Orionnnn/hub-stats huggingface dataset:
One Life to Learn: Inferring Symbolic World Models for Stochastic
  Environments from Unguided Exploration
Authors: name: Zaid Khan, name: Archiki Prasad, name: Elias Stengel-Eskin, name: Jaemin Cho, name: Mohit Bansal
Published: 2025-10-14 02:49:32

Symbolic world modeling requires inferring and representing an environment's
transitional dynamics as an executable program. Prior work has focused on
largely deterministic environments with abundant interaction data, simple
mechanics, and human guidance. We address a more realistic and challenging
setting, learning in a complex, stochastic environment where the agent has only
"one life" to explore a hostile environment without human guidance. We
introduce OneLife, a framework that models world dynamics through
conditionally-activated programmatic laws within a probabilistic programming
framework. Each law operates through a precondition-effect structure,
activating in relevant world states. This creates a dynamic computation graph
that routes inference and optimization only through relevant laws, avoiding
scaling challenges when all laws contribute to predictions about a complex,
hierarchical state, and enabling the learning of stochastic dynamics even with
sparse rule activation. To evaluate our approach under these demanding
constraints, we introduce a new evaluation protocol that measures (a) state
ranking, the ability to distinguish plausible future states from implausible
ones, and (b) state fidelity, the ability to generate future states that
closely resemble reality. We develop and evaluate our framework on Crafter-OO,
our reimplementation of the Crafter environment that exposes a structured,
object-oriented symbolic state and a pure transition function that operates on
that state alone. OneLife can successfully learn key environment dynamics from
minimal, unguided interaction, outperforming a strong baseline on 16 out of 23
scenarios tested. We also test OneLife's planning ability, with simulated
rollouts successfully identifying superior strategies. Our work establishes a
foundation for autonomously constructing programmatic world models of unknown,
complex environments.
Temporal Alignment Guidance: On-Manifold Sampling in Diffusion Models
Authors: name: Youngrok Park, name: Hojung Jung, name: Sangmin Bae, name: Se-Young Yun
Published: 2025-10-13 06:46:57

Diffusion models have achieved remarkable success as generative models.
However, even a well-trained model can accumulate errors throughout the
generation process. These errors become particularly problematic when arbitrary
guidance is applied to steer samples toward desired properties, which often
breaks sample fidelity. In this paper, we propose a general solution to address
the off-manifold phenomenon observed in diffusion models. Our approach
leverages a time predictor to estimate deviations from the desired data
manifold at each timestep, identifying that a larger time gap is associated
with reduced generation quality. We then design a novel guidance mechanism,
`Temporal Alignment Guidance' (TAG), attracting the samples back to the desired
manifold at every timestep during generation. Through extensive experiments, we
demonstrate that TAG consistently produces samples closely aligned with the
desired manifold at each timestep, leading to significant improvements in
generation quality across various downstream tasks.
MLLM as a UI Judge: Benchmarking Multimodal LLMs for Predicting Human
  Perception of User Interfaces
Authors: name: Reuben A. Luera, name: Ryan Rossi, name: Franck Dernoncourt, name: Samyadeep Basu, name: Sungchul Kim, name: Subhojyoti Mukherjee, name: Puneet Mathur, name: Ruiyi Zhang, name: Jihyung Kil, name: Nedim Lipka, name: Seunghyun Yoon, name: Jiuxiang Gu, name: Zichao Wang, name: Cindy Xiong Bearfield, name: Branislav Kveton
Published: 2025-10-09 20:00:41

In an ideal design pipeline, user interface (UI) design is intertwined with
user research to validate decisions, yet studies are often resource-constrained
during early exploration. Recent advances in multimodal large language models
(MLLMs) offer a promising opportunity to act as early evaluators, helping
designers narrow options before formal testing. Unlike prior work that
emphasizes user behavior in narrow domains such as e-commerce with metrics like
clicks or conversions, we focus on subjective user evaluations across varied
interfaces. We investigate whether MLLMs can mimic human preferences when
evaluating individual UIs and comparing them. Using data from a crowdsourcing
platform, we benchmark GPT-4o, Claude, and Llama across 30 interfaces and
examine alignment with human judgments on multiple UI factors. Our results show
that MLLMs approximate human preferences on some dimensions but diverge on
others, underscoring both their potential and limitations in supplementing
early UX research.
ExpVid: A Benchmark for Experiment Video Understanding & Reasoning
Authors: name: Yicheng Xu, name: Yue Wu, name: Jiashuo Yu, name: Ziang Yan, name: Tianxiang Jiang, name: Yinan He, name: Qingsong Zhao, name: Kai Chen, name: Yu Qiao, name: Limin Wang, name: Manabu Okumura, name: Yi Wang
Published: 2025-10-13 16:45:28

Multimodal Large Language Models (MLLMs) hold promise for accelerating
scientific discovery by interpreting complex experimental procedures. However,
their true capabilities are poorly understood, as existing benchmarks neglect
the fine-grained and long-horizon nature of authentic laboratory work,
especially in wet-lab settings. To bridge this gap, we introduce ExpVid, the
first benchmark designed to systematically evaluate MLLMs on scientific
experiment videos. Curated from peer-reviewed video publications, ExpVid
features a new three-level task hierarchy that mirrors the scientific process:
(1) Fine-grained Perception of tools, materials, and actions; (2) Procedural
Understanding of step order and completeness; and (3) Scientific Reasoning that
connects the full experiment to its published conclusions. Our vision-centric
annotation pipeline, combining automated generation with multi-disciplinary
expert validation, ensures that tasks require visual grounding. We evaluate 19
leading MLLMs on ExpVid and find that while they excel at coarse-grained
recognition, they struggle with disambiguating fine details, tracking state
changes over time, and linking experimental procedures to scientific outcomes.
Our results reveal a notable performance gap between proprietary and
open-source models, particularly in high-order reasoning. ExpVid not only
provides a diagnostic tool but also charts a roadmap for developing MLLMs
capable of becoming trustworthy partners in scientific experimentation.
ViCO: A Training Strategy towards Semantic Aware Dynamic High-Resolution
Authors: name: Long Cui, name: Weiyun Wang, name: Jie Shao, name: Zichen Wen, name: Gen Luo, name: Linfeng Zhang, name: Yanting Zhang, name: Yu Qiao, name: Wenhai Wang
Published: 2025-10-14 17:58:10

Existing Multimodal Large Language Models (MLLMs) suffer from increased
inference costs due to the additional vision tokens introduced by image inputs.
In this work, we propose Visual Consistency Learning (ViCO), a novel training
algorithm that enables the model to represent images of varying semantic
complexities using different numbers of vision tokens. The key idea behind our
method is to employ multiple MLP connectors, each with a different image
compression ratio, to downsample the vision tokens based on the semantic
complexity of the image. During training, we minimize the KL divergence between
the responses conditioned on different MLP connectors. At inference time, we
introduce an image router, termed Visual Resolution Router (ViR), that
automatically selects the appropriate compression rate for each image patch.
Compared with existing dynamic high-resolution strategies, which adjust the
number of visual tokens based on image resolutions, our method dynamically
adapts the number of visual tokens according to semantic complexity.
Experimental results demonstrate that our method can reduce the number of
vision tokens by up to 50% while maintaining the model's perception, reasoning,
and OCR capabilities. We hope this work will contribute to the development of
more efficient MLLMs. The code and models will be released to facilitate future
research.
Spatial Forcing: Implicit Spatial Representation Alignment for
  Vision-language-action Model
Authors: name: Fuhao Li, name: Wenxuan Song, name: Han Zhao, name: Jingbo Wang, name: Pengxiang Ding, name: Donglin Wang, name: Long Zeng, name: Haoang Li
Published: 2025-10-14 08:27:10

Vision-language-action (VLA) models have recently shown strong potential in
enabling robots to follow language instructions and execute precise actions.
However, most VLAs are built upon vision-language models pretrained solely on
2D data, which lack accurate spatial awareness and hinder their ability to
operate in the 3D physical world. Existing solutions attempt to incorporate
explicit 3D sensor inputs such as depth maps or point clouds, but these
approaches face challenges due to sensor noise, hardware heterogeneity, and
incomplete depth coverage in existing datasets. Alternative methods that
estimate 3D cues from 2D images also suffer from the limited performance of
depth estimators.We propose Spatial Forcing (SF), a simple yet effective
alignment strategy that implicitly forces VLA models to develop spatial
comprehension capabilities without relying on explicit 3D inputs or depth
estimators. SF aligns intermediate visual embeddings of VLAs with geometric
representations produced by pretrained 3D foundation models. By enforcing
alignment at intermediate layers, SF guides VLAs to encode richer spatial
representations that enhance action precision.Extensive experiments in
simulation and real-world environments demonstrate that SF achieves
state-of-the-art results, surpassing both 2D- and 3D-based VLAs. SF further
accelerates training by up to 3.8x and improves data efficiency across diverse
robotic tasks. Project page is at https://spatial-forcing.github.io/
Dr.LLM: Dynamic Layer Routing in LLMs
Authors: name: Ahmed Heakl, name: Martin Gubri, name: Salman Khan, name: Sangdoo Yun, name: Seong Joon Oh
Published: 2025-10-14 17:51:26

Large Language Models (LLMs) process every token through all layers of a
transformer stack, causing wasted computation on simple queries and
insufficient flexibility for harder ones that need deeper reasoning.
Adaptive-depth methods can improve efficiency, but prior approaches rely on
costly inference-time search, architectural changes, or large-scale retraining,
and in practice often degrade accuracy despite efficiency gains. We introduce
Dr.LLM, Dynamic routing of Layers for LLMs, a retrofittable framework that
equips pretrained models with lightweight per-layer routers deciding to skip,
execute, or repeat a block. Routers are trained with explicit supervision:
using Monte Carlo Tree Search (MCTS), we derive high-quality layer
configurations that preserve or improve accuracy under a compute budget. Our
design, windowed pooling for stable routing, focal loss with class balancing,
and bottleneck MLP routers, ensures robustness under class imbalance and long
sequences. On ARC (logic) and DART (math), Dr.LLM improves accuracy by up to
+3.4%p while saving 5 layers per example on average. Routers generalize to
out-of-domain tasks (MMLU, GSM8k, AIME, TruthfulQA, SQuADv2, GPQA, PIQA,
AGIEval) with only 0.85% accuracy drop while retaining efficiency, and
outperform prior routing methods by up to +7.7%p. Overall, Dr.LLM shows that
explicitly supervised routers retrofit frozen LLMs for budget-aware,
accuracy-driven inference without altering base weights.
Scaling Language-Centric Omnimodal Representation Learning
Authors: name: Chenghao Xiao, name: Hou Pong Chan, name: Hao Zhang, name: Weiwen Xu, name: Mahani Aljunied, name: Yu Rong
Published: 2025-10-13 17:53:52

Recent multimodal embedding approaches leveraging multimodal large language
models (MLLMs) fine-tuned with contrastive learning (CL) have shown promising
results, yet the underlying reasons behind their superiority remain
underexplored. This work argues that a crucial advantage of MLLM-based
approaches stems from implicit cross-modal alignment achieved during generative
pretraining, where the language decoder learns to exploit multimodal signals
within a shared representation space for generating unimodal outputs. Through
analysis of anisotropy and kernel similarity structure, we empirically confirm
that latent alignment emerges within MLLM representations, allowing CL to serve
as a lightweight refinement stage. Leveraging this insight, we propose a
Language-Centric Omnimodal Embedding framework, termed LCO-Emb. Extensive
experiments across diverse backbones and benchmarks demonstrate its
effectiveness, achieving state-of-the-art performance across modalities.
Furthermore, we identify a Generation-Representation Scaling Law (GRSL),
showing that the representational capabilities gained through contrastive
refinement scales positively with the MLLM's generative capabilities. This
suggests that improving generative abilities evolves as an effective paradigm
for enhancing representation quality. We provide a theoretical explanation of
GRSL, which formally links the MLLM's generative quality to the upper bound on
its representation performance, and validate it on a challenging, low-resource
visual-document retrieval task, showing that continual generative pretraining
before CL can further enhance the potential of a model's embedding
capabilities. Codes, models, and resources are available at
https://github.com/LCO-Embedding/LCO-Embedding.
Boundary-Guided Policy Optimization for Memory-efficient RL of Diffusion
  Large Language Models
Authors: name: Nianyi Lin, name: Jiajie Zhang, name: Lei Hou, name: Juanzi Li
Published: 2025-10-13 17:47:50

A key challenge in applying reinforcement learning (RL) to diffusion large
language models (dLLMs) lies in the intractability of their likelihood
functions, which are essential for the RL objective, necessitating
corresponding approximation in each training step. While existing methods
approximate the log-likelihoods by their evidence lower bounds (ELBOs) via
customized Monte Carlo (MC) sampling, the forward computational graphs of all
MC samples need to be retained for the gradient computation of non-linear terms
in the RL objective, resulting in significant memory overhead. This constraint
restricts feasible sample sizes, leading to imprecise likelihood approximations
and ultimately distorting the RL objective. To overcome this limitation, we
propose Boundary-Guided Policy Optimization (BGPO), a memory-efficient
RL algorithm that maximizes a specially constructed lower bound of the
ELBO-based objective. This lower bound is carefully designed to satisfy two key
properties: (1) Linearity: it is formulated in a linear sum where each term
depends only on a single MC sample, thereby enabling gradient accumulation
across samples and ensuring constant memory usage; (2) Equivalence: Both the
value and gradient of this lower bound are equal to those of the ELBO-based
objective in on-policy training, making it also an effective approximation for
the original RL objective. These properties allow BGPO to adopt a large MC
sample size, resulting in more accurate likelihood approximations and improved
RL objective estimation, which in turn leads to enhanced performance.
Experiments show that BGPO significantly outperforms previous RL algorithms for
dLLMs in math problem solving, code generation, and planning tasks.
ERA: Transforming VLMs into Embodied Agents via Embodied Prior Learning
  and Online Reinforcement Learning
Authors: name: Hanyang Chen, name: Mark Zhao, name: Rui Yang, name: Qinwei Ma, name: Ke Yang, name: Jiarui Yao, name: Kangrui Wang, name: Hao Bai, name: Zhenhailong Wang, name: Rui Pan, name: Mengchao Zhang, name: Jose Barreiros, name: Aykut Onol, name: ChengXiang Zhai, name: Heng Ji, name: Manling Li, name: Huan Zhang, name: Tong Zhang
Published: 2025-10-14 16:25:46

Recent advances in embodied AI highlight the potential of vision language
models (VLMs) as agents capable of perception, reasoning, and interaction in
complex environments. However, top-performing systems rely on large-scale
models that are costly to deploy, while smaller VLMs lack the necessary
knowledge and skills to succeed. To bridge this gap, we present
Embodied Reasoning Agent (ERA), a two-stage framework that integrates
prior knowledge learning and online reinforcement learning (RL). The first
stage, Embodied Prior Learning, distills foundational knowledge from
three types of data: (1) Trajectory-Augmented Priors, which enrich existing
trajectory data with structured reasoning generated by stronger models; (2)
Environment-Anchored Priors, which provide in-environment knowledge and
grounding supervision; and (3) External Knowledge Priors, which transfer
general knowledge from out-of-environment datasets. In the second stage, we
develop an online RL pipeline that builds on these priors to further enhance
agent performance. To overcome the inherent challenges in agent RL, including
long horizons, sparse rewards, and training instability, we introduce three key
designs: self-summarization for context management, dense reward shaping, and
turn-level policy optimization. Extensive experiments on both high-level
planning (EB-ALFRED) and low-level control (EB-Manipulation) tasks demonstrate
that ERA-3B surpasses both prompting-based large models and previous
training-based baselines. Specifically, it achieves overall improvements of
8.4\% on EB-ALFRED and 19.4\% on EB-Manipulation over GPT-4o, and exhibits
strong generalization to unseen tasks. Overall, ERA offers a practical path
toward scalable embodied intelligence, providing methodological insights for
future embodied AI systems.
HoneyBee: Data Recipes for Vision-Language Reasoners
Authors: name: Hritik Bansal, name: Devandra Singh Sachan, name: Kai-Wei Chang, name: Aditya Grover, name: Gargi Ghosh, name: Wen-tau Yih, name: Ramakanth Pasunuru
Published: 2025-10-14 07:23:44

Recent advances in vision-language models (VLMs) have made them highly
effective at reasoning tasks. However, the principles underlying the
construction of performant VL reasoning training datasets remain poorly
understood. In this work, we introduce several data curation approaches and
study their impacts on VL reasoning capabilities by carefully controlling
training and evaluation setups. We analyze the effects of context (image and
question pair) sources, implement targeted data interventions, and explore
scaling up images, questions, and chain-of-thought (CoT) solutions. Our
findings reveal that (a) context source strategies significantly affect VLM
performance, (b) interventions such as auxiliary signals from image captions
and the inclusion of text-only reasoning yield substantial gains, and (c)
scaling all data dimensions (e.g., unique questions per image and unique CoTs
per image-question pair) consistently improves reasoning capability. Motivated
by these insights, we introduce HoneyBee, a large-scale, high-quality CoT
reasoning dataset with 2.5M examples consisting 350K image-question pairs. VLMs
trained with HoneyBee outperform state-of-the-art models across model sizes.
For instance, a HoneyBee-trained VLM with 3B parameters outperforms the SOTA
model and the base model by 7.8% and 24.8%, respectively, on MathVerse.
Furthermore, we propose a test-time scaling strategy that reduces decoding cost
by 73% without sacrificing accuracy. Overall, this work presents improved
strategies for VL reasoning dataset curation research.
DITING: A Multi-Agent Evaluation Framework for Benchmarking Web Novel
  Translation
Authors: name: Enze Zhang, name: Jiaying Wang, name: Mengxi Xiao, name: Jifei Liu, name: Ziyan Kuang, name: Rui Dong, name: Eric Dong, name: Sophia Ananiadou, name: Min Peng, name: Qianqian Xie
Published: 2025-10-10 08:10:10

Large language models (LLMs) have substantially advanced machine translation
(MT), yet their effectiveness in translating web novels remains unclear.
Existing benchmarks rely on surface-level metrics that fail to capture the
distinctive traits of this genre. To address these gaps, we introduce DITING,
the first comprehensive evaluation framework for web novel translation,
assessing narrative and cultural fidelity across six dimensions: idiom
translation, lexical ambiguity, terminology localization, tense consistency,
zero-pronoun resolution, and cultural safety, supported by over 18K
expert-annotated Chinese-English sentence pairs. We further propose AgentEval,
a reasoning-driven multi-agent evaluation framework that simulates expert
deliberation to assess translation quality beyond lexical overlap, achieving
the highest correlation with human judgments among seven tested automatic
metrics. To enable metric comparison, we develop MetricAlign, a meta-evaluation
dataset of 300 sentence pairs annotated with error labels and scalar quality
scores. Comprehensive evaluation of fourteen open, closed, and commercial
models reveals that Chinese-trained LLMs surpass larger foreign counterparts,
and that DeepSeek-V3 delivers the most faithful and stylistically coherent
translations. Our work establishes a new paradigm for exploring LLM-based web
novel translation and provides public resources to advance future research.
What If : Understanding Motion Through Sparse Interactions
Authors: name: Stefan Andreas Baumann, name: Nick Stracke, name: Timy Phan, name: Björn Ommer
Published: 2025-10-14 17:52:17

Understanding the dynamics of a physical scene involves reasoning about the
diverse ways it can potentially change, especially as a result of local
interactions. We present the Flow Poke Transformer (FPT), a novel framework for
directly predicting the distribution of local motion, conditioned on sparse
interactions termed "pokes". Unlike traditional methods that typically only
enable dense sampling of a single realization of scene dynamics, FPT provides
an interpretable directly accessible representation of multi-modal scene
motion, its dependency on physical interactions and the inherent uncertainties
of scene dynamics. We also evaluate our model on several downstream tasks to
enable comparisons with prior methods and highlight the flexibility of our
approach. On dense face motion generation, our generic pre-trained model
surpasses specialized baselines. FPT can be fine-tuned in strongly
out-of-distribution tasks such as synthetic datasets to enable significant
improvements over in-domain methods in articulated object motion estimation.
Additionally, predicting explicit motion distributions directly enables our
method to achieve competitive performance on tasks like moving part
segmentation from pokes which further demonstrates the versatility of our FPT.
Code and models are publicly available at
https://compvis.github.io/flow-poke-transformer.