Orionnnn/hub-stats huggingface dataset:
Once Upon an Input: Reasoning via Per-Instance Program Synthesis
Authors: name: Adam Stein, name: Neelay Velingker, name: Mayur Naik, name: Eric Wong
Published: 2025-10-26 21:58:33

Large language models (LLMs) excel at zero-shot inference but continue to
struggle with complex, multi-step reasoning. Recent methods that augment LLMs
with intermediate reasoning steps such as Chain of Thought (CoT) and Program of
Thought (PoT) improve performance but often produce undesirable solutions,
especially in algorithmic domains. We introduce Per-Instance Program Synthesis
(PIPS), a method that generates and refines programs at the instance-level
using structural feedback without relying on task-specific guidance or explicit
test cases. To further improve performance, PIPS incorporates a confidence
metric that dynamically chooses between direct inference and program synthesis
on a per-instance basis. Experiments across three frontier LLMs and 30
benchmarks including all tasks of Big Bench Extra Hard (BBEH), visual question
answering tasks, relational reasoning tasks, and mathematical reasoning tasks
show that PIPS improves the absolute harmonic mean accuracy by up to 8.6% and
9.4% compared to PoT and CoT respectively, and reduces undesirable program
generations by 65.1% on the algorithmic tasks compared to PoT with
Gemini-2.0-Flash.
SyncHuman: Synchronizing 2D and 3D Generative Models for Single-view
  Human Reconstruction
Authors: name: Wenyue Chen, name: Peng Li, name: Wangguandong Zheng, name: Chengfeng Zhao, name: Mengfei Li, name: Yaolong Zhu, name: Zhiyang Dou, name: Ronggang Wang, name: Yuan Liu
Published: 2025-10-09 03:01:10

Photorealistic 3D full-body human reconstruction from a single image is a
critical yet challenging task for applications in films and video games due to
inherent ambiguities and severe self-occlusions. While recent approaches
leverage SMPL estimation and SMPL-conditioned image generative models to
hallucinate novel views, they suffer from inaccurate 3D priors estimated from
SMPL meshes and have difficulty in handling difficult human poses and
reconstructing fine details. In this paper, we propose SyncHuman, a novel
framework that combines 2D multiview generative model and 3D native generative
model for the first time, enabling high-quality clothed human mesh
reconstruction from single-view images even under challenging human poses.
Multiview generative model excels at capturing fine 2D details but struggles
with structural consistency, whereas 3D native generative model generates
coarse yet structurally consistent 3D shapes. By integrating the complementary
strengths of these two approaches, we develop a more effective generation
framework. Specifically, we first jointly fine-tune the multiview generative
model and the 3D native generative model with proposed pixel-aligned 2D-3D
synchronization attention to produce geometrically aligned 3D shapes and 2D
multiview images. To further improve details, we introduce a feature injection
mechanism that lifts fine details from 2D multiview images onto the aligned 3D
shapes, enabling accurate and high-fidelity reconstruction. Extensive
experiments demonstrate that SyncHuman achieves robust and photo-realistic 3D
human reconstruction, even for images with challenging poses. Our method
outperforms baseline methods in geometric accuracy and visual fidelity,
demonstrating a promising direction for future 3D generation models.
Scaling Laws for Deepfake Detection
Authors: name: Wenhao Wang, name: Longqi Cai, name: Taihong Xiao, name: Yuxiao Wang, name: Ming-Hsuan Yang
Published: 2025-10-18 03:08:10

This paper presents a systematic study of scaling laws for the deepfake
detection task. Specifically, we analyze the model performance against the
number of real image domains, deepfake generation methods, and training images.
Since no existing dataset meets the scale requirements for this research, we
construct ScaleDF, the largest dataset to date in this field, which contains
over 5.8 million real images from 51 different datasets (domains) and more than
8.8 million fake images generated by 102 deepfake methods. Using ScaleDF, we
observe power-law scaling similar to that shown in large language models
(LLMs). Specifically, the average detection error follows a predictable
power-law decay as either the number of real domains or the number of deepfake
methods increases. This key observation not only allows us to forecast the
number of additional real domains or deepfake methods required to reach a
target performance, but also inspires us to counter the evolving deepfake
technology in a data-centric manner. Beyond this, we examine the role of
pre-training and data augmentations in deepfake detection under scaling, as
well as the limitations of scaling itself.
DiffusionLane: Diffusion Model for Lane Detection
Authors: name: Kunyang Zhou, name: Yeqin Shao
Published: 2025-10-25 09:42:49

In this paper, we present a novel diffusion-based model for lane detection,
called DiffusionLane, which treats the lane detection task as a denoising
diffusion process in the parameter space of the lane. Firstly, we add the
Gaussian noise to the parameters (the starting point and the angle) of ground
truth lanes to obtain noisy lane anchors, and the model learns to refine the
noisy lane anchors in a progressive way to obtain the target lanes. Secondly,
we propose a hybrid decoding strategy to address the poor feature
representation of the encoder, resulting from the noisy lane anchors.
Specifically, we design a hybrid diffusion decoder to combine global-level and
local-level decoders for high-quality lane anchors. Then, to improve the
feature representation of the encoder, we employ an auxiliary head in the
training stage to adopt the learnable lane anchors for enriching the
supervision on the encoder. Experimental results on four benchmarks, Carlane,
Tusimple, CULane, and LLAMAS, show that DiffusionLane possesses a strong
generalization ability and promising detection performance compared to the
previous state-of-the-art methods. For example, DiffusionLane with ResNet18
surpasses the existing methods by at least 1\% accuracy on the domain
adaptation dataset Carlane. Besides, DiffusionLane with MobileNetV4 gets
81.32\% F1 score on CULane, 96.89\% accuracy on Tusimple with ResNet34, and
97.59\% F1 score on LLAMAS with ResNet101. Code will be available at
https://github.com/zkyntu/UnLanedet.
The Best of N Worlds: Aligning Reinforcement Learning with Best-of-N
  Sampling via max@k Optimisation
Authors: name: Farid Bagirov, name: Mikhail Arkhipov, name: Ksenia Sycheva, name: Evgeniy Glukhov, name: Egor Bogomolov
Published: 2025-10-27 14:47:30

The application of Reinforcement Learning with Verifiable Rewards (RLVR) to
mathematical and coding domains has demonstrated significant improvements in
the reasoning and problem-solving abilities of Large Language Models. Despite
its success in single generation problem solving, the reinforcement learning
fine-tuning process may harm the model's exploration ability, as reflected in
decreased diversity of generations and a resulting degradation of performance
during Best-of-N sampling for large N values. In this work, we focus on
optimizing the max@k metric, a continuous generalization of pass@k. We derive
an unbiased on-policy gradient estimate for direct optimization of this metric.
Furthermore, we extend our derivations to the off-policy updates, a common
element in modern RLVR algorithms, that allows better sample efficiency.
Empirically, we show that our objective effectively optimizes max@k metric in
off-policy scenarios, aligning the model with the Best-of-N inference strategy.
EchoDistill: Bidirectional Concept Distillation for One-Step Diffusion
  Personalization
Authors: name: Yixiong Yang, name: Tao Wu, name: Senmao Li, name: Shiqi Yang, name: Yaxing Wang, name: Joost van de Weijer, name: Kai Wang
Published: 2025-10-23 12:56:33

Recent advances in accelerating text-to-image (T2I) diffusion models have
enabled the synthesis of high-fidelity images even in a single step. However,
personalizing these models to incorporate novel concepts remains a challenge
due to the limited capacity of one-step models to capture new concept
distributions effectively. We propose a bidirectional concept distillation
framework, EchoDistill, to enable one-step diffusion personalization (1-SDP).
Our approach involves an end-to-end training process where a multi-step
diffusion model (teacher) and a one-step diffusion model (student) are trained
simultaneously. The concept is first distilled from the teacher model to the
student, and then echoed back from the student to the teacher. During the
EchoDistill, we share the text encoder between the two models to ensure
consistent semantic understanding. Following this, the student model is
optimized with adversarial losses to align with the real image distribution and
with alignment losses to maintain consistency with the teacher's output.
Furthermore, we introduce the bidirectional echoing refinement strategy,
wherein the student model leverages its faster generation capability to
feedback to the teacher model. This bidirectional concept distillation
mechanism not only enhances the student ability to personalize novel concepts
but also improves the generative quality of the teacher model. Our experiments
demonstrate that this collaborative framework significantly outperforms
existing personalization methods over the 1-SDP setup, establishing a novel
paradigm for rapid and effective personalization in T2I diffusion models.
Mitigating Attention Sinks and Massive Activations in Audio-Visual
  Speech Recognition with LLMS
Authors: name: Anand, name: Umberto Cappellazzo, name: Stavros Petridis, name: Maja Pantic
Published: 2025-10-26 09:44:20

Large language models (LLMs) have recently advanced auditory speech
recognition (ASR), visual speech recognition (VSR), and audio-visual speech
recognition (AVSR). However, understanding of their internal dynamics under
fine-tuning remains limited. In natural language processing, recent work has
revealed attention sinks, tokens that attract disproportionately high
attention, and associated massive activations in which some features of sink
tokens exhibit huge activation in LLMs. In this work, we are the first to study
these phenomena in multimodal speech recognition. Through a detailed analysis
of audio-visual LLMs, we identify attention sinks and massive activations not
only at the BOS token but also at intermediate low-semantic tokens across ASR,
VSR, and AVSR. We show that massive activations originate in the MLP layers and
correspond to fixed feature indices across all sink tokens. We further show
that intermediate sink tokens exhibit high cosine similarity to the BOS token,
thereby amplifying attention and activation. Building on these insights, we
introduce a simple decorrelation loss that reduces cosine similarity between
BOS and other tokens, effectively mitigating intermediate sinks and massive
activations. Furthermore, our method improves word error rate (WER) under high
audio-visual feature downsampling while remaining stable at lower downsampling
rates.
Memory-based Language Models: An Efficient, Explainable, and
  Eco-friendly Approach to Large Language Modeling
Authors: name: Antal van den Bosch, name: Ainhoa Risco Patón, name: Teun Buijse, name: Peter Berck, name: Maarten van Gompel
Published: 2025-10-25 14:34:18

We present memory-based language modeling as an efficient, eco-friendly
alternative to deep neural network-based language modeling. It offers
log-linearly scalable next-token prediction performance and strong memorization
capabilities. Implementing fast approximations of k-nearest neighbor
classification, memory-based language modeling leaves a relatively small
ecological footprint both in training and in inference mode, as it relies fully
on CPUs and attains low token latencies. Its internal workings are simple and
fully transparent. We compare our implementation of memory-based language
modeling, OLIFANT, with GPT-2 and GPT-Neo on next-token prediction accuracy,
estimated emissions and speeds, and offer some deeper analyses of the model.
A Survey of Data Agents: Emerging Paradigm or Overstated Hype?
Authors: name: Yizhang Zhu, name: Liangwei Wang, name: Chenyu Yang, name: Xiaotian Lin, name: Boyan Li, name: Wei Zhou, name: Xinyu Liu, name: Zhangyang Peng, name: Tianqi Luo, name: Yu Li, name: Chengliang Chai, name: Chong Chen, name: Shimin Di, name: Ju Fan, name: Ji Sun, name: Nan Tang, name: Fugee Tsung, name: Jiannan Wang, name: Chenglin Wu, name: Yanwei Xu, name: Shaolei Zhang, name: Yong Zhang, name: Xuanhe Zhou, name: Guoliang Li, name: Yuyu Luo
Published: 2025-10-27 17:54:07

The rapid advancement of large language models (LLMs) has spurred the
emergence of data agents--autonomous systems designed to orchestrate Data + AI
ecosystems for tackling complex data-related tasks. However, the term "data
agent" currently suffers from terminological ambiguity and inconsistent
adoption, conflating simple query responders with sophisticated autonomous
architectures. This terminological ambiguity fosters mismatched user
expectations, accountability challenges, and barriers to industry growth.
Inspired by the SAE J3016 standard for driving automation, this survey
introduces the first systematic hierarchical taxonomy for data agents,
comprising six levels that delineate and trace progressive shifts in autonomy,
from manual operations (L0) to a vision of generative, fully autonomous data
agents (L5), thereby clarifying capability boundaries and responsibility
allocation. Through this lens, we offer a structured review of existing
research arranged by increasing autonomy, encompassing specialized data agents
for data management, preparation, and analysis, alongside emerging efforts
toward versatile, comprehensive systems with enhanced autonomy. We further
analyze critical evolutionary leaps and technical gaps for advancing data
agents, especially the ongoing L2-to-L3 transition, where data agents evolve
from procedural execution to autonomous orchestration. Finally, we conclude
with a forward-looking roadmap, envisioning the advent of proactive, generative
data agents.
Code Aesthetics with Agentic Reward Feedback
Authors: name: Bang Xiao, name: Lingjie Jiang, name: Shaohan Huang, name: Tengchao Lv, name: Yupan Huang, name: Xun Wu, name: Lei Cui, name: Furu Wei
Published: 2025-10-27 12:32:33

Large Language Models (LLMs) have become valuable assistants for developers
in code-related tasks. While LLMs excel at traditional programming tasks such
as code generation and bug fixing, they struggle with visually-oriented coding
tasks, often producing suboptimal aesthetics. In this paper, we introduce a new
pipeline to enhance the aesthetic quality of LLM-generated code. We first
construct AesCode-358K, a large-scale instruction-tuning dataset focused on
code aesthetics. Next, we propose agentic reward feedback, a multi-agent system
that evaluates executability, static aesthetics, and interactive aesthetics.
Building on this, we develop GRPO-AR, which integrates these signals into the
GRPO algorithm for joint optimization of functionality and code aesthetics.
Finally, we develop OpenDesign, a benchmark for assessing code aesthetics.
Experimental results show that combining supervised fine-tuning on AesCode-358K
with reinforcement learning using agentic reward feedback significantly
improves performance on OpenDesign and also enhances results on existing
benchmarks such as PandasPlotBench. Notably, our AesCoder-4B surpasses GPT-4o
and GPT-4.1, and achieves performance comparable to large open-source models
with 480B-685B parameters, underscoring the effectiveness of our approach.
VoMP: Predicting Volumetric Mechanical Property Fields
Authors: name: Rishit Dagli, name: Donglai Xiang, name: Vismay Modi, name: Charles Loop, name: Clement Fuji Tsang, name: Anka He Chen, name: Anita Hu, name: Gavriel State, name: David I. W. Levin, name: Maria Shugrina
Published: 2025-10-27 03:56:25

Physical simulation relies on spatially-varying mechanical properties, often
laboriously hand-crafted. VoMP is a feed-forward method trained to predict
Young's modulus (E), Poisson's ratio (nu), and density (rho) throughout
the volume of 3D objects, in any representation that can be rendered and
voxelized. VoMP aggregates per-voxel multi-view features and passes them to our
trained Geometry Transformer to predict per-voxel material latent codes. These
latents reside on a manifold of physically plausible materials, which we learn
from a real-world dataset, guaranteeing the validity of decoded per-voxel
materials. To obtain object-level training data, we propose an annotation
pipeline combining knowledge from segmented 3D datasets, material databases,
and a vision-language model, along with a new benchmark. Experiments show that
VoMP estimates accurate volumetric properties, far outperforming prior art in
accuracy and speed.
Track, Inpaint, Resplat: Subject-driven 3D and 4D Generation with
  Progressive Texture Infilling
Authors: name: Shuhong Zheng, name: Ashkan Mirzaei, name: Igor Gilitschenski
Published: 2025-10-27 17:59:51

Current 3D/4D generation methods are usually optimized for photorealism,
efficiency, and aesthetics. However, they often fail to preserve the semantic
identity of the subject across different viewpoints. Adapting generation
methods with one or few images of a specific subject (also known as
Personalization or Subject-driven generation) allows generating visual content
that align with the identity of the subject. However, personalized 3D/4D
generation is still largely underexplored. In this work, we introduce TIRE
(Track, Inpaint, REsplat), a novel method for subject-driven 3D/4D generation.
It takes an initial 3D asset produced by an existing 3D generative model as
input and uses video tracking to identify the regions that need to be modified.
Then, we adopt a subject-driven 2D inpainting model for progressively infilling
the identified regions. Finally, we resplat the modified 2D multi-view
observations back to 3D while still maintaining consistency. Extensive
experiments demonstrate that our approach significantly improves identity
preservation in 3D/4D generation compared to state-of-the-art methods. Our
project website is available at
https://zsh2000.github.io/track-inpaint-resplat.github.io/.
Lookahead Anchoring: Preserving Character Identity in Audio-Driven Human
  Animation
Authors: name: Junyoung Seo, name: Rodrigo Mira, name: Alexandros Haliassos, name: Stella Bounareli, name: Honglie Chen, name: Linh Tran, name: Seungryong Kim, name: Zoe Landgraf, name: Jie Shen
Published: 2025-10-27 17:50:19

Audio-driven human animation models often suffer from identity drift during
temporal autoregressive generation, where characters gradually lose their
identity over time. One solution is to generate keyframes as intermediate
temporal anchors that prevent degradation, but this requires an additional
keyframe generation stage and can restrict natural motion dynamics. To address
this, we propose Lookahead Anchoring, which leverages keyframes from future
timesteps ahead of the current generation window, rather than within it. This
transforms keyframes from fixed boundaries into directional beacons: the model
continuously pursues these future anchors while responding to immediate audio
cues, maintaining consistent identity through persistent guidance. This also
enables self-keyframing, where the reference image serves as the lookahead
target, eliminating the need for keyframe generation entirely. We find that the
temporal lookahead distance naturally controls the balance between expressivity
and consistency: larger distances allow for greater motion freedom, while
smaller ones strengthen identity adherence. When applied to three recent human
animation models, Lookahead Anchoring achieves superior lip synchronization,
identity preservation, and visual quality, demonstrating improved temporal
conditioning across several different architectures. Video results are
available at the following link: https://lookahead-anchoring.github.io.
ReCode: Unify Plan and Action for Universal Granularity Control
Authors: name: Zhaoyang Yu, name: Jiayi Zhang, name: Huixue Su, name: Yufan Zhao, name: Yifan Wu, name: Mingyi Deng, name: Jinyu Xiang, name: Yizhang Lin, name: Lingxiao Tang, name: Yingchao Li, name: Yuyu Luo, name: Bang Liu, name: Chenglin Wu
Published: 2025-10-27 17:35:15

Real-world tasks require decisions at varying granularities, and humans excel
at this by leveraging a unified cognitive representation where planning is
fundamentally understood as a high-level form of action. However, current Large
Language Model (LLM)-based agents lack this crucial capability to operate
fluidly across decision granularities. This limitation stems from existing
paradigms that enforce a rigid separation between high-level planning and
low-level action, which impairs dynamic adaptability and limits generalization.
We propose ReCode (Recursive Code Generation), a novel paradigm that addresses
this limitation by unifying planning and action within a single code
representation. In this representation, ReCode treats high-level plans as
abstract placeholder functions, which the agent then recursively decomposes
into finer-grained sub-functions until reaching primitive actions. This
recursive approach dissolves the rigid boundary between plan and action,
enabling the agent to dynamically control its decision granularity.
Furthermore, the recursive structure inherently generates rich,
multi-granularity training data, enabling models to learn hierarchical
decision-making processes. Extensive experiments show ReCode significantly
surpasses advanced baselines in inference performance and demonstrates
exceptional data efficiency in training, validating our core insight that
unifying planning and action through recursive code generation is a powerful
and effective approach to achieving universal granularity control. The code is
available at https://github.com/FoundationAgents/ReCode.
IGGT: Instance-Grounded Geometry Transformer for Semantic 3D
  Reconstruction
Authors: name: Hao Li, name: Zhengyu Zou, name: Fangfu Liu, name: Xuanyang Zhang, name: Fangzhou Hong, name: Yukang Cao, name: Yushi Lan, name: Manyuan Zhang, name: Gang Yu, name: Dingwen Zhang, name: Ziwei Liu
Published: 2025-10-26 14:57:44

Humans naturally perceive the geometric structure and semantic content of a
3D world as intertwined dimensions, enabling coherent and accurate
understanding of complex scenes. However, most prior approaches prioritize
training large geometry models for low-level 3D reconstruction and treat
high-level spatial understanding in isolation, overlooking the crucial
interplay between these two fundamental aspects of 3D-scene analysis, thereby
limiting generalization and leading to poor performance in downstream 3D
understanding tasks. Recent attempts have mitigated this issue by simply
aligning 3D models with specific language models, thus restricting perception
to the aligned model's capacity and limiting adaptability to downstream tasks.
In this paper, we propose InstanceGrounded Geometry Transformer (IGGT), an
end-to-end large unified transformer to unify the knowledge for both spatial
reconstruction and instance-level contextual understanding. Specifically, we
design a 3D-Consistent Contrastive Learning strategy that guides IGGT to encode
a unified representation with geometric structures and instance-grounded
clustering through only 2D visual inputs. This representation supports
consistent lifting of 2D visual inputs into a coherent 3D scene with explicitly
distinct object instances. To facilitate this task, we further construct
InsScene-15K, a large-scale dataset with high-quality RGB images, poses, depth
maps, and 3D-consistent instance-level mask annotations with a novel data
curation pipeline.