Orionnnn/hub-stats huggingface dataset:
Search-R3: Unifying Reasoning and Embedding Generation in Large Language
  Models
Authors: name: Yuntao Gui, name: James Cheng
Published: 2025-10-08 14:16:20

Despite their remarkable natural language understanding capabilities, Large
Language Models (LLMs) have been underutilized for retrieval tasks. We present
Search-R3, a novel framework that addresses this limitation by adapting LLMs to
generate search embeddings as a direct output of their reasoning process. Our
approach exploits LLMs' chain-of-thought capabilities, allowing them to produce
more effective embeddings by reasoning step-by-step through complex semantic
analyses. We implement this through three complementary mechanisms. (1) a
supervised learning stage enables the model's ability to produce quality
embeddings, (2) a reinforcement learning (RL) methodology that optimizes
embedding generation alongside reasoning, and (3) a specialized RL environment
that efficiently handles evolving embedding representations without requiring
complete corpus re-encoding at each training iteration. Our extensive
evaluations on diverse benchmarks demonstrate that Search-R3 significantly
outperforms prior methods by unifying the reasoning and embedding generation
processes. This integrated post-training approach represents a substantial
advancement in handling complex knowledge-intensive tasks that require both
sophisticated reasoning and effective information retrieval. Project page:
https://github.com/ytgui/Search-R3
Taming Text-to-Sounding Video Generation via Advanced Modality Condition
  and Interaction
Authors: name: Kaisi Guan, name: Xihua Wang, name: Zhengfeng Lai, name: Xin Cheng, name: Peng Zhang, name: XiaoJiang Liu, name: Ruihua Song, name: Meng Cao
Published: 2025-10-03 15:43:56

This study focuses on a challenging yet promising task,
Text-to-Sounding-Video (T2SV) generation, which aims to generate a video with
synchronized audio from text conditions, meanwhile ensuring both modalities are
aligned with text. Despite progress in joint audio-video training, two critical
challenges still remain unaddressed: (1) a single, shared text caption where
the text for video is equal to the text for audio often creates modal
interference, confusing the pretrained backbones, and (2) the optimal mechanism
for cross-modal feature interaction remains unclear. To address these
challenges, we first propose the Hierarchical Visual-Grounded Captioning (HVGC)
framework that generates pairs of disentangled captions, a video caption, and
an audio caption, eliminating interference at the conditioning stage. Based on
HVGC, we further introduce BridgeDiT, a novel dual-tower diffusion transformer,
which employs a Dual CrossAttention (DCA) mechanism that acts as a robust
``bridge" to enable a symmetric, bidirectional exchange of information,
achieving both semantic and temporal synchronization. Extensive experiments on
three benchmark datasets, supported by human evaluations, demonstrate that our
method achieves state-of-the-art results on most metrics. Comprehensive
ablation studies further validate the effectiveness of our contributions,
offering key insights for the future T2SV task. All the codes and checkpoints
will be publicly released.
Fidelity-Aware Data Composition for Robust Robot Generalization
Authors: name: Zizhao Tong, name: Di Chen, name: Sicheng Hu, name: Hongwei Fan, name: Liliang Chen, name: Guanghui Ren, name: Hao Tang, name: Hao Dong, name: Ling Shao
Published: 2025-09-29 13:48:36

Generalist robot policies trained on large-scale, visually homogeneous
datasets can be susceptible to shortcut learning, which impairs their
out-of-distribution (OOD) generalization. While generative data augmentation is
a common approach to introduce diversity, it presents a subtle challenge: data
composition. Naively mixing real and synthetic data can corrupt the learning
signal, as this process often prioritizes visual diversity at the expense of
information fidelity. This paper suggests that robust generalization depends on
principled, fidelity-aware data composition. We introduce Coherent Information
Fidelity Tuning (CIFT), a framework that treats data composition as an
optimization problem. CIFT uses a practical proxy for Information Fidelity
based on the feature-space geometry of a dataset. This enables the
identification of a phase transition, termed the Decoherence Point, where
training stability degrades. The framework includes a generative engine,
Multi-View Video Augmentation (MVAug), to synthesize a causally disentangled
data spectrum for this tuning process. Applying CIFT to policy architectures
such as pi_0 and Diffusion Policy improves OOD success rates by over 54\%.
These results indicate that fidelity-aware composition, beyond data synthesis
alone, is an important component for developing robust, general-purpose robots.
Learning on the Job: An Experience-Driven Self-Evolving Agent for
  Long-Horizon Tasks
Authors: name: Cheng Yang, name: Xuemeng Yang, name: Licheng Wen, name: Daocheng Fu, name: Jianbiao Mei, name: Rong Wu, name: Pinlong Cai, name: Yufan Shen, name: Nianchen Deng, name: Botian Shi, name: Yu Qiao, name: Haifeng Li
Published: 2025-10-09 09:40:34

Large Language Models have demonstrated remarkable capabilities across
diverse domains, yet significant challenges persist when deploying them as AI
agents for real-world long-horizon tasks. Existing LLM agents suffer from a
critical limitation: they are test-time static and cannot learn from
experience, lacking the ability to accumulate knowledge and continuously
improve on the job. To address this challenge, we propose MUSE, a novel agent
framework that introduces an experience-driven, self-evolving system centered
around a hierarchical Memory Module. MUSE organizes diverse levels of
experience and leverages them to plan and execute long-horizon tasks across
multiple applications. After each sub-task execution, the agent autonomously
reflects on its trajectory, converting the raw trajectory into structured
experience and integrating it back into the Memory Module. This mechanism
enables the agent to evolve beyond its static pretrained parameters, fostering
continuous learning and self-evolution. We evaluate MUSE on the long-horizon
productivity benchmark TAC. It achieves new SOTA performance by a significant
margin using only a lightweight Gemini-2.5 Flash model. Sufficient Experiments
demonstrate that as the agent autonomously accumulates experience, it exhibits
increasingly superior task completion capabilities, as well as robust
continuous learning and self-evolution capabilities. Moreover, the accumulated
experience from MUSE exhibits strong generalization properties, enabling
zero-shot improvement on new tasks. MUSE establishes a new paradigm for AI
agents capable of real-world productivity task automation.
Memory Retrieval and Consolidation in Large Language Models through
  Function Tokens
Authors: name: Shaohua Zhang, name: Yuan Lin, name: Hang Li
Published: 2025-10-09 13:31:20

The remarkable success of large language models (LLMs) stems from their
ability to consolidate vast amounts of knowledge into the memory during
pre-training and to retrieve it from the memory during inference, enabling
advanced capabilities such as knowledge memorization, instruction-following and
reasoning. However, the mechanisms of memory retrieval and consolidation in
LLMs remain poorly understood. In this paper, we propose the function token
hypothesis to explain the workings of LLMs: During inference, function tokens
activate the most predictive features from context and govern next token
prediction (memory retrieval). During pre-training, predicting the next tokens
(usually content tokens) that follow function tokens increases the number of
learned features of LLMs and updates the model parameters (memory
consolidation). Function tokens here roughly correspond to function words in
linguistics, including punctuation marks, articles, prepositions, and
conjunctions, in contrast to content tokens. We provide extensive experimental
evidence supporting this hypothesis. Using bipartite graph analysis, we show
that a small number of function tokens activate the majority of features. Case
studies further reveal how function tokens activate the most predictive
features from context to direct next token prediction. We also find that during
pre-training, the training loss is dominated by predicting the next content
tokens following function tokens, which forces the function tokens to select
the most predictive features from context.
Beyond Outliers: A Study of Optimizers Under Quantization
Authors: name: Georgios Vlassis, name: Saleh Ashkboos, name: Alexandra Volkova, name: Torsten Hoefler, name: Dan Alistarh
Published: 2025-09-27 21:15:22

As new optimizers gain traction and model quantization becomes standard for
efficient deployment, a key question arises: how does the choice of optimizer
affect model performance in the presence of quantization? Despite progress in
both areas, systematic evidence on optimizer-quantization interactions remains
limited. To fill this gap, we study the impact of optimizer choice on model
robustness under quantization, considering both post-training quantization
(PTQ), and quantization-aware training (QAT). We first train full-precision
models, ranging from 50M to 1.5B parameters, with six optimizers, to explore
the hyperparameter landscape, and establish well-tuned baselines. We then apply
PTQ to evaluate how model performance degrades when trained with different
optimizers. We find that outlier-related metrics, such as the max-to-mean ratio
(MMR) and Kurtosis, fail to predict the PTQ performance across different
optimizers. We show analytically that this is due to the MMR capturing only
isolated layer errors, while ignoring how quantization errors accumulate and
propagate through the network. To study the QAT degradation, we train quantized
models from scratch and compare them to our original-precision baselines. We
find that optimizers performing well in the original pretraining setup may not
remain optimal under QAT, and that models trained with Shampoo show the lowest
accuracy degradation. Finally, we derive scaling laws for quantization-aware
training under different optimizers, showing that Shampoo achieves the highest
parameter efficiency of all tested optimizers.
UP2You: Fast Reconstruction of Yourself from Unconstrained Photo
  Collections
Authors: name: Zeyu Cai, name: Ziyang Li, name: Xiaoben Li, name: Boqian Li, name: Zeyu Wang, name: Zhenyu Zhang, name: Yuliang Xiu
Published: 2025-09-29 14:06:00

We present UP2You, the first tuning-free solution for reconstructing
high-fidelity 3D clothed portraits from extremely unconstrained in-the-wild 2D
photos. Unlike previous approaches that require "clean" inputs (e.g., full-body
images with minimal occlusions, or well-calibrated cross-view captures), UP2You
directly processes raw, unstructured photographs, which may vary significantly
in pose, viewpoint, cropping, and occlusion. Instead of compressing data into
tokens for slow online text-to-3D optimization, we introduce a data rectifier
paradigm that efficiently converts unconstrained inputs into clean, orthogonal
multi-view images in a single forward pass within seconds, simplifying the 3D
reconstruction. Central to UP2You is a pose-correlated feature aggregation
module (PCFA), that selectively fuses information from multiple reference
images w.r.t. target poses, enabling better identity preservation and nearly
constant memory footprint, with more observations. We also introduce a
perceiver-based multi-reference shape predictor, removing the need for
pre-captured body templates. Extensive experiments on 4D-Dress, PuzzleIOI, and
in-the-wild captures demonstrate that UP2You consistently surpasses previous
methods in both geometric accuracy (Chamfer-15%, P2S-18% on PuzzleIOI) and
texture fidelity (PSNR-21%, LPIPS-46% on 4D-Dress). UP2You is efficient (1.5
minutes per person), and versatile (supports arbitrary pose control, and
training-free multi-garment 3D virtual try-on), making it practical for
real-world scenarios where humans are casually captured. Both models and code
will be released to facilitate future research on this underexplored task.
Project Page: https://zcai0612.github.io/UP2You
CoMAS: Co-Evolving Multi-Agent Systems via Interaction Rewards
Authors: name: Xiangyuan Xue, name: Yifan Zhou, name: Guibin Zhang, name: Zaibin Zhang, name: Yijiang Li, name: Chen Zhang, name: Zhenfei Yin, name: Philip Torr, name: Wanli Ouyang, name: Lei Bai
Published: 2025-10-09 17:50:26

Self-evolution is a central research topic in enabling large language model
(LLM)-based agents to continually improve their capabilities after pretraining.
Recent research has witnessed a transition from reinforcement learning
(RL)-free to RL-based methods. Current RL-based methods either rely on dense
external reward signals or extract intrinsic reward signals from LLMs
themselves. However, these approaches diverge from the self-evolution
mechanisms observed in human intelligence, where individuals learn and improve
through mutual discussion and collaboration. In this work, we introduce
Co-Evolving Multi-Agent Systems (CoMAS), a novel framework that enables agents
to improve autonomously by learning from inter-agent interactions without
external supervision. CoMAS generates intrinsic rewards from rich discussion
dynamics, employs an LLM-as-a-judge mechanism to formulate these rewards, and
optimizes each agent's policy through RL, thereby enabling decentralized and
scalable co-evolution. Experimental results demonstrate that CoMAS consistently
outperforms untrained agents and achieves state-of-the-art performance across
most evaluation settings. Ablation studies confirm the necessity of
interaction-based reward signals and reveal promising scalability as the number
and diversity of agents increase. These findings establish CoMAS as a novel and
effective paradigm for self-evolution in LLM-based agents.
MemMamba: Rethinking Memory Patterns in State Space Model
Authors: name: Youjin Wang, name: Yangjingyi Chen, name: Jiahao Yan, name: Jiaxuan Lu, name: Xiao Sun
Published: 2025-09-28 14:40:58

With the explosive growth of data, long-sequence modeling has become
increasingly important in tasks such as natural language processing and
bioinformatics. However, existing methods face inherent trade-offs between
efficiency and memory. Recurrent neural networks suffer from gradient vanishing
and explosion, making them hard to scale. Transformers can model global
dependencies but are constrained by quadratic complexity. Recently, selective
state-space models such as Mamba have demonstrated high efficiency with O(n)
time and O(1) recurrent inference, yet their long-range memory decays
exponentially. In this work, we conduct mathematical derivations and
information-theoretic analysis to systematically uncover the memory decay
mechanism of Mamba, answering a fundamental question: what is the nature of
Mamba's long-range memory and how does it retain information? To quantify key
information loss, we further introduce horizontal-vertical memory fidelity
metrics that capture degradation both within and across layers. Inspired by how
humans distill and retain salient information when reading long documents, we
propose MemMamba, a novel architectural framework that integrates state
summarization mechanism together with cross-layer and cross-token attention,
which alleviates long-range forgetting while preserving linear complexity.
MemMamba achieves significant improvements over existing Mamba variants and
Transformers on long-sequence benchmarks such as PG19 and Passkey Retrieval,
while delivering a 48% speedup in inference efficiency. Both theoretical
analysis and empirical results demonstrate that MemMamba achieves a
breakthrough in the complexity-memory trade-off, offering a new paradigm for
ultra-long sequence modeling.
Meta-Awareness Enhances Reasoning Models: Self-Alignment Reinforcement
  Learning
Authors: name: Yoonjeon Kim, name: Doohyuk Jang, name: Eunho Yang
Published: 2025-09-26 14:05:48

Recent studies on reasoning models explore the meta-awareness of language
models, the ability to know how to think by itself. We argue that large
reasoning models lack this meta-awareness property by proving severe
misalignment between true rollouts and predicted meta information. We posit
that aligning meta-prediction with true rollouts will lead to significant
performance gains. To verify this hypothesis, we design a training pipeline
that boosts Meta-Awareness via Self-Alignment (MASA), and prove that enhanced
meta-awareness directly translates to improved accuracy. Unlike existing
meta-cognitive reasoning models, our method does not require external training
sources but leverages self-generated signals to train meta-awareness. Moreover,
our method enables efficient training by i) filtering out zero-variance prompts
that are either trivial or unsolvable and ii) cutting off lengthy rollouts when
they are unlikely to lead to correct answers. The results are inspiring: our
strategy yields significant improvements in both accuracy and training
efficiency on in-domain tasks and shows strong generalization to out-of-domain
benchmarks. More specifically, our method can speed up GRPO training by over
1.28x to reach the same performance, and achieve a 19.3% gain in accuracy on
AIME25, and a 6.2 % average gain over six mathematics benchmarks. Training with
meta-cognitive guidance enhances out-of-domain generalization, giving a 3.87 %
boost on GPQA-Diamond and a 2.08 % overall accuracy gain across 13 benchmarks
spanning logical, scientific, and coding domains.
From What to Why: A Multi-Agent System for Evidence-based Chemical
  Reaction Condition Reasoning
Authors: name: Cheng Yang, name: Jiaxuan Lu, name: Haiyuan Wan, name: Junchi Yu, name: Feiwei Qin
Published: 2025-09-28 09:34:35

The chemical reaction recommendation is to select proper reaction condition
parameters for chemical reactions, which is pivotal to accelerating chemical
science. With the rapid development of large language models (LLMs), there is
growing interest in leveraging their reasoning and planning capabilities for
reaction condition recommendation. Despite their success, existing methods
rarely explain the rationale behind the recommended reaction conditions,
limiting their utility in high-stakes scientific workflows. In this work, we
propose ChemMAS, a multi-agent system that reframes condition prediction as an
evidence-based reasoning task. ChemMAS decomposes the task into mechanistic
grounding, multi-channel recall, constraint-aware agentic debate, and rationale
aggregation. Each decision is backed by interpretable justifications grounded
in chemical knowledge and retrieved precedents. Experiments show that ChemMAS
achieves 20-35% gains over domain-specific baselines and outperforms
general-purpose LLMs by 10-15% in Top-1 accuracy, while offering falsifiable,
human-trustable rationales, which establishes a new paradigm for explainable AI
in scientific discovery.
InstructX: Towards Unified Visual Editing with MLLM Guidance
Authors: name: Chong Mou, name: Qichao Sun, name: Yanze Wu, name: Pengze Zhang, name: Xinghui Li, name: Fulong Ye, name: Songtao Zhao, name: Qian He
Published: 2025-10-09 17:26:09

With recent advances in Multimodal Large Language Models (MLLMs) showing
strong visual understanding and reasoning, interest is growing in using them to
improve the editing performance of diffusion models. Despite rapid progress,
most studies lack an in-depth analysis of MLLM design choices. Moreover, the
integration of MLLMs and diffusion models remains an open challenge in some
difficult tasks, such as video editing. In this paper, we present InstructX, a
unified framework for image and video editing. Specifically, we conduct a
comprehensive study on integrating MLLMs and diffusion models for
instruction-driven editing across diverse tasks. Building on this study, we
analyze the cooperation and distinction between images and videos in unified
modeling. (1) We show that training on image data can lead to emergent video
editing capabilities without explicit supervision, thereby alleviating the
constraints imposed by scarce video training data. (2) By incorporating
modality-specific MLLM features, our approach effectively unifies image and
video editing tasks within a single model. Extensive experiments demonstrate
that our method can handle a broad range of image and video editing tasks and
achieves state-of-the-art performance.
Towards Scalable and Consistent 3D Editing
Authors: name: Ruihao Xia, name: Yang Tang, name: Pan Zhou
Published: 2025-10-03 13:34:55

3D editing - the task of locally modifying the geometry or appearance of a 3D
asset - has wide applications in immersive content creation, digital
entertainment, and AR/VR. However, unlike 2D editing, it remains challenging
due to the need for cross-view consistency, structural fidelity, and
fine-grained controllability. Existing approaches are often slow, prone to
geometric distortions, or dependent on manual and accurate 3D masks that are
error-prone and impractical. To address these challenges, we advance both the
data and model fronts. On the data side, we introduce 3DEditVerse, the largest
paired 3D editing benchmark to date, comprising 116,309 high-quality training
pairs and 1,500 curated test pairs. Built through complementary pipelines of
pose-driven geometric edits and foundation model-guided appearance edits,
3DEditVerse ensures edit locality, multi-view consistency, and semantic
alignment. On the model side, we propose 3DEditFormer, a
3D-structure-preserving conditional transformer. By enhancing image-to-3D
generation with dual-guidance attention and time-adaptive gating, 3DEditFormer
disentangles editable regions from preserved structure, enabling precise and
consistent edits without requiring auxiliary 3D masks. Extensive experiments
demonstrate that our framework outperforms state-of-the-art baselines both
quantitatively and qualitatively, establishing a new standard for practical and
scalable 3D editing. Dataset and code will be released. Project:
https://www.lv-lab.org/3DEditFormer/
LLMs Learn to Deceive Unintentionally: Emergent Misalignment in
  Dishonesty from Misaligned Samples to Biased Human-AI Interactions
Authors: name: XuHao Hu, name: Peng Wang, name: Xiaoya Lu, name: Dongrui Liu, name: Xuanjing Huang, name: Jing Shao
Published: 2025-10-09 13:35:19

Previous research has shown that LLMs finetuned on malicious or incorrect
completions within narrow domains (e.g., insecure code or incorrect medical
advice) can become broadly misaligned to exhibit harmful behaviors, which is
called emergent misalignment. In this work, we investigate whether this
phenomenon can extend beyond safety behaviors to a broader spectrum of
dishonesty and deception under high-stakes scenarios (e.g., lying under
pressure and deceptive behavior). To explore this, we finetune open-sourced
LLMs on misaligned completions across diverse domains. Experimental results
demonstrate that LLMs show broadly misaligned behavior in dishonesty.
Additionally, we further explore this phenomenon in a downstream combined
finetuning setting, and find that introducing as little as 1% of misalignment
data into a standard downstream task is sufficient to decrease honest behavior
over 20%. Furthermore, we consider a more practical human-AI interaction
environment where we simulate both benign and biased users to interact with the
assistant LLM. Notably, we find that the assistant can be misaligned
unintentionally to exacerbate its dishonesty with only 10% biased user
population. In summary, we extend the study of emergent misalignment to the
domain of dishonesty and deception under high-stakes scenarios, and demonstrate
that this risk arises not only through direct finetuning, but also in
downstream mixture tasks and practical human-AI interactions.