Orionnnn/hub-stats huggingface dataset:
DC-Gen: Post-Training Diffusion Acceleration with Deeply Compressed
  Latent Space
Authors: name: Wenkun He, name: Yuchao Gu, name: Junyu Chen, name: Dongyun Zou, name: Yujun Lin, name: Zhekai Zhang, name: Haocheng Xi, name: Muyang Li, name: Ligeng Zhu, name: Jincheng Yu, name: Junsong Chen, name: Enze Xie, name: Song Han, name: Han Cai
Published: 2025-09-29 17:59:25

Existing text-to-image diffusion models excel at generating high-quality
images, but face significant efficiency challenges when scaled to high
resolutions, like 4K image generation. While previous research accelerates
diffusion models in various aspects, it seldom handles the inherent redundancy
within the latent space. To bridge this gap, this paper introduces DC-Gen, a
general framework that accelerates text-to-image diffusion models by leveraging
a deeply compressed latent space. Rather than a costly training-from-scratch
approach, DC-Gen uses an efficient post-training pipeline to preserve the
quality of the base model. A key challenge in this paradigm is the
representation gap between the base model's latent space and a deeply
compressed latent space, which can lead to instability during direct
fine-tuning. To overcome this, DC-Gen first bridges the representation gap with
a lightweight embedding alignment training. Once the latent embeddings are
aligned, only a small amount of LoRA fine-tuning is needed to unlock the base
model's inherent generation quality. We verify DC-Gen's effectiveness on SANA
and FLUX.1-Krea. The resulting DC-Gen-SANA and DC-Gen-FLUX models achieve
quality comparable to their base models but with a significant speedup.
Specifically, DC-Gen-FLUX reduces the latency of 4K image generation by 53x on
the NVIDIA H100 GPU. When combined with NVFP4 SVDQuant, DC-Gen-FLUX generates a
4K image in just 3.5 seconds on a single NVIDIA 5090 GPU, achieving a total
latency reduction of 138x compared to the base FLUX.1-Krea model. Code:
https://github.com/dc-ai-projects/DC-Gen.
Where LLM Agents Fail and How They can Learn From Failures
Authors: name: Kunlun Zhu, name: Zijia Liu, name: Bingxuan Li, name: Muxin Tian, name: Yingxuan Yang, name: Jiaxun Zhang, name: Pengrui Han, name: Qipeng Xie, name: Fuyang Cui, name: Weijia Zhang, name: Xiaoteng Ma, name: Xiaodong Yu, name: Gowtham Ramesh, name: Jialian Wu, name: Zicheng Liu, name: Pan Lu, name: James Zou, name: Jiaxuan You
Published: 2025-09-29 18:20:27

Large Language Model (LLM) agents, which integrate planning, memory,
reflection, and tool-use modules, have shown promise in solving complex,
multi-step tasks. Yet their sophisticated architectures amplify vulnerability
to cascading failures, where a single root-cause error propagates through
subsequent decisions, leading to task failure. Current systems lack a framework
that can comprehensively understand agent error in a modular and systemic way,
and therefore fail to detect these errors accordingly. We address this gap with
three contributions. First, we introduce the AgentErrorTaxonomy, a modular
classification of failure modes spanning memory, reflection, planning, action,
and system-level operations. Second, we construct AgentErrorBench, the first
dataset of systematically annotated failure trajectories from ALFWorld, GAIA,
and WebShop, grounding error analysis in real-world agent rollouts. Third, we
propose AgentDebug, a debugging framework that isolates root-cause failures and
provides corrective feedback, enabling agents to recover and iteratively
improve. Experiments on AgentErrorBench show that AgentDebug achieves 24%
higher all-correct accuracy and 17% higher step accuracy compared to the
strongest baseline. Beyond detection, the targeted feedback generated by
AgentDebug enables LLM agents to iteratively recover from failures, yielding up
to 26% relative improvements in task success across ALFWorld, GAIA, and
WebShop. These results establish principled debugging as a pathway to more
reliable and adaptive LLM agents. The code and data will be available at
https://github.com/ulab-uiuc/AgentDebug
Scalable GANs with Transformers
Authors: name: Sangeek Hyun, name: MinKyu Lee, name: Jae-Pil Heo
Published: 2025-09-29 15:36:15

Scalability has driven recent advances in generative modeling, yet its
principles remain underexplored for adversarial learning. We investigate the
scalability of Generative Adversarial Networks (GANs) through two design
choices that have proven to be effective in other types of generative models:
training in a compact Variational Autoencoder latent space and adopting purely
transformer-based generators and discriminators. Training in latent space
enables efficient computation while preserving perceptual fidelity, and this
efficiency pairs naturally with plain transformers, whose performance scales
with computational budget. Building on these choices, we analyze failure modes
that emerge when naively scaling GANs. Specifically, we find issues as
underutilization of early layers in the generator and optimization instability
as the network scales. Accordingly, we provide simple and scale-friendly
solutions as lightweight intermediate supervision and width-aware learning-rate
adjustment. Our experiments show that GAT, a purely transformer-based and
latent-space GANs, can be easily trained reliably across a wide range of
capacities (S through XL). Moreover, GAT-XL/2 achieves state-of-the-art
single-step, class-conditional generation performance (FID of 2.96) on
ImageNet-256 in just 40 epochs, 6x fewer epochs than strong baselines.
DepthLM: Metric Depth From Vision Language Models
Authors: name: Zhipeng Cai, name: Ching-Feng Yeh, name: Hu Xu, name: Zhuang Liu, name: Gregory Meyer, name: Xinjie Lei, name: Changsheng Zhao, name: Shang-Wen Li, name: Vikas Chandra, name: Yangyang Shi
Published: 2025-09-29 19:12:13

Vision language models (VLMs) can flexibly address various vision tasks
through text interactions. Although successful in semantic understanding,
state-of-the-art VLMs including GPT-5 still struggle in understanding 3D from
2D inputs. On the other hand, expert pure vision models achieve super-human
accuracy in metric depth estimation, a key 3D understanding task. However, they
require task-specific architectures and losses. Such difference motivates us to
ask: Can VLMs reach expert-level accuracy without architecture or loss change?
We take per-pixel metric depth estimation as the representative task and show
that the answer is yes! Surprisingly, comprehensive analysis shows that
text-based supervised-finetuning with sparse labels is sufficient for VLMs to
unlock strong 3D understanding, no dense prediction head or complex
regression/regularization loss is needed. The bottleneck for VLMs lies actually
in pixel reference and cross-dataset camera ambiguity, which we address through
visual prompting and intrinsic-conditioned augmentation. With much smaller
models, our method DepthLM surpasses the accuracy of most advanced VLMs by over
2x, making VLMs for the first time comparable with pure vision models.
Interestingly, without explicit enforcement during training, VLMs trained with
DepthLM naturally avoids over-smoothing, having much fewer flying points at
boundary regions than pure vision models. The simplicity of DepthLM also
enables a single VLM to cover various 3D tasks beyond metric depth. Our code
and model will be released at the link below.
TR2-D2: Tree Search Guided Trajectory-Aware Fine-Tuning for Discrete
  Diffusion
Authors: name: Sophia Tang, name: Yuchen Zhu, name: Molei Tao, name: Pranam Chatterjee
Published: 2025-09-29 17:58:45

Reinforcement learning with stochastic optimal control offers a promising
framework for diffusion fine-tuning, where a pre-trained diffusion model is
optimized to generate paths that lead to a reward-tilted distribution. While
these approaches enable optimization without access to explicit samples from
the optimal distribution, they require training on rollouts under the current
fine-tuned model, making them susceptible to reinforcing sub-optimal
trajectories that yield poor rewards. To overcome this challenge, we introduce
TRee Search Guided TRajectory-Aware Fine-Tuning for Discrete Diffusion
(TR2-D2), a novel framework that optimizes reward-guided discrete diffusion
trajectories with tree search to construct replay buffers for trajectory-aware
fine-tuning. These buffers are generated using Monte Carlo Tree Search (MCTS)
and subsequently used to fine-tune a pre-trained discrete diffusion model under
a stochastic optimal control objective. We validate our framework on single-
and multi-objective fine-tuning of biological sequence diffusion models,
highlighting the overall effectiveness of TR2-D2 for reliable reward-guided
fine-tuning in discrete sequence generation.
The Photographer Eye: Teaching Multimodal Large Language Models to See
  and Critique like Photographers
Authors: name: Daiqing Qi, name: Handong Zhao, name: Jing Shi, name: Simon Jenni, name: Yifei Fan, name: Franck Dernoncourt, name: Scott Cohen, name: Sheng Li
Published: 2025-09-23 02:59:41

While editing directly from life, photographers have found it too difficult
to see simultaneously both the blue and the sky. Photographer and curator,
Szarkowski insightfully revealed one of the notable gaps between general and
aesthetic visual understanding: while the former focuses on identifying the
factual element in an image (sky), the latter transcends such object
identification, viewing it instead as an aesthetic component--a pure color
block (blue). Such fundamental distinctions between general (detection,
localization, etc.) and aesthetic (color, lighting, composition, etc.) visual
understanding present a significant challenge for Multimodal Large Language
Models (MLLMs). Although some recent works have made initial explorations, they
are often limited to general and basic aesthetic commonsense. As a result, they
frequently fall short in real-world scenarios (Fig. 1), which require extensive
expertise--including photographic techniques, photo pre/post-processing
knowledge, and more, to provide a detailed analysis and description. To
fundamentally enhance the aesthetics understanding of MLLMs, we first introduce
a novel dataset, PhotoCritique, derived from extensive discussions among
professional photographers and enthusiasts, and characterized by the large
scale, expertise, and diversity. Then, to better learn visual aesthetics from
PhotoCritique, we furthur propose a novel model, PhotoEye, featuring a
languageguided multi-view vision fusion mechanism to understand image
aesthetics from multiple perspectives. Finally, we present a novel benchmark,
PhotoBench, a comprehensive and professional benchmark for aesthetic visual
understanding. On existing benchmarks and PhotoBench, our model demonstrates
clear advantages over existing models.
Rethinking JEPA: Compute-Efficient Video SSL with Frozen Teachers
Authors: name: Xianhang Li, name: Chen Huang, name: Chun-Liang Li, name: Eran Malach, name: Josh Susskind, name: Vimal Thilak, name: Etai Littwin
Published: 2025-09-29 05:55:17

Video Joint Embedding Predictive Architectures (V-JEPA) learn generalizable
off-the-shelf video representation by predicting masked regions in latent space
with an exponential moving average (EMA)-updated teacher. While EMA prevents
representation collapse, it complicates scalable model selection and couples
teacher and student architectures. We revisit masked-latent prediction and show
that a frozen teacher suffices. Concretely, we (i) train a target encoder with
a simple pixel-reconstruction objective under V-JEPA masking, then (ii) freeze
it and train a student to predict the teacher's latents on masked regions. This
leads to a two-stage, unregularized scheme that we refer to as SALT
(Static-teacher Asymmetric Latent Training). SALT decouples optimization into
pixel reconstruction (teacher) and masked latent prediction (student),
increasing transparency, efficiency, and scalability while preserving the
ability of representation to generalize under frozen evaluation. Empirically,
our student models outperform recently proposed V-JEPA 2 encoders under frozen
backbone evaluation across diverse benchmarks. They are also more
compute-optimal: at matched pretraining FLOPs, our method achieves higher
probing accuracy, and its scaling curves dominate V-JEPA's accuracy-FLOPs
Pareto frontier. Finally, we find that student quality is remarkably robust to
teacher quality: high-performing students emerge even with small, sub-optimal
teachers. This points to a compute budget allocation that should overwhelmingly
favor the student. These results position SALT as a simple, scalable, and
compute-efficient alternative to EMA-based self-distillation for video
representation learning.
ADAM: A Diverse Archive of Mankind for Evaluating and Enhancing LLMs in
  Biographical Reasoning
Authors: name: Jasin Cekinmez, name: Omid Ghahroodi, name: Saad Fowad Chandle, name: Dhiman Gupta, name: Ehsaneddin Asgari
Published: 2025-09-26 23:04:28

We introduce ADAM (A Diverse Archive of Mankind), a framework for evaluating
and improving multimodal large language models (MLLMs) in biographical
reasoning. To the best of our knowledge, this is the first work to
systematically examine LLM capabilities in biography, a critical yet
underexplored dimension of factual knowledge. At its core, AdamDB is a
multilingual and multimodal dataset covering over 4 million individuals across
geography, time, and profession, while AdamBench provides cognitively
structured evaluations based on Bloom's taxonomy, spanning six reasoning levels
in both English and native languages. To address hallucinations, particularly
for lesser-known individuals, we propose AdamRAG, a retrieval-augmented
generation system tailored to biographical contexts. Experiments show that
AdamRAG substantially improves open-source models and modestly benefits
closed-source ones, with the largest gains on lower-order reasoning. Popularity
strongly mediates accuracy, and multimodal input via face images offers
smaller, less consistent improvements than retrieval. ADAM establishes the
first benchmark and framework for cognitively, culturally, and multimodally
grounded biographical evaluation, advancing the development of multilingual,
accurate, and hallucination-resistant MLLMs.
Advantage Weighted Matching: Aligning RL with Pretraining in Diffusion
  Models
Authors: name: Shuchen Xue, name: Chongjian Ge, name: Shilong Zhang, name: Yichen Li, name: Zhi-Ming Ma
Published: 2025-09-29 17:02:20

Reinforcement Learning (RL) has emerged as a central paradigm for advancing
Large Language Models (LLMs), where pre-training and RL post-training share the
same log-likelihood formulation. In contrast, recent RL approaches for
diffusion models, most notably Denoising Diffusion Policy Optimization (DDPO),
optimize an objective different from the pretraining objectives--score/flow
matching loss. In this work, we establish a novel theoretical analysis: DDPO is
an implicit form of score/flow matching with noisy targets, which increases
variance and slows convergence. Building on this analysis, we introduce
Advantage Weighted Matching (AWM), a policy-gradient method for
diffusion. It uses the same score/flow-matching loss as pretraining to obtain a
lower-variance objective and reweights each sample by its advantage. In effect,
AWM raises the influence of high-reward samples and suppresses low-reward ones
while keeping the modeling objective identical to pretraining. This unifies
pretraining and RL conceptually and practically, is consistent with
policy-gradient theory, reduces variance, and yields faster convergence. This
simple yet effective design yields substantial benefits: on GenEval, OCR, and
PickScore benchmarks, AWM delivers up to a 24times speedup over Flow-GRPO
(which builds on DDPO), when applied to Stable Diffusion 3.5 Medium and FLUX,
without compromising generation quality. Code is available at
https://github.com/scxue/advantage_weighted_matching.
Understanding Language Prior of LVLMs by Contrasting Chain-of-Embedding
Authors: name: Lin Long, name: Changdae Oh, name: Seongheon Park, name: Yixuan Li
Published: 2025-09-27 02:12:05

Large vision-language models (LVLMs) achieve strong performance on multimodal
tasks, yet they often default to their language prior (LP) -- memorized textual
patterns from pre-training while under-utilizing visual evidence. Prior
analyses of LP mostly rely on input-output probing, which fails to reveal the
internal mechanisms governing when and how vision influences model behavior. To
address this gap, we present the first systematic analysis of language prior
through the lens of chain-of-embedding, which examines the layer-wise
representation dynamics within LVLMs. Our analysis reveals a universal
phenomenon: each model exhibits a Visual Integration Point (VIP), a critical
layer at which visual information begins to meaningfully reshape hidden
representations and influence decoding. Building on this observation, we
introduce the Total Visual Integration (TVI) estimator, which aggregates
representation distance beyond the VIP to quantify how strongly visual query
influences response generation. Across 54 model-dataset combinations spanning 9
contemporary LVLMs and 6 benchmarks, we demonstrate that VIP consistently
emerges, and that TVI reliably predicts the strength of language prior. This
offers a principled toolkit for diagnosing and understanding language prior in
LVLMs.
ThermalGen: Style-Disentangled Flow-Based Generative Models for
  RGB-to-Thermal Image Translation
Authors: name: Jiuhong Xiao, name: Roshan Nayak, name: Ning Zhang, name: Daniel Tortei, name: Giuseppe Loianno
Published: 2025-09-29 14:55:51

Paired RGB-thermal data is crucial for visual-thermal sensor fusion and
cross-modality tasks, including important applications such as multi-modal
image alignment and retrieval. However, the scarcity of synchronized and
calibrated RGB-thermal image pairs presents a major obstacle to progress in
these areas. To overcome this challenge, RGB-to-Thermal (RGB-T) image
translation has emerged as a promising solution, enabling the synthesis of
thermal images from abundant RGB datasets for training purposes. In this study,
we propose ThermalGen, an adaptive flow-based generative model for RGB-T image
translation, incorporating an RGB image conditioning architecture and a
style-disentangled mechanism. To support large-scale training, we curated eight
public satellite-aerial, aerial, and ground RGB-T paired datasets, and
introduced three new large-scale satellite-aerial RGB-T datasets--DJI-day,
Bosonplus-day, and Bosonplus-night--captured across diverse times, sensor
types, and geographic regions. Extensive evaluations across multiple RGB-T
benchmarks demonstrate that ThermalGen achieves comparable or superior
translation performance compared to existing GAN-based and diffusion-based
methods. To our knowledge, ThermalGen is the first RGB-T image translation
model capable of synthesizing thermal images that reflect significant
variations in viewpoints, sensor characteristics, and environmental conditions.
Project page: http://xjh19971.github.io/ThermalGen
TENET: Leveraging Tests Beyond Validation for Code Generation
Authors: name: Yiran Hu, name: Nan Jiang, name: Shanchao Liang, name: Yi Wu, name: Lin Tan
Published: 2025-09-29 00:53:16

Test-Driven Development (TDD) is a widely adopted software engineering
practice that requires developers to create and execute tests alongside code
implementation, ensuring that software behavior is continuously validated and
refined. In the era of vibe coding, where developers increasingly delegate code
writing to large language models (LLMs) by specifying high-level intentions,
TDD becomes even more crucial, as test cases serve as executable specifications
that explicitly define and verify intended functionality beyond what
natural-language descriptions and code context can convey. While vibe coding
under TDD is promising, there are three main challenges: (1) selecting a small
yet effective test suite to improve the generation accuracy and control the
execution workload, (2) retrieving context such as relevant code effectively,
and (3) systematically using test feedback for effective code refinement. To
address these challenges, we introduce TENET, an LLM agent for generating
functions in complex real-world repositories under the TDD setting. TENET
features three components: (1) a novel test harness mechanism that selects a
concise test suite to maximize diversity of target usage scenarios; (2) a
tailored agent toolset that performs efficient retrieval of relevant code with
interactive debugging; and (3) a reflection-based refinement workflow that
iteratively analyzes failures, replenishes context, and applies code
refinement. TENET achieves 69.08% and 81.77% Pass@1 on RepoCod and RepoEval
benchmarks, outperforming the best agentic baselines by 9.49 and 2.17
percentage points, respectively. In addition, this is the first study of
test-driven code generation with repository-level context, examining how
different aspects of test suites affect the performance of LLM agents under the
TDD setting.
Combinatorial Creativity: A New Frontier in Generalization Abilities
Authors: name: Samuel Schapiro, name: Sumuk Shashidhar, name: Alexi Gladstone, name: Jonah Black, name: Royce Moon, name: Dilek Hakkani-Tur, name: Lav R. Varshney
Published: 2025-09-25 11:48:37

Artificial intelligence (AI) systems, and Large Language Models (LLMs) in
particular, are increasingly employed for creative tasks like scientific idea
generation, constituting a form of generalization from training data
unaddressed by existing conceptual frameworks. Despite its similarities to
compositional generalization (CG), combinatorial creativity (CC) is an
open-ended ability. Instead of evaluating for accuracy or correctness against
fixed targets, which would contradict the open-ended nature of CC, we propose a
theoretical framework and algorithmic task for evaluating outputs by their
degrees of novelty and utility. From here, we make several important empirical
contributions: (1) We obtain the first insights into the scaling behavior of
creativity for LLMs. (2) We discover that, for fixed compute budgets, there
exist optimal model depths and widths for creative ability. (3) We find that
the ideation-execution gap, whereby LLMs excel at generating novel scientific
ideas but struggle to ensure their practical feasibility, may be explained by a
more fundamental novelty-utility tradeoff characteristic of creativity
algorithms in general. Importantly, this tradeoff remains persistent even at
scale, casting doubt on the long-term creative potential of LLMs in their
current form. Together, our conceptual framework and empirical findings provide
a foundation for understanding and improving creativity in modern AI models,
bridging the gap between human and machine intelligence.