Orionnnn/hub-stats huggingface dataset:
NExT-Mol: 3D Diffusion Meets 1D Language Modeling for 3D Molecule
  Generation
Authors: name: Zhiyuan Liu, name: Yanchen Luo, name: Han Huang, name: Enzhi Zhang, name: Sihang Li, name: Junfeng Fang, name: Yaorui Shi, name: Xiang Wang, name: Kenji Kawaguchi, name: Tat-Seng Chua
Published: 2025-02-18 08:40:13

3D molecule generation is crucial for drug discovery and material design.
While prior efforts focus on 3D diffusion models for their benefits in modeling
continuous 3D conformers, they overlook the advantages of 1D SELFIES-based
Language Models (LMs), which can generate 100% valid molecules and leverage the
billion-scale 1D molecule datasets. To combine these advantages for 3D molecule
generation, we propose a foundation model -- NExT-Mol: 3D Diffusion Meets 1D
Language Modeling for 3D Molecule Generation. NExT-Mol uses an extensively
pretrained molecule LM for 1D molecule generation, and subsequently predicts
the generated molecule's 3D conformers with a 3D diffusion model. We enhance
NExT-Mol's performance by scaling up the LM's model size, refining the
diffusion neural architecture, and applying 1D to 3D transfer learning.
Notably, our 1D molecule LM significantly outperforms baselines in
distributional similarity while ensuring validity, and our 3D diffusion model
achieves leading performances in conformer prediction. Given these improvements
in 1D and 3D modeling, NExT-Mol achieves a 26% relative improvement in 3D FCD
for de novo 3D generation on GEOM-DRUGS, and a 13% average relative gain for
conditional 3D generation on QM9-2014. Our codes and pretrained checkpoints are
available at https://github.com/acharkq/NExT-Mol.
AdaptiveStep: Automatically Dividing Reasoning Step through Model
  Confidence
Authors: name: Yuliang Liu, name: Junjie Lu, name: Zhaoling Chen, name: Chaofeng Qu, name: Jason Klein Liu, name: Chonghan Liu, name: Zefan Cai, name: Yunhui Xia, name: Li Zhao, name: Jiang Bian, name: Chuheng Zhang, name: Wei Shen, name: Zhouhan Lin
Published: 2025-02-19 18:35:55

Current approaches for training Process Reward Models (PRMs) often involve
breaking down responses into multiple reasoning steps using rule-based
techniques, such as using predefined placeholder tokens or setting the
reasoning step's length into a fixed size. These approaches overlook the fact
that specific words do not typically mark true decision points in a text. To
address this, we propose AdaptiveStep, a method that divides reasoning steps
based on the model's confidence in predicting the next word. This division
method provides more decision-making information at each step, enhancing
downstream tasks, such as reward model learning. Moreover, our method does not
require manual annotation. We demonstrate its effectiveness through experiments
with AdaptiveStep-trained PRMs in mathematical reasoning and code generation
tasks. Experimental results indicate that the outcome PRM achieves
state-of-the-art Best-of-N performance, surpassing greedy search strategy with
token-level value-guided decoding, while also reducing construction costs by
over 30% compared to existing open-source PRMs. In addition, we provide a
thorough analysis and case study on the PRM's performance, transferability, and
generalization capabilities.
Craw4LLM: Efficient Web Crawling for LLM Pretraining
Authors: name: Shi Yu, name: Zhiyuan Liu, name: Chenyan Xiong
Published: 2025-02-19 00:31:43

Web crawl is a main source of large language models' (LLMs) pretraining data,
but the majority of crawled web pages are discarded in pretraining due to low
data quality. This paper presents Crawl4LLM, an efficient web crawling method
that explores the web graph based on the preference of LLM pretraining.
Specifically, it leverages the influence of a webpage in LLM pretraining as the
priority score of the web crawler's scheduler, replacing the standard graph
connectivity based priority. Our experiments on a web graph containing 900
million webpages from a commercial search engine's index demonstrate the
efficiency of Crawl4LLM in obtaining high-quality pretraining data. With just
21% URLs crawled, LLMs pretrained on Crawl4LLM data reach the same downstream
performances of previous crawls, significantly reducing the crawling waste and
alleviating the burdens on websites. Our code is publicly available at
https://github.com/cxcscmu/Crawl4LLM.
Autellix: An Efficient Serving Engine for LLM Agents as General Programs
Authors: name: Michael Luo, name: Xiaoxiang Shi, name: Colin Cai, name: Tianjun Zhang, name: Justin Wong, name: Yichuan Wang, name: Chi Wang, name: Yanping Huang, name: Zhifeng Chen, name: Joseph E. Gonzalez, name: Ion Stoica
Published: 2025-02-19 18:59:30

Large language model (LLM) applications are evolving beyond simple chatbots
into dynamic, general-purpose agentic programs, which scale LLM calls and
output tokens to help AI agents reason, explore, and solve complex tasks.
However, existing LLM serving systems ignore dependencies between programs and
calls, missing significant opportunities for optimization. Our analysis reveals
that programs submitted to LLM serving engines experience long cumulative wait
times, primarily due to head-of-line blocking at both the individual LLM
request and the program. To address this, we introduce Autellix, an LLM serving
system that treats programs as first-class citizens to minimize their
end-to-end latencies. Autellix intercepts LLM calls submitted by programs,
enriching schedulers with program-level context. We propose two scheduling
algorithms-for single-threaded and distributed programs-that preempt and
prioritize LLM calls based on their programs' previously completed calls. Our
evaluation demonstrates that across diverse LLMs and agentic workloads,
Autellix improves throughput of programs by 4-15x at the same latency compared
to state-of-the-art systems, such as vLLM.
SearchRAG: Can Search Engines Be Helpful for LLM-based Medical Question
  Answering?
Authors: name: Yucheng Shi, name: Tianze Yang, name: Canyu Chen, name: Quanzheng Li, name: Tianming Liu, name: Xiang Li, name: Ninghao Liu
Published: 2025-02-18 19:12:15

Large Language Models (LLMs) have shown remarkable capabilities in general
domains but often struggle with tasks requiring specialized knowledge.
Conventional Retrieval-Augmented Generation (RAG) techniques typically retrieve
external information from static knowledge bases, which can be outdated or
incomplete, missing fine-grained clinical details essential for accurate
medical question answering. In this work, we propose SearchRAG, a novel
framework that overcomes these limitations by leveraging real-time search
engines. Our method employs synthetic query generation to convert complex
medical questions into search-engine-friendly queries and utilizes
uncertainty-based knowledge selection to filter and incorporate the most
relevant and informative medical knowledge into the LLM's input. Experimental
results demonstrate that our method significantly improves response accuracy in
medical question answering tasks, particularly for complex questions requiring
detailed and up-to-date knowledge.
RAD: Training an End-to-End Driving Policy via Large-Scale 3DGS-based
  Reinforcement Learning
Authors: name: Hao Gao, name: Shaoyu Chen, name: Bo Jiang, name: Bencheng Liao, name: Yiang Shi, name: Xiaoyang Guo, name: Yuechuan Pu, name: Haoran Yin, name: Xiangyu Li, name: Xinbang Zhang, name: Ying Zhang, name: Wenyu Liu, name: Qian Zhang, name: Xinggang Wang
Published: 2025-02-18 18:59:21

Existing end-to-end autonomous driving (AD) algorithms typically follow the
Imitation Learning (IL) paradigm, which faces challenges such as causal
confusion and the open-loop gap. In this work, we establish a 3DGS-based
closed-loop Reinforcement Learning (RL) training paradigm. By leveraging 3DGS
techniques, we construct a photorealistic digital replica of the real physical
world, enabling the AD policy to extensively explore the state space and learn
to handle out-of-distribution scenarios through large-scale trial and error. To
enhance safety, we design specialized rewards that guide the policy to
effectively respond to safety-critical events and understand real-world causal
relationships. For better alignment with human driving behavior, IL is
incorporated into RL training as a regularization term. We introduce a
closed-loop evaluation benchmark consisting of diverse, previously unseen 3DGS
environments. Compared to IL-based methods, RAD achieves stronger performance
in most closed-loop metrics, especially 3x lower collision rate. Abundant
closed-loop results are presented at https://hgao-cv.github.io/RAD.
Small Models Struggle to Learn from Strong Reasoners
Authors: name: Yuetai Li, name: Xiang Yue, name: Zhangchen Xu, name: Fengqing Jiang, name: Luyao Niu, name: Bill Yuchen Lin, name: Bhaskar Ramasubramanian, name: Radha Poovendran
Published: 2025-02-17 18:56:15

Large language models (LLMs) excel in complex reasoning tasks, and distilling
their reasoning capabilities into smaller models has shown promise. However, we
uncover an interesting phenomenon, which we term the Small Model Learnability
Gap: small models (leq3B parameters) do not consistently benefit from long
chain-of-thought (CoT) reasoning or distillation from larger models. Instead,
they perform better when fine-tuned on shorter, simpler reasoning chains that
better align with their intrinsic learning capacity. To address this, we
propose Mix Distillation, a simple yet effective strategy that balances
reasoning complexity by combining long and short CoT examples or reasoning from
both larger and smaller models. Our experiments demonstrate that Mix
Distillation significantly improves small model reasoning performance compared
to training on either data alone. These findings highlight the limitations of
direct strong model distillation and underscore the importance of adapting
reasoning complexity for effective reasoning capability transfer.
LongPO: Long Context Self-Evolution of Large Language Models through
  Short-to-Long Preference Optimization
Authors: name: Guanzheng Chen, name: Xin Li, name: Michael Qizhe Shieh, name: Lidong Bing
Published: 2025-02-19 17:59:03

Large Language Models (LLMs) have demonstrated remarkable capabilities
through pretraining and alignment. However, superior short-context LLMs may
underperform in long-context scenarios due to insufficient long-context
alignment. This alignment process remains challenging due to the impracticality
of human annotation for extended contexts and the difficulty in balancing
short- and long-context performance. To address these challenges, we introduce
LongPO, that enables short-context LLMs to self-evolve to excel on long-context
tasks by internally transferring short-context capabilities. LongPO harnesses
LLMs to learn from self-generated short-to-long preference data, comprising
paired responses generated for identical instructions with long-context inputs
and their compressed short-context counterparts, respectively. This preference
reveals capabilities and potentials of LLMs cultivated during short-context
alignment that may be diminished in under-aligned long-context scenarios.
Additionally, LongPO incorporates a short-to-long KL constraint to mitigate
short-context performance decline during long-context alignment. When applied
to Mistral-7B-Instruct-v0.2 from 128K to 512K context lengths, LongPO fully
retains short-context performance and largely outperforms naive SFT and DPO in
both long- and short-context tasks. Specifically, \ourMethod-trained models can
achieve results on long-context benchmarks comparable to, or even surpassing,
those of superior LLMs (e.g., GPT-4-128K) that involve extensive long-context
annotation and larger parameter scales.
The Hidden Risks of Large Reasoning Models: A Safety Assessment of R1
Authors: name: Kaiwen Zhou, name: Chengzhi Liu, name: Xuandong Zhao, name: Shreedhar Jangam, name: Jayanth Srinivasa, name: Gaowen Liu, name: Dawn Song, name: Xin Eric Wang
Published: 2025-02-18 09:06:07

The rapid development of large reasoning models, such as OpenAI-o3 and
DeepSeek-R1, has led to significant improvements in complex reasoning over
non-reasoning large language models~(LLMs). However, their enhanced
capabilities, combined with the open-source access of models like DeepSeek-R1,
raise serious safety concerns, particularly regarding their potential for
misuse. In this work, we present a comprehensive safety assessment of these
reasoning models, leveraging established safety benchmarks to evaluate their
compliance with safety regulations. Furthermore, we investigate their
susceptibility to adversarial attacks, such as jailbreaking and prompt
injection, to assess their robustness in real-world applications. Through our
multi-faceted analysis, we uncover four key findings: (1) There is a
significant safety gap between the open-source R1 models and the o3-mini model,
on both safety benchmark and attack, suggesting more safety effort on R1 is
needed. (2) The distilled reasoning model shows poorer safety performance
compared to its safety-aligned base models. (3) The stronger the model's
reasoning ability, the greater the potential harm it may cause when answering
unsafe questions. (4) The thinking process in R1 models pose greater safety
concerns than their final answers. Our study provides insights into the
security implications of reasoning models and highlights the need for further
advancements in R1 models' safety to close the gap.
Scaling Autonomous Agents via Automatic Reward Modeling And Planning
Authors: name: Zhenfang Chen, name: Delin Chen, name: Rui Sun, name: Wenjun Liu, name: Chuang Gan
Published: 2025-02-17 18:49:25

Large language models (LLMs) have demonstrated remarkable capabilities across
a range of text-generation tasks. However, LLMs still struggle with problems
requiring multi-step decision-making and environmental feedback, such as online
shopping, scientific reasoning, and mathematical problem-solving. Unlike pure
text data, collecting large-scale decision-making data is challenging.
Moreover, many powerful LLMs are only accessible through APIs, which hinders
their fine-tuning for agent tasks due to cost and complexity. To address LLM
agents' limitations, we propose a framework that can automatically learn a
reward model from the environment without human annotations. This model can be
used to evaluate the action trajectories of LLM agents and provide heuristics
for task planning. Specifically, our approach involves employing one LLM-based
agent to navigate an environment randomly, generating diverse action
trajectories. Subsequently, a separate LLM is leveraged to assign a task intent
and synthesize a negative response alongside the correct response for each
trajectory. These triplets (task intent, positive response, and negative
response) are then utilized as training data to optimize a reward model capable
of scoring action trajectories. The effectiveness and generalizability of our
framework are demonstrated through evaluations conducted on different agent
benchmarks. In conclusion, our proposed framework represents a significant
advancement in enhancing LLM agents' decision-making capabilities. By
automating the learning of reward models, we overcome the challenges of data
scarcity and API limitations, potentially revolutionizing the application of
LLMs in complex and interactive environments. This research paves the way for
more sophisticated AI agents capable of tackling a wide range of real-world
problems requiring multi-step decision-making.
YOLOv12: Attention-Centric Real-Time Object Detectors
Authors: name: Yunjie Tian, name: Qixiang Ye, name: David Doermann
Published: 2025-02-18 04:20:14

Enhancing the network architecture of the YOLO framework has been crucial for
a long time, but has focused on CNN-based improvements despite the proven
superiority of attention mechanisms in modeling capabilities. This is because
attention-based models cannot match the speed of CNN-based models. This paper
proposes an attention-centric YOLO framework, namely YOLOv12, that matches the
speed of previous CNN-based ones while harnessing the performance benefits of
attention mechanisms. YOLOv12 surpasses all popular real-time object detectors
in accuracy with competitive speed. For example, YOLOv12-N achieves 40.6% mAP
with an inference latency of 1.64 ms on a T4 GPU, outperforming advanced
YOLOv10-N / YOLOv11-N by 2.1%/1.2% mAP with a comparable speed. This advantage
extends to other model scales. YOLOv12 also surpasses end-to-end real-time
detectors that improve DETR, such as RT-DETR / RT-DETRv2: YOLOv12-S beats
RT-DETR-R18 / RT-DETRv2-R18 while running 42% faster, using only 36% of the
computation and 45% of the parameters. More comparisons are shown in Figure 1.
Harnessing Vision Models for Time Series Analysis: A Survey
Authors: name: Jingchao Ni, name: Ziming Zhao, name: ChengAo Shen, name: Hanghang Tong, name: Dongjin Song, name: Wei Cheng, name: Dongsheng Luo, name: Haifeng Chen
Published: 2025-02-13 00:42:11

Time series analysis has witnessed the inspiring development from traditional
autoregressive models, deep learning models, to recent Transformers and Large
Language Models (LLMs). Efforts in leveraging vision models for time series
analysis have also been made along the way but are less visible to the
community due to the predominant research on sequence modeling in this domain.
However, the discrepancy between continuous time series and the discrete token
space of LLMs, and the challenges in explicitly modeling the correlations of
variates in multivariate time series have shifted some research attentions to
the equally successful Large Vision Models (LVMs) and Vision Language Models
(VLMs). To fill the blank in the existing literature, this survey discusses the
advantages of vision models over LLMs in time series analysis. It provides a
comprehensive and in-depth overview of the existing methods, with dual views of
detailed taxonomy that answer the key research questions including how to
encode time series as images and how to model the imaged time series for
various tasks. Additionally, we address the challenges in the pre- and
post-processing steps involved in this framework and outline future directions
to further advance time series analysis with vision models.
Flow-of-Options: Diversified and Improved LLM Reasoning by Thinking
  Through Options
Authors: name: Lakshmi Nair, name: Ian Trase, name: Mark Kim
Published: 2025-02-18 15:11:46

We present a novel reasoning approach called Flow-of-Options (FoO), designed
to address intrinsic biases in Large Language Models (LLMs). FoO enables LLMs
to systematically explore a diverse range of possibilities in their reasoning,
as demonstrated by an FoO-based agentic system for autonomously solving Machine
Learning tasks (AutoML). Our framework outperforms state-of-the-art baselines,
achieving improvements of 38.2% - 69.2% on standard data science tasks, and
37.4% - 47.9% on therapeutic chemistry tasks. With an overall operation cost
under $1 per task, our framework is well-suited for cost-sensitive
applications. Beyond classification and regression, we illustrate the broader
applicability of our FoO-based agentic system to tasks such as reinforcement
learning and image generation. Our framework presents significant advancements
compared to current state-of-the-art agentic systems for AutoML, due to the
benefits of FoO in enforcing diversity in LLM solutions through compressed,
explainable representations that also support long-term memory when combined
with case-based reasoning.
Text2World: Benchmarking Large Language Models for Symbolic World Model
  Generation
Authors: name: Mengkang Hu, name: Tianxing Chen, name: Yude Zou, name: Yuheng Lei, name: Qiguang Chen, name: Ming Li, name: Hongyuan Zhang, name: Wenqi Shao, name: Ping Luo
Published: 2025-02-18 17:59:48

Recently, there has been growing interest in leveraging large language models
(LLMs) to generate symbolic world models from textual descriptions. Although
LLMs have been extensively explored in the context of world modeling, prior
studies encountered several challenges, including evaluation randomness,
dependence on indirect metrics, and a limited domain scope. To address these
limitations, we introduce a novel benchmark, Text2World, based on planning
domain definition language (PDDL), featuring hundreds of diverse domains and
employing multi-criteria, execution-based metrics for a more robust evaluation.
We benchmark current LLMs using Text2World and find that reasoning models
trained with large-scale reinforcement learning outperform others. However,
even the best-performing model still demonstrates limited capabilities in world
modeling. Building on these insights, we examine several promising strategies
to enhance the world modeling capabilities of LLMs, including test-time
scaling, agent training, and more. We hope that Text2World can serve as a
crucial resource, laying the groundwork for future research in leveraging LLMs
as world models. The project page is available at
https://text-to-world.github.io/.
Atom of Thoughts for Markov LLM Test-Time Scaling
Authors: name: Fengwei Teng, name: Zhaoyang Yu, name: Quan Shi, name: Jiayi Zhang, name: Chenglin Wu, name: Yuyu Luo
Published: 2025-02-17 16:52:42

Large Language Models (LLMs) achieve superior performance through
training-time scaling, and test-time scaling further enhances their
capabilities by conducting effective reasoning during inference. However, as
the scale of reasoning increases, existing test-time scaling methods suffer
from accumulated historical information, which not only wastes computational
resources but also interferes with effective reasoning. To address this issue,
we observe that complex reasoning progress is often achieved by solving a
sequence of independent subquestions, each being self-contained and verifiable.
These subquestions are essentially atomic questions, relying primarily on their
current state rather than accumulated history, similar to the memoryless
transitions in a Markov process. Based on this observation, we propose Atom of
Thoughts (AoT), where each state transition in the reasoning process consists
of decomposing the current question into a dependency-based directed acyclic
graph and contracting its subquestions, forming a new atomic question state.
This iterative decomposition-contraction process continues until reaching
directly solvable atomic questions, naturally realizing Markov transitions
between question states. Furthermore, these atomic questions can be seamlessly
integrated into existing test-time scaling methods, enabling AoT to serve as a
plug-in enhancement for improving reasoning capabilities. Experiments across
six benchmarks demonstrate the effectiveness of AoT both as a standalone
framework and a plug-in enhancement. Notably, on HotpotQA, when applied to
gpt-4o-mini, AoT achieves an 80.6% F1 score, surpassing o3-mini by 3.4% and
DeepSeek-R1 by 10.6%. The code will be available at
https://github.com/qixucen/atom.
Eager Updates For Overlapped Communication and Computation in DiLoCo
Authors: name: Satyen Kale, name: Arthur Douillard, name: Yanislav Donchev
Published: 2025-02-18 16:16:14

Distributed optimization methods such as DiLoCo have been shown to be
effective in training very large models across multiple distributed workers,
such as datacenters. These methods split updates into two parts: an inner
optimization phase, where the workers independently execute multiple
optimization steps on their own local data, and an outer optimization step,
where the inner updates are synchronized. While such approaches require orders
of magnitude less communication than standard data-parallel training, in
settings where the workers are datacenters, even the limited communication
requirements of these approaches can still cause significant slow downs due to
the blocking necessary at each outer optimization step. In this paper, we
investigate techniques to mitigate this issue by overlapping communication with
computation in a manner that allows the outer optimization step to fully
overlap with the inner optimization phase. We show that a particular variant,
dubbed eager updates, provides competitive performance with standard DiLoCo in
settings with low bandwidth between workers.