date stringdate 2023-05-04 00:00:00 2025-08-27 00:00:00 | arxiv_id stringlengths 10 10 | votes int32 0 110M | title stringlengths 8 206 | abstract stringlengths 165 1.92k | url stringlengths 40 40 |
|---|---|---|---|---|---|
2025-08-27 | 2508.19242 | 7 | Autoregressive Universal Video Segmentation Model | Recent video foundation models such as SAM2 excel at prompted video
segmentation by treating masks as a general-purpose primitive. However, many
real-world settings require unprompted segmentation that aims to detect and
track all objects in a video without external cues, leaving today's landscape
fragmented across task-specific models and pipelines. We recast streaming video
segmentation as sequential mask prediction, analogous to language modeling, and
introduce the Autoregressive Universal Segmentation Model (AUSM), a single
architecture that unifies both prompted and unprompted video segmentation.
Built on recent state-space models, AUSM maintains a fixed-size spatial state
and scales to video streams of arbitrary length. Furthermore, all components of
AUSM are designed for parallel training across frames, yielding substantial
speedups over iterative training. On standard benchmarks (DAVIS17, YouTube-VOS
2018 & 2019, MOSE, YouTube-VIS 2019 & 2021, and OVIS) AUSM outperforms prior
universal streaming video segmentation methods and achieves up to 2.5x faster
training on 16-frame sequences. | https://huggingface.co/papers/2508.19242 |
2025-08-27 | 2508.18621 | 6 | Wan-S2V: Audio-Driven Cinematic Video Generation | Current state-of-the-art (SOTA) methods for audio-driven character animation
demonstrate promising performance for scenarios primarily involving speech and
singing. However, they often fall short in more complex film and television
productions, which demand sophisticated elements such as nuanced character
interactions, realistic body movements, and dynamic camera work. To address
this long-standing challenge of achieving film-level character animation, we
propose an audio-driven model, which we refere to as Wan-S2V, built upon Wan.
Our model achieves significantly enhanced expressiveness and fidelity in
cinematic contexts compared to existing approaches. We conducted extensive
experiments, benchmarking our method against cutting-edge models such as
Hunyuan-Avatar and Omnihuman. The experimental results consistently demonstrate
that our approach significantly outperforms these existing solutions.
Additionally, we explore the versatility of our method through its applications
in long-form video generation and precise video lip-sync editing. | https://huggingface.co/papers/2508.18621 |
2025-08-27 | 2508.15774 | 6 | CineScale: Free Lunch in High-Resolution Cinematic Visual Generation | Visual diffusion models achieve remarkable progress, yet they are typically
trained at limited resolutions due to the lack of high-resolution data and
constrained computation resources, hampering their ability to generate
high-fidelity images or videos at higher resolutions. Recent efforts have
explored tuning-free strategies to exhibit the untapped potential
higher-resolution visual generation of pre-trained models. However, these
methods are still prone to producing low-quality visual content with repetitive
patterns. The key obstacle lies in the inevitable increase in high-frequency
information when the model generates visual content exceeding its training
resolution, leading to undesirable repetitive patterns deriving from the
accumulated errors. In this work, we propose CineScale, a novel inference
paradigm to enable higher-resolution visual generation. To tackle the various
issues introduced by the two types of video generation architectures, we
propose dedicated variants tailored to each. Unlike existing baseline methods
that are confined to high-resolution T2I and T2V generation, CineScale broadens
the scope by enabling high-resolution I2V and V2V synthesis, built atop
state-of-the-art open-source video generation frameworks. Extensive experiments
validate the superiority of our paradigm in extending the capabilities of
higher-resolution visual generation for both image and video models.
Remarkably, our approach enables 8k image generation without any fine-tuning,
and achieves 4k video generation with only minimal LoRA fine-tuning. Generated
video samples are available at our website:
https://eyeline-labs.github.io/CineScale/. | https://huggingface.co/papers/2508.15774 |
2025-08-27 | 2508.19188 | 5 | FastMesh:Efficient Artistic Mesh Generation via Component Decoupling | Recent mesh generation approaches typically tokenize triangle meshes into
sequences of tokens and train autoregressive models to generate these tokens
sequentially. Despite substantial progress, such token sequences inevitably
reuse vertices multiple times to fully represent manifold meshes, as each
vertex is shared by multiple faces. This redundancy leads to excessively long
token sequences and inefficient generation processes. In this paper, we propose
an efficient framework that generates artistic meshes by treating vertices and
faces separately, significantly reducing redundancy. We employ an
autoregressive model solely for vertex generation, decreasing the token count
to approximately 23\% of that required by the most compact existing tokenizer.
Next, we leverage a bidirectional transformer to complete the mesh in a single
step by capturing inter-vertex relationships and constructing the adjacency
matrix that defines the mesh faces. To further improve the generation quality,
we introduce a fidelity enhancer to refine vertex positioning into more natural
arrangements and propose a post-processing framework to remove undesirable edge
connections. Experimental results show that our method achieves more than
8$\times$ faster speed on mesh generation compared to state-of-the-art
approaches, while producing higher mesh quality. | https://huggingface.co/papers/2508.19188 |
2025-08-27 | 2508.15804 | 16,305 | ReportBench: Evaluating Deep Research Agents via Academic Survey Tasks | The advent of Deep Research agents has substantially reduced the time
required for conducting extensive research tasks. However, these tasks
inherently demand rigorous standards of factual accuracy and comprehensiveness,
necessitating thorough evaluation before widespread adoption. In this paper, we
propose ReportBench, a systematic benchmark designed to evaluate the content
quality of research reports generated by large language models (LLMs). Our
evaluation focuses on two critical dimensions: (1) the quality and relevance of
cited literature, and (2) the faithfulness and veracity of the statements
within the generated reports. ReportBench leverages high-quality published
survey papers available on arXiv as gold-standard references, from which we
apply reverse prompt engineering to derive domain-specific prompts and
establish a comprehensive evaluation corpus. Furthermore, we develop an
agent-based automated framework within ReportBench that systematically analyzes
generated reports by extracting citations and statements, checking the
faithfulness of cited content against original sources, and validating
non-cited claims using web-based resources. Empirical evaluations demonstrate
that commercial Deep Research agents such as those developed by OpenAI and
Google consistently generate more comprehensive and reliable reports than
standalone LLMs augmented with search or browsing tools. However, there remains
substantial room for improvement in terms of the breadth and depth of research
coverage, as well as factual consistency. The complete code and data will be
released at the following link: https://github.com/ByteDance-BandAI/ReportBench | https://huggingface.co/papers/2508.15804 |
2025-08-27 | 2508.18773 | 4 | ThinkDial: An Open Recipe for Controlling Reasoning Effort in Large
Language Models | Large language models (LLMs) with chain-of-thought reasoning have
demonstrated remarkable problem-solving capabilities, but controlling their
computational effort remains a significant challenge for practical deployment.
Recent proprietary systems like OpenAI's gpt-oss series have introduced
discrete operational modes for intuitive reasoning control, but the open-source
community has largely failed to achieve such capabilities. In this paper, we
introduce ThinkDial, the first open-recipe end-to-end framework that
successfully implements gpt-oss-style controllable reasoning through discrete
operational modes. Our system enables seamless switching between three distinct
reasoning regimes: High mode (full reasoning capability), Medium mode (50
percent token reduction with <10 percent performance degradation), and Low mode
(75 percent token reduction with <15 percent performance degradation). We
achieve this through an end-to-end training paradigm that integrates
budget-mode control throughout the entire pipeline: budget-mode supervised
fine-tuning that embeds controllable reasoning capabilities directly into the
learning process, and two-phase budget-aware reinforcement learning with
adaptive reward shaping. Extensive experiments demonstrate that ThinkDial
achieves target compression-performance trade-offs with clear response length
reductions while maintaining performance thresholds. The framework also
exhibits strong generalization capabilities on out-of-distribution tasks. | https://huggingface.co/papers/2508.18773 |
2025-08-27 | 2508.19026 | 3 | MovieCORE: COgnitive REasoning in Movies | This paper introduces MovieCORE, a novel video question answering (VQA)
dataset designed to probe deeper cognitive understanding of movie content.
Unlike existing datasets that focus on surface-level comprehension, MovieCORE
emphasizes questions that engage System-2 thinking while remaining specific to
the video material. We present an innovative agentic brainstorming approach,
utilizing multiple large language models (LLMs) as thought agents to generate
and refine high-quality question-answer pairs. To evaluate dataset quality, we
develop a set of cognitive tests assessing depth, thought-provocation
potential, and syntactic complexity. We also propose a comprehensive evaluation
scheme for assessing VQA model performance on deeper cognitive tasks. To
address the limitations of existing video-language models (VLMs), we introduce
an agentic enhancement module, Agentic Choice Enhancement (ACE), which improves
model reasoning capabilities post-training by up to 25%. Our work contributes
to advancing movie understanding in AI systems and provides valuable insights
into the capabilities and limitations of current VQA models when faced with
more challenging, nuanced questions about cinematic content. Our project page,
dataset and code can be found at
https://joslefaure.github.io/assets/html/moviecore.html. | https://huggingface.co/papers/2508.19026 |
2025-08-27 | 2508.18672 | 3,243 | Optimal Sparsity of Mixture-of-Experts Language Models for Reasoning
Tasks | Empirical scaling laws have driven the evolution of large language models
(LLMs), yet their coefficients shift whenever the model architecture or data
pipeline changes. Mixture-of-Experts (MoE) models, now standard in
state-of-the-art systems, introduce a new sparsity dimension that current
dense-model frontiers overlook. We investigate how MoE sparsity influences two
distinct capability regimes: memorization and reasoning. We train families of
MoE Transformers that systematically vary total parameters, active parameters,
and top-k routing while holding the compute budget fixed. For every model we
record pre-training loss, downstream task loss, and task accuracy, allowing us
to separate the train-test generalization gap from the loss-accuracy gap.
Memorization benchmarks improve monotonically with total parameters, mirroring
training loss. By contrast, reasoning performance saturates and can even
regress despite continued gains in both total parameters and training loss.
Altering top-k alone has little effect when active parameters are constant,
and classic hyperparameters such as learning rate and initialization modulate
the generalization gap in the same direction as sparsity. Neither post-training
reinforcement learning (GRPO) nor extra test-time compute rescues the reasoning
deficit of overly sparse models. Our model checkpoints, code and logs are
open-source at https://github.com/rioyokotalab/optimal-sparsity. | https://huggingface.co/papers/2508.18672 |
2025-08-27 | 2508.18370 | 2 | Training Language Model Agents to Find Vulnerabilities with CTF-Dojo | Large language models (LLMs) have demonstrated exceptional capabilities when
trained within executable runtime environments, notably excelling at software
engineering tasks through verified feedback loops. Yet, scalable and
generalizable execution-grounded environments remain scarce, limiting progress
in training more capable ML agents. We introduce CTF-Dojo, the first
large-scale executable runtime tailored for training LLMs with verifiable
feedback, featuring 658 fully functional Capture-The-Flag (CTF)-style
challenges containerized in Docker with guaranteed reproducibility. To enable
rapid scaling without manual intervention, we develop CTF-Forge, an automated
pipeline that transforms publicly available artifacts into ready-to-use
execution environments in minutes, eliminating weeks of expert configuration
traditionally required. We trained LLM-based agents on just 486 high-quality,
execution-verified trajectories from CTF-Dojo, achieving up to 11.6% absolute
gains over strong baselines across three competitive benchmarks: InterCode-CTF,
NYU CTF Bench, and Cybench. Our best-performing 32B model reaches 31.9% Pass@1,
establishing a new open-weight state-of-the-art that rivals frontier models
like DeepSeek-V3-0324 and Gemini-2.5-Flash. By framing CTF-style tasks as a
benchmark for executable-agent learning, CTF-Dojo demonstrates that
execution-grounded training signals are not only effective but pivotal in
advancing high-performance ML agents without dependence on costly proprietary
systems. | https://huggingface.co/papers/2508.18370 |
2025-08-27 | 2508.16697 | 2 | QueryBandits for Hallucination Mitigation: Exploiting Semantic Features
for No-Regret Rewriting | Advanced reasoning capabilities in Large Language Models (LLMs) have caused
higher hallucination prevalence; yet most mitigation work focuses on
after-the-fact filtering rather than shaping the queries that trigger them. We
introduce QueryBandits, a bandit framework that designs rewrite strategies to
maximize a reward model, that encapsulates hallucination propensity based upon
the sensitivities of 17 linguistic features of the input query-and therefore,
proactively steer LLMs away from generating hallucinations. Across 13 diverse
QA benchmarks and 1,050 lexically perturbed queries per dataset, our top
contextual QueryBandit (Thompson Sampling) achieves an 87.5% win rate over a
no-rewrite baseline and also outperforms zero-shot static prompting
("paraphrase" or "expand") by 42.6% and 60.3% respectively. Therefore, we
empirically substantiate the effectiveness of QueryBandits in mitigating
hallucination via the intervention that takes the form of a query rewrite.
Interestingly, certain static prompting strategies, which constitute a
considerable number of current query rewriting literature, have a higher
cumulative regret than the no-rewrite baseline, signifying that static rewrites
can worsen hallucination. Moreover, we discover that the converged per-arm
regression feature weight vectors substantiate that there is no single rewrite
strategy optimal for all queries. In this context, guided rewriting via
exploiting semantic features with QueryBandits can induce significant shifts in
output behavior through forward-pass mechanisms, bypassing the need for
retraining or gradient-based adaptation. | https://huggingface.co/papers/2508.16697 |
2025-08-27 | 2508.18271 | 1 | ObjFiller-3D: Consistent Multi-view 3D Inpainting via Video Diffusion
Models | 3D inpainting often relies on multi-view 2D image inpainting, where the
inherent inconsistencies across different inpainted views can result in blurred
textures, spatial discontinuities, and distracting visual artifacts. These
inconsistencies pose significant challenges when striving for accurate and
realistic 3D object completion, particularly in applications that demand high
fidelity and structural coherence. To overcome these limitations, we propose
ObjFiller-3D, a novel method designed for the completion and editing of
high-quality and consistent 3D objects. Instead of employing a conventional 2D
image inpainting model, our approach leverages a curated selection of
state-of-the-art video editing model to fill in the masked regions of 3D
objects. We analyze the representation gap between 3D and videos, and propose
an adaptation of a video inpainting model for 3D scene inpainting. In addition,
we introduce a reference-based 3D inpainting method to further enhance the
quality of reconstruction. Experiments across diverse datasets show that
compared to previous methods, ObjFiller-3D produces more faithful and
fine-grained reconstructions (PSNR of 26.6 vs. NeRFiller (15.9) and LPIPS of
0.19 vs. Instant3dit (0.25)). Moreover, it demonstrates strong potential for
practical deployment in real-world 3D editing applications. Project page:
https://objfiller3d.github.io/ Code:
https://github.com/objfiller3d/ObjFiller-3D . | https://huggingface.co/papers/2508.18271 |
2025-08-27 | 2508.19202 | 2,020 | Demystifying Scientific Problem-Solving in LLMs by Probing Knowledge and
Reasoning | Scientific problem solving poses unique challenges for LLMs, requiring both
deep domain knowledge and the ability to apply such knowledge through complex
reasoning. While automated scientific reasoners hold great promise for
assisting human scientists, there is currently no widely adopted holistic
benchmark for evaluating scientific reasoning, and few approaches
systematically disentangle the distinct roles of knowledge and reasoning in
these tasks. To address these gaps, we introduce SciReas, a diverse suite of
existing benchmarks for scientific reasoning tasks, and SciReas-Pro, a
selective subset that requires more complex reasoning. Our holistic evaluation
surfaces insights about scientific reasoning performance that remain hidden
when relying on individual benchmarks alone. We then propose KRUX, a probing
framework for studying the distinct roles of reasoning and knowledge in
scientific tasks. Combining the two, we conduct an in-depth analysis that
yields several key findings: (1) Retrieving task-relevant knowledge from model
parameters is a critical bottleneck for LLMs in scientific reasoning; (2)
Reasoning models consistently benefit from external knowledge added in-context
on top of the reasoning enhancement; (3) Enhancing verbalized reasoning
improves LLMs' ability to surface task-relevant knowledge. Finally, we conduct
a lightweight analysis, comparing our science-focused data composition with
concurrent efforts on long CoT SFT, and release SciLit01, a strong 8B baseline
for scientific reasoning. | https://huggingface.co/papers/2508.19202 |
2025-08-27 | 2508.18192 | 11 | Unraveling the cognitive patterns of Large Language Models through
module communities | Large Language Models (LLMs) have reshaped our world with significant
advancements in science, engineering, and society through applications ranging
from scientific discoveries and medical diagnostics to Chatbots. Despite their
ubiquity and utility, the underlying mechanisms of LLM remain concealed within
billions of parameters and complex structures, making their inner architecture
and cognitive processes challenging to comprehend. We address this gap by
adopting approaches to understanding emerging cognition in biology and
developing a network-based framework that links cognitive skills, LLM
architectures, and datasets, ushering in a paradigm shift in foundation model
analysis. The skill distribution in the module communities demonstrates that
while LLMs do not strictly parallel the focalized specialization observed in
specific biological systems, they exhibit unique communities of modules whose
emergent skill patterns partially mirror the distributed yet interconnected
cognitive organization seen in avian and small mammalian brains. Our numerical
results highlight a key divergence from biological systems to LLMs, where skill
acquisition benefits substantially from dynamic, cross-regional interactions
and neural plasticity. By integrating cognitive science principles with machine
learning, our framework provides new insights into LLM interpretability and
suggests that effective fine-tuning strategies should leverage distributed
learning dynamics rather than rigid modular interventions. | https://huggingface.co/papers/2508.18192 |
Subsets and Splits
No community queries yet
The top public SQL queries from the community will appear here once available.