Orionnnn/hub-stats huggingface dataset:
System-1.5 Reasoning: Traversal in Language and Latent Spaces with
  Dynamic Shortcuts
Authors: name: Xiaoqiang Wang, name: Suyuchen Wang, name: Yun Zhu, name: Bang Liu
Published: 2025-05-25 03:35:49

Chain-of-thought (CoT) reasoning enables large language models (LLMs) to move
beyond fast System-1 responses and engage in deliberative System-2 reasoning.
However, this comes at the cost of significant inefficiency due to verbose
intermediate output. Recent latent-space reasoning methods improve efficiency
by operating on hidden states without decoding into language, yet they treat
all steps uniformly, failing to distinguish critical deductions from auxiliary
steps and resulting in suboptimal use of computational resources. In this
paper, we propose System-1.5 Reasoning, an adaptive reasoning framework that
dynamically allocates computation across reasoning steps through shortcut paths
in latent space. Specifically, System-1.5 Reasoning introduces two types of
dynamic shortcuts. The model depth shortcut (DS) adaptively reasons along the
vertical depth by early exiting non-critical tokens through lightweight adapter
branches, while allowing critical tokens to continue through deeper Transformer
layers. The step shortcut (SS) reuses hidden states across the decoding steps
to skip trivial steps and reason horizontally in latent space. Training
System-1.5 Reasoning involves a two-stage self-distillation process: first
distilling natural language CoT into latent-space continuous thought, and then
distilling full-path System-2 latent reasoning into adaptive shortcut paths
(System-1.5 Reasoning). Experiments on reasoning tasks demonstrate the superior
performance of our method. For example, on GSM8K, System-1.5 Reasoning achieves
reasoning performance comparable to traditional CoT fine-tuning methods while
accelerating inference by over 20x and reducing token generation by 92.31% on
average.
Towards Safety Reasoning in LLMs: AI-agentic Deliberation for
  Policy-embedded CoT Data Creation
Authors: name: Tharindu Kumarage, name: Ninareh Mehrabi, name: Anil Ramakrishna, name: Xinyan Zhao, name: Richard Zemel, name: Kai-Wei Chang, name: Aram Galstyan, name: Rahul Gupta, name: Charith Peris
Published: 2025-05-27 21:34:40

Safety reasoning is a recent paradigm where LLMs reason over safety policies
before generating responses, thereby mitigating limitations in existing safety
measures such as over-refusal and jailbreak vulnerabilities. However,
implementing this paradigm is challenging due to the resource-intensive process
of creating high-quality policy-embedded chain-of-thought (CoT) datasets while
ensuring reasoning remains accurate and free from hallucinations or policy
conflicts. To tackle this, we propose AIDSAFE: Agentic Iterative Deliberation
for Safety Reasoning, a novel data generation recipe that leverages multi-agent
deliberation to iteratively expand reasoning on safety policies. A data refiner
stage in AIDSAFE ensures high-quality outputs by eliminating repetitive,
redundant, and deceptive thoughts. AIDSAFE-generated CoTs provide a strong
foundation for supervised fine-tuning (SFT)-based safety training.
Additionally, to address the need of preference data in alignment stages, such
as DPO training, we introduce a supplemental recipe that uses belief
augmentation to create distinct selected and rejected CoT samples. Our
evaluations demonstrate that AIDSAFE-generated CoTs achieve superior policy
adherence and reasoning quality. Consequently, we show that fine-tuning
open-source LLMs on these CoTs can significantly improve safety generalization
and jailbreak robustness while maintaining acceptable utility and over-refusal
accuracy. AIDSAFE-generated CoT datasets can be found here:
https://huggingface.co/datasets/AmazonScience/AIDSAFE
ATI: Any Trajectory Instruction for Controllable Video Generation
Authors: name: Angtian Wang, name: Haibin Huang, name: Jacob Zhiyuan Fang, name: Yiding Yang, name: Chongyang Ma
Published: 2025-05-28 23:49:18

We propose a unified framework for motion control in video generation that
seamlessly integrates camera movement, object-level translation, and
fine-grained local motion using trajectory-based inputs. In contrast to prior
methods that address these motion types through separate modules or
task-specific designs, our approach offers a cohesive solution by projecting
user-defined trajectories into the latent space of pre-trained image-to-video
generation models via a lightweight motion injector. Users can specify
keypoints and their motion paths to control localized deformations, entire
object motion, virtual camera dynamics, or combinations of these. The injected
trajectory signals guide the generative process to produce temporally
consistent and semantically aligned motion sequences. Our framework
demonstrates superior performance across multiple video motion control tasks,
including stylized motion effects (e.g., motion brushes), dynamic viewpoint
changes, and precise local motion manipulation. Experiments show that our
method provides significantly better controllability and visual quality
compared to prior approaches and commercial solutions, while remaining broadly
compatible with various state-of-the-art video generation backbones. Project
page: https://anytraj.github.io/.
MMSI-Bench: A Benchmark for Multi-Image Spatial Intelligence
Authors: name: Sihan Yang, name: Runsen Xu, name: Yiman Xie, name: Sizhe Yang, name: Mo Li, name: Jingli Lin, name: Chenming Zhu, name: Xiaochen Chen, name: Haodong Duan, name: Xiangyu Yue, name: Dahua Lin, name: Tai Wang, name: Jiangmiao Pang
Published: 2025-05-29 17:59:52

Spatial intelligence is essential for multimodal large language models
(MLLMs) operating in the complex physical world. Existing benchmarks, however,
probe only single-image relations and thus fail to assess the multi-image
spatial reasoning that real-world deployments demand. We introduce MMSI-Bench,
a VQA benchmark dedicated to multi-image spatial intelligence. Six 3D-vision
researchers spent more than 300 hours meticulously crafting 1,000 challenging,
unambiguous multiple-choice questions from over 120,000 images, each paired
with carefully designed distractors and a step-by-step reasoning process. We
conduct extensive experiments and thoroughly evaluate 34 open-source and
proprietary MLLMs, observing a wide gap: the strongest open-source model
attains roughly 30% accuracy and OpenAI's o3 reasoning model reaches 40%, while
humans score 97%. These results underscore the challenging nature of MMSI-Bench
and the substantial headroom for future research. Leveraging the annotated
reasoning processes, we also provide an automated error analysis pipeline that
diagnoses four dominant failure modes, including (1) grounding errors, (2)
overlap-matching and scene-reconstruction errors, (3) situation-transformation
reasoning errors, and (4) spatial-logic errors, offering valuable insights for
advancing multi-image spatial intelligence. Project page:
https://runsenxu.com/projects/MMSI_Bench .
ZPressor: Bottleneck-Aware Compression for Scalable Feed-Forward 3DGS
Authors: name: Weijie Wang, name: Donny Y. Chen, name: Zeyu Zhang, name: Duochao Shi, name: Akide Liu, name: Bohan Zhuang
Published: 2025-05-29 17:57:04

Feed-forward 3D Gaussian Splatting (3DGS) models have recently emerged as a
promising solution for novel view synthesis, enabling one-pass inference
without the need for per-scene 3DGS optimization. However, their scalability is
fundamentally constrained by the limited capacity of their encoders, leading to
degraded performance or excessive memory consumption as the number of input
views increases. In this work, we analyze feed-forward 3DGS frameworks through
the lens of the Information Bottleneck principle and introduce ZPressor, a
lightweight architecture-agnostic module that enables efficient compression of
multi-view inputs into a compact latent state Z that retains essential scene
information while discarding redundancy. Concretely, ZPressor enables existing
feed-forward 3DGS models to scale to over 100 input views at 480P resolution on
an 80GB GPU, by partitioning the views into anchor and support sets and using
cross attention to compress the information from the support views into anchor
views, forming the compressed latent state Z. We show that integrating
ZPressor into several state-of-the-art feed-forward 3DGS models consistently
improves performance under moderate input views and enhances robustness under
dense view settings on two large-scale benchmarks DL3DV-10K and RealEstate10K.
The video results, code and trained models are available on our project page:
https://lhmd.top/zpressor.
How Animals Dance (When You're Not Looking)
Authors: name: Xiaojuan Wang, name: Aleksander Holynski, name: Brian Curless, name: Ira Kemelmacher, name: Steve Seitz
Published: 2025-05-29 17:58:02

We present a keyframe-based framework for generating music-synchronized,
choreography aware animal dance videos. Starting from a few keyframes
representing distinct animal poses -- generated via text-to-image prompting or
GPT-4o -- we formulate dance synthesis as a graph optimization problem: find
the optimal keyframe structure that satisfies a specified choreography pattern
of beats, which can be automatically estimated from a reference dance video. We
also introduce an approach for mirrored pose image generation, essential for
capturing symmetry in dance. In-between frames are synthesized using an video
diffusion model. With as few as six input keyframes, our method can produce up
to 30 second dance videos across a wide range of animals and music tracks.
TokBench: Evaluating Your Visual Tokenizer before Visual Generation
Authors: name: Junfeng Wu, name: Dongliang Luo, name: Weizhi Zhao, name: Zhihao Xie, name: Yuanhao Wang, name: Junyi Li, name: Xudong Xie, name: Yuliang Liu, name: Xiang Bai
Published: 2025-05-23 17:52:16

In this work, we reveal the limitations of visual tokenizers and VAEs in
preserving fine-grained features, and propose a benchmark to evaluate
reconstruction performance for two challenging visual contents: text and face.
Visual tokenizers and VAEs have significantly advanced visual generation and
multimodal modeling by providing more efficient compressed or quantized image
representations. However, while helping production models reduce computational
burdens, the information loss from image compression fundamentally limits the
upper bound of visual generation quality. To evaluate this upper bound, we
focus on assessing reconstructed text and facial features since they typically:
1) exist at smaller scales, 2) contain dense and rich textures, 3) are prone to
collapse, and 4) are highly sensitive to human vision. We first collect and
curate a diverse set of clear text and face images from existing datasets.
Unlike approaches using VLM models, we employ established OCR and face
recognition models for evaluation, ensuring accuracy while maintaining an
exceptionally lightweight assessment process <span style="font-weight: bold;
color: rgb(214, 21, 21);">requiring just 2GB memory and 4 minutes</span> to
complete. Using our benchmark, we analyze text and face reconstruction quality
across various scales for different image tokenizers and VAEs. Our results show
modern visual tokenizers still struggle to preserve fine-grained features,
especially at smaller scales. We further extend this evaluation framework to
video, conducting comprehensive analysis of video tokenizers. Additionally, we
demonstrate that traditional metrics fail to accurately reflect reconstruction
performance for faces and text, while our proposed metrics serve as an
effective complement.
Puzzled by Puzzles: When Vision-Language Models Can't Take a Hint
Authors: name: Heekyung Lee, name: Jiaxin Ge, name: Tsung-Han Wu, name: Minwoo Kang, name: Trevor Darrell, name: David M. Chan
Published: 2025-05-29 17:59:47

Rebus puzzles, visual riddles that encode language through imagery, spatial
arrangement, and symbolic substitution, pose a unique challenge to current
vision-language models (VLMs). Unlike traditional image captioning or question
answering tasks, rebus solving requires multi-modal abstraction, symbolic
reasoning, and a grasp of cultural, phonetic and linguistic puns. In this
paper, we investigate the capacity of contemporary VLMs to interpret and solve
rebus puzzles by constructing a hand-generated and annotated benchmark of
diverse English-language rebus puzzles, ranging from simple pictographic
substitutions to spatially-dependent cues ("head" over "heels"). We analyze how
different VLMs perform, and our findings reveal that while VLMs exhibit some
surprising capabilities in decoding simple visual clues, they struggle
significantly with tasks requiring abstract reasoning, lateral thinking, and
understanding visual metaphors.
Grounded Reinforcement Learning for Visual Reasoning
Authors: name: Gabriel Sarch, name: Snigdha Saha, name: Naitik Khandelwal, name: Ayush Jain, name: Michael J. Tarr, name: Aviral Kumar, name: Katerina Fragkiadaki
Published: 2025-05-29 17:20:26

While reinforcement learning (RL) over chains of thought has significantly
advanced language models in tasks such as mathematics and coding, visual
reasoning introduces added complexity by requiring models to direct visual
attention, interpret perceptual inputs, and ground abstract reasoning in
spatial evidence. We introduce ViGoRL (Visually Grounded Reinforcement
Learning), a vision-language model trained with RL to explicitly anchor each
reasoning step to specific visual coordinates. Inspired by human visual
decision-making, ViGoRL learns to produce spatially grounded reasoning traces,
guiding visual attention to task-relevant regions at each step. When
fine-grained exploration is required, our novel multi-turn RL framework enables
the model to dynamically zoom into predicted coordinates as reasoning unfolds.
Across a diverse set of visual reasoning benchmarks--including SAT-2 and BLINK
for spatial reasoning, V*bench for visual search, and ScreenSpot and
VisualWebArena for web-based grounding--ViGoRL consistently outperforms both
supervised fine-tuning and conventional RL baselines that lack explicit
grounding mechanisms. Incorporating multi-turn RL with zoomed-in visual
feedback significantly improves ViGoRL's performance on localizing small GUI
elements and visual search, achieving 86.4% on V*Bench. Additionally, we find
that grounding amplifies other visual behaviors such as region exploration,
grounded subgoal setting, and visual verification. Finally, human evaluations
show that the model's visual references are not only spatially accurate but
also helpful for understanding model reasoning steps. Our results show that
visually grounded RL is a strong paradigm for imbuing models with
general-purpose visual reasoning.
Lunguage: A Benchmark for Structured and Sequential Chest X-ray
  Interpretation
Authors: name: Jong Hak Moon, name: Geon Choi, name: Paloma Rabaey, name: Min Gwan Kim, name: Hyuk Gi Hong, name: Jung-Oh Lee, name: Hangyul Yoon, name: Eun Woo Doe, name: Jiyoun Kim, name: Harshita Sharma, name: Daniel C. Castro, name: Javier Alvarez-Valle, name: Edward Choi
Published: 2025-05-27 13:40:00

Radiology reports convey detailed clinical observations and capture
diagnostic reasoning that evolves over time. However, existing evaluation
methods are limited to single-report settings and rely on coarse metrics that
fail to capture fine-grained clinical semantics and temporal dependencies. We
introduce LUNGUAGE,a benchmark dataset for structured radiology report
generation that supports both single-report evaluation and longitudinal
patient-level assessment across multiple studies. It contains 1,473 annotated
chest X-ray reports, each reviewed by experts, and 80 of them contain
longitudinal annotations to capture disease progression and inter-study
intervals, also reviewed by experts. Using this benchmark, we develop a
two-stage framework that transforms generated reports into fine-grained,
schema-aligned structured representations, enabling longitudinal
interpretation. We also propose LUNGUAGESCORE, an interpretable metric that
compares structured outputs at the entity, relation, and attribute level while
modeling temporal consistency across patient timelines. These contributions
establish the first benchmark dataset, structuring framework, and evaluation
metric for sequential radiology reporting, with empirical results demonstrating
that LUNGUAGESCORE effectively supports structured report evaluation. The code
is available at: https://github.com/SuperSupermoon/Lunguage
Large Language Models Meet Knowledge Graphs for Question Answering:
  Synthesis and Opportunities
Authors: name: Chuangtao Ma, name: Yongrui Chen, name: Tianxing Wu, name: Arijit Khan, name: Haofen Wang
Published: 2025-05-26 15:08:23

Large language models (LLMs) have demonstrated remarkable performance on
question-answering (QA) tasks because of their superior capabilities in natural
language understanding and generation. However, LLM-based QA struggles with
complex QA tasks due to poor reasoning capacity, outdated knowledge, and
hallucinations. Several recent works synthesize LLMs and knowledge graphs (KGs)
for QA to address the above challenges. In this survey, we propose a new
structured taxonomy that categorizes the methodology of synthesizing LLMs and
KGs for QA according to the categories of QA and the KG's role when integrating
with LLMs. We systematically survey state-of-the-art advances in synthesizing
LLMs and KGs for QA and compare and analyze these approaches in terms of
strength, limitations, and KG requirements. We then align the approaches with
QA and discuss how these approaches address the main challenges of different
complex QA. Finally, we summarize the advancements, evaluation metrics, and
benchmark datasets and highlight open challenges and opportunities.
Concise Reasoning, Big Gains: Pruning Long Reasoning Trace with
  Difficulty-Aware Prompting
Authors: name: Yifan Wu, name: Jingze Shi, name: Bingheng Wu, name: Jiayi Zhang, name: Xiaotian Lin, name: Nan Tang, name: Yuyu Luo
Published: 2025-05-26 09:04:44

Existing chain-of-thought (CoT) distillation methods can effectively transfer
reasoning abilities to base models but suffer from two major limitations:
excessive verbosity of reasoning traces and inadequate adaptability to problem
difficulty. Long reasoning traces significantly increase inference costs, and
uniform-length solutions prevent base models from learning adaptive reasoning
strategies. To address these issues, we propose a difficulty-aware prompting
(DAP) method to dynamically shorten reasoning traces without performance loss.
In our approach, a large teacher model first judges each problem's difficulty
and then rewrites its reasoning traces to an appropriate shorter length,
yielding concise yet complete reasoning traces. Leveraging the DAP pipeline, we
curate a distilled dataset called LiteCoT consisting of 100K concise reasoning
examples, with solutions averaging only 720 tokens (an order of magnitude
shorter than typical CoTs). Using LiteCoT, we distilled a new family of
reasoning models called Liter (1.5B, 7B, and 32B) based on the Qwen2.5
architecture. Experiments show that a student model fine-tuned on just 100K of
these difficulty-pruned CoT samples outperforms a model distilled on 800K
original Long CoT samples, while significantly reducing training and inference
costs. Our method also generalizes well: across 11 diverse benchmarks, the
shorter difficulty-aware CoTs achieve equal or better accuracy than Long
chains, using far fewer tokens. For example, on the challenging AIME24 exam,
our approach reaches 74.2% Pass@1 using only about 5K inference tokens,
surpassing other methods that consume many more tokens. Our code and data are
available at https://github.com/Evanwu1125/LiteCoT.
REOrdering Patches Improves Vision Models
Authors: name: Declan Kutscher, name: David M. Chan, name: Yutong Bai, name: Trevor Darrell, name: Ritwik Gupta
Published: 2025-05-29 17:59:30

Sequence models such as transformers require inputs to be represented as
one-dimensional sequences. In vision, this typically involves flattening images
using a fixed row-major (raster-scan) order. While full self-attention is
permutation-equivariant, modern long-sequence transformers increasingly rely on
architectural approximations that break this invariance and introduce
sensitivity to patch ordering. We show that patch order significantly affects
model performance in such settings, with simple alternatives like column-major
or Hilbert curves yielding notable accuracy shifts. Motivated by this, we
propose REOrder, a two-stage framework for discovering task-optimal patch
orderings. First, we derive an information-theoretic prior by evaluating the
compressibility of various patch sequences. Then, we learn a policy over
permutations by optimizing a Plackett-Luce policy using REINFORCE. This
approach enables efficient learning in a combinatorial permutation space.
REOrder improves top-1 accuracy over row-major ordering on ImageNet-1K by up to
3.01% and Functional Map of the World by 13.35%.
CLIPGaussian: Universal and Multimodal Style Transfer Based on Gaussian
  Splatting
Authors: name: Kornel Howil, name: Joanna Waczyńska, name: Piotr Borycki, name: Tadeusz Dziarmaga, name: Marcin Mazur, name: Przemysław Spurek
Published: 2025-05-28 20:41:24

Gaussian Splatting (GS) has recently emerged as an efficient representation
for rendering 3D scenes from 2D images and has been extended to images, videos,
and dynamic 4D content. However, applying style transfer to GS-based
representations, especially beyond simple color changes, remains challenging.
In this work, we introduce CLIPGaussians, the first unified style transfer
framework that supports text- and image-guided stylization across multiple
modalities: 2D images, videos, 3D objects, and 4D scenes. Our method operates
directly on Gaussian primitives and integrates into existing GS pipelines as a
plug-in module, without requiring large generative models or retraining from
scratch. CLIPGaussians approach enables joint optimization of color and
geometry in 3D and 4D settings, and achieves temporal coherence in videos,
while preserving a model size. We demonstrate superior style fidelity and
consistency across all tasks, validating CLIPGaussians as a universal and
efficient solution for multimodal style transfer.
When Models Reason in Your Language: Controlling Thinking Trace Language
  Comes at the Cost of Accuracy
Authors: name: Jirui Qi, name: Shan Chen, name: Zidi Xiong, name: Raquel Fernández, name: Danielle S. Bitterman, name: Arianna Bisazza
Published: 2025-05-28 21:44:12

Recent Large Reasoning Models (LRMs) with thinking traces have shown strong
performance on English reasoning tasks. However, their ability to think in
other languages is less studied. This capability is as important as answer
accuracy for real world applications because users may find the reasoning trace
useful for oversight only when it is expressed in their own language. We
comprehensively evaluate two leading families of LRMs on our XReasoning
benchmark and find that even the most advanced models often revert to English
or produce fragmented reasoning in other languages, revealing a substantial gap
in multilingual reasoning. Prompt based interventions that force models to
reason in the users language improve readability and oversight but reduce
answer accuracy, exposing an important trade off. We further show that targeted
post training on just 100 examples mitigates this mismatch, though some
accuracy loss remains. Our results highlight the limited multilingual reasoning
capabilities of current LRMs and outline directions for future work. Code and
data are available at https://github.com/Betswish/mCoT-XReasoning.