Orionnnn/hub-stats huggingface dataset:
MathBode: Frequency-Domain Fingerprints of LLM Mathematical Reasoning
Authors: name: Charles L. Wang
Published: 2025-09-27 06:06:36

This paper presents MathBode, a dynamic diagnostic for mathematical reasoning
in large language models (LLMs). Instead of one-shot accuracy, MathBode treats
each parametric problem as a system: we drive a single parameter sinusoidally
and fit first-harmonic responses of model outputs and exact solutions. This
yields interpretable, frequency-resolved metrics -- gain (amplitude tracking)
and phase (lag) -- that form Bode-style fingerprints. Across five closed-form
families (linear solve, ratio/saturation, compound interest, 2x2 linear
systems, similar triangles), the diagnostic surfaces systematic low-pass
behavior and growing phase lag that accuracy alone obscures. We compare several
models against a symbolic baseline that calibrates the instrument (G approx
1, phi approx 0). Results separate frontier from mid-tier models on
dynamics, providing a compact, reproducible protocol that complements standard
benchmarks with actionable measurements of reasoning fidelity and consistency.
We open-source the dataset and code to enable further research and adoption.
Dynamic Experts Search: Enhancing Reasoning in Mixture-of-Experts LLMs
  at Test Time
Authors: name: Yixuan Han, name: Fan Ma, name: Ruijie Quan, name: Yi Yang
Published: 2025-09-26 16:49:10

Test-Time Scaling (TTS) enhances the reasoning ability of large language
models (LLMs) by allocating additional computation during inference. However,
existing approaches primarily rely on output-level sampling while overlooking
the role of model architecture. In mainstream Mixture-of-Experts (MoE) LLMs, we
observe that varying the number of activated experts yields complementary
solution sets with stable accuracy, revealing a new and underexplored source of
diversity. Motivated by this observation, we propose Dynamic Experts Search
(DES), a TTS strategy that elevates expert activation into a controllable
dimension of the search space. DES integrates two key components: (1) Dynamic
MoE, which enables direct control of expert counts during inference to generate
diverse reasoning trajectories without additional cost; and (2) Expert
Configuration Inheritance, which preserves consistent expert counts within a
reasoning path while varying them across runs, thereby balancing stability and
diversity throughout the search. Extensive experiments across MoE
architectures, verifiers and reasoning benchmarks (i.e., math, code and
knowledge) demonstrate that DES reliably outperforms TTS baselines, enhancing
accuracy and stability without additional cost. These results highlight DES as
a practical and scalable form of architecture-aware TTS, illustrating how
structural flexibility in modern LLMs can advance reasoning.
OpenGPT-4o-Image: A Comprehensive Dataset for Advanced Image Generation
  and Editing
Authors: name: Zhihong Chen, name: Xuehai Bai, name: Yang Shi, name: Chaoyou Fu, name: Huanyu Zhang, name: Haotian Wang, name: Xiaoyan Sun, name: Zhang Zhang, name: Liang Wang, name: Yuanxing Zhang, name: Pengfei Wan, name: Yi-Fan Zhang
Published: 2025-09-29 15:11:09

The performance of unified multimodal models for image generation and editing
is fundamentally constrained by the quality and comprehensiveness of their
training data. While existing datasets have covered basic tasks like style
transfer and simple object manipulation, they often lack the systematic
structure and challenging scenarios required for real-world applications. To
address this bottleneck, we introduce OpenGPT-4o-Image, a large-scale dataset
constructed using a novel methodology that combines hierarchical task taxonomy
with automated data generation. Our taxonomy not only includes fundamental
capabilities such as text rendering and style control but also introduces
highly practical yet challenging categories like scientific imagery for
chemistry illustrations and complex instruction editing requiring simultaneous
execution of multiple operations. Through an automated pipeline leveraging
structured resource pools and GPT-4o, we generate 80k high-quality
instruction-image pairs with controlled diversity, covering 11 major domains
and 51 subtasks. Extensive experiments show that fine-tuning leading models on
our dataset achieves significant performance gains across multiple benchmarks,
with improvements of up to 18\% on editing tasks (UniWorld-V1 on ImgEdit-Bench)
and 13% on generation tasks (Harmon on GenEval). Our work demonstrates that
systematic data construction is key to advancing multimodal AI capabilities.
RealUnify: Do Unified Models Truly Benefit from Unification? A
  Comprehensive Benchmark
Authors: name: Yang Shi, name: Yuhao Dong, name: Yue Ding, name: Yuran Wang, name: Xuanyu Zhu, name: Sheng Zhou, name: Wenting Liu, name: Haochen Tian, name: Rundong Wang, name: Huanqian Wang, name: Zuyan Liu, name: Bohan Zeng, name: Ruizhe Chen, name: Qixun Wang, name: Zhuoran Zhang, name: Xinlong Chen, name: Chengzhuo Tong, name: Bozhou Li, name: Chaoyou Fu, name: Qiang Liu, name: Haotian Wang, name: Wenjing Yang, name: Yuanxing Zhang, name: Pengfei Wan, name: Yi-Fan Zhang, name: Ziwei Liu
Published: 2025-09-29 15:07:28

The integration of visual understanding and generation into unified
multimodal models represents a significant stride toward general-purpose AI.
However, a fundamental question remains unanswered by existing benchmarks: does
this architectural unification actually enable synergetic interaction between
the constituent capabilities? Existing evaluation paradigms, which primarily
assess understanding and generation in isolation, are insufficient for
determining whether a unified model can leverage its understanding to enhance
its generation, or use generative simulation to facilitate deeper
comprehension. To address this critical gap, we introduce RealUnify, a
benchmark specifically designed to evaluate bidirectional capability synergy.
RealUnify comprises 1,000 meticulously human-annotated instances spanning 10
categories and 32 subtasks. It is structured around two core axes: 1)
Understanding Enhances Generation, which requires reasoning (e.g., commonsense,
logic) to guide image generation, and 2) Generation Enhances Understanding,
which necessitates mental simulation or reconstruction (e.g., of transformed or
disordered visual inputs) to solve reasoning tasks. A key contribution is our
dual-evaluation protocol, which combines direct end-to-end assessment with a
diagnostic stepwise evaluation that decomposes tasks into distinct
understanding and generation phases. This protocol allows us to precisely
discern whether performance bottlenecks stem from deficiencies in core
abilities or from a failure to integrate them. Through large-scale evaluations
of 12 leading unified models and 6 specialized baselines, we find that current
unified models still struggle to achieve effective synergy, indicating that
architectural unification alone is insufficient. These results highlight the
need for new training strategies and inductive biases to fully unlock the
potential of unified modeling.
EasySteer: A Unified Framework for High-Performance and Extensible LLM
  Steering
Authors: name: Haolei Xu, name: Xinyu Mei, name: Yuchen Yan, name: Rui Zhou, name: Wenqi Zhang, name: Weiming Lu, name: Yueting Zhuang, name: Yongliang Shen
Published: 2025-09-29 17:59:07

Large language model (LLM) steering has emerged as a promising paradigm for
controlling model behavior at inference time through targeted manipulation of
hidden states, offering a lightweight alternative to expensive retraining.
However, existing steering frameworks suffer from critical limitations:
computational inefficiency, limited extensibility, and restricted functionality
that hinder both research progress and practical deployment. We present
EasySteer, a unified framework for high-performance, extensible LLM steering
built on vLLM. Our system features modular architecture with pluggable
interfaces for both analysis-based and learning-based methods, fine-grained
parameter control, pre-computed steering vectors for eight application domains,
and an interactive demonstration system. Through deep integration with vLLM's
optimized inference engine, EasySteer achieves 5.5-11.4times speedup over
existing frameworks. Extensive experiments demonstrate its effectiveness in
overthinking mitigation, hallucination reduction, and other key applications.
EasySteer transforms steering from research technique to production-ready
capability, establishing critical infrastructure for deployable, controllable
language models.
SANA-Video: Efficient Video Generation with Block Linear Diffusion
  Transformer
Authors: name: Junsong Chen, name: Yuyang Zhao, name: Jincheng Yu, name: Ruihang Chu, name: Junyu Chen, name: Shuai Yang, name: Xianbang Wang, name: Yicheng Pan, name: Daquan Zhou, name: Huan Ling, name: Haozhe Liu, name: Hongwei Yi, name: Hao Zhang, name: Muyang Li, name: Yukang Chen, name: Han Cai, name: Sanja Fidler, name: Ping Luo, name: Song Han, name: Enze Xie
Published: 2025-09-29 12:28:09

We introduce SANA-Video, a small diffusion model that can efficiently
generate videos up to 720x1280 resolution and minute-length duration.
SANA-Video synthesizes high-resolution, high-quality and long videos with
strong text-video alignment at a remarkably fast speed, deployable on RTX 5090
GPU. Two core designs ensure our efficient, effective and long video
generation: (1) Linear DiT: We leverage linear attention as the core operation,
which is more efficient than vanilla attention given the large number of tokens
processed in video generation. (2) Constant-Memory KV cache for Block Linear
Attention: we design block-wise autoregressive approach for long video
generation by employing a constant-memory state, derived from the cumulative
properties of linear attention. This KV cache provides the Linear DiT with
global context at a fixed memory cost, eliminating the need for a traditional
KV cache and enabling efficient, minute-long video generation. In addition, we
explore effective data filters and model training strategies, narrowing the
training cost to 12 days on 64 H100 GPUs, which is only 1% of the cost of
MovieGen. Given its low cost, SANA-Video achieves competitive performance
compared to modern state-of-the-art small diffusion models (e.g., Wan 2.1-1.3B
and SkyReel-V2-1.3B) while being 16x faster in measured latency. Moreover,
SANA-Video can be deployed on RTX 5090 GPUs with NVFP4 precision, accelerating
the inference speed of generating a 5-second 720p video from 71s to 29s (2.4x
speedup). In summary, SANA-Video enables low-cost, high-quality video
generation.
UniMIC: Token-Based Multimodal Interactive Coding for Human-AI
  Collaboration
Authors: name: Qi Mao, name: Tinghan Yang, name: Jiahao Li, name: Bin Li, name: Libiao Jin, name: Yan Lu
Published: 2025-09-26 16:46:12

The rapid progress of Large Multimodal Models (LMMs) and cloud-based AI
agents is transforming human-AI collaboration into bidirectional, multimodal
interaction. However, existing codecs remain optimized for unimodal, one-way
communication, resulting in repeated degradation under conventional
compress-transmit-reconstruct pipelines. To address this limitation, we propose
UniMIC, a Unified token-based Multimodal Interactive Coding framework that
bridges edge devices and cloud AI agents. Instead of transmitting raw pixels or
plain text, UniMIC employs compact tokenized representations as the
communication medium, enabling efficient low-bitrate transmission while
maintaining compatibility with LMMs. To further enhance compression,
lightweight Transformer-based entropy models with scenario-specific
designs-generic, masked, and text-conditioned-effectively minimize inter-token
redundancy. Extensive experiments on text-to-image generation, text-guided
inpainting, outpainting, and visual question answering show that UniMIC
achieves substantial bitrate savings and remains robust even at ultra-low
bitrates (<0.05bpp), without compromising downstream task performance. These
results establish UniMIC as a practical and forward-looking paradigm for
next-generation multimodal interactive communication.
AdvChain: Adversarial Chain-of-Thought Tuning for Robust Safety
  Alignment of Large Reasoning Models
Authors: name: Zihao Zhu, name: Xinyu Wu, name: Gehan Hu, name: Siwei Lyu, name: Ke Xu, name: Baoyuan Wu
Published: 2025-09-29 04:27:23

Large Reasoning Models (LRMs) have demonstrated remarkable capabilities in
complex problem-solving through Chain-of-Thought (CoT) reasoning. However, the
multi-step nature of CoT introduces new safety challenges that extend beyond
conventional language model alignment. We identify a failure mode in current
safety CoT tuning methods: the snowball effect, where minor reasoning
deviations progressively amplify throughout the thought process, leading to
either harmful compliance or excessive refusal. This effect stems from models
being trained to imitate perfect reasoning scripts without learning to
self-correct. To address this limitation, we propose AdvChain, an alignment
paradigm that teaches models dynamic self-correction through adversarial CoT
tuning. Our method involves constructing a dataset containing
Temptation-Correction and Hesitation-Correction samples, where models learn to
recover from harmful reasoning drifts and unnecessary cautions. Extensive
experiments show that AdvChain significantly enhances robustness against
jailbreak attacks and CoT hijacking while substantially reducing over-refusal
on benign prompts, achieving a superior safety-utility balance without
compromising reasoning capabilities. Our work establishes a new direction for
building more robust and reliable reasoning models.
Sequential Diffusion Language Models
Authors: name: Yangzhou Liu, name: Yue Cao, name: Hao Li, name: Gen Luo, name: Zhe Chen, name: Weiyun Wang, name: Xiaobo Liang, name: Biqing Qi, name: Lijun Wu, name: Changyao Tian, name: Yanting Zhang, name: Yuqiang Li, name: Tong Lu, name: Yu Qiao, name: Jifeng Dai, name: Wenhai Wang
Published: 2025-09-28 17:59:15

Diffusion language models (DLMs) have strong theoretical efficiency but are
limited by fixed-length decoding and incompatibility with key-value (KV)
caches. Block diffusion mitigates these issues, yet still enforces a fixed
block size and requires expensive training. We introduce Next Sequence
Prediction (NSP), which unifies next-token and next-block prediction, enabling
the model to adaptively determine the generation length at each step. When the
length is fixed to 1, NSP reduces to standard next-token prediction. Building
on NSP, we propose Sequential Diffusion Language Model (SDLM), which can
retrofit pre-trained autoregressive language models (ALMs) at minimal cost.
Specifically, SDLM performs diffusion inference within fixed-size mask blocks,
but dynamically decodes consecutive subsequences based on model confidence,
thereby preserving KV-cache compatibility and improving robustness to varying
uncertainty and semantics across the sequence. Experiments show that SDLM
matches or surpasses strong autoregressive baselines using only 3.5M training
samples, while achieving 2.1 higher throughput than Qwen-2.5. Notably, the
SDLM-32B model delivers even more pronounced efficiency gains, demonstrating
the strong scalability potential of our modeling paradigm. Project page and
codes: https://github.com/OpenGVLab/SDLM
SIRI: Scaling Iterative Reinforcement Learning with Interleaved
  Compression
Authors: name: Haoming Wen, name: Yushi Bai, name: Juanzi Li, name: Jie Tang
Published: 2025-09-29 17:59:08

We introduce SIRI, Scaling Iterative Reinforcement Learning with Interleaved
Compression, a simple yet effective RL approach for Large Reasoning Models
(LRMs) that enables more efficient and accurate reasoning. Existing studies
have observed repetitive thinking patterns in LRMs, and attempts to reduce them
often come at the cost of performance. In this paper, we show that this
trade-off can be overcome through a training regime that iteratively alternates
between compressing and expanding the reasoning budget, by dynamically
adjusting the maximum rollout length during training. The compression phase
cuts the rollout length, forcing the model to make precise and valuable
decisions within a limited context, which effectively reduces redundant tokens
and increases reasoning density. The expansion phase then relaxes the length
limit, providing space for the model to explore and plan in long-horizon
settings. Remarkably, we find that after each compression-expansion cycle, the
model's performance improves even as its output length decreases, steadily
pushing it closer to the Pareto frontier in the performance-efficiency
trade-off. Training on DeepSeek-R1-Distill-Qwen-1.5B, SIRI-low improves
performance on AIME24 by 43.2% while reducing token usage by 46.9% after three
iterations, and SIRI-high achieves the highest accuracy compared to all other
methods (Figure 1). Our findings shed light on the potential of periodically
oscillating the LRM's output truncation length during training to dynamically
balance exploration and efficiency in reasoning, converging towards an optimal
"sweet spot" between the two. Our models are publicly available.
From Harm to Help: Turning Reasoning In-Context Demos into Assets for
  Reasoning LMs
Authors: name: Haonan Wang, name: Weida Liang, name: Zihang Fu, name: Nie Zheng, name: Yifan Zhang, name: Yao Tong, name: Tongyao Zhu, name: Hao Jiang, name: Chuang Li, name: Jiaying Wu, name: Kenji Kawaguchi
Published: 2025-09-27 08:59:31

Recent reasoning LLMs (RLMs), especially those trained with verifier-based
reinforcement learning, often perform worse with few-shot CoT than with direct
answering. We revisit this paradox using high-quality reasoning traces from
DeepSeek-R1 as demonstrations and find that adding more exemplars consistently
degrades accuracy, even when demonstrations are optimal. A detailed analysis
reveals two mechanisms behind this decline: (i) semantic misguidance, where
high textual similarity leads the model to treat the target as the same as the
exemplar and to copy intermediate steps verbatim; and (ii) strategy transfer
failure, where the model struggles to extract useful reasoning strategies and
apply them to target questions. Guided by these, we introduce Insight-to-Solve
(I2S), a sequential test-time procedure that turns demonstrations into
explicit, reusable insights and derives a target-specific reasoning trace;
optionally, the reasoning is self-refined for coherence and correctness (I2S+).
Extensive experiments on diverse benchmarks show that I2S and I2S+ consistently
outperform both direct answering and test-time scaling baselines across open-
and closed-source models. Even for GPT models, our method helps: on AIME'25,
GPT-4.1 rises by +14.0%, and o1-mini improves by +2.7% on AIME and +1.7% on
GPQA, indicating that in-context demonstrations can be harnessed effectively
via insight-refine-solve framework.
Hyperspherical Latents Improve Continuous-Token Autoregressive
  Generation
Authors: name: Guolin Ke, name: Hui Xue
Published: 2025-09-29 06:34:24

Autoregressive (AR) models are promising for image generation, yet
continuous-token AR variants often trail latent diffusion and masked-generation
models. The core issue is heterogeneous variance in VAE latents, which is
amplified during AR decoding, especially under classifier-free guidance (CFG),
and can cause variance collapse. We propose SphereAR to address this issue. Its
core design is to constrain all AR inputs and outputs -- including after CFG --
to lie on a fixed-radius hypersphere (constant ell_2 norm), leveraging
hyperspherical VAEs. Our theoretical analysis shows that hyperspherical
constraint removes the scale component (the primary cause of variance
collapse), thereby stabilizing AR decoding. Empirically, on ImageNet
generation, SphereAR-H (943M) sets a new state of the art for AR models,
achieving FID 1.34. Even at smaller scales, SphereAR-L (479M) reaches FID 1.54
and SphereAR-B (208M) reaches 1.92, matching or surpassing much larger
baselines such as MAR-H (943M, 1.55) and VAR-d30 (2B, 1.92). To our knowledge,
this is the first time a pure next-token AR image generator with raster order
surpasses diffusion and masked-generation models at comparable parameter
scales.
Euclid's Gift: Enhancing Spatial Perception and Reasoning in
  Vision-Language Models via Geometric Surrogate Tasks
Authors: name: Shijie Lian, name: Changti Wu, name: Laurence Tianruo Yang, name: Hang Yuan, name: Bin Yu, name: Lei Zhang, name: Kai Chen
Published: 2025-09-29 08:49:21

Spatial intelligence spans a rich suite of abilities, including visualising
and transforming shapes, mentally rotating objects, judging relational
positions and containment, and estimating numerosity. However, it still remains
a critical unresolved challenge for Multimodal Large Language Models (MLLMs).To
fill this gap, we propose to treat Euclidean geometry problem-solving as a
surrogate task. Specifically, we meticulously constructed a curated multimodal
dataset, called Euclid30K, comprising approximately 30K plane and solid
geometry problems. To enable the model to acquire and apply Euclidean
principles from these geometry problems, we employed Group Relative Policy
Optimization (GRPO) to finetune the Qwen2.5VL family and RoboBrain2.0 family,
inspiring the models to identify shapes, count, and relate entities, and
perform multi-step deductive reasoning using Euclidean principles. Our
experiments demonstrate that the resulting models achieve substantial zero-shot
gains across four spatial reasoning benchmarks (Super-CLEVR, Omni3DBench,
VSI-Bench, and MindCube) without any task-specific adaptations. Notably, after
training on the Euclid30K, the mean VSI-Bench accuracy of all evaluated models
rose from 34.5% to 40.5%, improving by 5.5 percentage points. Among them,
RoboBrain2.0-Euclid-7B achieves 49.6\% accuracy, surpassing the previous
state-of-the-art model, Spatial-MLLM.To our knowledge, this is the first
systematic study showing that geometry-centric fine-tuning can confer
vision-language models with broadly transferable spatial skills. Code and
Euclid30K dataset can be found in https://zgca-ai4edu.github.io/Euclids_Gift.
ChatInject: Abusing Chat Templates for Prompt Injection in LLM Agents
Authors: name: Hwan Chang, name: Yonghyun Jun, name: Hwanhee Lee
Published: 2025-09-26 18:38:07

The growing deployment of large language model (LLM) based agents that
interact with external environments has created new attack surfaces for
adversarial manipulation. One major threat is indirect prompt injection, where
attackers embed malicious instructions in external environment output, causing
agents to interpret and execute them as if they were legitimate prompts. While
previous research has focused primarily on plain-text injection attacks, we
find a significant yet underexplored vulnerability: LLMs' dependence on
structured chat templates and their susceptibility to contextual manipulation
through persuasive multi-turn dialogues. To this end, we introduce ChatInject,
an attack that formats malicious payloads to mimic native chat templates,
thereby exploiting the model's inherent instruction-following tendencies.
Building on this foundation, we develop a persuasion-driven Multi-turn variant
that primes the agent across conversational turns to accept and execute
otherwise suspicious actions. Through comprehensive experiments across frontier
LLMs, we demonstrate three critical findings: (1) ChatInject achieves
significantly higher average attack success rates than traditional prompt
injection methods, improving from 5.18% to 32.05% on AgentDojo and from 15.13%
to 45.90% on InjecAgent, with multi-turn dialogues showing particularly strong
performance at average 52.33% success rate on InjecAgent, (2)
chat-template-based payloads demonstrate strong transferability across models
and remain effective even against closed-source LLMs, despite their unknown
template structures, and (3) existing prompt-based defenses are largely
ineffective against this attack approach, especially against Multi-turn
variants. These findings highlight vulnerabilities in current agent systems.