Orionnnn/hub-stats huggingface dataset:
OnePiece: Bringing Context Engineering and Reasoning to Industrial
  Cascade Ranking System
Authors: name: Sunhao Dai, name: Jiakai Tang, name: Jiahua Wu, name: Kun Wang, name: Yuxuan Zhu, name: Bingjun Chen, name: Bangyang Hong, name: Yu Zhao, name: Cong Fu, name: Kangle Wu, name: Yabo Ni, name: Anxiang Zeng, name: Wenjie Wang, name: Xu Chen, name: Jun Xu, name: See-Kiong Ng
Published: 2025-09-22 17:59:07

Despite the growing interest in replicating the scaled success of large
language models (LLMs) in industrial search and recommender systems, most
existing industrial efforts remain limited to transplanting Transformer
architectures, which bring only incremental improvements over strong Deep
Learning Recommendation Models (DLRMs). From a first principle perspective, the
breakthroughs of LLMs stem not only from their architectures but also from two
complementary mechanisms: context engineering, which enriches raw input queries
with contextual cues to better elicit model capabilities, and multi-step
reasoning, which iteratively refines model outputs through intermediate
reasoning paths. However, these two mechanisms and their potential to unlock
substantial improvements remain largely underexplored in industrial ranking
systems.
  In this paper, we propose OnePiece, a unified framework that seamlessly
integrates LLM-style context engineering and reasoning into both retrieval and
ranking models of industrial cascaded pipelines. OnePiece is built on a pure
Transformer backbone and further introduces three key innovations: (1)
structured context engineering, which augments interaction history with
preference and scenario signals and unifies them into a structured tokenized
input sequence for both retrieval and ranking; (2) block-wise latent reasoning,
which equips the model with multi-step refinement of representations and scales
reasoning bandwidth via block size; (3) progressive multi-task training, which
leverages user feedback chains to effectively supervise reasoning steps during
training. OnePiece has been deployed in the main personalized search scenario
of Shopee and achieves consistent online gains across different key business
metrics, including over +2% GMV/UU and a +2.90% increase in advertising
revenue.
EpiCache: Episodic KV Cache Management for Long Conversational Question
  Answering
Authors: name: Minsoo Kim, name: Arnav Kundu, name: Han-Byul Kim, name: Richa Dixit, name: Minsik Cho
Published: 2025-09-22 06:56:35

Recent advances in large language models (LLMs) have extended context
lengths, enabling assistants to sustain long histories for coherent,
personalized responses. This ability, however, hinges on Key-Value (KV)
caching, whose memory grows linearly with dialogue length and quickly dominates
under strict resource constraints. An active line of research for reducing this
overhead is KV cache compression, which seeks to limit cache size while
preserving accuracy. Yet existing methods face two major limitations: (i)
evicting entries after full-context prefill causes unbounded peak memory, and
(ii) query-dependent eviction narrows the cache to a single query, leading to
degraded accuracy in multi-turn conversations. We introduce EpiCache, a
training-free KV cache management framework for long conversational question
answering (LongConvQA) under fixed memory budgets. EpiCache bounds cache growth
through block-wise prefill and preserves topic-relevant context via episodic KV
compression, which clusters conversation history into coherent episodes and
applies episode-specific KV cache eviction. We further design an adaptive
layer-wise budget allocation strategy that measures each layer's sensitivity to
eviction and distributes the memory budget across layers accordingly. Across
three LongConvQA benchmarks, EpiCache improves accuracy by up to 40% over
recent baselines, sustains near-full KV accuracy under 4-6x compression, and
reduces latency and memory by up to 2.4x and 3.5x, thereby enabling efficient
multi-turn interaction under strict resource constraints.
ContextFlow: Training-Free Video Object Editing via Adaptive Context
  Enrichment
Authors: name: Yiyang Chen, name: Xuanhua He, name: Xiujun Ma, name: Yue Ma
Published: 2025-09-22 14:13:31

Training-free video object editing aims to achieve precise object-level
manipulation, including object insertion, swapping, and deletion. However, it
faces significant challenges in maintaining fidelity and temporal consistency.
Existing methods, often designed for U-Net architectures, suffer from two
primary limitations: inaccurate inversion due to first-order solvers, and
contextual conflicts caused by crude "hard" feature replacement. These issues
are more challenging in Diffusion Transformers (DiTs), where the unsuitability
of prior layer-selection heuristics makes effective guidance challenging. To
address these limitations, we introduce ContextFlow, a novel training-free
framework for DiT-based video object editing. In detail, we first employ a
high-order Rectified Flow solver to establish a robust editing foundation. The
core of our framework is Adaptive Context Enrichment (for specifying what to
edit), a mechanism that addresses contextual conflicts. Instead of replacing
features, it enriches the self-attention context by concatenating Key-Value
pairs from parallel reconstruction and editing paths, empowering the model to
dynamically fuse information. Additionally, to determine where to apply this
enrichment (for specifying where to edit), we propose a systematic, data-driven
analysis to identify task-specific vital layers. Based on a novel Guidance
Responsiveness Metric, our method pinpoints the most influential DiT blocks for
different tasks (e.g., insertion, swapping), enabling targeted and highly
effective guidance. Extensive experiments show that ContextFlow significantly
outperforms existing training-free methods and even surpasses several
state-of-the-art training-based approaches, delivering temporally coherent,
high-fidelity results.
Synthetic bootstrapped pretraining
Authors: name: Zitong Yang, name: Aonan Zhang, name: Hong Liu, name: Tatsunori Hashimoto, name: Emmanuel Candès, name: Chong Wang, name: Ruoming Pang
Published: 2025-09-17 22:28:27

We introduce Synthetic Bootstrapped Pretraining (SBP), a language model (LM)
pretraining procedure that first learns a model of relations between documents
from the pretraining dataset and then leverages it to synthesize a vast new
corpus for joint training. While the standard pretraining teaches LMs to learn
causal correlations among tokens within a single document, it is not designed
to efficiently model the rich, learnable inter-document correlations that can
potentially lead to better performance. We validate SBP by designing a
compute-matched pretraining setup and pretrain a 3B-parameter model on up to 1T
tokens from scratch. We find SBP consistently improves upon a strong repetition
baseline and delivers a significant fraction of performance improvement
attainable by an oracle upper bound with access to 20x more unique data.
Qualitative analysis reveals that the synthesized documents go beyond mere
paraphrases -- SBP first abstracts a core concept from the seed material and
then crafts a new narration on top of it. Besides strong empirical performance,
SBP admits a natural Bayesian interpretation: the synthesizer implicitly learns
to abstract the latent concepts shared between related documents.
DiffusionNFT: Online Diffusion Reinforcement with Forward Process
Authors: name: Kaiwen Zheng, name: Huayu Chen, name: Haotian Ye, name: Haoxiang Wang, name: Qinsheng Zhang, name: Kai Jiang, name: Hang Su, name: Stefano Ermon, name: Jun Zhu, name: Ming-Yu Liu
Published: 2025-09-19 16:09:33

Online reinforcement learning (RL) has been central to post-training language
models, but its extension to diffusion models remains challenging due to
intractable likelihoods. Recent works discretize the reverse sampling process
to enable GRPO-style training, yet they inherit fundamental drawbacks,
including solver restrictions, forward-reverse inconsistency, and complicated
integration with classifier-free guidance (CFG). We introduce Diffusion
Negative-aware FineTuning (DiffusionNFT), a new online RL paradigm that
optimizes diffusion models directly on the forward process via flow matching.
DiffusionNFT contrasts positive and negative generations to define an implicit
policy improvement direction, naturally incorporating reinforcement signals
into the supervised learning objective. This formulation enables training with
arbitrary black-box solvers, eliminates the need for likelihood estimation, and
requires only clean images rather than sampling trajectories for policy
optimization. DiffusionNFT is up to 25times more efficient than FlowGRPO in
head-to-head comparisons, while being CFG-free. For instance, DiffusionNFT
improves the GenEval score from 0.24 to 0.98 within 1k steps, while FlowGRPO
achieves 0.95 with over 5k steps and additional CFG employment. By leveraging
multiple reward models, DiffusionNFT significantly boosts the performance of
SD3.5-Medium in every benchmark tested.
Analyzing the Effects of Supervised Fine-Tuning on Model Knowledge from
  Token and Parameter Levels
Authors: name: Junjie Ye, name: Yuming Yang, name: Yang Nan, name: Shuo Li, name: Qi Zhang, name: Tao Gui, name: Xuanjing Huang, name: Peng Wang, name: Zhongchao Shi, name: Jianping Fan
Published: 2025-09-20 09:40:32

Large language models (LLMs) acquire substantial world knowledge during
pre-training, which is further shaped by post-training techniques such as
supervised fine-tuning (SFT). However, the impact of SFT on a model's knowledge
remains underexplored, limiting our ability to control knowledge change
behavior in fine-tuned models. To address this gap, we evaluate closed-book
question answering (CBQA) performance across five LLMs from the LLaMA-2 and
LLaMA-3 families. Surprisingly, models fine-tuned on 1,920 samples perform up
to 14% worse than those fine-tuned on only 240 samples. Furthermore, varying
the level of knowledge mastery in the fine-tuning data leads to performance
fluctuations of over 12%. To investigate these effects, we analyze model
behavior at both the token and parameter levels. Our analysis reveals that up
to 90% of parameter updates during SFT do not contribute to knowledge
enhancement. Restoring these updates can improve performance on the CBQA task,
depending on the characteristics of the fine-tuning data. These insights offer
practical guidance for developing fine-tuning strategies that more effectively
strengthen model knowledge.
ByteWrist: A Parallel Robotic Wrist Enabling Flexible and
  Anthropomorphic Motion for Confined Spaces
Authors: name: Jiawen Tian, name: Liqun Huang, name: Zhongren Cui, name: Jingchao Qiao, name: Jiafeng Xu, name: Xiao Ma, name: Zeyu Ren
Published: 2025-09-22 17:57:07

This paper introduces ByteWrist, a novel highly-flexible and anthropomorphic
parallel wrist for robotic manipulation. ByteWrist addresses the critical
limitations of existing serial and parallel wrists in narrow-space operations
through a compact three-stage parallel drive mechanism integrated with
arc-shaped end linkages. The design achieves precise RPY (Roll-Pitch-Yaw)
motion while maintaining exceptional compactness, making it particularly
suitable for complex unstructured environments such as home services, medical
assistance, and precision assembly. The key innovations include: (1) a nested
three-stage motor-driven linkages that minimize volume while enabling
independent multi-DOF control, (2) arc-shaped end linkages that optimize force
transmission and expand motion range, and (3) a central supporting ball
functioning as a spherical joint that enhances structural stiffness without
compromising flexibility. Meanwhile, we present comprehensive kinematic
modeling including forward / inverse kinematics and a numerical Jacobian
solution for precise control. Empirically, we observe ByteWrist demonstrates
strong performance in narrow-space maneuverability and dual-arm cooperative
manipulation tasks, outperforming Kinova-based systems. Results indicate
significant improvements in compactness, efficiency, and stiffness compared to
traditional designs, establishing ByteWrist as a promising solution for
next-generation robotic manipulation in constrained environments.
Audio-Conditioned Diffusion LLMs for ASR and Deliberation Processing
Authors: name: Mengqi Wang, name: Zhan Liu, name: Zengrui Jin, name: Guangzhi Sun, name: Chao Zhang, name: Philip C. Woodland
Published: 2025-09-20 10:48:06

Diffusion-based large language models (DLLMs) have recently attracted growing
interest as an alternative to autoregressive decoders. In this work, we present
an empirical study on using the diffusion-based large language model LLaDA for
automatic speech recognition (ASR). We first investigate its use as an external
deliberation-based processing module for Whisper-LLaMA transcripts. By
leveraging the bidirectional attention and denoising capabilities of LLaDA, we
explore random masking, low-confidence masking, and semi-autoregressive
strategies, showing that Whisper-LLaDA substantially reduces WER compared with
the baseline. On LibriSpeech, the best cascade system achieves 2.25%/4.94% WER
on test-clean/test-other, representing a 12.3% relative improvement over the
Whisper-LLaMA baseline on the test-other split. In contrast, a plain-text LLaDA
without acoustic features fails to improve accuracy, highlighting the
importance of audio-conditioned embeddings. We further evaluate Whisper-LLaDA
as a standalone decoder for ASR with diffusion-based and semi-autoregressive
decoding. Most experimental configurations achieve faster inference than the
Whisper-LLaMA baseline, although recognition accuracy is slightly lower. These
findings offer an empirical view of diffusion-based LLMs for ASR and point to
promising directions for improvements.
Towards Human-like Multimodal Conversational Agent by Generating
  Engaging Speech
Authors: name: Taesoo Kim, name: Yongsik Jo, name: Hyunmin Song, name: Taehwan Kim
Published: 2025-09-18 05:14:10

Human conversation involves language, speech, and visual cues, with each
medium providing complementary information. For instance, speech conveys a vibe
or tone not fully captured by text alone. While multimodal LLMs focus on
generating text responses from diverse inputs, less attention has been paid to
generating natural and engaging speech. We propose a human-like agent that
generates speech responses based on conversation mood and responsive style
information. To achieve this, we build a novel MultiSensory Conversation
dataset focused on speech to enable agents to generate natural speech. We then
propose a multimodal LLM-based model for generating text responses and voice
descriptions, which are used to generate speech covering paralinguistic
information. Experimental results demonstrate the effectiveness of utilizing
both visual and audio modalities in conversation to generate engaging speech.
The source code is available in https://github.com/kimtaesu24/MSenC
Do You Hear What I Mean? Quantifying the Instruction-Perception Gap in
  Instruction-Guided Expressive Text-To-Speech Systems
Authors: name: Yi-Cheng Lin, name: Huang-Cheng Chou, name: Tzu-Chieh Wei, name: Kuan-Yu Chen, name: Hung-yi Lee
Published: 2025-09-17 14:00:45

Instruction-guided text-to-speech (ITTS) enables users to control speech
generation through natural language prompts, offering a more intuitive
interface than traditional TTS. However, the alignment between user style
instructions and listener perception remains largely unexplored. This work
first presents a perceptual analysis of ITTS controllability across two
expressive dimensions (adverbs of degree and graded emotion intensity) and
collects human ratings on speaker age and word-level emphasis attributes. To
comprehensively reveal the instruction-perception gap, we provide a data
collection with large-scale human evaluations, named Expressive VOice Control
(E-VOC) corpus. Furthermore, we reveal that (1) gpt-4o-mini-tts is the most
reliable ITTS model with great alignment between instruction and generated
utterances across acoustic dimensions. (2) The 5 analyzed ITTS systems tend to
generate Adult voices even when the instructions ask to use child or Elderly
voices. (3) Fine-grained control remains a major challenge, indicating that
most ITTS systems have substantial room for improvement in interpreting
slightly different attribute instructions.
Latent Zoning Network: A Unified Principle for Generative Modeling,
  Representation Learning, and Classification
Authors: name: Zinan Lin, name: Enshu Liu, name: Xuefei Ning, name: Junyi Zhu, name: Wenyu Wang, name: Sergey Yekhanin
Published: 2025-09-19 04:47:16

Generative modeling, representation learning, and classification are three
core problems in machine learning (ML), yet their state-of-the-art (SoTA)
solutions remain largely disjoint. In this paper, we ask: Can a unified
principle address all three? Such unification could simplify ML pipelines and
foster greater synergy across tasks. We introduce Latent Zoning Network (LZN)
as a step toward this goal. At its core, LZN creates a shared Gaussian latent
space that encodes information across all tasks. Each data type (e.g., images,
text, labels) is equipped with an encoder that maps samples to disjoint latent
zones, and a decoder that maps latents back to data. ML tasks are expressed as
compositions of these encoders and decoders: for example, label-conditional
image generation uses a label encoder and image decoder; image embedding uses
an image encoder; classification uses an image encoder and label decoder. We
demonstrate the promise of LZN in three increasingly complex scenarios: (1) LZN
can enhance existing models (image generation): When combined with the SoTA
Rectified Flow model, LZN improves FID on CIFAR10 from 2.76 to 2.59-without
modifying the training objective. (2) LZN can solve tasks independently
(representation learning): LZN can implement unsupervised representation
learning without auxiliary loss functions, outperforming the seminal MoCo and
SimCLR methods by 9.3% and 0.2%, respectively, on downstream linear
classification on ImageNet. (3) LZN can solve multiple tasks simultaneously
(joint generation and classification): With image and label encoders/decoders,
LZN performs both tasks jointly by design, improving FID and achieving SoTA
classification accuracy on CIFAR10. The code and trained models are available
at https://github.com/microsoft/latent-zoning-networks. The project website is
at https://zinanlin.me/blogs/latent_zoning_networks.html.
SPATIALGEN: Layout-guided 3D Indoor Scene Generation
Authors: name: Chuan Fang, name: Heng Li, name: Yixun Liang, name: Jia Zheng, name: Yongsen Mao, name: Yuan Liu, name: Rui Tang, name: Zihan Zhou, name: Ping Tan
Published: 2025-09-18 14:12:32

Creating high-fidelity 3D models of indoor environments is essential for
applications in design, virtual reality, and robotics. However, manual 3D
modeling remains time-consuming and labor-intensive. While recent advances in
generative AI have enabled automated scene synthesis, existing methods often
face challenges in balancing visual quality, diversity, semantic consistency,
and user control. A major bottleneck is the lack of a large-scale, high-quality
dataset tailored to this task. To address this gap, we introduce a
comprehensive synthetic dataset, featuring 12,328 structured annotated scenes
with 57,440 rooms, and 4.7M photorealistic 2D renderings. Leveraging this
dataset, we present SpatialGen, a novel multi-view multi-modal diffusion model
that generates realistic and semantically consistent 3D indoor scenes. Given a
3D layout and a reference image (derived from a text prompt), our model
synthesizes appearance (color image), geometry (scene coordinate map), and
semantic (semantic segmentation map) from arbitrary viewpoints, while
preserving spatial consistency across modalities. SpatialGen consistently
generates superior results to previous methods in our experiments. We are
open-sourcing our data and models to empower the community and advance the
field of indoor scene understanding and generation.
Ask-to-Clarify: Resolving Instruction Ambiguity through Multi-turn
  Dialogue
Authors: name: Xingyao Lin, name: Xinghao Zhu, name: Tianyi Lu, name: Sicheng Xie, name: Hui Zhang, name: Xipeng Qiu, name: Zuxuan Wu, name: Yu-Gang Jiang
Published: 2025-09-18 15:25:31

The ultimate goal of embodied agents is to create collaborators that can
interact with humans, not mere executors that passively follow instructions.
This requires agents to communicate, coordinate, and adapt their actions based
on human feedback. Recently, advances in VLAs have offered a path toward this
goal. However, most current VLA-based embodied agents operate in a one-way
mode: they receive an instruction and execute it without feedback. This
approach fails in real-world scenarios where instructions are often ambiguous.
In this paper, we address this problem with the Ask-to-Clarify framework. Our
framework first resolves ambiguous instructions by asking questions in a
multi-turn dialogue. Then it generates low-level actions end-to-end.
Specifically, the Ask-to-Clarify framework consists of two components, one VLM
for collaboration and one diffusion for action. We also introduce a connection
module that generates conditions for the diffusion based on the output of the
VLM. This module adjusts the observation by instructions to create reliable
conditions. We train our framework with a two-stage knowledge-insulation
strategy. First, we fine-tune the collaboration component using
ambiguity-solving dialogue data to handle ambiguity. Then, we integrate the
action component while freezing the collaboration one. This preserves the
interaction abilities while fine-tuning the diffusion to generate actions. The
training strategy guarantees our framework can first ask questions, then
generate actions. During inference, a signal detector functions as a router
that helps our framework switch between asking questions and taking actions. We
evaluate the Ask-to-Clarify framework in 8 real-world tasks, where it
outperforms existing state-of-the-art VLAs. The results suggest that our
proposed framework, along with the training strategy, provides a path toward
collaborative embodied agents.