Orionnnn/hub-stats huggingface dataset:
MARS: A Multi-Agent Framework Incorporating Socratic Guidance for
  Automated Prompt Optimization
Authors: name: Jian Zhang, name: Zhangqi Wang, name: Haiping Zhu, name: Jun Liu, name: Qika Lin, name: Erik Cambria
Published: 2025-03-21 06:19:55

The basic question-answering format of large language models involves
inputting a prompt and receiving a response, and the quality of the prompt
directly impacts the effectiveness of the response. Automated Prompt
Optimization (APO) aims to break free from the cognitive biases of manually
designed prompts and explores a broader design space for prompts. However,
existing APO methods suffer from limited flexibility of fixed templates and
inefficient search in prompt spaces as key issues. To this end, we propose a
Multi-Agent framework Incorporating Socratic guidance (MARS), which utilizes
multi-agent fusion technology for automatic planning, with gradual continuous
optimization and evaluation. Specifically, MARS comprises seven agents, each
with distinct functionalities, which autonomously use the Planner to devise an
optimization path that ensures flexibility. Additionally, it employs a
Teacher-Critic-Student Socratic dialogue pattern to iteratively optimize the
prompts while conducting effective search. We conduct extensive experiments on
various datasets to validate the effectiveness of our method, and perform
additional analytical experiments to assess the model's advancement as well as
the interpretability.
MAPS: A Multi-Agent Framework Based on Big Seven Personality and
  Socratic Guidance for Multimodal Scientific Problem Solving
Authors: name: Jian Zhang, name: Zhiyuan Wang, name: Zhangqi Wang, name: Xinyu Zhang, name: Fangzhi Xu, name: Qika Lin, name: Rui Mao, name: Erik Cambria, name: Jun Liu
Published: 2025-03-21 07:13:45

Multimodal scientific problems (MSPs) involve complex issues that require the
integration of multiple modalities, such as text and diagrams, presenting a
significant challenge in artificial intelligence. While progress has been made
in addressing traditional scientific problems, MSPs still face two primary
issues: the challenge of multi-modal comprehensive reasoning in scientific
problem-solving and the lack of reflective and rethinking capabilities. To
address these issues, we introduce a Multi-Agent framework based on the Big
Seven Personality and Socratic guidance (MAPS). This framework employs seven
distinct agents that leverage feedback mechanisms and the Socratic method to
guide the resolution of MSPs. To tackle the first issue, we propose a
progressive four-agent solving strategy, where each agent focuses on a specific
stage of the problem-solving process. For the second issue, we introduce a
Critic agent, inspired by Socratic questioning, which prompts critical thinking
and stimulates autonomous learning. We conduct extensive experiments on the
EMMA, Olympiad, and MathVista datasets, achieving promising results that
outperform the current SOTA model by 15.84% across all tasks. Meanwhile, the
additional analytical experiments also verify the model's progress as well as
generalization ability.
Bridging Continuous and Discrete Tokens for Autoregressive Visual
  Generation
Authors: name: Yuqing Wang, name: Zhijie Lin, name: Yao Teng, name: Yuanzhi Zhu, name: Shuhuai Ren, name: Jiashi Feng, name: Xihui Liu
Published: 2025-03-20 17:59:59

Autoregressive visual generation models typically rely on tokenizers to
compress images into tokens that can be predicted sequentially. A fundamental
dilemma exists in token representation: discrete tokens enable straightforward
modeling with standard cross-entropy loss, but suffer from information loss and
tokenizer training instability; continuous tokens better preserve visual
details, but require complex distribution modeling, complicating the generation
pipeline. In this paper, we propose TokenBridge, which bridges this gap by
maintaining the strong representation capacity of continuous tokens while
preserving the modeling simplicity of discrete tokens. To achieve this, we
decouple discretization from the tokenizer training process through
post-training quantization that directly obtains discrete tokens from
continuous representations. Specifically, we introduce a dimension-wise
quantization strategy that independently discretizes each feature dimension,
paired with a lightweight autoregressive prediction mechanism that efficiently
model the resulting large token space. Extensive experiments show that our
approach achieves reconstruction and generation quality on par with continuous
methods while using standard categorical prediction. This work demonstrates
that bridging discrete and continuous paradigms can effectively harness the
strengths of both approaches, providing a promising direction for high-quality
visual generation with simple autoregressive modeling. Project page:
https://yuqingwang1029.github.io/TokenBridge.
FastCuRL: Curriculum Reinforcement Learning with Progressive Context
  Extension for Efficient Training R1-like Reasoning Models
Authors: name: Mingyang Song, name: Mao Zheng, name: Zheng Li, name: Wenjie Yang, name: Xuan Luo, name: Yue Pan, name: Feng Zhang
Published: 2025-03-21 16:35:31

In this paper, we propose \textsc{FastCuRL}, a simple yet efficient
Curriculum Reinforcement Learning approach with
context window extending strategy to accelerate the reinforcement learning
training efficiency for R1-like reasoning models while enhancing their
performance in tackling complex reasoning tasks with long chain-of-thought
rationales, particularly with a 1.5B parameter language model.
\textsc{FastCuRL} consists of two main procedures: length-aware
training data segmentation and context window extension training. Specifically,
the former first splits the original training data into three different levels
by the input prompt length, and then the latter leverages segmented training
datasets with a progressively increasing context window length to train the
reasoning model. Experimental results demonstrate that
\textsc{FastCuRL}-1.5B-Preview surpasses DeepScaleR-1.5B-Preview
across all five datasets (including MATH 500, AIME 2024, AMC 2023, Minerva
Math, and OlympiadBench) while only utilizing 50\% of training steps.
Furthermore, all training stages for FastCuRL-1.5B-Preview are completed using
just a single node with 8 GPUs.
Modifying Large Language Model Post-Training for Diverse Creative
  Writing
Authors: name: John Joon Young Chung, name: Vishakh Padmakumar, name: Melissa Roemmele, name: Yuqian Sun, name: Max Kreminski
Published: 2025-03-21 13:21:45

As creative writing tasks do not have singular correct answers, large
language models (LLMs) trained to perform these tasks should be able to
generate diverse valid outputs. However, LLM post-training often focuses on
improving generation quality but neglects to facilitate output diversity.
Hence, in creative writing generation, we investigate post-training approaches
to promote both output diversity and quality. Our core idea is to include
deviation -- the degree of difference between a training sample and all other
samples with the same prompt -- in the training objective to facilitate
learning from rare high-quality instances. By adopting our approach to direct
preference optimization (DPO) and odds ratio preference optimization (ORPO), we
demonstrate that we can promote the output diversity of trained models while
minimally decreasing quality. Our best model with 8B parameters could achieve
on-par diversity as a human-created dataset while having output quality similar
to the best instruction-tuned models we examined, GPT-4o and DeepSeek-R1. We
further validate our approaches with a human evaluation, an ablation, and a
comparison to an existing diversification approach, DivPO.
OpenVLThinker: An Early Exploration to Complex Vision-Language Reasoning
  via Iterative Self-Improvement
Authors: name: Yihe Deng, name: Hritik Bansal, name: Fan Yin, name: Nanyun Peng, name: Wei Wang, name: Kai-Wei Chang
Published: 2025-03-21 17:52:43

Recent advancements demonstrated by DeepSeek-R1 have shown that complex
reasoning abilities in large language models (LLMs), including sophisticated
behaviors such as self-verification and self-correction, can be achieved by RL
with verifiable rewards and significantly improves model performance on
challenging tasks such as AIME. Motivated by these findings, our study
investigates whether similar reasoning capabilities can be successfully
integrated into large vision-language models (LVLMs) and assesses their impact
on challenging multimodal reasoning tasks. We consider an approach that
iteratively leverages supervised fine-tuning (SFT) on lightweight training data
and Reinforcement Learning (RL) to further improve model generalization.
Initially, reasoning capabilities were distilled from pure-text R1 models by
generating reasoning steps using high-quality captions of the images sourced
from diverse visual datasets. Subsequently, iterative RL training further
enhance reasoning skills, with each iteration's RL-improved model generating
refined SFT datasets for the next round. This iterative process yielded
OpenVLThinker, a LVLM exhibiting consistently improved reasoning performance on
challenging benchmarks such as MathVista, MathVerse, and MathVision,
demonstrating the potential of our strategy for robust vision-language
reasoning. The code, model and data are held at
https://github.com/yihedeng9/OpenVLThinker.
When Preferences Diverge: Aligning Diffusion Models with Minority-Aware
  Adaptive DPO
Authors: name: Lingfan Zhang, name: Chen Liu, name: Chengming Xu, name: Kai Hu, name: Donghao Luo, name: Chengjie Wang, name: Yanwei Fu, name: Yuan Yao
Published: 2025-03-21 07:33:44

In recent years, the field of image generation has witnessed significant
advancements, particularly in fine-tuning methods that align models with
universal human preferences. This paper explores the critical role of
preference data in the training process of diffusion models, particularly in
the context of Diffusion-DPO and its subsequent adaptations. We investigate the
complexities surrounding universal human preferences in image generation,
highlighting the subjective nature of these preferences and the challenges
posed by minority samples in preference datasets. Through pilot experiments, we
demonstrate the existence of minority samples and their detrimental effects on
model performance. We propose Adaptive-DPO -- a novel approach that
incorporates a minority-instance-aware metric into the DPO objective. This
metric, which includes intra-annotator confidence and inter-annotator
stability, distinguishes between majority and minority samples. We introduce an
Adaptive-DPO loss function which improves the DPO loss in two ways: enhancing
the model's learning of majority labels while mitigating the negative impact of
minority samples. Our experiments demonstrate that this method effectively
handles both synthetic minority data and real-world preference data, paving the
way for more effective training methodologies in image generation tasks.
Painting with Words: Elevating Detailed Image Captioning with Benchmark
  and Alignment Learning
Authors: name: Qinghao Ye, name: Xianhan Zeng, name: Fu Li, name: Chunyuan Li, name: Haoqi Fan
Published: 2025-03-10 22:53:56

Image captioning has long been a pivotal task in visual understanding, with
recent advancements in vision-language models (VLMs) significantly enhancing
the ability to generate detailed image captions. However, the evaluation of
detailed image captioning remains underexplored due to outdated evaluation
metrics and coarse annotations. In this paper, we introduce DeCapBench along
with a novel metric, DCScore, specifically designed for detailed captioning
tasks. DCScore evaluates hallucinations and fine-grained comprehensiveness by
deconstructing responses into the smallest self-sufficient units, termed
primitive information units, and assessing them individually. Our evaluation
shows that DCScore aligns more closely with human judgment than other
rule-based or model-based metrics. Concurrently, DeCapBench exhibits a high
correlation with VLM arena results on descriptive tasks, surpassing existing
benchmarks for vision-language models. Additionally, we present an automatic
fine-grained feedback collection method, FeedQuill, for preference optimization
based on our advanced metric, showing robust generalization capabilities across
auto-generated preference data. Extensive experiments on multiple VLMs
demonstrate that our method not only significantly reduces hallucinations but
also enhances performance across various benchmarks, achieving superior detail
captioning performance while surpassing GPT-4o.
Where do Large Vision-Language Models Look at when Answering Questions?
Authors: name: Xiaoying Xing, name: Chia-Wen Kuo, name: Li Fuxin, name: Yulei Niu, name: Fan Chen, name: Ming Li, name: Ying Wu, name: Longyin Wen, name: Sijie Zhu
Published: 2025-03-18 04:34:43

Large Vision-Language Models (LVLMs) have shown promising performance in
vision-language understanding and reasoning tasks. However, their visual
understanding behaviors remain underexplored. A fundamental question arises: to
what extent do LVLMs rely on visual input, and which image regions contribute
to their responses? It is non-trivial to interpret the free-form generation of
LVLMs due to their complicated visual architecture (e.g., multiple encoders and
multi-resolution) and variable-length outputs. In this paper, we extend
existing heatmap visualization methods (e.g., iGOS++) to support LVLMs for
open-ended visual question answering. We propose a method to select visually
relevant tokens that reflect the relevance between generated answers and input
image. Furthermore, we conduct a comprehensive analysis of state-of-the-art
LVLMs on benchmarks designed to require visual information to answer. Our
findings offer several insights into LVLM behavior, including the relationship
between focus region and answer correctness, differences in visual attention
across architectures, and the impact of LLM scale on visual understanding. The
code and data are available at
https://github.com/bytedance/LVLM_Interpretation.
Why Personalizing Deep Learning-Based Code Completion Tools Matters
Authors: name: Alessandro Giagnorio, name: Alberto Martin-Lopez, name: Gabriele Bavota
Published: 2025-03-18 12:26:06

Deep learning (DL)-based code completion tools have transformed software
development by enabling advanced code generation. These tools leverage models
trained on vast amounts of code from numerous repositories, capturing general
coding patterns. However, the impact of fine-tuning these models for specific
organizations or developers to boost their performance on such subjects remains
unexplored. In this work, we fill this gap by presenting solid empirical
evidence answering this question. More specifically, we consider 136 developers
from two organizations (Apache and Spring), two model architectures (T5 and
Code Llama), and three model sizes (60M, 750M, and 7B trainable parameters). T5
models (60M, 750M) were pre-trained and fine-tuned on over 2,000 open-source
projects, excluding the subject organizations' data, and compared against
versions fine-tuned on organization- and developer-specific datasets. For the
Code Llama model (7B), we compared the performance of the already pre-trained
model publicly available online with the same model fine-tuned via
parameter-efficient fine-tuning on organization- and developer-specific
datasets. Our results show that there is a boost in prediction capabilities
provided by both an organization-specific and a developer-specific additional
fine-tuning, with the former being particularly performant. Such a finding
generalizes across (i) the two subject organizations (i.e., Apache and Spring)
and (ii) models of completely different magnitude (from 60M to 7B trainable
parameters). Finally, we show that DL models fine-tuned on an
organization-specific dataset achieve the same completion performance of
pre-trained code models used out of the box and being sim10times larger,
with consequent savings in terms of deployment and inference cost (e.g.,
smaller GPUs needed).
GASP: Unifying Geometric and Semantic Self-Supervised Pre-training for
  Autonomous Driving
Authors: name: William Ljungbergh, name: Adam Lilja, name: Adam Tonderski. Arvid Laveno Ling, name: Carl Lindström, name: Willem Verbeke, name: Junsheng Fu, name: Christoffer Petersson, name: Lars Hammarstrand, name: Michael Felsberg
Published: 2025-03-19 20:00:27

Self-supervised pre-training based on next-token prediction has enabled large
language models to capture the underlying structure of text, and has led to
unprecedented performance on a large array of tasks when applied at scale.
Similarly, autonomous driving generates vast amounts of spatiotemporal data,
alluding to the possibility of harnessing scale to learn the underlying
geometric and semantic structure of the environment and its evolution over
time. In this direction, we propose a geometric and semantic self-supervised
pre-training method, GASP, that learns a unified representation by predicting,
at any queried future point in spacetime, (1) general occupancy, capturing the
evolving structure of the 3D scene; (2) ego occupancy, modeling the ego vehicle
path through the environment; and (3) distilled high-level features from a
vision foundation model. By modeling geometric and semantic 4D occupancy fields
instead of raw sensor measurements, the model learns a structured,
generalizable representation of the environment and its evolution through time.
We validate GASP on multiple autonomous driving benchmarks, demonstrating
significant improvements in semantic occupancy forecasting, online mapping, and
ego trajectory prediction. Our results demonstrate that continuous 4D geometric
and semantic occupancy prediction provides a scalable and effective
pre-training paradigm for autonomous driving. For code and additional
visualizations, see \href{https://research.zenseact.com/publications/gasp/.
TikZero: Zero-Shot Text-Guided Graphics Program Synthesis
Authors: name: Jonas Belouadi, name: Eddy Ilg, name: Margret Keuper, name: Hideki Tanaka, name: Masao Utiyama, name: Raj Dabre, name: Steffen Eger, name: Simone Paolo Ponzetto
Published: 2025-03-14 15:29:58

With the rise of generative AI, synthesizing figures from text captions
becomes a compelling application. However, achieving high geometric precision
and editability requires representing figures as graphics programs in languages
like TikZ, and aligned training data (i.e., graphics programs with captions)
remains scarce. Meanwhile, large amounts of unaligned graphics programs and
captioned raster images are more readily available. We reconcile these
disparate data sources by presenting TikZero, which decouples graphics program
generation from text understanding by using image representations as an
intermediary bridge. It enables independent training on graphics programs and
captioned images and allows for zero-shot text-guided graphics program
synthesis during inference. We show that our method substantially outperforms
baselines that can only operate with caption-aligned graphics programs.
Furthermore, when leveraging caption-aligned graphics programs as a
complementary training signal, TikZero matches or exceeds the performance of
much larger models, including commercial systems like GPT-4o. Our code,
datasets, and select models are publicly available.
SynCity: Training-Free Generation of 3D Worlds
Authors: name: Paul Engstler, name: Aleksandar Shtedritski, name: Iro Laina, name: Christian Rupprecht, name: Andrea Vedaldi
Published: 2025-03-20 17:59:40

We address the challenge of generating 3D worlds from textual descriptions.
We propose SynCity, a training- and optimization-free approach, which leverages
the geometric precision of pre-trained 3D generative models and the artistic
versatility of 2D image generators to create large, high-quality 3D spaces.
While most 3D generative models are object-centric and cannot generate
large-scale worlds, we show how 3D and 2D generators can be combined to
generate ever-expanding scenes. Through a tile-based approach, we allow
fine-grained control over the layout and the appearance of scenes. The world is
generated tile-by-tile, and each new tile is generated within its world-context
and then fused with the scene. SynCity generates compelling and immersive
scenes that are rich in detail and diversity.
Agents Play Thousands of 3D Video Games
Authors: name: Zhongwen Xu, name: Xianliang Wang, name: Siyi Li, name: Tao Yu, name: Liang Wang, name: Qiang Fu, name: Wei Yang
Published: 2025-03-17 16:42:34

We present PORTAL, a novel framework for developing artificial intelligence
agents capable of playing thousands of 3D video games through language-guided
policy generation. By transforming decision-making problems into language
modeling tasks, our approach leverages large language models (LLMs) to generate
behavior trees represented in domain-specific language (DSL). This method
eliminates the computational burden associated with traditional reinforcement
learning approaches while preserving strategic depth and rapid adaptability.
Our framework introduces a hybrid policy structure that combines rule-based
nodes with neural network components, enabling both high-level strategic
reasoning and precise low-level control. A dual-feedback mechanism
incorporating quantitative game metrics and vision-language model analysis
facilitates iterative policy improvement at both tactical and strategic levels.
The resulting policies are instantaneously deployable, human-interpretable, and
capable of generalizing across diverse gaming environments. Experimental
results demonstrate PORTAL's effectiveness across thousands of first-person
shooter (FPS) games, showcasing significant improvements in development
efficiency, policy generalization, and behavior diversity compared to
traditional approaches. PORTAL represents a significant advancement in game AI
development, offering a practical solution for creating sophisticated agents
that can operate across thousands of commercial video games with minimal
development overhead. Experiment results on the 3D video games are best viewed
on https://zhongwen.one/projects/portal .
UVE: Are MLLMs Unified Evaluators for AI-Generated Videos?
Authors: name: Yuanxin Liu, name: Rui Zhu, name: Shuhuai Ren, name: Jiacong Wang, name: Haoyuan Guo, name: Xu Sun, name: Lu Jiang
Published: 2025-03-13 01:52:27

With the rapid growth of video generative models (VGMs), it is essential to
develop reliable and comprehensive automatic metrics for AI-generated videos
(AIGVs). Existing methods either use off-the-shelf models optimized for other
tasks or rely on human assessment data to train specialized evaluators. These
approaches are constrained to specific evaluation aspects and are difficult to
scale with the increasing demands for finer-grained and more comprehensive
evaluations. To address this issue, this work investigates the feasibility of
using multimodal large language models (MLLMs) as a unified evaluator for
AIGVs, leveraging their strong visual perception and language understanding
capabilities. To evaluate the performance of automatic metrics in unified AIGV
evaluation, we introduce a benchmark called UVE-Bench. UVE-Bench collects
videos generated by state-of-the-art VGMs and provides pairwise human
preference annotations across 15 evaluation aspects. Using UVE-Bench, we
extensively evaluate 16 MLLMs. Our empirical results suggest that while
advanced MLLMs (e.g., Qwen2VL-72B and InternVL2.5-78B) still lag behind human
evaluators, they demonstrate promising ability in unified AIGV evaluation,
significantly surpassing existing specialized evaluation methods. Additionally,
we conduct an in-depth analysis of key design choices that impact the
performance of MLLM-driven evaluators, offering valuable insights for future
research on AIGV evaluation. The code is available at
https://github.com/bytedance/UVE.