Orionnnn/hub-stats huggingface dataset:
Analysing Chain of Thought Dynamics: Active Guidance or Unfaithful
  Post-hoc Rationalisation?
Authors: name: Samuel Lewis-Lim, name: Xingwei Tan, name: Zhixue Zhao, name: Nikolaos Aletras
Published: 2025-08-27 12:25:29

Recent work has demonstrated that Chain-of-Thought (CoT) often yields limited
gains for soft-reasoning problems such as analytical and commonsense reasoning.
CoT can also be unfaithful to a model's actual reasoning. We investigate the
dynamics and faithfulness of CoT in soft-reasoning tasks across
instruction-tuned, reasoning and reasoning-distilled models. Our findings
reveal differences in how these models rely on CoT, and show that CoT influence
and faithfulness are not always aligned.
SEAM: Semantically Equivalent Across Modalities Benchmark for
  Vision-Language Models
Authors: name: Zhenwei Tang, name: Difan Jiao, name: Blair Yang, name: Ashton Anderson
Published: 2025-08-25 16:33:07

Evaluating whether vision-language models (VLMs) reason consistently across
representations is challenging because modality comparisons are typically
confounded by task differences and asymmetric information. We introduce SEAM, a
benchmark that pairs semantically equivalent inputs across four domains that
have existing standardized textual and visual notations. By employing distinct
notation systems across modalities, in contrast to OCR-based image-text
pairing, SEAM provides a rigorous comparative assessment of the
textual-symbolic and visual-spatial reasoning capabilities of VLMs. Across 21
contemporary models, we observe systematic modality imbalance: vision
frequently lags language in overall performance, despite the problems
containing semantically equivalent information, and cross-modal agreement is
relatively low. Our error analysis reveals two main drivers: textual perception
failures from tokenization in domain notation and visual perception failures
that induce hallucinations. We also show that our results are largely robust to
visual transformations. SEAM establishes a controlled, semantically equivalent
setting for measuring and improving modality-agnostic reasoning.
Gaze into the Heart: A Multi-View Video Dataset for rPPG and Health
  Biomarkers Estimation
Authors: name: Konstantin Egorov, name: Stepan Botman, name: Pavel Blinov, name: Galina Zubkova, name: Anton Ivaschenko, name: Alexander Kolsanov, name: Andrey Savchenko
Published: 2025-08-25 11:46:40

Progress in remote PhotoPlethysmoGraphy (rPPG) is limited by the critical
issues of existing publicly available datasets: small size, privacy concerns
with facial videos, and lack of diversity in conditions. The paper introduces a
novel comprehensive large-scale multi-view video dataset for rPPG and health
biomarkers estimation. Our dataset comprises 3600 synchronized video recordings
from 600 subjects, captured under varied conditions (resting and post-exercise)
using multiple consumer-grade cameras at different angles. To enable multimodal
analysis of physiological states, each recording is paired with a 100 Hz PPG
signal and extended health metrics, such as electrocardiogram, arterial blood
pressure, biomarkers, temperature, oxygen saturation, respiratory rate, and
stress level. Using this data, we train an efficient rPPG model and compare its
quality with existing approaches in cross-dataset scenarios. The public release
of our dataset and model should significantly speed up the progress in the
development of AI medical assistants.
Beyond Transcription: Mechanistic Interpretability in ASR
Authors: name: Neta Glazer, name: Yael Segal-Feldman, name: Hilit Segev, name: Aviv Shamsian, name: Asaf Buchnick, name: Gill Hetz, name: Ethan Fetaya, name: Joseph Keshet, name: Aviv Navon
Published: 2025-08-21 15:42:53

Interpretability methods have recently gained significant attention,
particularly in the context of large language models, enabling insights into
linguistic representations, error detection, and model behaviors such as
hallucinations and repetitions. However, these techniques remain underexplored
in automatic speech recognition (ASR), despite their potential to advance both
the performance and interpretability of ASR systems. In this work, we adapt and
systematically apply established interpretability methods such as logit lens,
linear probing, and activation patching, to examine how acoustic and semantic
information evolves across layers in ASR systems. Our experiments reveal
previously unknown internal dynamics, including specific encoder-decoder
interactions responsible for repetition hallucinations and semantic biases
encoded deep within acoustic representations. These insights demonstrate the
benefits of extending and applying interpretability techniques to speech
recognition, opening promising directions for future research on improving
model transparency and robustness.
MIDAS: Multimodal Interactive Digital-human Synthesis via Real-time
  Autoregressive Video Generation
Authors: name: Ming Chen, name: Liyuan Cui, name: Wenyuan Zhang, name: Haoxian Zhang, name: Yan Zhou, name: Xiaohan Li, name: Xiaoqiang Liu, name: Pengfei Wan
Published: 2025-08-26 14:00:16

Recently, interactive digital human video generation has attracted widespread
attention and achieved remarkable progress. However, building such a practical
system that can interact with diverse input signals in real time remains
challenging to existing methods, which often struggle with high latency, heavy
computational cost, and limited controllability. In this work, we introduce an
autoregressive video generation framework that enables interactive multimodal
control and low-latency extrapolation in a streaming manner. With minimal
modifications to a standard large language model (LLM), our framework accepts
multimodal condition encodings including audio, pose, and text, and outputs
spatially and semantically coherent representations to guide the denoising
process of a diffusion head. To support this, we construct a large-scale
dialogue dataset of approximately 20,000 hours from multiple sources, providing
rich conversational scenarios for training. We further introduce a deep
compression autoencoder with up to 64times reduction ratio, which
effectively alleviates the long-horizon inference burden of the autoregressive
model. Extensive experiments on duplex conversation, multilingual human
synthesis, and interactive world model highlight the advantages of our approach
in low latency, high efficiency, and fine-grained multimodal controllability.
Diffusion Language Models Know the Answer Before Decoding
Authors: name: Pengxiang Li, name: Yefan Zhou, name: Dilxat Muhtar, name: Lu Yin, name: Shilin Yan, name: Li Shen, name: Yi Liang, name: Soroush Vosoughi, name: Shiwei Liu
Published: 2025-08-27 15:40:25

Diffusion language models (DLMs) have recently emerged as an alternative to
autoregressive approaches, offering parallel sequence generation and flexible
token orders. However, their inference remains slower than that of
autoregressive models, primarily due to the cost of bidirectional attention and
the large number of refinement steps required for high quality outputs. In this
work, we highlight and leverage an overlooked property of DLMs early answer
convergence: in many cases, the correct answer can be internally identified by
half steps before the final decoding step, both under semi-autoregressive and
random remasking schedules. For example, on GSM8K and MMLU, up to 97% and 99%
of instances, respectively, can be decoded correctly using only half of the
refinement steps. Building on this observation, we introduce Prophet, a
training-free fast decoding paradigm that enables early commit decoding.
Specifically, Prophet dynamically decides whether to continue refinement or to
go "all-in" (i.e., decode all remaining tokens in one step), using the
confidence gap between the top-2 prediction candidates as the criterion. It
integrates seamlessly into existing DLM implementations, incurs negligible
overhead, and requires no additional training. Empirical evaluations of
LLaDA-8B and Dream-7B across multiple tasks show that Prophet reduces the
number of decoding steps by up to 3.4x while preserving high generation
quality. These results recast DLM decoding as a problem of when to stop
sampling, and demonstrate that early decode convergence provides a simple yet
powerful mechanism for accelerating DLM inference, complementary to existing
speedup techniques. Our code is publicly available at
https://github.com/pixeli99/Prophet.
DeepScholar-Bench: A Live Benchmark and Automated Evaluation for
  Generative Research Synthesis
Authors: name: Liana Patel, name: Negar Arabzadeh, name: Harshit Gupta, name: Ankita Sundar, name: Ion Stoica, name: Matei Zaharia, name: Carlos Guestrin
Published: 2025-08-27 16:36:34

The ability to research and synthesize knowledge is central to human
expertise and progress. An emerging class of systems promises these exciting
capabilities through generative research synthesis, performing retrieval over
the live web and synthesizing discovered sources into long-form, cited
summaries. However, evaluating such systems remains an open challenge: existing
question-answering benchmarks focus on short-form factual responses, while
expert-curated datasets risk staleness and data contamination. Both fail to
capture the complexity and evolving nature of real research synthesis tasks. In
this work, we introduce DeepScholar-bench, a live benchmark and holistic,
automated evaluation framework designed to evaluate generative research
synthesis. DeepScholar-bench draws queries from recent, high-quality ArXiv
papers and focuses on a real research synthesis task: generating the related
work sections of a paper by retrieving, synthesizing, and citing prior
research. Our evaluation framework holistically assesses performance across
three key dimensions, knowledge synthesis, retrieval quality, and
verifiability. We also develop DeepScholar-base, a reference pipeline
implemented efficiently using the LOTUS API. Using the DeepScholar-bench
framework, we perform a systematic evaluation of prior open-source systems,
search AI's, OpenAI's DeepResearch, and DeepScholar-base. We find that
DeepScholar-base establishes a strong baseline, attaining competitive or higher
performance than each other method. We also find that DeepScholar-bench remains
far from saturated, with no system exceeding a score of 19% across all
metrics. These results underscore the difficulty of DeepScholar-bench, as well
as its importance for progress towards AI systems capable of generative
research synthesis. We make our code available at
https://github.com/guestrin-lab/deepscholar-bench.
Taming the Chaos: Coordinated Autoscaling for Heterogeneous and
  Disaggregated LLM Inference
Authors: name: Rongzhi Li, name: Ruogu Du, name: Zefang Chu, name: Sida Zhao, name: Chunlei Han, name: Zuocheng Shi, name: Yiwen Shao, name: Huanle Han, name: Long Huang, name: Zherui Liu, name: Shufan Liu
Published: 2025-08-27 04:22:02

Serving Large Language Models (LLMs) is a GPU-intensive task where
traditional autoscalers fall short, particularly for modern Prefill-Decode
(P/D) disaggregated architectures. This architectural shift, while powerful,
introduces significant operational challenges, including inefficient use of
heterogeneous hardware, network bottlenecks, and critical imbalances between
prefill and decode stages. We introduce HeteroScale, a coordinated autoscaling
framework that addresses the core challenges of P/D disaggregated serving.
HeteroScale combines a topology-aware scheduler that adapts to heterogeneous
hardware and network constraints with a novel metric-driven policy derived from
the first large-scale empirical study of autoscaling signals in production. By
leveraging a single, robust metric to jointly scale prefill and decode pools,
HeteroScale maintains architectural balance while ensuring efficient, adaptive
resource management. Deployed in a massive production environment on tens of
thousands of GPUs, HeteroScale has proven its effectiveness, increasing average
GPU utilization by a significant 26.6 percentage points and saving hundreds of
thousands of GPU-hours daily, all while upholding stringent service level
objectives.
MotionFlux: Efficient Text-Guided Motion Generation through Rectified
  Flow Matching and Preference Alignment
Authors: name: Zhiting Gao, name: Dan Song, name: Diqiong Jiang, name: Chao Xue, name: An-An Liu
Published: 2025-08-27 02:45:09

Motion generation is essential for animating virtual characters and embodied
agents. While recent text-driven methods have made significant strides, they
often struggle with achieving precise alignment between linguistic descriptions
and motion semantics, as well as with the inefficiencies of slow, multi-step
inference. To address these issues, we introduce TMR++ Aligned Preference
Optimization (TAPO), an innovative framework that aligns subtle motion
variations with textual modifiers and incorporates iterative adjustments to
reinforce semantic grounding. To further enable real-time synthesis, we propose
MotionFLUX, a high-speed generation framework based on deterministic rectified
flow matching. Unlike traditional diffusion models, which require hundreds of
denoising steps, MotionFLUX constructs optimal transport paths between noise
distributions and motion spaces, facilitating real-time synthesis. The
linearized probability paths reduce the need for multi-step sampling typical of
sequential methods, significantly accelerating inference time without
sacrificing motion quality. Experimental results demonstrate that, together,
TAPO and MotionFLUX form a unified system that outperforms state-of-the-art
approaches in both semantic consistency and motion quality, while also
accelerating generation speed. The code and pretrained models will be released.
AudioStory: Generating Long-Form Narrative Audio with Large Language
  Models
Authors: name: Yuxin Guo, name: Teng Wang, name: Yuying Ge, name: Shijie Ma, name: Yixiao Ge, name: Wei Zou, name: Ying Shan
Published: 2025-08-27 17:55:38

Recent advances in text-to-audio (TTA) generation excel at synthesizing short
audio clips but struggle with long-form narrative audio, which requires
temporal coherence and compositional reasoning. To address this gap, we propose
AudioStory, a unified framework that integrates large language models (LLMs)
with TTA systems to generate structured, long-form audio narratives. AudioStory
possesses strong instruction-following reasoning generation capabilities. It
employs LLMs to decompose complex narrative queries into temporally ordered
sub-tasks with contextual cues, enabling coherent scene transitions and
emotional tone consistency. AudioStory has two appealing features: (1)
Decoupled bridging mechanism: AudioStory disentangles LLM-diffuser
collaboration into two specialized components, i.e., a bridging query for
intra-event semantic alignment and a residual query for cross-event coherence
preservation. (2) End-to-end training: By unifying instruction comprehension
and audio generation within a single end-to-end framework, AudioStory
eliminates the need for modular training pipelines while enhancing synergy
between components. Furthermore, we establish a benchmark AudioStory-10K,
encompassing diverse domains such as animated soundscapes and natural sound
narratives. Extensive experiments show the superiority of AudioStory on both
single-audio generation and narrative audio generation, surpassing prior TTA
baselines in both instruction-following ability and audio fidelity. Our code is
available at https://github.com/TencentARC/AudioStory
Predicting the Order of Upcoming Tokens Improves Language Modeling
Authors: name: Zayd M. K. Zuhri, name: Erland Hilman Fuadi, name: Alham Fikri Aji
Published: 2025-08-26 17:43:30

Multi-Token Prediction (MTP) has been proposed as an auxiliary objective to
improve next-token prediction (NTP) in language model training but shows
inconsistent improvements, underperforming in standard NLP benchmarks. We argue
that MTP's exact future token prediction is too difficult as an auxiliary loss.
Instead, we propose Token Order Prediction (TOP), which trains models to order
upcoming tokens by their proximity using a learning-to-rank loss. TOP requires
only a single additional unembedding layer compared to MTP's multiple
transformer layers. We pretrain models of 340M, 1.8B, and 7B parameters using
NTP, MTP, and TOP objectives. Results on eight standard NLP benchmarks show
that TOP overall outperforms both NTP and MTP even at scale. Our code is
available at https://github.com/zaydzuhri/token-order-prediction
Discrete Diffusion VLA: Bringing Discrete Diffusion to Action Decoding
  in Vision-Language-Action Policies
Authors: name: Zhixuan Liang, name: Yizhuo Li, name: Tianshuo Yang, name: Chengyue Wu, name: Sitong Mao, name: Liuao Pei, name: Xiaokang Yang, name: Jiangmiao Pang, name: Yao Mu, name: Ping Luo
Published: 2025-08-27 17:39:11

Vision-Language-Action (VLA) models adapt large vision-language backbones to
map images and instructions to robot actions. However, prevailing VLA decoders
either generate actions autoregressively in a fixed left-to-right order or
attach continuous diffusion or flow matching heads outside the backbone,
demanding specialized training and iterative sampling that hinder a unified,
scalable architecture. We present Discrete Diffusion VLA, a single-transformer
policy that models discretized action chunks with discrete diffusion and is
trained with the same cross-entropy objective as the VLM backbone. The design
retains diffusion's progressive refinement paradigm while remaining natively
compatible with the discrete token interface of VLMs. Our method achieves an
adaptive decoding order that resolves easy action elements before harder ones
and uses secondary remasking to revisit uncertain predictions across refinement
rounds, which improves consistency and enables robust error correction. This
unified decoder preserves pretrained vision language priors, supports parallel
decoding, breaks the autoregressive bottleneck, and reduces the number of
function evaluations. Discrete Diffusion VLA achieves 96.3% avg. SR on LIBERO,
71.2% visual matching on SimplerEnv Fractal and 49.3% overall on SimplerEnv
Bridge, improving over both autoregressive and continuous diffusion baselines.
These findings indicate that discrete-diffusion action decoder supports precise
action modeling and consistent training, laying groundwork for scaling VLA to
larger models and datasets.
CODA: Coordinating the Cerebrum and Cerebellum for a Dual-Brain Computer
  Use Agent with Decoupled Reinforcement Learning
Authors: name: Zeyi Sun, name: Yuhang Cao, name: Jianze Liang, name: Qiushi Sun, name: Ziyu Liu, name: Zhixiong Zhang, name: Yuhang Zang, name: Xiaoyi Dong, name: Kai Chen, name: Dahua Lin, name: Jiaqi Wang
Published: 2025-08-27 17:59:50

Autonomous agents for Graphical User Interfaces (GUIs) face significant
challenges in specialized domains such as scientific computing, where both
long-horizon planning and precise execution are required. Existing approaches
suffer from a trade-off: generalist agents excel at planning but perform poorly
in execution, while specialized agents demonstrate the opposite weakness.
Recent compositional frameworks attempt to bridge this gap by combining a
planner and an actor, but they are typically static and non-trainable, which
prevents adaptation from experience. This is a critical limitation given the
scarcity of high-quality data in scientific domains. To address these
limitations, we introduce CODA, a novel and trainable compositional framework
that integrates a generalist planner (Cerebrum) with a specialist executor
(Cerebellum), trained via a dedicated two-stage pipeline. In the first stage,
Specialization, we apply a decoupled GRPO approach to train an expert planner
for each scientific application individually, bootstrapping from a small set of
task trajectories. In the second stage, Generalization, we aggregate all
successful trajectories from the specialized experts to build a consolidated
dataset, which is then used for supervised fine-tuning of the final planner.
This equips CODA with both robust execution and cross-domain generalization.
Evaluated on four challenging applications from the ScienceBoard benchmark,
CODA significantly outperforms baselines and establishes a new state of the art
among open-source models.
StepWiser: Stepwise Generative Judges for Wiser Reasoning
Authors: name: Wei Xiong, name: Wenting Zhao, name: Weizhe Yuan, name: Olga Golovneva, name: Tong Zhang, name: Jason Weston, name: Sainbayar Sukhbaatar
Published: 2025-08-26 17:45:05

As models increasingly leverage multi-step reasoning strategies to solve
complex problems, supervising the logical validity of these intermediate steps
has become a critical research challenge. Process reward models address this by
providing step-by-step feedback, but current approaches have two major
drawbacks: they typically function as classifiers without providing
explanations, and their reliance on supervised fine-tuning with static datasets
limits generalization. Inspired by recent advances, we reframe stepwise reward
modeling from a classification task to a reasoning task itself. We thus propose
a generative judge that reasons about the policy model's reasoning steps (i.e.,
meta-reasons), outputting thinking tokens before delivering a final verdict.
Our model, StepWiser, is trained by reinforcement learning using relative
outcomes of rollouts. We show it provides (i) better judgment accuracy on
intermediate steps than existing methods; (ii) can be used to improve the
policy model at training time; and (iii) improves inference-time search.
Mind the Third Eye! Benchmarking Privacy Awareness in MLLM-powered
  Smartphone Agents
Authors: name: Zhixin Lin, name: Jungang Li, name: Shidong Pan, name: Yibo Shi, name: Yue Yao, name: Dongliang Xu
Published: 2025-08-27 00:41:28

Smartphones bring significant convenience to users but also enable devices to
extensively record various types of personal information. Existing smartphone
agents powered by Multimodal Large Language Models (MLLMs) have achieved
remarkable performance in automating different tasks. However, as the cost,
these agents are granted substantial access to sensitive users' personal
information during this operation. To gain a thorough understanding of the
privacy awareness of these agents, we present the first large-scale benchmark
encompassing 7,138 scenarios to the best of our knowledge. In addition, for
privacy context in scenarios, we annotate its type (e.g., Account Credentials),
sensitivity level, and location. We then carefully benchmark seven available
mainstream smartphone agents. Our results demonstrate that almost all
benchmarked agents show unsatisfying privacy awareness (RA), with performance
remaining below 60% even with explicit hints. Overall, closed-source agents
show better privacy ability than open-source ones, and Gemini 2.0-flash
achieves the best, achieving an RA of 67%. We also find that the agents'
privacy detection capability is highly related to scenario sensitivity level,
i.e., the scenario with a higher sensitivity level is typically more
identifiable. We hope the findings enlighten the research community to rethink
the unbalanced utility-privacy tradeoff about smartphone agents. Our code and
benchmark are available at https://zhixin-l.github.io/SAPA-Bench.