Orionnnn/hub-stats huggingface dataset:
MM-HELIX: Boosting Multimodal Long-Chain Reflective Reasoning with
  Holistic Platform and Adaptive Hybrid Policy Optimization
Authors: name: Xiangyu Zhao, name: Junming Lin, name: Tianhao Liang, name: Yifan Zhou, name: Wenhao Chai, name: Yuzhe Gu, name: Weiyun Wang, name: Kai Chen, name: Gen Luo, name: Wenwei Zhang, name: Junchi Yan, name: Hua Yang, name: Haodong Duan, name: Xue Yang
Published: 2025-10-09 17:53:58

While current Multimodal Large Language Models (MLLMs) have demonstrated
proficiency in reasoning tasks such as mathematics and logic, their capacity
for long-chain reflective reasoning, a prerequisite for solving complex
real-world problems, remains largely underexplored. In this work, we first
conduct an extensive empirical investigation to evaluate this capability.
Leveraging a carefully designed data synthesis engine, we construct MM-HELIX, a
multimodal benchmark consisting 1,260 samples of 42 challenging synthetic tasks
that require iterative thinking and backtracking. Empirical results on this
benchmark reveal that existing MLLMs exhibit significant performance deficits
in long-chain reflective reasoning. To address this limitation, we generate
post-training data and further explore learning paradigms for exploiting such
data. We first develop the Step-Elicited Response Generation pipeline to create
MM-HELIX-100K, a large-scale dataset of 100k high-quality, reflective reasoning
traces for instruction-tuning stage. Given that standard Reinforcement Learning
fails on complex tasks due to sparse reward signals and catastrophic forgetting
after Supervised Fine-Tuning, we propose Adaptive Hybrid Policy Optimization
(AHPO), a novel training strategy that dynamically unifies offline supervision
and online optimization into a single stage. This strategy enables the model to
learn from expert data when rewards are sparse and conduct independent
exploration once proficient. When applied to the Qwen2.5-VL-7B baseline, our
method achieves a +18.6\% accuracy improvement on MM-HELIX benchmark and
demonstrates strong generalization with a +5.7\% average performance gain on
general mathematic and logic tasks. Our work demonstrate that reflective
reasoning in MLLMs can be effectively learned and generalized, paving the way
for developing more capable MLLMs.
Large Scale Diffusion Distillation via Score-Regularized Continuous-Time
  Consistency
Authors: name: Kaiwen Zheng, name: Yuji Wang, name: Qianli Ma, name: Huayu Chen, name: Jintao Zhang, name: Yogesh Balaji, name: Jianfei Chen, name: Ming-Yu Liu, name: Jun Zhu, name: Qinsheng Zhang
Published: 2025-10-09 16:45:30

This work represents the first effort to scale up continuous-time consistency
distillation to general application-level image and video diffusion models.
Although continuous-time consistency model (sCM) is theoretically principled
and empirically powerful for accelerating academic-scale diffusion, its
applicability to large-scale text-to-image and video tasks remains unclear due
to infrastructure challenges in Jacobian-vector product (JVP) computation and
the limitations of standard evaluation benchmarks. We first develop a
parallelism-compatible FlashAttention-2 JVP kernel, enabling sCM training on
models with over 10 billion parameters and high-dimensional video tasks. Our
investigation reveals fundamental quality limitations of sCM in fine-detail
generation, which we attribute to error accumulation and the "mode-covering"
nature of its forward-divergence objective. To remedy this, we propose the
score-regularized continuous-time consistency model (rCM), which incorporates
score distillation as a long-skip regularizer. This integration complements sCM
with the "mode-seeking" reverse divergence, effectively improving visual
quality while maintaining high generation diversity. Validated on large-scale
models (Cosmos-Predict2, Wan2.1) up to 14B parameters and 5-second videos, rCM
matches or surpasses the state-of-the-art distillation method DMD2 on quality
metrics while offering notable advantages in diversity, all without GAN tuning
or extensive hyperparameter searches. The distilled models generate
high-fidelity samples in only 1sim4 steps, accelerating diffusion sampling
by 15timessim50times. These results position rCM as a practical and
theoretically grounded framework for advancing large-scale diffusion
distillation.
NewtonBench: Benchmarking Generalizable Scientific Law Discovery in LLM
  Agents
Authors: name: Tianshi Zheng, name: Kelvin Kiu-Wai Tam, name: Newt Hue-Nam K. Nguyen, name: Baixuan Xu, name: Zhaowei Wang, name: Jiayang Cheng, name: Hong Ting Tsang, name: Weiqi Wang, name: Jiaxin Bai, name: Tianqing Fang, name: Yangqiu Song, name: Ginny Y. Wong, name: Simon See
Published: 2025-10-08 16:12:11

Large language models are emerging as powerful tools for scientific law
discovery, a foundational challenge in AI-driven science. However, existing
benchmarks for this task suffer from a fundamental methodological trilemma,
forcing a trade-off between scientific relevance, scalability, and resistance
to memorization. Furthermore, they oversimplify discovery as static function
fitting, failing to capture the authentic scientific process of uncovering
embedded laws through the interactive exploration of complex model systems. To
address these critical gaps, we introduce NewtonBench, a benchmark comprising
324 scientific law discovery tasks across 12 physics domains. Our design
mitigates the evaluation trilemma by using metaphysical shifts - systematic
alterations of canonical laws - to generate a vast suite of problems that are
scalable, scientifically relevant, and memorization-resistant. Moreover, we
elevate the evaluation from static function fitting to interactive model
discovery, requiring agents to experimentally probe simulated complex systems
to uncover hidden principles. Our extensive experiment reveals a clear but
fragile capability for discovery in frontier LLMs: this ability degrades
precipitously with increasing system complexity and exhibits extreme
sensitivity to observational noise. Notably, we uncover a paradoxical effect of
tool assistance: providing a code interpreter can hinder more capable models by
inducing a premature shift from exploration to exploitation, causing them to
satisfice on suboptimal solutions. These results demonstrate that robust,
generalizable discovery in complex, interactive environments remains the core
challenge. By providing a scalable, robust, and scientifically authentic
testbed, NewtonBench offers a crucial tool for measuring true progress and
guiding the development of next-generation AI agents capable of genuine
scientific discovery.
Agent Learning via Early Experience
Authors: name: Kai Zhang, name: Xiangchao Chen, name: Bo Liu, name: Tianci Xue, name: Zeyi Liao, name: Zhihan Liu, name: Xiyao Wang, name: Yuting Ning, name: Zhaorun Chen, name: Xiaohan Fu, name: Jian Xie, name: Yuxuan Sun, name: Boyu Gou, name: Qi Qi, name: Zihang Meng, name: Jianwei Yang, name: Ning Zhang, name: Xian Li, name: Ashish Shah, name: Dat Huynh, name: Hengduo Li, name: Zi Yang, name: Sara Cao, name: Lawrence Jang, name: Shuyan Zhou, name: Jiacheng Zhu, name: Huan Sun, name: Jason Weston, name: Yu Su, name: Yifan Wu
Published: 2025-10-09 17:59:17

A long-term goal of language agents is to learn and improve through their own
experience, ultimately outperforming humans in complex, real-world tasks.
However, training agents from experience data with reinforcement learning
remains difficult in many environments, which either lack verifiable rewards
(e.g., websites) or require inefficient long-horizon rollouts (e.g., multi-turn
tool use). As a result, most current agents rely on supervised fine-tuning on
expert data, which is challenging to scale and generalizes poorly. This
limitation stems from the nature of expert demonstrations: they capture only a
narrow range of scenarios and expose the agent to limited environment
diversity. We address this limitation with a middle-ground paradigm we call
early experience: interaction data generated by the agent's own actions, where
the resulting future states serve as supervision without reward signals. Within
this paradigm we study two strategies of using such data: (1) Implicit world
modeling, which uses collected states to ground the policy in environment
dynamics; and (2) Self-reflection, where the agent learns from its suboptimal
actions to improve reasoning and decision-making. We evaluate across eight
diverse environments and multiple model families. Our approaches consistently
improve effectiveness and out-of-domain generalization, highlighting the value
of early experience. Moreover, in environments with verifiable rewards, our
results provide promising signals that early experience offers a strong
foundation for subsequent reinforcement learning, positioning it as a practical
bridge between imitation learning and fully experience-driven agents.
VideoCanvas: Unified Video Completion from Arbitrary Spatiotemporal
  Patches via In-Context Conditioning
Authors: name: Minghong Cai, name: Qiulin Wang, name: Zongli Ye, name: Wenze Liu, name: Quande Liu, name: Weicai Ye, name: Xintao Wang, name: Pengfei Wan, name: Kun Gai, name: Xiangyu Yue
Published: 2025-10-09 17:58:59

We introduce the task of arbitrary spatio-temporal video completion, where a
video is generated from arbitrary, user-specified patches placed at any spatial
location and timestamp, akin to painting on a video canvas. This flexible
formulation naturally unifies many existing controllable video generation
tasks--including first-frame image-to-video, inpainting, extension, and
interpolation--under a single, cohesive paradigm. Realizing this vision,
however, faces a fundamental obstacle in modern latent video diffusion models:
the temporal ambiguity introduced by causal VAEs, where multiple pixel frames
are compressed into a single latent representation, making precise frame-level
conditioning structurally difficult. We address this challenge with
VideoCanvas, a novel framework that adapts the In-Context Conditioning (ICC)
paradigm to this fine-grained control task with zero new parameters. We propose
a hybrid conditioning strategy that decouples spatial and temporal control:
spatial placement is handled via zero-padding, while temporal alignment is
achieved through Temporal RoPE Interpolation, which assigns each condition a
continuous fractional position within the latent sequence. This resolves the
VAE's temporal ambiguity and enables pixel-frame-aware control on a frozen
backbone. To evaluate this new capability, we develop VideoCanvasBench, the
first benchmark for arbitrary spatio-temporal video completion, covering both
intra-scene fidelity and inter-scene creativity. Experiments demonstrate that
VideoCanvas significantly outperforms existing conditioning paradigms,
establishing a new state of the art in flexible and unified video generation.
SciVideoBench: Benchmarking Scientific Video Reasoning in Large
  Multimodal Models
Authors: name: Andong Deng, name: Taojiannan Yang, name: Shoubin Yu, name: Lincoln Spencer, name: Mohit Bansal, name: Chen Chen, name: Serena Yeung-Levy, name: Xiaohan Wang
Published: 2025-10-09 17:59:23

Large Multimodal Models (LMMs) have achieved remarkable progress across
various capabilities; however, complex video reasoning in the scientific domain
remains a significant and challenging frontier. Current video benchmarks
predominantly target general scenarios where perception/recognition is heavily
relied on, while with relatively simple reasoning tasks, leading to saturation
and thus failing to effectively evaluate advanced multimodal cognitive skills.
To address this critical gap, we introduce SciVideoBench, a rigorous benchmark
specifically designed to assess advanced video reasoning in scientific
contexts. SciVideoBench consists of 1,000 carefully crafted multiple-choice
questions derived from cutting-edge scientific experimental videos spanning
over 25 specialized academic subjects and verified by a semi-automatic system.
Each question demands sophisticated domain-specific knowledge, precise
spatiotemporal perception, and intricate logical reasoning, effectively
challenging models' higher-order cognitive abilities. Our evaluation highlights
significant performance deficits in state-of-the-art proprietary and
open-source LMMs, including Gemini 2.5 Pro and Qwen2.5-VL, indicating
substantial room for advancement in video reasoning capabilities. Detailed
analyses of critical factors such as reasoning complexity and visual grounding
provide valuable insights and clear direction for future developments in LMMs,
driving the evolution of truly capable multimodal AI co-scientists. We hope
SciVideoBench could fit the interests of the community and help to push the
boundary of cutting-edge AI for border science.
Learning to Route LLMs from Bandit Feedback: One Policy, Many Trade-offs
Authors: name: Wang Wei, name: Tiankai Yang, name: Hongjie Chen, name: Yue Zhao, name: Franck Dernoncourt, name: Ryan A. Rossi, name: Hoda Eldardiry
Published: 2025-10-08 18:24:59

Efficient use of large language models (LLMs) is critical for deployment at
scale: without adaptive routing, systems either overpay for strong models or
risk poor performance from weaker ones. Selecting the right LLM for each query
is fundamentally an online decision problem: models differ in strengths, prices
fluctuate, and users value accuracy and cost differently. Yet most routers are
trained offline with labels for all candidate models, an assumption that breaks
in deployment, where only the outcome of the chosen model is observed. We
bridge this gap with BaRP, a Bandit-feedback Routing with Preferences approach
that trains under the same partial-feedback restriction as deployment, while
supporting preference-tunable inference: operators can dial the
performance/cost trade-off at test time without retraining. Framed as a
contextual bandit over prompt features and a user preference vector, our method
simulates an online feedback setting during training and adapts its routing
decisions to each new prompt, rather than depending on full-information offline
supervision. Comprehensive experiments show that our method consistently
outperforms strong offline routers by at least 12.46% and the largest LLM by at
least 2.45%, and generalizes robustly for unseen tasks.
R2RGEN: Real-to-Real 3D Data Generation for Spatially Generalized
  Manipulation
Authors: name: Xiuwei Xu, name: Angyuan Ma, name: Hankun Li, name: Bingyao Yu, name: Zheng Zhu, name: Jie Zhou, name: Jiwen Lu
Published: 2025-10-09 17:55:44

Towards the aim of generalized robotic manipulation, spatial generalization
is the most fundamental capability that requires the policy to work robustly
under different spatial distribution of objects, environment and agent itself.
To achieve this, substantial human demonstrations need to be collected to cover
different spatial configurations for training a generalized visuomotor policy
via imitation learning. Prior works explore a promising direction that
leverages data generation to acquire abundant spatially diverse data from
minimal source demonstrations. However, most approaches face significant
sim-to-real gap and are often limited to constrained settings, such as
fixed-base scenarios and predefined camera viewpoints. In this paper, we
propose a real-to-real 3D data generation framework (R2RGen) that directly
augments the pointcloud observation-action pairs to generate real-world data.
R2RGen is simulator- and rendering-free, thus being efficient and
plug-and-play. Specifically, given a single source demonstration, we introduce
an annotation mechanism for fine-grained parsing of scene and trajectory. A
group-wise augmentation strategy is proposed to handle complex multi-object
compositions and diverse task constraints. We further present camera-aware
processing to align the distribution of generated data with real-world 3D
sensor. Empirically, R2RGen substantially enhances data efficiency on extensive
experiments and demonstrates strong potential for scaling and application on
mobile manipulation.
First Try Matters: Revisiting the Role of Reflection in Reasoning Models
Authors: name: Liwei Kang, name: Yue Deng, name: Yao Xiao, name: Zhanfeng Mo, name: Wee Sun Lee, name: Lidong Bing
Published: 2025-10-09 14:57:10

Large language models have recently demonstrated significant gains in
reasoning ability, often attributed to their capacity to generate longer chains
of thought and engage in reflective reasoning. However, the contribution of
reflections to performance improvement remains unclear. In this paper, we
systematically analyze the rollouts of eight reasoning models on five
mathematical datasets. We focus on reflective behaviours where the model has
already produced an answer but continues reflecting before finalizing its
output. Our analysis reveals that reflections are predominantly confirmatory
and rarely alter the model's initial answer, a pattern consistent across models
and datasets. To understand the role of reflections in training, we construct
supervised fine-tuning (SFT) datasets with varying amounts of reflection steps.
We observe that training models on rollouts with more reflection steps
primarily enhances first-answer correctness rather than the ability to correct
initially wrong answers through reflections. This motivates us to propose a
question-aware early-stopping method that enhances inference-time token
efficiency by stopping the reasoning process once a few plausible candidate
answers are generated, thereby reducing unnecessary reflection steps. Motivated
by this, we further propose to dynamically truncate the reflections after a
candidate answer has appeared during generation, which reduces reasoning tokens
by 24.5% across five mathematical datasets, within a 2.9% drop in accuracy.
UNIDOC-BENCH: A Unified Benchmark for Document-Centric Multimodal RAG
Authors: name: Xiangyu Peng, name: Cab Qin, name: Zeyuan Chen, name: Ran Xu, name: Caiming Xiong, name: Chien-Sheng Wu
Published: 2025-10-04 04:30:13

Multimodal retrieval-augmented generation (MM-RAG) is a key approach for
applying large language models (LLMs) and agents to real-world knowledge bases,
yet current evaluations are fragmented, focusing on either text or images in
isolation or on simplified multimodal setups that fail to capture
document-centric multimodal use cases. In this paper, we introduce
UniDoc-Bench, the first large-scale, realistic benchmark for MM-RAG built from
70k real-world PDF pages across eight domains. Our pipeline extracts and links
evidence from text, tables, and figures, then generates 1,600 multimodal QA
pairs spanning factual retrieval, comparison, summarization, and logical
reasoning queries. To ensure reliability, 20% of QA pairs are validated by
multiple annotators and expert adjudication. UniDoc-Bench supports
apples-to-apples comparison across four paradigms: (1) text-only, (2)
image-only, (3) multimodal text-image fusion, and (4) multimodal joint
retrieval -- under a unified protocol with standardized candidate pools,
prompts, and evaluation metrics. Our experiments show that multimodal
text-image fusion RAG systems consistently outperform both unimodal and jointly
multimodal embedding-based retrieval, indicating that neither text nor images
alone are sufficient and that current multimodal embeddings remain inadequate.
Beyond benchmarking, our analysis reveals when and how visual context
complements textual evidence, uncovers systematic failure modes, and offers
actionable guidance for developing more robust MM-RAG pipelines.
LongRM: Revealing and Unlocking the Context Boundary of Reward Modeling
Authors: name: Zecheng Tang, name: Baibei Ji, name: Quantong Qiu, name: Haitian Wang, name: Xiaobo Liang, name: Juntao Li, name: Min Zhang
Published: 2025-10-08 11:48:16

Reward model (RM) plays a pivotal role in aligning large language model (LLM)
with human preferences. As real-world applications increasingly involve long
history trajectories, e.g., LLM agent, it becomes indispensable to evaluate
whether a model's responses are not only high-quality but also grounded in and
consistent with the provided context. Yet, current RMs remain confined to
short-context settings and primarily focus on response-level attributes (e.g.,
safety or helpfulness), while largely neglecting the critical dimension of long
context-response consistency. In this work, we introduce Long-RewardBench, a
benchmark specifically designed for long-context RM evaluation, featuring both
Pairwise Comparison and Best-of-N tasks. Our preliminary study reveals that
even state-of-the-art generative RMs exhibit significant fragility in
long-context scenarios, failing to maintain context-aware preference judgments.
Motivated by the analysis of failure patterns observed in model outputs, we
propose a general multi-stage training strategy that effectively scales
arbitrary models into robust Long-context RMs (LongRMs). Experiments show that
our approach not only substantially improves performance on long-context
evaluation but also preserves strong short-context capability. Notably, our 8B
LongRM outperforms much larger 70B-scale baselines and matches the performance
of the proprietary Gemini 2.5 Pro model.
DeepPrune: Parallel Scaling without Inter-trace Redundancy
Authors: name: Shangqing Tu, name: Yaxuan Li, name: Yushi Bai, name: Lei Hou, name: Juanzi Li
Published: 2025-10-09 17:24:54

Parallel scaling has emerged as a powerful paradigm to enhance reasoning
capabilities in large language models (LLMs) by generating multiple
Chain-of-Thought (CoT) traces simultaneously. However, this approach introduces
significant computational inefficiency due to inter-trace redundancy -- our
analysis reveals that over 80% of parallel reasoning traces yield identical
final answers, representing substantial wasted computation. To address this
critical efficiency bottleneck, we propose DeepPrune, a novel framework that
enables efficient parallel scaling through dynamic pruning. Our method features
a specialized judge model trained with focal loss and oversampling techniques
to accurately predict answer equivalence from partial reasoning traces which
realizes 0.87 AUROC on equivalence prediction, combined with an online greedy
clustering algorithm that dynamically prunes redundant paths while preserving
answer diversity. Comprehensive evaluations across three challenging benchmarks
(AIME 2024, AIME 2025, and GPQA) and multiple reasoning models demonstrate that
DeepPrune achieves remarkable token reduction by over 80% compared to
conventional consensus sampling on most cases, while maintaining competitive
accuracy within 3 percentage points. Our work establishes a new standard for
efficient parallel reasoning, making high-performance reasoning more efficient.
Our code and data are here: https://deepprune.github.io/