of base models by up to 13.50%
using Med-PRM. Moreover, we demonstrate the generality of Med-PRM by
integrating it in a plug-and-play fashion with strong policy models such as
Meerkat, achieving over 80\% accuracy on MedQA for the first time using
small-scale models of 8 billion parameters. Our code and data are available at:
https://med-prm.github.io/ | 2025-06-13T05:36:30Z | null | null | null | null | null | null | null | null | null | null |
2,506.11515 | Manager: Aggregating Insights from Unimodal Experts in Two-Tower VLMs
and MLLMs | ['Xiao Xu', 'Libo Qin', 'Wanxiang Che', 'Min-Yen Kan'] | ['cs.CV', 'cs.CL', 'cs.LG'] | Two-Tower Vision--Language Models (VLMs) have demonstrated strong performance
across various downstream VL tasks. While BridgeTower further enhances
performance by building bridges between encoders, it \textit{(i)} suffers from
ineffective layer-by-layer utilization of unimodal representations,
\textit{(ii)} restricts the flexible exploitation of different levels of
unimodal semantic knowledge, and \textit{(iii)} is limited to the evaluation on
traditional low-resolution datasets only with the Two-Tower VLM architecture.
In this work, we propose Manager, a lightweight, efficient and effective plugin
that adaptively aggregates insights from different levels of pre-trained
unimodal experts to facilitate more comprehensive VL alignment and fusion.
First, under the Two-Tower VLM architecture, we introduce ManagerTower, a novel
VLM that introduces the manager in each cross-modal layer. Whether with or
without VL pre-training, ManagerTower outperforms previous strong baselines and
achieves superior performance on 4 downstream VL tasks. Moreover, we extend our
exploration to the latest Multimodal Large Language Model (MLLM) architecture.
We demonstrate that LLaVA-OV-Manager significantly boosts the zero-shot
performance of LLaVA-OV across different categories of capabilities, images,
and resolutions on 20 downstream datasets, whether the multi-grid algorithm is
enabled or not. In-depth analysis reveals that both our manager and the
multi-grid algorithm can be viewed as a plugin that improves the visual
representation by capturing more diverse visual details from two orthogonal
perspectives (depth and width). Their synergy can mitigate the semantic
ambiguity caused by the multi-grid algorithm and further improve performance.
Code and models are available at https://github.com/LooperXX/ManagerTower. | 2025-06-13T07:16:41Z | Accepted by IEEE Transactions on Circuits and Systems for Video
Technology (TCSVT). June 2025. DOI:
https://doi.org/10.1109/TCSVT.2025.3578266 | null | 10.1109/TCSVT.2025.3578266 | Manager: Aggregating Insights from Unimodal Experts in Two-Tower VLMs and MLLMs | ['Xiao Xu', 'Libo Qin', 'Wanxiang Che', 'Min-Yen Kan'] | 2,025 | IEEE transactions on circuits and systems for video technology (Print) | 0 | 143 | ['Computer Science'] |
2,506.11543 | FIMA-Q: Post-Training Quantization for Vision Transformers by Fisher
Information Matrix Approximation | ['Zhuguanyu Wu', 'Shihe Wang', 'Jiayi Zhang', 'Jiaxin Chen', 'Yunhong Wang'] | ['cs.CV', 'cs.AI', 'cs.LG'] | Post-training quantization (PTQ) has stood out as a cost-effective and
promising model compression paradigm in recent years, as it avoids
computationally intensive model retraining. Nevertheless, current PTQ methods
for Vision Transformers (ViTs) still suffer from significant accuracy
degradation, especially under low-bit quantization. To address these
shortcomings, we analyze the prevailing Hessian-guided quantization loss, and
uncover certain limitations of conventional Hessian approximations. By
following the block-wise reconstruction framework, we propose a novel PTQ
method for ViTs, dubbed FIMA-Q. Specifically, we firstly establish the
connection between KL divergence and FIM, which enables fast computation of the
quantization loss during reconstruction. We further propose an efficient FIM
approximation method, namely DPLR-FIM, by employing the diagonal plus low-rank
principle, and formulate the ultimate quantization loss. Our extensive
experiments, conducted across various vision tasks with representative
ViT-based architectures on public datasets, demonstrate that our method
substantially promotes the accuracy compared to the state-of-the-art
approaches, especially in the case of low-bit quantization. The source code is
available at https://github.com/ShiheWang/FIMA-Q. | 2025-06-13T07:57:38Z | CVPR 2025 Highlight | null | null | null | null | null | null | null | null | null |
2,506.11702 | Configurable Preference Tuning with Rubric-Guided Synthetic Data | ['Víctor Gallego'] | ['cs.CL', 'cs.AI'] | Models of human feedback for AI alignment, such as those underpinning Direct
Preference Optimization (DPO), often bake in a singular, static set of
preferences, limiting adaptability. This paper challenges the assumption of
monolithic preferences by introducing Configurable Preference Tuning (CPT), a
novel framework for endowing language models with the ability to dynamically
adjust their behavior based on explicit, human-interpretable directives. CPT
leverages synthetically generated preference data, conditioned on system
prompts derived from structured, fine-grained rubrics that define desired
attributes like writing style. By fine-tuning with these rubric-guided
preferences, the LLM learns to modulate its outputs at inference time in
response to the system prompt, without retraining. This approach not only
offers fine-grained control but also provides a mechanism for modeling more
nuanced and context-dependent human feedback. Several experimental artifacts,
such as training code, generated datasets and fine-tuned models are released at
https://github.com/vicgalle/configurable-preference-tuning | 2025-06-13T12:17:38Z | Accepted to ICML 2025 Workshop on Models of Human Feedback for AI
Alignment | null | null | null | null | null | null | null | null | null |
2,506.11903 | GeistBERT: Breathing Life into German NLP | ['Raphael Scheible-Schmitt', 'Johann Frei'] | ['cs.CL'] | Advances in transformer-based language models have highlighted the benefits
of language-specific pre-training on high-quality corpora. In this context,
German NLP stands to gain from updated architectures and modern datasets
tailored to the linguistic characteristics of the German language. GeistBERT
seeks to improve German language processing by incrementally training on a
diverse corpus and optimizing model performance across various NLP tasks. We
pre-trained GeistBERT using fairseq, following the RoBERTa base configuration
with Whole Word Masking (WWM), and initialized from GottBERT weights. The model
was trained on a 1.3 TB German corpus with dynamic masking and a fixed sequence
length of 512 tokens. For evaluation, we fine-tuned the model on standard
downstream tasks, including NER (CoNLL 2003, GermEval 2014), text
classification (GermEval 2018 coarse/fine, 10kGNAD), and NLI (German XNLI),
using $F_1$ score and accuracy as evaluation metrics. GeistBERT achieved strong
results across all tasks, leading among base models and setting a new
state-of-the-art (SOTA) in GermEval 2018 fine text classification. It also
outperformed several larger models, particularly in classification benchmarks.
To support research in German NLP, we release GeistBERT under the MIT license. | 2025-06-13T15:53:17Z | null | null | null | null | null | null | null | null | null | null |
2,506.11991 | VGR: Visual Grounded Reasoning | ['Jiacong Wang', 'Zijian Kang', 'Haochen Wang', 'Haiyong Jiang', 'Jiawen Li', 'Bohong Wu', 'Ya Wang', 'Jiao Ran', 'Xiao Liang', 'Chao Feng', 'Jun Xiao'] | ['cs.CV', 'cs.AI', 'cs.CL'] | In the field of multimodal chain-of-thought (CoT) reasoning, existing
approaches predominantly rely on reasoning on pure language space, which
inherently suffers from language bias and is largely confined to math or
science domains. This narrow focus limits their ability to handle complex
visual reasoning tasks that demand comprehensive understanding of image
details. To address these limitations, this paper introduces VGR, a novel
reasoning multimodal large language model (MLLM) with enhanced fine-grained
visual perception capabilities. Unlike traditional MLLMs that answer the
question or reasoning solely on the language space, our VGR first detects
relevant regions that may help to solve problems, and then provides precise
answers based on replayed image regions. To achieve this, we conduct a
large-scale SFT dataset called VGR -SFT that contains reasoning data with mixed
vision grounding and language deduction. The inference pipeline of VGR allows
the model to choose bounding boxes for visual reference and a replay stage is
introduced to integrates the corresponding regions into the reasoning process,
enhancing multimodel comprehension. Experiments on the LLaVA-NeXT-7B baseline
show that VGR achieves superior performance on multi-modal benchmarks requiring
comprehensive image detail understanding. Compared to the baseline, VGR uses
only 30\% of the image token count while delivering scores of +4.1 on MMStar,
+7.1 on AI2D, and a +12.9 improvement on ChartQA. | 2025-06-13T17:47:43Z | 9 pages, 4 figures | null | null | null | null | null | null | null | null | null |
2,506.12119 | Can Mixture-of-Experts Surpass Dense LLMs Under Strictly Equal
Resources? | ['Houyi Li', 'Ka Man Lo', 'Ziqi Wang', 'Zili Wang', 'Wenzhen Zheng', 'Shuigeng Zhou', 'Xiangyu Zhang', 'Daxin Jiang'] | ['cs.CL', 'cs.AI'] | Mixture-of-Experts (MoE) language models dramatically expand model capacity
and achieve remarkable performance without increasing per-token compute.
However, can MoEs surpass dense architectures under strictly equal resource
constraints - that is, when the total parameter count, training compute, and
data budget are identical? This question remains under-explored despite its
significant practical value and potential. In this paper, we propose a novel
perspective and methodological framework to study this question thoroughly.
First, we comprehensively investigate the architecture of MoEs and achieve an
optimal model design that maximizes the performance. Based on this, we
subsequently find that an MoE model with activation rate in an optimal region
is able to outperform its dense counterpart under the same total parameter,
training compute and data resource. More importantly, this optimal region
remains consistent across different model sizes. Although additional amount of
data turns out to be a trade-off for the enhanced performance, we show that
this can be resolved via reusing data. We validate our findings through
extensive experiments, training nearly 200 language models at 2B scale and over
50 at 7B scale, cumulatively processing 50 trillion tokens. All models will be
released publicly. | 2025-06-13T17:59:05Z | null | null | null | Can Mixture-of-Experts Surpass Dense LLMs Under Strictly Equal Resources? | ['Houyi Li', 'Ka Man Lo', 'Ziqi Wang', 'Zili Wang', 'Wenzheng Zheng', 'Shuigeng Zhou', 'Xiangyu Zhang', 'Daxin Jiang'] | 2,025 | arXiv.org | 0 | 63 | ['Computer Science'] |
2,506.12242 | Large Language Models for History, Philosophy, and Sociology of Science:
Interpretive Uses, Methodological Challenges, and Critical Perspectives | ['Arno Simons', 'Michael Zichert', 'Adrian Wüthrich'] | ['cs.CL', 'cs.AI', 'cs.CY', 'A.1; I.2.1; I.2.7; J.4; J.5'] | This paper explores the use of large language models (LLMs) as research tools
in the history, philosophy, and sociology of science (HPSS). LLMs are
remarkably effective at processing unstructured text and inferring meaning from
context, offering new affordances that challenge long-standing divides between
computational and interpretive methods. This raises both opportunities and
challenges for HPSS, which emphasizes interpretive methodologies and
understands meaning as context-dependent, ambiguous, and historically situated.
We argue that HPSS is uniquely positioned not only to benefit from LLMs'
capabilities but also to interrogate their epistemic assumptions and
infrastructural implications. To this end, we first offer a concise primer on
LLM architectures and training paradigms tailored to non-technical readers. We
frame LLMs not as neutral tools but as epistemic infrastructures that encode
assumptions about meaning, context, and similarity, conditioned by their
training data, architecture, and patterns of use. We then examine how
computational techniques enhanced by LLMs, such as structuring data, detecting
patterns, and modeling dynamic processes, can be applied to support
interpretive research in HPSS. Our analysis compares full-context and
generative models, outlines strategies for domain and task adaptation (e.g.,
continued pretraining, fine-tuning, and retrieval-augmented generation), and
evaluates their respective strengths and limitations for interpretive inquiry
in HPSS. We conclude with four lessons for integrating LLMs into HPSS: (1)
model selection involves interpretive trade-offs; (2) LLM literacy is
foundational; (3) HPSS must define its own benchmarks and corpora; and (4) LLMs
should enhance, not replace, interpretive methods. | 2025-06-13T21:44:13Z | 27 pages, 2 tables | null | null | Large Language Models for History, Philosophy, and Sociology of Science: Interpretive Uses, Methodological Challenges, and Critical Perspectives | ['Arno Simons', 'Michael Zichert', 'Adrian Wüthrich'] | 2,025 | arXiv.org | 0 | 79 | ['Computer Science'] |
2,506.12364 | MM-R5: MultiModal Reasoning-Enhanced ReRanker via Reinforcement Learning
for Document Retrieval | ['Mingjun Xu', 'Jinhan Dong', 'Jue Hou', 'Zehui Wang', 'Sihang Li', 'Zhifeng Gao', 'Renxin Zhong', 'Hengxing Cai'] | ['cs.AI', 'cs.CL', 'cs.CV'] | Multimodal document retrieval systems enable information access across text,
images, and layouts, benefiting various domains like document-based question
answering, report analysis, and interactive content summarization. Rerankers
improve retrieval precision by reordering retrieved candidates. However,
current multimodal reranking methods remain underexplored, with significant
room for improvement in both training strategies and overall effectiveness.
Moreover, the lack of explicit reasoning makes it difficult to analyze and
optimize these methods further. In this paper, We propose MM-R5, a MultiModal
Reasoning-Enhanced ReRanker via Reinforcement Learning for Document Retrieval,
aiming to provide a more effective and reliable solution for multimodal
reranking tasks. MM-R5 is trained in two stages: supervised fine-tuning (SFT)
and reinforcement learning (RL). In the SFT stage, we focus on improving
instruction-following and guiding the model to generate complete and
high-quality reasoning chains. To support this, we introduce a novel data
construction strategy that produces rich, high-quality reasoning data. In the
RL stage, we design a task-specific reward framework, including a reranking
reward tailored for multimodal candidates and a composite template-based reward
to further refine reasoning quality. We conduct extensive experiments on
MMDocIR, a challenging public benchmark spanning multiple domains. MM-R5
achieves state-of-the-art performance on most metrics and delivers comparable
results to much larger models on the remaining ones. Moreover, compared to the
best retrieval-only method, MM-R5 improves recall@1 by over 4%. These results
validate the effectiveness of our reasoning-enhanced training pipeline. Our
code is available at https://github.com/i2vec/MM-R5 . | 2025-06-14T05:55:00Z | null | null | null | MM-R5: MultiModal Reasoning-Enhanced ReRanker via Reinforcement Learning for Document Retrieval | ['Mingjun Xu', 'Jinhan Dong', 'Jue Hou', 'Zehui Wang', 'Sihang Li', 'Zhifeng Gao', 'Renxin Zhong', 'Hengxing Cai'] | 2,025 | arXiv.org | 0 | 47 | ['Computer Science'] |
2,506.12473 | TagRouter: Learning Route to LLMs through Tags for Open-Domain Text
Generation Tasks | ['Zhou Chen', 'Zhiqiang Wei', 'Yuqi Bai', 'Xue Xiong', 'Jianmin Wu'] | ['cs.CL'] | Model routing allocates queries to the suitable model, improving system
performance while reducing costs. However, existing routing methods face
practical limitations that hinder scalability in large-scale applications and
struggle to keep up with the rapid growth of the large language model (LLM)
ecosystem. To tackle these challenges, we propose TagRouter, a training-free
model routing method designed to optimize the synergy among multiple LLMs for
open-domain text generation tasks. Experimental results demonstrate that
TagRouter outperforms 13 baseline methods, increasing the accept rate of system
by 6.15% and reducing costs by 17.20%, achieving optimal cost-efficiency. Our
findings provides the LLM community with an efficient and scalable solution for
model ensembling, offering users an evolvable "super model." | 2025-06-14T12:17:47Z | ACL 2025, 26 pages, 13 figures, 14 tables | null | null | null | null | null | null | null | null | null |
2,506.12479 | AI Flow: Perspectives, Scenarios, and Approaches | ['Hongjun An', 'Wenhan Hu', 'Sida Huang', 'Siqi Huang', 'Ruanjun Li', 'Yuanzhi Liang', 'Jiawei Shao', 'Yiliang Song', 'Zihan Wang', 'Cheng Yuan', 'Chi Zhang', 'Hongyuan Zhang', 'Wenhao Zhuang', 'Xuelong Li'] | ['cs.AI', 'cs.CL', 'cs.CV', 'cs.DC', 'eess.SP'] | Pioneered by the foundational information theory by Claude Shannon and the
visionary framework of machine intelligence by Alan Turing, the convergent
evolution of information and communication technologies (IT/CT) has created an
unbroken wave of connectivity and computation. This synergy has sparked a
technological revolution, now reaching its peak with large artificial
intelligence (AI) models that are reshaping industries and redefining
human-machine collaboration. However, the realization of ubiquitous
intelligence faces considerable challenges due to substantial resource
consumption in large models and high communication bandwidth demands. To
address these challenges, AI Flow has been introduced as a multidisciplinary
framework that integrates cutting-edge IT and CT advancements, with a
particular emphasis on the following three key points. First, device-edge-cloud
framework serves as the foundation, which integrates end devices, edge servers,
and cloud clusters to optimize scalability and efficiency for low-latency model
inference. Second, we introduce the concept of familial models, which refers to
a series of different-sized models with aligned hidden features, enabling
effective collaboration and the flexibility to adapt to varying resource
constraints and dynamic scenarios. Third, connectivity- and interaction-based
intelligence emergence is a novel paradigm of AI Flow. By leveraging
communication networks to enhance connectivity, the collaboration among AI
models across heterogeneous nodes achieves emergent intelligence that surpasses
the capability of any single model. The innovations of AI Flow provide enhanced
intelligence, timely responsiveness, and ubiquitous accessibility to AI
services, paving the way for the tighter fusion of AI techniques and
communication systems. | 2025-06-14T12:43:07Z | Authors are with Institute of Artificial Intelligence (TeleAI), China
Telecom, China. Author names are listed alphabetically by surname. This work
was conducted at TeleAI, facilitated by Dr. Jiawei Shao (e-mail:
shaojw2@chinatelecom.cn) under the leadership of Prof. Xuelong Li. The
corresponding author is Prof. Xuelong Li (e-mail: xuelong li@ieee.org), the
CTO and Chief Scientist of China Telecom | null | null | null | null | null | null | null | null | null |
2,506.12704 | Flexible Realignment of Language Models | ['Wenhong Zhu', 'Ruobing Xie', 'Weinan Zhang', 'Rui Wang'] | ['cs.CL', 'cs.AI'] | Realignment becomes necessary when a language model (LM) fails to meet
expected performance. We propose a flexible realignment framework that supports
quantitative control of alignment degree during training and inference. This
framework incorporates Training-time Realignment (TrRa), which efficiently
realigns the reference model by leveraging the controllable fusion of logits
from both the reference and already aligned models. For example, TrRa reduces
token usage by 54.63% on DeepSeek-R1-Distill-Qwen-1.5B without any performance
degradation, outperforming DeepScaleR-1.5B's 33.86%. To complement TrRa during
inference, we introduce a layer adapter that enables smooth Inference-time
Realignment (InRa). This adapter is initialized to perform an identity
transformation at the bottom layer and is inserted preceding the original
layers. During inference, input embeddings are simultaneously processed by the
adapter and the original layer, followed by the remaining layers, and then
controllably interpolated at the logit level. We upgraded
DeepSeek-R1-Distill-Qwen-7B from a slow-thinking model to one that supports
both fast and slow thinking, allowing flexible alignment control even during
inference. By encouraging deeper reasoning, it even surpassed its original
performance. | 2025-06-15T03:26:59Z | null | null | null | null | null | null | null | null | null | null |
2,506.12776 | Native Visual Understanding: Resolving Resolution Dilemmas in
Vision-Language Models | ['Junbo Niu', 'Yuanhong Zheng', 'Ziyang Miao', 'Hejun Dong', 'Chunjiang Ge', 'Hao Liang', 'Ma Lu', 'Bohan Zeng', 'Qiahao Zheng', 'Conghui He', 'Wentao Zhang'] | ['cs.CV'] | Vision-Language Models (VLMs) face significant challenges when dealing with
the diverse resolutions and aspect ratios of real-world images, as most
existing models rely on fixed, low-resolution inputs. While recent studies have
explored integrating native resolution visual encoding to improve model
performance, such efforts remain fragmented and lack a systematic framework
within the open-source community. Moreover, existing benchmarks fall short in
evaluating VLMs under varied visual conditions, often neglecting resolution as
a critical factor. To address the "Resolution Dilemma" stemming from both model
design and benchmark limitations, we introduce RC-Bench, a novel benchmark
specifically designed to systematically evaluate VLM capabilities under extreme
visual conditions, with an emphasis on resolution and aspect ratio variations.
In conjunction, we propose NativeRes-LLaVA, an open-source training framework
that empowers VLMs to effectively process images at their native resolutions
and aspect ratios. Based on RC-Bench and NativeRes-LLaVA, we conduct
comprehensive experiments on existing visual encoding strategies. The results
show that Native Resolution Visual Encoding significantly improves the
performance of VLMs on RC-Bench as well as other resolution-centric benchmarks.
Code is available at https://github.com/Niujunbo2002/NativeRes-LLaVA. | 2025-06-15T08:58:09Z | null | null | null | Native Visual Understanding: Resolving Resolution Dilemmas in Vision-Language Models | ['Junbo Niu', 'Yuanhong Zheng', 'Ziyang Miao', 'Hejun Dong', 'Chunjiang Ge', 'Hao Liang', 'Ma Lu', 'Bohan Zeng', 'Qiahao Zheng', 'Conghui He', 'Wentao Zhang'] | 2,025 | arXiv.org | 0 | 62 | ['Computer Science'] |
2,506.1286 | QFFT, Question-Free Fine-Tuning for Adaptive Reasoning | ['Wanlong Liu', 'Junxiao Xu', 'Fei Yu', 'Yukang Lin', 'Ke Ji', 'Wenyu Chen', 'Yan Xu', 'Yasheng Wang', 'Lifeng Shang', 'Benyou Wang'] | ['cs.CL'] | Recent advancements in Long Chain-of-Thought (CoT) reasoning models have
improved performance on complex tasks, but they suffer from overthinking, which
generates redundant reasoning steps, especially for simple questions. This
paper revisits the reasoning patterns of Long and Short CoT models, observing
that the Short CoT patterns offer concise reasoning efficiently, while the Long
CoT patterns excel in challenging scenarios where the Short CoT patterns
struggle. To enable models to leverage both patterns, we propose Question-Free
Fine-Tuning (QFFT), a fine-tuning approach that removes the input question
during training and learns exclusively from Long CoT responses. This approach
enables the model to adaptively employ both reasoning patterns: it prioritizes
the Short CoT patterns and activates the Long CoT patterns only when necessary.
Experiments on various mathematical datasets demonstrate that QFFT reduces
average response length by more than 50\%, while achieving performance
comparable to Supervised Fine-Tuning (SFT). Additionally, QFFT exhibits
superior performance compared to SFT in noisy, out-of-domain, and low-resource
scenarios. | 2025-06-15T14:21:28Z | 23 pages | null | null | QFFT, Question-Free Fine-Tuning for Adaptive Reasoning | ['Wanlong Liu', 'Junxiao Xu', 'Fei Yu', 'Yukang Lin', 'Ke Ji', 'Wenyu Chen', 'Yan Xu', 'Yasheng Wang', 'Lifeng Shang', 'Benyou Wang'] | 2,025 | arXiv.org | 0 | 48 | ['Computer Science'] |
2,506.13006 | Antibody Foundational Model : Ab-RoBERTa | ['Eunna Huh', 'Hyeonsu Lee', 'Hyunjin Shin'] | ['cs.LG', '68T50 (Primary) 68U15 (Secondary)'] | With the growing prominence of antibody-based therapeutics, antibody
engineering has gained increasing attention as a critical area of research and
development. Recent progress in transformer-based protein large language models
(LLMs) has demonstrated promising applications in protein sequence design and
structural prediction. Moreover, the availability of large-scale antibody
datasets such as the Observed Antibody Space (OAS) database has opened new
avenues for the development of LLMs specialized for processing antibody
sequences. Among these, RoBERTa has demonstrated improved performance relative
to BERT, while maintaining a smaller parameter count (125M) compared to the
BERT-based protein model, ProtBERT (420M). This reduced model size enables more
efficient deployment in antibody-related applications. However, despite the
numerous advantages of the RoBERTa architecture, antibody-specific foundational
models built upon it have remained inaccessible to the research community. In
this study, we introduce Ab-RoBERTa, a RoBERTa-based antibody-specific LLM,
which is publicly available at https://huggingface.co/mogam-ai/Ab-RoBERTa. This
resource is intended to support a wide range of antibody-related research
applications including paratope prediction or humanness assessment. | 2025-06-16T00:22:07Z | 14 page, 3 figures, 5 tables | null | null | null | null | null | null | null | null | null |
2,506.13044 | Just Go Parallel: Improving the Multilingual Capabilities of Large
Language Models | ['Muhammad Reza Qorib', 'Junyi Li', 'Hwee Tou Ng'] | ['cs.CL', 'cs.AI'] | Large language models (LLMs) have demonstrated impressive translation
capabilities even without being explicitly trained on parallel data. This
remarkable property has led some to believe that parallel data is no longer
necessary for building multilingual language models. While some attribute this
to the emergent abilities of LLMs due to scale, recent work suggests that it is
actually caused by incidental bilingual signals present in the training data.
Various methods have been proposed to maximize the utility of parallel data to
enhance the multilingual capabilities of multilingual encoder-based and
encoder-decoder language models. However, some decoder-based LLMs opt to ignore
parallel data instead. In this work, we conduct a systematic study on the
impact of adding parallel data on LLMs' multilingual capabilities, focusing
specifically on translation and multilingual common-sense reasoning. Through
controlled experiments, we demonstrate that parallel data can significantly
improve LLMs' multilingual capabilities. | 2025-06-16T02:21:15Z | ACL 2025 | null | null | null | null | null | null | null | null | null |
2,506.13053 | ZipVoice: Fast and High-Quality Zero-Shot Text-to-Speech with Flow
Matching | ['Han Zhu', 'Wei Kang', 'Zengwei Yao', 'Liyong Guo', 'Fangjun Kuang', 'Zhaoqing Li', 'Weiji Zhuang', 'Long Lin', 'Daniel Povey'] | ['eess.AS', 'cs.SD'] | Existing large-scale zero-shot text-to-speech (TTS) models deliver high
speech quality but suffer from slow inference speeds due to massive parameters.
To address this issue, this paper introduces ZipVoice, a high-quality
flow-matching-based zero-shot TTS model with a compact model size and fast
inference speed. Key designs include: 1) a Zipformer-based flow-matching
decoder to maintain adequate modeling capabilities under constrained size; 2)
Average upsampling-based initial speech-text alignment and Zipformer-based text
encoder to improve speech intelligibility; 3) A flow distillation method to
reduce sampling steps and eliminate the inference overhead associated with
classifier-free guidance. Experiments on 100k hours multilingual datasets show
that ZipVoice matches state-of-the-art models in speech quality, while being 3
times smaller and up to 30 times faster than a DiT-based flow-matching
baseline. Codes, model checkpoints and demo samples are publicly available. | 2025-06-16T02:48:17Z | null | null | null | null | null | null | null | null | null | null |
2,506.13056 | Metis-RISE: RL Incentivizes and SFT Enhances Multimodal Reasoning Model
Learning | ['Haibo Qiu', 'Xiaohan Lan', 'Fanfan Liu', 'Xiaohu Sun', 'Delian Ruan', 'Peng Shi', 'Lin Ma'] | ['cs.AI', 'cs.CV', 'cs.LG'] | Recent advancements in large language models (LLMs) have witnessed a surge in
the development of advanced reasoning paradigms, which are now being integrated
into multimodal large language models (MLLMs). However, existing approaches
often fall short: methods solely employing reinforcement learning (RL) can
struggle with sample inefficiency and activating entirely absent reasoning
capabilities, while conventional pipelines that initiate with a cold-start
supervised fine-tuning (SFT) phase before RL may restrict the model's
exploratory capacity and face suboptimal convergence. In this work, we
introduce \textbf{Metis-RISE} (\textbf{R}L \textbf{I}ncentivizes and
\textbf{S}FT \textbf{E}nhances) for multimodal reasoning model learning. Unlike
conventional approaches, Metis-RISE distinctively omits an initial SFT stage,
beginning instead with an RL phase (e.g., using a Group Relative Policy
Optimization variant) to incentivize and activate the model's latent reasoning
capacity. Subsequently, the targeted SFT stage addresses two key challenges
identified during RL: (1) \textit{inefficient trajectory sampling} for tasks
where the model possesses but inconsistently applies correct reasoning, which
we tackle using self-distilled reasoning trajectories from the RL model itself;
and (2) \textit{fundamental capability absence}, which we address by injecting
expert-augmented knowledge for prompts where the model entirely fails. This
strategic application of RL for incentivization followed by SFT for enhancement
forms the core of Metis-RISE, leading to two versions of our MLLMs (7B and 72B
parameters). Evaluations on the OpenCompass Multimodal Reasoning Leaderboard
demonstrate that both models achieve state-of-the-art performance among
similar-sized models, with the 72B version ranking fourth overall. Please refer
to our project page for open-source information. | 2025-06-16T02:56:13Z | Project Page: https://github.com/MM-Thinking/Metis-RISE | null | null | null | null | null | null | null | null | null |
2,506.13277 | SeqPE: Transformer with Sequential Position Encoding | ['Huayang Li', 'Yahui Liu', 'Hongyu Sun', 'Deng Cai', 'Leyang Cui', 'Wei Bi', 'Peilin Zhao', 'Taro Watanabe'] | ['cs.LG', 'cs.AI', 'cs.CL', 'cs.CV'] | Since self-attention layers in Transformers are permutation invariant by
design, positional encodings must be explicitly incorporated to enable spatial
understanding. However, fixed-size lookup tables used in traditional learnable
position embeddings (PEs) limit extrapolation capabilities beyond pre-trained
sequence lengths. Expert-designed methods such as ALiBi and RoPE, mitigate this
limitation but demand extensive modifications for adapting to new modalities,
underscoring fundamental challenges in adaptability and scalability. In this
work, we present SeqPE, a unified and fully learnable position encoding
framework that represents each $n$-dimensional position index as a symbolic
sequence and employs a lightweight sequential position encoder to learn their
embeddings in an end-to-end manner. To regularize SeqPE's embedding space, we
introduce two complementary objectives: a contrastive objective that aligns
embedding distances with a predefined position-distance function, and a
knowledge distillation loss that anchors out-of-distribution position
embeddings to in-distribution teacher representations, further enhancing
extrapolation performance. Experiments across language modeling, long-context
question answering, and 2D image classification demonstrate that SeqPE not only
surpasses strong baselines in perplexity, exact match (EM), and
accuracy--particularly under context length extrapolation--but also enables
seamless generalization to multi-dimensional inputs without requiring manual
architectural redesign. We release our code, data, and checkpoints at
https://github.com/ghrua/seqpe. | 2025-06-16T09:16:40Z | null | null | null | SeqPE: Transformer with Sequential Position Encoding | ['Huyang Li', 'Yahui Liu', 'Hongyu Sun', 'Deng Cai', 'Leyang Cui', 'Wei Bi', 'Peilin Zhao', 'Taro Watanabe'] | 2,025 | arXiv.org | 0 | 54 | ['Computer Science'] |
2,506.13284 | AceReason-Nemotron 1.1: Advancing Math and Code Reasoning through SFT
and RL Synergy | ['Zihan Liu', 'Zhuolin Yang', 'Yang Chen', 'Chankyu Lee', 'Mohammad Shoeybi', 'Bryan Catanzaro', 'Wei Ping'] | ['cs.CL', 'cs.AI', 'cs.LG'] | In this work, we investigate the synergy between supervised fine-tuning (SFT)
and reinforcement learning (RL) in developing strong reasoning models. We begin
by curating the SFT training data through two scaling strategies: increasing
the number of collected prompts and the number of generated responses per
prompt. Both approaches yield notable improvements in reasoning performance,
with scaling the number of prompts resulting in more substantial gains. We then
explore the following questions regarding the synergy between SFT and RL: (i)
Does a stronger SFT model consistently lead to better final performance after
large-scale RL training? (ii) How can we determine an appropriate sampling
temperature during RL training to effectively balance exploration and
exploitation for a given SFT initialization? Our findings suggest that (i)
holds true, provided effective RL training is conducted, particularly when the
sampling temperature is carefully chosen to maintain the temperature-adjusted
entropy around 0.3, a setting that strikes a good balance between exploration
and exploitation. Notably, the performance gap between initial SFT models
narrows significantly throughout the RL process. Leveraging a strong SFT
foundation and insights into the synergistic interplay between SFT and RL, our
AceReason-Nemotron-1.1 7B model significantly outperforms
AceReason-Nemotron-1.0 and achieves new state-of-the-art performance among
Qwen2.5-7B-based reasoning models on challenging math and code benchmarks,
thereby demonstrating the effectiveness of our post-training recipe. We release
the model and data at: https://huggingface.co/nvidia/AceReason-Nemotron-1.1-7B | 2025-06-16T09:27:48Z | The AceReason-Nemotron collection:
https://huggingface.co/collections/nvidia/acereason-682f4e1261dc22f697fd1485 | null | null | AceReason-Nemotron 1.1: Advancing Math and Code Reasoning through SFT and RL Synergy | ['Zihan Liu', 'Zhuoling Yang', 'Yang Chen', 'Chankyu Lee', 'M. Shoeybi', 'Bryan Catanzaro', 'Wei Ping'] | 2,025 | arXiv.org | 0 | 42 | ['Computer Science'] |
2,506.13342 | Verifying the Verifiers: Unveiling Pitfalls and Potentials in Fact
Verifiers | ['Wooseok Seo', 'Seungju Han', 'Jaehun Jung', 'Benjamin Newman', 'Seungwon Lim', 'Seungbeen Lee', 'Ximing Lu', 'Yejin Choi', 'Youngjae Yu'] | ['cs.AI', 'cs.CL', 'cs.LG'] | Fact verification is essential for ensuring the reliability of LLM
applications. In this study, we evaluate 12 pre-trained LLMs and one
specialized fact-verifier, including frontier LLMs and open-weight reasoning
LLMs, using a collection of examples from 14 fact-checking benchmarks. We share
three findings intended to guide future development of more robust fact
verifiers. First, we highlight the importance of addressing annotation errors
and ambiguity in datasets, demonstrating that approximately 16\% of ambiguous
or incorrectly labeled data substantially influences model rankings. Neglecting
this issue may result in misleading conclusions during comparative evaluations,
and we suggest using a systematic pipeline utilizing LLM-as-a-judge to help
identify these issues at scale. Second, we discover that frontier LLMs with
few-shot in-context examples, often overlooked in previous works, achieve
top-tier performance. We therefore recommend future studies include comparisons
with these simple yet highly effective baselines. Lastly, despite their
effectiveness, frontier LLMs incur substantial costs, motivating the
development of small, fine-tuned fact verifiers. We show that these small
models still have room for improvement, particularly on instances that require
complex reasoning. Encouragingly, we demonstrate that augmenting training with
synthetic multi-hop reasoning data significantly enhances their capabilities in
such instances. We release our code, model, and dataset at
https://github.com/just1nseo/verifying-the-verifiers | 2025-06-16T10:32:10Z | null | null | null | null | null | null | null | null | null | null |
2,506.13355 | DicFace: Dirichlet-Constrained Variational Codebook Learning for
Temporally Coherent Video Face Restoration | ['Yan Chen', 'Hanlin Shang', 'Ce Liu', 'Yuxuan Chen', 'Hui Li', 'Weihao Yuan', 'Hao Zhu', 'Zilong Dong', 'Siyu Zhu'] | ['cs.CV'] | Video face restoration faces a critical challenge in maintaining temporal
consistency while recovering fine facial details from degraded inputs. This
paper presents a novel approach that extends Vector-Quantized Variational
Autoencoders (VQ-VAEs), pretrained on static high-quality portraits, into a
video restoration framework through variational latent space modeling. Our key
innovation lies in reformulating discrete codebook representations as
Dirichlet-distributed continuous variables, enabling probabilistic transitions
between facial features across frames. A spatio-temporal Transformer
architecture jointly models inter-frame dependencies and predicts latent
distributions, while a Laplacian-constrained reconstruction loss combined with
perceptual (LPIPS) regularization enhances both pixel accuracy and visual
quality. Comprehensive evaluations on blind face restoration, video inpainting,
and facial colorization tasks demonstrate state-of-the-art performance. This
work establishes an effective paradigm for adapting intensive image priors,
pretrained on high-quality images, to video restoration while addressing the
critical challenge of flicker artifacts. The source code has been open-sourced
and is available at https://github.com/fudan-generative-vision/DicFace. | 2025-06-16T10:54:28Z | null | null | null | null | null | null | null | null | null | null |
2,506.13414 | BUT System for the MLC-SLM Challenge | ['Alexander Polok', 'Jiangyu Han', 'Dominik Klement', 'Samuele Cornell', 'Jan Černocký', 'Lukáš Burget'] | ['eess.AS'] | We present a two-speaker automatic speech recognition (ASR) system that
combines DiCoW -- a diarization-conditioned variant of Whisper -- with
DiariZen, a diarization pipeline built on top of Pyannote. We first evaluate
both systems in out-of-domain (OOD) multilingual scenarios without any
fine-tuning. In this scenario, DiariZen consistently outperforms the baseline
Pyannote diarization model, demonstrating strong generalization. Despite being
fine-tuned on English-only data for target-speaker ASR, DiCoW retains solid
multilingual performance, indicating that encoder modifications preserve
Whisper's multilingual capabilities. We then fine-tune both DiCoW and DiariZen
on the MLC-SLM challenge data. The fine-tuned DiariZen continues to outperform
the fine-tuned Pyannote baseline, while DiCoW sees further gains from domain
adaptation. Our final system achieves a micro-average tcpWER/CER of 16.75% and
ranks second in Task 2 of the MLC-SLM challenge. Lastly, we identify several
labeling inconsistencies in the training data -- such as missing speech
segments and incorrect silence annotations -- which can hinder diarization
fine-tuning. We propose simple mitigation strategies to address these issues
and improve system robustness. | 2025-06-16T12:28:35Z | null | null | null | null | null | null | null | null | null | null |
2,506.13585 | MiniMax-M1: Scaling Test-Time Compute Efficiently with Lightning
Attention | ['MiniMax', ':', 'Aili Chen', 'Aonian Li', 'Bangwei Gong', 'Binyang Jiang', 'Bo Fei', 'Bo Yang', 'Boji Shan', 'Changqing Yu', 'Chao Wang', 'Cheng Zhu', 'Chengjun Xiao', 'Chengyu Du', 'Chi Zhang', 'Chu Qiao', 'Chunhao Zhang', 'Chunhui Du', 'Congchao Guo', 'Da Chen', 'Deming Ding', 'Dianjun Sun', 'Dong Li', 'Enwei Jiao', 'Haigang Zhou', 'Haimo Zhang', 'Han Ding', 'Haohai Sun', 'Haoyu Feng', 'Huaiguang Cai', 'Haichao Zhu', 'Jian Sun', 'Jiaqi Zhuang', 'Jiaren Cai', 'Jiayuan Song', 'Jin Zhu', 'Jingyang Li', 'Jinhao Tian', 'Jinli Liu', 'Junhao Xu', 'Junjie Yan', 'Junteng Liu', 'Junxian He', 'Kaiyi Feng', 'Ke Yang', 'Kecheng Xiao', 'Le Han', 'Leyang Wang', 'Lianfei Yu', 'Liheng Feng', 'Lin Li', 'Lin Zheng', 'Linge Du', 'Lingyu Yang', 'Lunbin Zeng', 'Minghui Yu', 'Mingliang Tao', 'Mingyuan Chi', 'Mozhi Zhang', 'Mujie Lin', 'Nan Hu', 'Nongyu Di', 'Peng Gao', 'Pengfei Li', 'Pengyu Zhao', 'Qibing Ren', 'Qidi Xu', 'Qile Li', 'Qin Wang', 'Rong Tian', 'Ruitao Leng', 'Shaoxiang Chen', 'Shaoyu Chen', 'Shengmin Shi', 'Shitong Weng', 'Shuchang Guan', 'Shuqi Yu', 'Sichen Li', 'Songquan Zhu', 'Tengfei Li', 'Tianchi Cai', 'Tianrun Liang', 'Weiyu Cheng', 'Weize Kong', 'Wenkai Li', 'Xiancai Chen', 'Xiangjun Song', 'Xiao Luo', 'Xiao Su', 'Xiaobo Li', 'Xiaodong Han', 'Xinzhu Hou', 'Xuan Lu', 'Xun Zou', 'Xuyang Shen', 'Yan Gong', 'Yan Ma', 'Yang Wang', 'Yiqi Shi', 'Yiran Zhong', 'Yonghong Duan', 'Yongxiang Fu', 'Yongyi Hu', 'Yu Gao', 'Yuanxiang Fan', 'Yufeng Yang', 'Yuhao Li', 'Yulin Hu', 'Yunan Huang', 'Yunji Li', 'Yunzhi Xu', 'Yuxin Mao', 'Yuxuan Shi', 'Yuze Wenren', 'Zehan Li', 'Zelin Li', 'Zhanxu Tian', 'Zhengmao Zhu', 'Zhenhua Fan', 'Zhenzhen Wu', 'Zhichao Xu', 'Zhihang Yu', 'Zhiheng Lyu', 'Zhuo Jiang', 'Zibo Gao', 'Zijia Wu', 'Zijian Song', 'Zijun Sun'] | ['cs.CL', 'cs.LG'] | We introduce MiniMax-M1, the world's first open-weight, large-scale
hybrid-attention reasoning model. MiniMax-M1 is powered by a hybrid
Mixture-of-Experts (MoE) architecture combined with a lightning attention
mechanism. The model is developed based on our previous MiniMax-Text-01 model,
which contains a total of 456 billion parameters with 45.9 billion parameters
activated per token. The M1 model natively supports a context length of 1
million tokens, 8x the context size of DeepSeek R1. Furthermore, the lightning
attention mechanism in MiniMax-M1 enables efficient scaling of test-time
compute. These properties make M1 particularly suitable for complex tasks that
require processing long inputs and thinking extensively. MiniMax-M1 is trained
using large-scale reinforcement learning (RL) on diverse problems including
sandbox-based, real-world software engineering environments. In addition to
M1's inherent efficiency advantage for RL training, we propose CISPO, a novel
RL algorithm to further enhance RL efficiency. CISPO clips importance sampling
weights rather than token updates, outperforming other competitive RL variants.
Combining hybrid-attention and CISPO enables MiniMax-M1's full RL training on
512 H800 GPUs to complete in only three weeks, with a rental cost of just
$534,700. We release two versions of MiniMax-M1 models with 40K and 80K
thinking budgets respectively, where the 40K model represents an intermediate
phase of the 80K training. Experiments on standard benchmarks show that our
models are comparable or superior to strong open-weight models such as the
original DeepSeek-R1 and Qwen3-235B, with particular strengths in complex
software engineering, tool utilization, and long-context tasks. We publicly
release MiniMax-M1 at https://github.com/MiniMax-AI/MiniMax-M1. | 2025-06-16T15:08:02Z | A technical report from MiniMax. The authors are listed in
alphabetical order. We open-source our MiniMax-M1 at
https://github.com/MiniMax-AI/MiniMax-M1 | null | null | null | null | null | null | null | null | null |
2,506.13642 | Stream-Omni: Simultaneous Multimodal Interactions with Large
Language-Vision-Speech Model | ['Shaolei Zhang', 'Shoutao Guo', 'Qingkai Fang', 'Yan Zhou', 'Yang Feng'] | ['cs.AI', 'cs.CL', 'cs.CV', 'cs.SD', 'eess.AS'] | The emergence of GPT-4o-like large multimodal models (LMMs) has raised the
exploration of integrating text, vision, and speech modalities to support more
flexible multimodal interaction. Existing LMMs typically concatenate
representation of modalities along the sequence dimension and feed them into a
large language model (LLM) backbone. While sequence-dimension concatenation is
straightforward for modality integration, it often relies heavily on
large-scale data to learn modality alignments. In this paper, we aim to model
the relationships between modalities more purposefully, thereby achieving more
efficient and flexible modality alignments. To this end, we propose
Stream-Omni, a large language-vision-speech model with efficient modality
alignments, which can simultaneously support interactions under various
modality combinations. Stream-Omni employs LLM as the backbone and aligns the
vision and speech to the text based on their relationships. For vision that is
semantically complementary to text, Stream-Omni uses sequence-dimension
concatenation to achieve vision-text alignment. For speech that is semantically
consistent with text, Stream-Omni introduces a CTC-based layer-dimension
mapping to achieve speech-text alignment. In this way, Stream-Omni can achieve
modality alignments with less data (especially speech), enabling the transfer
of text capabilities to other modalities. Experiments on various benchmarks
demonstrate that Stream-Omni achieves strong performance on visual
understanding, speech interaction, and vision-grounded speech interaction
tasks. Owing to the layer-dimensional mapping, Stream-Omni can simultaneously
provide intermediate text outputs (such as ASR transcriptions and model
responses) during speech interaction, offering users a comprehensive multimodal
experience. | 2025-06-16T16:06:45Z | Code: https://github.com/ictnlp/Stream-Omni , Model:
https://huggingface.co/ICTNLP/stream-omni-8b | null | null | Stream-Omni: Simultaneous Multimodal Interactions with Large Language-Vision-Speech Model | ['Shaolei Zhang', 'Shoutao Guo', 'Qingkai Fang', 'Yan Zhou', 'Yang Feng'] | 2,025 | arXiv.org | 0 | 55 | ['Computer Science', 'Engineering'] |
2,506.13691 | UltraVideo: High-Quality UHD Video Dataset with Comprehensive Captions | ['Zhucun Xue', 'Jiangning Zhang', 'Teng Hu', 'Haoyang He', 'Yinan Chen', 'Yuxuan Cai', 'Yabiao Wang', 'Chengjie Wang', 'Yong Liu', 'Xiangtai Li', 'Dacheng Tao'] | ['cs.CV'] | The quality of the video dataset (image quality, resolution, and fine-grained
caption) greatly influences the performance of the video generation model. The
growing demand for video applications sets higher requirements for high-quality
video generation models. For example, the generation of movie-level Ultra-High
Definition (UHD) videos and the creation of 4K short video content. However,
the existing public datasets cannot support related research and applications.
In this paper, we first propose a high-quality open-sourced UHD-4K (22.4\% of
which are 8K) text-to-video dataset named UltraVideo, which contains a wide
range of topics (more than 100 kinds), and each video has 9 structured captions
with one summarized caption (average of 824 words). Specifically, we carefully
design a highly automated curation process with four stages to obtain the final
high-quality dataset: \textit{i)} collection of diverse and high-quality video
clips. \textit{ii)} statistical data filtering. \textit{iii)} model-based data
purification. \textit{iv)} generation of comprehensive, structured captions. In
addition, we expand Wan to UltraWan-1K/-4K, which can natively generate
high-quality 1K/4K videos with more consistent text controllability,
demonstrating the effectiveness of our data curation.We believe that this work
can make a significant contribution to future research on UHD video generation.
UltraVideo dataset and UltraWan models are available at
https://xzc-zju.github.io/projects/UltraVideo. | 2025-06-16T16:52:52Z | null | null | null | null | null | null | null | null | null | null |
2,506.13705 | TimeMaster: Training Time-Series Multimodal LLMs to Reason via
Reinforcement Learning | ['Junru Zhang', 'Lang Feng', 'Xu Guo', 'Yuhan Wu', 'Yabo Dong', 'Duanqing Xu'] | ['cs.LG', 'cs.AI'] | Time-series reasoning remains a significant challenge in multimodal large
language models (MLLMs) due to the dynamic temporal patterns, ambiguous
semantics, and lack of temporal priors. In this work, we introduce TimeMaster,
a reinforcement learning (RL)-based method that enables time-series MLLMs to
perform structured, interpretable reasoning directly over visualized
time-series inputs and task prompts. TimeMaster adopts a three-part structured
output format, reasoning, classification, and domain-specific extension, and is
optimized via a composite reward function that aligns format adherence,
prediction accuracy, and open-ended insight quality. The model is trained using
a two-stage pipeline: we first apply supervised fine-tuning (SFT) to establish
a good initialization, followed by Group Relative Policy Optimization (GRPO) at
the token level to enable stable and targeted reward-driven improvement in
time-series reasoning. We evaluate TimeMaster on the TimerBed benchmark across
six real-world classification tasks based on Qwen2.5-VL-3B-Instruct. TimeMaster
achieves state-of-the-art performance, outperforming both classical time-series
models and few-shot GPT-4o by over 14.6% and 7.3% performance gain,
respectively. Notably, TimeMaster goes beyond time-series classification: it
also exhibits expert-like reasoning behavior, generates context-aware
explanations, and delivers domain-aligned insights. Our results highlight that
reward-driven RL can be a scalable and promising path toward integrating
temporal understanding into time-series MLLMs. | 2025-06-16T17:12:26Z | Preprint | null | null | TimeMaster: Training Time-Series Multimodal LLMs to Reason via Reinforcement Learning | ['Junru Zhang', 'Lang Feng', 'Xu Guo', 'Yuhan Wu', 'Yabo Dong', 'Duanqing Xu'] | 2,025 | arXiv.org | 0 | 59 | ['Computer Science'] |
2,506.13725 | CEED-VLA: Consistency Vision-Language-Action Model with Early-Exit
Decoding | ['Wenxuan Song', 'Jiayi Chen', 'Pengxiang Ding', 'Yuxin Huang', 'Han Zhao', 'Donglin Wang', 'Haoang Li'] | ['cs.RO'] | In recent years, Vision-Language-Action (VLA) models have become a vital
research direction in robotics due to their impressive multimodal understanding
and generalization capabilities. Despite the progress, their practical
deployment is severely constrained by inference speed bottlenecks, particularly
in high-frequency and dexterous manipulation tasks. While recent studies have
explored Jacobi decoding as a more efficient alternative to traditional
autoregressive decoding, its practical benefits are marginal due to the lengthy
iterations. To address it, we introduce consistency distillation training to
predict multiple correct action tokens in each iteration, thereby achieving
acceleration. Besides, we design mixed-label supervision to mitigate the error
accumulation during distillation. Although distillation brings acceptable
speedup, we identify that certain inefficient iterations remain a critical
bottleneck. To tackle this, we propose an early-exit decoding strategy that
moderately relaxes convergence conditions, which further improves average
inference efficiency. Experimental results show that the proposed method
achieves more than 4 times inference acceleration across different baselines
while maintaining high task success rates in both simulated and real-world
robot tasks. These experiments validate that our approach provides an efficient
and general paradigm for accelerating multimodal decision-making in robotics.
Our project page is available at https://irpn-eai.github.io/CEED-VLA/. | 2025-06-16T17:31:16Z | 16 pages | null | null | null | null | null | null | null | null | null |
2,506.13793 | Med-REFL: Medical Reasoning Enhancement via Self-Corrected Fine-grained
Reflection | ['Zongxian Yang', 'Jiayu Qian', 'Zegao Peng', 'Haoyu Zhang', 'Zhi-An Huang'] | ['cs.AI'] | Large reasoning models have recently made significant strides in mathematical
and code reasoning, yet their success has not transferred smoothly to the
medical domain. While multiple factors contribute to this disparity, a critical
issue is the inadequate focus on the quality of intermediate reflection steps,
which is particularly crucial in high-stakes medical scenarios. To address this
challenge, we propose Med-REFL, a \underline{\textbf{Med}}ical
\underline{\textbf{R}}easoning \underline{\textbf{E}}nhancement via
self-corrected \underline{\textbf{F}}ine-grained
ref\underline{\textbf{L}}ection. Our method leverages a tree-of-thought
approach to decompose medical questions into fine-grained reasoning paths,
quantitatively evaluating each step and its subsequent reflections. These
assessments enable automatic construction of direct preference optimization
data, reducing reliance on expensive expert annotations while guiding models to
identify and correct reasoning errors. Experimental results on the MedQA-USMLE
benchmark demonstrate Med-REFL achieves consistent improvements, with average
gains up to 4.11\%. Notably, it further boosts the state-of-the-art performance
of 7B/8B models by an additional 4.13\%. Furthermore, Med-REFL exhibits strong
generalization capabilities and robustness across several challenging medical
question-answering datasets. Our work illustrates that prioritizing reflection
quality leads to more accurate and trustworthy reasoning in medical AI
applications. Checkpoints, code, and data can be found in
https://github.com/TianYin123/Med-REFL. | 2025-06-11T14:58:38Z | null | null | null | null | null | null | null | null | null | null |
Subsets and Splits
No community queries yet
The top public SQL queries from the community will appear here once available.