Orionnnn/hub-stats huggingface dataset:
MOSAIC: Multi-Subject Personalized Generation via Correspondence-Aware
  Alignment and Disentanglement
Authors: name: Dong She, name: Siming Fu, name: Mushui Liu, name: Qiaoqiao Jin, name: Hualiang Wang, name: Mu Liu, name: Jidong Jiang
Published: 2025-09-02 05:40:07

Multi-subject personalized generation presents unique challenges in
maintaining identity fidelity and semantic coherence when synthesizing images
conditioned on multiple reference subjects. Existing methods often suffer from
identity blending and attribute leakage due to inadequate modeling of how
different subjects should interact within shared representation spaces. We
present MOSAIC, a representation-centric framework that rethinks multi-subject
generation through explicit semantic correspondence and orthogonal feature
disentanglement. Our key insight is that multi-subject generation requires
precise semantic alignment at the representation level - knowing exactly which
regions in the generated image should attend to which parts of each reference.
To enable this, we introduce SemAlign-MS, a meticulously annotated dataset
providing fine-grained semantic correspondences between multiple reference
subjects and target images, previously unavailable in this domain. Building on
this foundation, we propose the semantic correspondence attention loss to
enforce precise point-to-point semantic alignment, ensuring high consistency
from each reference to its designated regions. Furthermore, we develop the
multi-reference disentanglement loss to push different subjects into orthogonal
attention subspaces, preventing feature interference while preserving
individual identity characteristics. Extensive experiments demonstrate that
MOSAIC achieves state-of-the-art performance on multiple benchmarks. Notably,
while existing methods typically degrade beyond 3 subjects, MOSAIC maintains
high fidelity with 4+ reference subjects, opening new possibilities for complex
multi-subject synthesis applications.
Open Data Synthesis For Deep Research
Authors: name: Ziyi Xia, name: Kun Luo, name: Hongjin Qian, name: Zheng Liu
Published: 2025-08-30 06:02:56

Large language models (LLMs) are increasingly expected to go beyond simple
factual queries toward Deep Research-tasks that require decomposing questions
into sub-problems, coordinating multi-step reasoning, and synthesizing evidence
from diverse sources. We formalize Deep Research tasks with verifiable answers
as Hierarchical Constraint Satisfaction Problems (HCSPs), which are
fundamentally different from single-constraint, multi-hop, or flat CSP
formulations. However, existing benchmarks (e.g., Natural Questions, HotpotQA)
fail to capture this complexity, while recent synthetic datasets often
introduce shortcut reasoning, knowledge leakage, or lack sufficient structural
depth. To address this gap, we introduce InfoSeek, a scalable framework for
synthesizing complex Deep Research tasks. InfoSeek uses a dual-agent system to
recursively build a Research Tree from large-scale webpages, blurring
intermediate nodes into valid sub-problems, and converting these trees into
natural language questions that require traversing the full hierarchy. It also
enables rapid scaling, yielding over 50K training examples, a curated test set,
and reasoning trajectories generated via reject sampling. Experiments show that
models trained on InfoSeek consistently outperform strong baselines. On a
challenging benchmark BrowseComp-Plus, 3B LLMs optimized with InfoSeek surpass
much larger 32B models and lightweight commercial APIs (e.g., Gemini2.5-Flash),
while achieving performance comparable to stronger APIs (e.g., Gemini2.5-Pro).
By preserving meta-information such as intermediate steps and retrieval labels,
InfoSeek further supports advanced optimization strategies, including compound
reward design and trajectory-level exploration. We provide our codes and
datasets in https://github.com/VectorSpaceLab/InfoSeek{this repository}.
Robix: A Unified Model for Robot Interaction, Reasoning and Planning
Authors: name: Huang Fang, name: Mengxi Zhang, name: Heng Dong, name: Wei Li, name: Zixuan Wang, name: Qifeng Zhang, name: Xueyun Tian, name: Yucheng Hu, name: Hang Li
Published: 2025-09-01 03:53:47

We introduce Robix, a unified model that integrates robot reasoning, task
planning, and natural language interaction within a single vision-language
architecture. Acting as the high-level cognitive layer in a hierarchical robot
system, Robix dynamically generates atomic commands for the low-level
controller and verbal responses for human interaction, enabling robots to
follow complex instructions, plan long-horizon tasks, and interact naturally
with human within an end-to-end framework. Robix further introduces novel
capabilities such as proactive dialogue, real-time interruption handling, and
context-aware commonsense reasoning during task execution. At its core, Robix
leverages chain-of-thought reasoning and adopts a three-stage training
strategy: (1) continued pretraining to enhance foundational embodied reasoning
abilities including 3D spatial understanding, visual grounding, and
task-centric reasoning; (2) supervised finetuning to model human-robot
interaction and task planning as a unified reasoning-action sequence; and (3)
reinforcement learning to improve reasoning-action consistency and long-horizon
task coherence. Extensive experiments demonstrate that Robix outperforms both
open-source and commercial baselines (e.g., GPT-4o and Gemini 2.5 Pro) in
interactive task execution, demonstrating strong generalization across diverse
instruction types (e.g., open-ended, multi-stage, constrained, invalid, and
interrupted) and various user-involved tasks such as table bussing, grocery
shopping, and dietary filtering.
On the Theoretical Limitations of Embedding-Based Retrieval
Authors: name: Orion Weller, name: Michael Boratko, name: Iftekhar Naim, name: Jinhyuk Lee
Published: 2025-08-28 17:43:53

Vector embeddings have been tasked with an ever-increasing set of retrieval
tasks over the years, with a nascent rise in using them for reasoning,
instruction-following, coding, and more. These new benchmarks push embeddings
to work for any query and any notion of relevance that could be given. While
prior works have pointed out theoretical limitations of vector embeddings,
there is a common assumption that these difficulties are exclusively due to
unrealistic queries, and those that are not can be overcome with better
training data and larger models. In this work, we demonstrate that we may
encounter these theoretical limitations in realistic settings with extremely
simple queries. We connect known results in learning theory, showing that the
number of top-k subsets of documents capable of being returned as the result of
some query is limited by the dimension of the embedding. We empirically show
that this holds true even if we restrict to k=2, and directly optimize on the
test set with free parameterized embeddings. We then create a realistic dataset
called LIMIT that stress tests models based on these theoretical results, and
observe that even state-of-the-art models fail on this dataset despite the
simple nature of the task. Our work shows the limits of embedding models under
the existing single vector paradigm and calls for future research to develop
methods that can resolve this fundamental limitation.
Flavors of Moonshine: Tiny Specialized ASR Models for Edge Devices
Authors: name: Evan King, name: Adam Sabra, name: Manjunath Kudlur, name: James Wang, name: Pete Warden
Published: 2025-09-02 17:22:54

We present the Flavors of Moonshine, a suite of tiny automatic speech
recognition (ASR) models specialized for a range of underrepresented languages.
Prevailing wisdom suggests that multilingual ASR models outperform monolingual
counterparts by exploiting cross-lingual phonetic similarities. We challenge
this assumption, showing that for sufficiently small models (27M parameters),
training monolingual systems on a carefully balanced mix of high-quality
human-labeled, pseudo-labeled, and synthetic data yields substantially superior
performance. On average, our models achieve error rates 48% lower than the
comparably sized Whisper Tiny model, outperform the 9x larger Whisper Small
model, and in most cases match or outperform the 28x larger Whisper Medium
model. These results advance the state of the art for models of this size,
enabling accurate on-device ASR for languages that previously had limited
support. We release Arabic, Chinese, Japanese, Korean, Ukrainian, and
Vietnamese Moonshine models under a permissive open-source license.
Flaw or Artifact? Rethinking Prompt Sensitivity in Evaluating LLMs
Authors: name: Andong Hua, name: Kenan Tang, name: Chenhe Gu, name: Jindong Gu, name: Eric Wong, name: Yao Qin
Published: 2025-09-01 21:38:28

Prompt sensitivity, referring to the phenomenon where paraphrasing (i.e.,
repeating something written or spoken using different words) leads to
significant changes in large language model (LLM) performance, has been widely
accepted as a core limitation of LLMs. In this work, we revisit this issue and
ask: Is the widely reported high prompt sensitivity truly an inherent weakness
of LLMs, or is it largely an artifact of evaluation processes? To answer this
question, we systematically evaluate 7 LLMs (e.g., GPT and Gemini family)
across 6 benchmarks, including both multiple-choice and open-ended tasks on 12
diverse prompt templates. We find that much of the prompt sensitivity stems
from heuristic evaluation methods, including log-likelihood scoring and rigid
answer matching, which often overlook semantically correct responses expressed
through alternative phrasings, such as synonyms or paraphrases. When we adopt
LLM-as-a-Judge evaluations, we observe a substantial reduction in performance
variance and a consistently higher correlation in model rankings across
prompts. Our findings suggest that modern LLMs are more robust to prompt
templates than previously believed, and that prompt sensitivity may be more an
artifact of evaluation than a flaw in the models.
Gated Associative Memory: A Parallel O(N) Architecture for Efficient
  Sequence Modeling
Authors: name: Rishiraj Acharya
Published: 2025-08-30 20:59:46

The Transformer architecture, underpinned by the self-attention mechanism,
has become the de facto standard for sequence modeling tasks. However, its core
computational primitive scales quadratically with sequence length (O(N^2)),
creating a significant bottleneck for processing long contexts. In this paper,
we propose the Gated Associative Memory (GAM) network, a novel, fully parallel
architecture for sequence modeling that exhibits linear complexity (O(N)) with
respect to sequence length. The GAM block replaces the self-attention layer
with two parallel pathways: a causal convolution to efficiently capture local,
position-dependent context, and a parallel associative memory retrieval
mechanism to model global, content-based patterns. These pathways are
dynamically fused using a gating mechanism, allowing the model to flexibly
combine local and global information for each token. We implement GAM from
scratch and conduct a rigorous comparative analysis against a standard
Transformer model and a modern linear-time baseline (Mamba) on the WikiText-2
benchmark, as well as against the Transformer on the TinyStories dataset. Our
experiments demonstrate that GAM is consistently faster, outperforming both
baselines on training speed, and achieves a superior or competitive final
validation perplexity across all datasets, establishing it as a promising and
efficient alternative for sequence modeling.
DynaGuard: A Dynamic Guardrail Model With User-Defined Policies
Authors: name: Monte Hoover, name: Vatsal Baherwani, name: Neel Jain, name: Khalid Saifullah, name: Joseph Vincent, name: Chirag Jain, name: Melissa Kazemi Rad, name: C. Bayan Bruss, name: Ashwinee Panda, name: Tom Goldstein
Published: 2025-09-02 17:57:56

Guardian models are used to supervise and moderate the outputs of user-facing
chatbots, enforcing guardrails and detecting bad behaviors. Standard guardian
models like LlamaGuard detect predefined, static categories of harms. We
propose dynamic guardian models that evaluate text based on user-defined
policies, making them useful for different application domains that are not
addressed by standard guardian models. Our dynamic guardian models can be used
for fast detection of policy violations or with chain-of-thought reasoning that
articulates and justifies the model outputs. Our dynamic guardian models match
static models in detection accuracy for static harm categories while
identifying violations of free-form policies with accuracy comparable to
frontier reasoning models in a fraction of the time.
Stairway to Fairness: Connecting Group and Individual Fairness
Authors: name: Theresia Veronika Rampisela, name: Maria Maistro, name: Tuukka Ruotsalo, name: Falk Scholer, name: Christina Lioma
Published: 2025-08-29 05:25:05

Fairness in recommender systems (RSs) is commonly categorised into group
fairness and individual fairness. However, there is no established scientific
understanding of the relationship between the two fairness types, as prior work
on both types has used different evaluation measures or evaluation objectives
for each fairness type, thereby not allowing for a proper comparison of the
two. As a result, it is currently not known how increasing one type of fairness
may affect the other. To fill this gap, we study the relationship of group and
individual fairness through a comprehensive comparison of evaluation measures
that can be used for both fairness types. Our experiments with 8 runs across 3
datasets show that recommendations that are highly fair for groups can be very
unfair for individuals. Our finding is novel and useful for RS practitioners
aiming to improve the fairness of their systems. Our code is available at:
https://github.com/theresiavr/stairway-to-fairness.
ELV-Halluc: Benchmarking Semantic Aggregation Hallucinations in Long
  Video Understanding
Authors: name: Hao Lu, name: Jiahao Wang, name: Yaolun Zhang, name: Ruohui Wang, name: Xuanyu Zheng, name: Yepeng Tang, name: Dahua Lin, name: Lewei Lu
Published: 2025-08-29 10:25:03

Video multimodal large language models (Video-MLLMs) have achieved remarkable
progress in video understanding. However, they remain vulnerable to
hallucination-producing content inconsistent with or unrelated to video inputs.
Previous video hallucination benchmarks primarily focus on short-videos. They
attribute hallucinations to factors such as strong language priors, missing
frames, or vision-language biases introduced by the visual encoder. While these
causes indeed account for most hallucinations in short videos, they still
oversimplify the cause of hallucinations. Sometimes, models generate incorrect
outputs but with correct frame-level semantics. We refer to this type of
hallucination as Semantic Aggregation Hallucination (SAH), which arises during
the process of aggregating frame-level semantics into event-level semantic
groups. Given that SAH becomes particularly critical in long videos due to
increased semantic complexity across multiple events, it is essential to
separate and thoroughly investigate the causes of this type of hallucination.
To address the above issues, we introduce ELV-Halluc, the first benchmark
dedicated to long-video hallucination, enabling a systematic investigation of
SAH. Our experiments confirm the existence of SAH and show that it increases
with semantic complexity. Additionally, we find that models are more prone to
SAH on rapidly changing semantics. Moreover, we discuss potential approaches to
mitigate SAH. We demonstrate that positional encoding strategy contributes to
alleviating SAH, and further adopt DPO strategy to enhance the model's ability
to distinguish semantics within and across events. To support this, we curate a
dataset of 8K adversarial data pairs and achieve improvements on both
ELV-Halluc and Video-MME, including a substantial 27.7% reduction in SAH ratio.
FastFit: Accelerating Multi-Reference Virtual Try-On via Cacheable
  Diffusion Models
Authors: name: Zheng Chong, name: Yanwei Lei, name: Shiyue Zhang, name: Zhuandi He, name: Zhen Wang, name: Xujie Zhang, name: Xiao Dong, name: Yiling Wu, name: Dongmei Jiang, name: Xiaodan Liang
Published: 2025-08-28 09:25:52

Despite its great potential, virtual try-on technology is hindered from
real-world application by two major challenges: the inability of current
methods to support multi-reference outfit compositions (including garments and
accessories), and their significant inefficiency caused by the redundant
re-computation of reference features in each denoising step. To address these
challenges, we propose FastFit, a high-speed multi-reference virtual try-on
framework based on a novel cacheable diffusion architecture. By employing a
Semi-Attention mechanism and substituting traditional timestep embeddings with
class embeddings for reference items, our model fully decouples reference
feature encoding from the denoising process with negligible parameter overhead.
This allows reference features to be computed only once and losslessly reused
across all steps, fundamentally breaking the efficiency bottleneck and
achieving an average 3.5x speedup over comparable methods. Furthermore, to
facilitate research on complex, multi-reference virtual try-on, we introduce
DressCode-MR, a new large-scale dataset. It comprises 28,179 sets of
high-quality, paired images covering five key categories (tops, bottoms,
dresses, shoes, and bags), constructed through a pipeline of expert models and
human feedback refinement. Extensive experiments on the VITON-HD, DressCode,
and our DressCode-MR datasets show that FastFit surpasses state-of-the-art
methods on key fidelity metrics while offering its significant advantage in
inference efficiency.
DCPO: Dynamic Clipping Policy Optimization
Authors: name: Shihui Yang, name: Chengfeng Dou, name: Peidong Guo, name: Kai Lu, name: Qiang Ju, name: Fei Deng, name: Rihui Xin
Published: 2025-09-02 14:01:07

Reinforcement Learning from Verifiable Rewards (RLVR) has emerged as a
promising framework for enhancing the reasoning capabilities of large language
models. However, existing approaches such as GRPO often suffer from zero
gradients. This problem arises primarily due to fixed clipping bounds for
token-level probability ratios and the standardization of identical rewards,
which can lead to ineffective gradient updates and underutilization of
generated responses. In this work, we propose Dynamic Clipping Policy
Optimization (DCPO), which introduces a dynamic clipping strategy that
adaptively adjusts the clipping bounds based on token-specific prior
probabilities to enhance token-level exploration, and a smooth advantage
standardization technique that standardizes rewards across cumulative training
steps to improve the response-level effective utilization of generated
responses. DCPO achieved state-of-the-art performance on four benchmarks based
on four different models. In particular, DCPO achieved an Avg@1 of 46.7 under
greedy decoding and an Avg@32 of 38.8 under 32 times sampling on the AIME24
benchmark, surpassing both DAPO (36.7/31.6) and GRPO (36.7/32.1) on the
Qwen2.5-Math-7B model. On the AIME25 benchmark based on Qwen2.5-14B, DCPO
achieves a performance of (23.3/19.0), surpassing GRPO (13.3/10.5) and DAPO
(20.0/15.3). Furthermore, DCPO achieved an average 28% improvement in the
nonzero advantage over GRPO in four models, doubled the training efficiency
over DAPO, and significantly reduced the token clipping ratio by an order of
magnitude compared to both GRPO and DAPO, while achieving superior performance.
These results highlight DCPO's effectiveness in leveraging generated data more
efficiently for reinforcement learning in large language models.
Benchmarking Optimizers for Large Language Model Pretraining
Authors: name: Andrei Semenov, name: Matteo Pagliardini, name: Martin Jaggi
Published: 2025-09-01 12:50:30

The recent development of Large Language Models (LLMs) has been accompanied
by an effervescence of novel ideas and methods to better optimize the loss of
deep learning models. Claims from those methods are myriad: from faster
convergence to removing reliance on certain hyperparameters. However, the
diverse experimental protocols used to validate these claims make direct
comparisons between methods challenging. This study presents a comprehensive
evaluation of recent optimization techniques across standardized LLM
pretraining scenarios, systematically varying model size, batch size, and
training duration. Through careful tuning of each method, we provide guidance
to practitioners on which optimizer is best suited for each scenario. For
researchers, our work highlights promising directions for future optimization
research. Finally, by releasing our code and making all experiments fully
reproducible, we hope our efforts can help the development and rigorous
benchmarking of future methods.
ViSTA-SLAM: Visual SLAM with Symmetric Two-view Association
Authors: name: Ganlin Zhang, name: Shenhan Qian, name: Xi Wang, name: Daniel Cremers
Published: 2025-09-01 16:12:23

We present ViSTA-SLAM as a real-time monocular visual SLAM system that
operates without requiring camera intrinsics, making it broadly applicable
across diverse camera setups. At its core, the system employs a lightweight
symmetric two-view association (STA) model as the frontend, which
simultaneously estimates relative camera poses and regresses local pointmaps
from only two RGB images. This design reduces model complexity significantly,
the size of our frontend is only 35\% that of comparable state-of-the-art
methods, while enhancing the quality of two-view constraints used in the
pipeline. In the backend, we construct a specially designed Sim(3) pose graph
that incorporates loop closures to address accumulated drift. Extensive
experiments demonstrate that our approach achieves superior performance in both
camera tracking and dense 3D reconstruction quality compared to current
methods. Github repository: https://github.com/zhangganlin/vista-slam
The Gold Medals in an Empty Room: Diagnosing Metalinguistic Reasoning in
  LLMs with Camlang
Authors: name: Fenghua Liu, name: Yulong Chen, name: Yixuan Liu, name: Zhujun Jin, name: Solomon Tsai, name: Ming Zhong
Published: 2025-08-30 09:13:10

Large Language Models (LLMs) achieve gold-medal performance across many
benchmarks, yet it remains unclear whether such success reflects genuine
reasoning or pattern matching. From a cognitive science perspective, an
informative test is whether models can master an unfamiliar language through
explicit metalinguistic deductive learning, a paradigm where human learners can
reliably internalise grammatical systems through metalinguistic reasoning. We
address this question with Camlang, a novel constructed language that exhibits
naturalistic yet unattested feature combinations. Camlang consists of two
explicit resources, a grammar book and a bilingual dictionary, which mirror
adult second-language learning via explicit grammar rules and lexical lookup,
and enable us to disentangle errors in morpho-syntax, lexical semantics, and
sentence-level reasoning. Human experiments show that these resources are
sufficient for participants to acquire Camlang and successfully solve Camlang
tasks. To operationalise evaluation, we adapt CommonsenseQA into Camlang,
creating Camlang-CSQA-v0, the first task in a broader suite where solving
questions requires applying grammar rules and lexical mappings. Experimental
results show that GPT-5 achieves 98\% EM accuracy in English but only 47\% in
Camlang, far below human performance at 87\%, while other state-of-the-art
reasoning LLMs perform even worse. Human verification further reveals that most
model successes stem from shallow lexical alignment while GPT-5 shows emerging
metalinguistic awareness to a limited extent but not systematic grammatical
mastery as humans. Camlang establishes a cognitively grounded evaluation
paradigm that exposes fundamental gaps between current models and human
metalinguistic competence.