Orionnnn/hub-stats huggingface dataset:
MARS-M: When Variance Reduction Meets Matrices
Authors: name: Yifeng Liu, name: Angela Yuan, name: Quanquan Gu
Published: 2025-10-20 16:49:22

Matrix-based preconditioned optimizers, such as Muon, have recently been
shown to be more efficient than scalar-based optimizers for training
large-scale neural networks, including large language models (LLMs). On the
other hand, recent benchmarks on optimizers for LLM pre-training have
demonstrated that variance-reduction techniques such as MARS can achieve
substantial speedups over standard optimizers that do not employ variance
reduction. In this paper, to achieve the best of both worlds, we introduce
MARS-M, a new optimizer that integrates the variance reduction technique in
MARS with Muon. Under standard regularity conditions, we prove that Muon-M
converges to a first-order stationary point at a rate of
mathcal{O}(T^{-1/3}), which improves upon
mathcal{O}(T^{-1/4}) rate attained by Muon. Our empirical results on
language modeling and computer vision tasks demonstrate that MARS-M
consistently yields lower losses and improved performance across various
downstream benchmarks. The implementation of MARS-M is available at
https://github.com/AGI-Arena/MARS/MARS_M.
ACG: Action Coherence Guidance for Flow-based VLA models
Authors: name: Minho Park, name: Kinam Kim, name: Junha Hyung, name: Hyojin Jang, name: Hoiyeong Jin, name: Jooyeol Yun, name: Hojoon Lee, name: Jaegul Choo
Published: 2025-10-25 07:44:33

Diffusion and flow matching models have emerged as powerful robot policies,
enabling Vision-Language-Action (VLA) models to generalize across diverse
scenes and instructions. Yet, when trained via imitation learning, their high
generative capacity makes them sensitive to noise in human demonstrations:
jerks, pauses, and jitter which reduce action coherence. Reduced action
coherence causes instability and trajectory drift during deployment, failures
that are catastrophic in fine-grained manipulation where precision is crucial.
In this paper, we present Action Coherence Guidance (ACG) for VLA models, a
training-free test-time guidance algorithm that improves action coherence and
thereby yields performance gains. Evaluated on RoboCasa, DexMimicGen, and
real-world SO-101 tasks, ACG consistently improves action coherence and boosts
success rates across diverse manipulation tasks. Code and project page are
available at https://github.com/DAVIAN-Robotics/ACG and
https://DAVIAN-Robotics.github.io/ACG , respectively.
Redefining Retrieval Evaluation in the Era of LLMs
Authors: name: Giovanni Trappolini, name: Florin Cuconasu, name: Simone Filice, name: Yoelle Maarek, name: Fabrizio Silvestri
Published: 2025-10-24 13:17:00

Traditional Information Retrieval (IR) metrics, such as nDCG, MAP, and MRR,
assume that human users sequentially examine documents with diminishing
attention to lower ranks. This assumption breaks down in Retrieval Augmented
Generation (RAG) systems, where search results are consumed by Large Language
Models (LLMs), which, unlike humans, process all retrieved documents as a whole
rather than sequentially. Additionally, traditional IR metrics do not account
for related but irrelevant documents that actively degrade generation quality,
rather than merely being ignored. Due to these two major misalignments, namely
human vs. machine position discount and human relevance vs. machine utility,
classical IR metrics do not accurately predict RAG performance. We introduce a
utility-based annotation schema that quantifies both the positive contribution
of relevant passages and the negative impact of distracting ones. Building on
this foundation, we propose UDCG (Utility and Distraction-aware Cumulative
Gain), a metric using an LLM-oriented positional discount to directly optimize
the correlation with the end-to-end answer accuracy. Experiments on five
datasets and six LLMs demonstrate that UDCG improves correlation by up to 36%
compared to traditional metrics. Our work provides a critical step toward
aligning IR evaluation with LLM consumers and enables more reliable assessment
of RAG components
ALICE-LRI: A General Method for Lossless Range Image Generation for
  Spinning LiDAR Sensors without Calibration Metadata
Authors: name: Samuel Soutullo, name: Miguel Yermo, name: David L. Vilariño, name: Óscar G. Lorenzo, name: José C. Cabaleiro, name: Francisco F. Rivera
Published: 2025-10-23 16:22:58

3D LiDAR sensors are essential for autonomous navigation, environmental
monitoring, and precision mapping in remote sensing applications. To
efficiently process the massive point clouds generated by these sensors, LiDAR
data is often projected into 2D range images that organize points by their
angular positions and distances. While these range image representations enable
efficient processing, conventional projection methods suffer from fundamental
geometric inconsistencies that cause irreversible information loss,
compromising high-fidelity applications. We present ALICE-LRI (Automatic LiDAR
Intrinsic Calibration Estimation for Lossless Range Images), the first general,
sensor-agnostic method that achieves lossless range image generation from
spinning LiDAR point clouds without requiring manufacturer metadata or
calibration files. Our algorithm automatically reverse-engineers the intrinsic
geometry of any spinning LiDAR sensor by inferring critical parameters
including laser beam configuration, angular distributions, and per-beam
calibration corrections, enabling lossless projection and complete point cloud
reconstruction with zero point loss. Comprehensive evaluation across the
complete KITTI and DurLAR datasets demonstrates that ALICE-LRI achieves perfect
point preservation, with zero points lost across all point clouds. Geometric
accuracy is maintained well within sensor precision limits, establishing
geometric losslessness with real-time performance. We also present a
compression case study that validates substantial downstream benefits,
demonstrating significant quality improvements in practical applications. This
paradigm shift from approximate to lossless LiDAR projections opens new
possibilities for high-precision remote sensing applications requiring complete
geometric preservation.
Foley Control: Aligning a Frozen Latent Text-to-Audio Model to Video
Authors: name: Ciara Rowles, name: Varun Jampani, name: Simon Donné, name: Shimon Vainer, name: Julian Parker, name: Zach Evans
Published: 2025-10-24 15:49:54

Foley Control is a lightweight approach to video-guided Foley that keeps
pretrained single-modality models frozen and learns only a small
cross-attention bridge between them. We connect V-JEPA2 video embeddings to a
frozen Stable Audio Open DiT text-to-audio (T2A) model by inserting compact
video cross-attention after the model's existing text cross-attention, so
prompts set global semantics while video refines timing and local dynamics. The
frozen backbones retain strong marginals (video; audio given text) and the
bridge learns the audio-video dependency needed for synchronization -- without
retraining the audio prior. To cut memory and stabilize training, we pool video
tokens before conditioning. On curated video-audio benchmarks, Foley Control
delivers competitive temporal and semantic alignment with far fewer trainable
parameters than recent multi-modal systems, while preserving prompt-driven
controllability and production-friendly modularity (swap/upgrade encoders or
the T2A backbone without end-to-end retraining). Although we focus on
Video-to-Foley, the same bridge design can potentially extend to other audio
modalities (e.g., speech).
Soft Instruction De-escalation Defense
Authors: name: Nils Philipp Walter, name: Chawin Sitawarin, name: Jamie Hayes, name: David Stutz, name: Ilia Shumailov
Published: 2025-10-24 00:04:07

Large Language Models (LLMs) are increasingly deployed in agentic systems
that interact with an external environment; this makes them susceptible to
prompt injections when dealing with untrusted data. To overcome this
limitation, we propose SIC (Soft Instruction Control)-a simple yet effective
iterative prompt sanitization loop designed for tool-augmented LLM agents. Our
method repeatedly inspects incoming data for instructions that could compromise
agent behavior. If such content is found, the malicious content is rewritten,
masked, or removed, and the result is re-evaluated. The process continues until
the input is clean or a maximum iteration limit is reached; if imperative
instruction-like content remains, the agent halts to ensure security. By
allowing multiple passes, our approach acknowledges that individual rewrites
may fail but enables the system to catch and correct missed injections in later
steps. Although immediately useful, worst-case analysis shows that SIC is not
infallible; strong adversary can still get a 15% ASR by embedding
non-imperative workflows. This nonetheless raises the bar.
Stabilizing MoE Reinforcement Learning by Aligning Training and
  Inference Routers
Authors: name: Wenhan Ma, name: Hailin Zhang, name: Liang Zhao, name: Yifan Song, name: Yudong Wang, name: Zhifang Sui, name: Fuli Luo
Published: 2025-10-13 13:11:27

Reinforcement learning (RL) has emerged as a crucial approach for enhancing
the capabilities of large language models. However, in Mixture-of-Experts (MoE)
models, the routing mechanism often introduces instability, even leading to
catastrophic RL training collapse. We analyze the training-inference
consistency of MoE models and identify a notable discrepancy in routing
behaviors between the two phases. Moreover, even under identical conditions,
the routing framework can yield divergent expert selections across repeated
forward passes. To address this foundational inconsistency, we propose Rollout
Routing Replay (R3), a method that records routing distributions from the
inference engine and replays them during training. R3 significantly reduces
training-inference policy KL divergence and mitigates extreme discrepancies
without compromising training speed. Extensive experiments on various settings
confirm that R3 succeeds in stabilizing RL training, preventing collapse and
outperforming methods such as GSPO and TIS. We believe this work can offer a
new solution for stabilizing RL in MoE models.
PhysVLM-AVR: Active Visual Reasoning for Multimodal Large Language
  Models in Physical Environments
Authors: name: Weijie Zhou, name: Xuantang Xiong, name: Yi Peng, name: Manli Tao, name: Chaoyang Zhao, name: Honghui Dong, name: Ming Tang, name: Jinqiao Wang
Published: 2025-10-24 02:59:00

Visual reasoning in multimodal large language models (MLLMs) has primarily
been studied in static, fully observable settings, limiting their effectiveness
in real-world environments where information is often incomplete due to
occlusion or limited field of view. Humans, in contrast, actively explore and
interact with their environment-moving, examining, and manipulating objects-to
gather information through a closed-loop process integrating perception,
reasoning, and action. Inspired by this human capability, we introduce the
Active Visual Reasoning (AVR) task, extending visual reasoning to partially
observable, interactive environments. AVR necessitates agents to: (1) actively
acquire information via sequential physical actions, (2) integrate observations
across multiple steps for coherent reasoning, and (3) dynamically adjust
decisions based on evolving visual feedback. To rigorously evaluate AVR, we
introduce CLEVR-AVR, a simulation benchmark featuring multi-round interactive
environments designed to assess both reasoning correctness and
information-gathering efficiency. We present AVR-152k, a large-scale dataset
that offers rich Chain-of-Thought (CoT) annotations detailing iterative
reasoning for uncertainty identification, action-conditioned information gain
prediction, and information-maximizing action selection, crucial for training
agents in a higher-order Markov Decision Process. Building on this, we develop
PhysVLM-AVR, an MLLM achieving state-of-the-art performance on CLEVR-AVR,
embodied reasoning (OpenEQA, RoboVQA), and passive visual reasoning (GeoMath,
Geometry30K). Our analysis also reveals that current embodied MLLMs, despite
detecting information incompleteness, struggle to actively acquire and
integrate new information through interaction, highlighting a fundamental gap
in active reasoning capabilities.
Sample By Step, Optimize By Chunk: Chunk-Level GRPO For Text-to-Image
  Generation
Authors: name: Yifu Luo, name: Penghui Du, name: Bo Li, name: Sinan Du, name: Tiantian Zhang, name: Yongzhe Chang, name: Kai Wu, name: Kun Gai, name: Xueqian Wang
Published: 2025-10-24 15:50:36

Group Relative Policy Optimization (GRPO) has shown strong potential for
flow-matching-based text-to-image (T2I) generation, but it faces two key
limitations: inaccurate advantage attribution, and the neglect of temporal
dynamics of generation. In this work, we argue that shifting the optimization
paradigm from the step level to the chunk level can effectively alleviate these
issues. Building on this idea, we propose Chunk-GRPO, the first chunk-level
GRPO-based approach for T2I generation. The insight is to group consecutive
steps into coherent 'chunk's that capture the intrinsic temporal dynamics of
flow matching, and to optimize policies at the chunk level. In addition, we
introduce an optional weighted sampling strategy to further enhance
performance. Extensive experiments show that ChunkGRPO achieves superior
results in both preference alignment and image quality, highlighting the
promise of chunk-level optimization for GRPO-based methods.
Reasoning with Sampling: Your Base Model is Smarter Than You Think
Authors: name: Aayush Karan, name: Yilun Du
Published: 2025-10-16 17:18:11

Frontier reasoning models have exhibited incredible capabilities across a
wide array of disciplines, driven by posttraining large language models (LLMs)
with reinforcement learning (RL). However, despite the widespread success of
this paradigm, much of the literature has been devoted to disentangling truly
novel behaviors that emerge during RL but are not present in the base models.
In our work, we approach this question from a different angle, instead asking
whether comparable reasoning capabilites can be elicited from base models at
inference time by pure sampling, without any additional training. Inspired by
Markov chain Monte Carlo (MCMC) techniques for sampling from sharpened
distributions, we propose a simple iterative sampling algorithm leveraging the
base models' own likelihoods. Over different base models, we show that our
algorithm offers substantial boosts in reasoning that nearly match and even
outperform those from RL on a wide variety of single-shot tasks, including
MATH500, HumanEval, and GPQA. Moreover, our sampler avoids the collapse in
diversity over multiple samples that is characteristic of RL-posttraining.
Crucially, our method does not require training, curated datasets, or a
verifier, suggesting broad applicability beyond easily verifiable domains.
Are Large Reasoning Models Good Translation Evaluators? Analysis and
  Performance Boost
Authors: name: Runzhe Zhan, name: Zhihong Huang, name: Xinyi Yang, name: Lidia S. Chao, name: Min Yang, name: Derek F. Wong
Published: 2025-10-23 17:48:36

Recent advancements in large reasoning models (LRMs) have introduced an
intermediate "thinking" process prior to generating final answers, improving
their reasoning capabilities on complex downstream tasks. However, the
potential of LRMs as evaluators for machine translation (MT) quality remains
underexplored. We provides the first systematic analysis of LRM-as-a-judge in
MT evaluation. We identify key challenges, revealing LRMs require tailored
evaluation materials, tend to "overthink" simpler instances and have issues
with scoring mechanisms leading to overestimation. To address these, we propose
to calibrate LRM thinking by training them on synthetic, human-like thinking
trajectories. Our experiments on WMT24 Metrics benchmarks demonstrate that this
approach largely reduces thinking budgets by ~35x while concurrently improving
evaluation performance across different LRM scales from 7B to 32B (e.g.,
R1-Distill-Qwen-7B achieves a +8.7 correlation point improvement). These
findings highlight the potential of efficiently calibrated LRMs to advance
fine-grained automatic MT evaluation.
Map the Flow: Revealing Hidden Pathways of Information in VideoLLMs
Authors: name: Minji Kim, name: Taekyung Kim, name: Bohyung Han
Published: 2025-10-15 07:59:06

Video Large Language Models (VideoLLMs) extend the capabilities of
vision-language models to spatiotemporal inputs, enabling tasks such as video
question answering (VideoQA). Despite recent advances in VideoLLMs, their
internal mechanisms on where and how they extract and propagate video and
textual information remain less explored. In this study, we investigate the
internal information flow of VideoLLMs using mechanistic interpretability
techniques. Our analysis reveals consistent patterns across diverse VideoQA
tasks: (1) temporal reasoning in VideoLLMs initiates with active cross-frame
interactions in early-to-middle layers, (2) followed by progressive
video-language integration in middle layers. This is facilitated by alignment
between video representations and linguistic embeddings containing temporal
concepts. (3) Upon completion of this integration, the model is ready to
generate correct answers in middle-to-late layers. (4) Based on our analysis,
we show that VideoLLMs can retain their VideoQA performance by selecting these
effective information pathways while suppressing a substantial amount of
attention edges, e.g., 58% in LLaVA-NeXT-7B-Video-FT. These findings provide a
blueprint on how VideoLLMs perform temporal reasoning and offer practical
insights for improving model interpretability and downstream generalization.
Our project page with the source code is available at
https://map-the-flow.github.io
Sparser Block-Sparse Attention via Token Permutation
Authors: name: Xinghao Wang, name: Pengyu Wang, name: Dong Zhang, name: Chenkun Tan, name: Shaojun Zhou, name: Zhaoxiang Liu, name: Shiguo Lian, name: Fangxu Liu, name: Kai Song, name: Xipeng Qiu
Published: 2025-10-24 09:11:50

Scaling the context length of large language models (LLMs) offers significant
benefits but is computationally expensive. This expense stems primarily from
the self-attention mechanism, whose O(N^2) complexity with respect to
sequence length presents a major bottleneck for both memory and latency.
Fortunately, the attention matrix is often sparse, particularly for long
sequences, suggesting an opportunity for optimization. Block-sparse attention
has emerged as a promising solution that partitions sequences into blocks and
skips computation for a subset of these blocks. However, the effectiveness of
this method is highly dependent on the underlying attention patterns, which can
lead to sub-optimal block-level sparsity. For instance, important key tokens
for queries within a single block may be scattered across numerous other
blocks, leading to computational redundancy. In this work, we propose Permuted
Block-Sparse Attention (PBS-Attn), a plug-and-play method that
leverages the permutation properties of attention to increase block-level
sparsity and enhance the computational efficiency of LLM prefilling. We conduct
comprehensive experiments on challenging real-world long-context datasets,
demonstrating that PBS-Attn consistently outperforms existing block-sparse
attention methods in model accuracy and closely matches the full attention
baseline. Powered by our custom permuted-FlashAttention kernels, PBS-Attn
achieves an end-to-end speedup of up to 2.75times in long-context
prefilling, confirming its practical viability. Code available at
https://github.com/xinghaow99/pbs-attn
RAPO++: Cross-Stage Prompt Optimization for Text-to-Video Generation via
  Data Alignment and Test-Time Scaling
Authors: name: Bingjie Gao, name: Qianli Ma, name: Xiaoxue Wu, name: Shuai Yang, name: Guanzhou Lan, name: Haonan Zhao, name: Jiaxuan Chen, name: Qingyang Liu, name: Yu Qiao, name: Xinyuan Chen, name: Yaohui Wang, name: Li Niu
Published: 2025-10-23 04:45:09

Prompt design plays a crucial role in text-to-video (T2V) generation, yet
user-provided prompts are often short, unstructured, and misaligned with
training data, limiting the generative potential of diffusion-based T2V models.
We present RAPO++, a cross-stage prompt optimization framework that
unifies training-data--aligned refinement, test-time iterative scaling, and
large language model (LLM) fine-tuning to substantially improve T2V generation
without modifying the underlying generative backbone. In Stage 1,
Retrieval-Augmented Prompt Optimization (RAPO) enriches user prompts with
semantically relevant modifiers retrieved from a relation graph and refactors
them to match training distributions, enhancing compositionality and
multi-object fidelity. Stage 2 introduces Sample-Specific Prompt
Optimization (SSPO), a closed-loop mechanism that iteratively refines prompts
using multi-source feedback -- including semantic alignment, spatial fidelity,
temporal coherence, and task-specific signals such as optical flow -- yielding
progressively improved video generation quality. Stage 3 leverages
optimized prompt pairs from SSPO to fine-tune the rewriter LLM, internalizing
task-specific optimization patterns and enabling efficient, high-quality prompt
generation even before inference. Extensive experiments across five
state-of-the-art T2V models and five benchmarks demonstrate that RAPO++
achieves significant gains in semantic alignment, compositional reasoning,
temporal stability, and physical plausibility, outperforming existing methods
by large margins. Our results highlight RAPO++ as a model-agnostic,
cost-efficient, and scalable solution that sets a new standard for prompt
optimization in T2V generation. The code is available at
https://github.com/Vchitect/RAPO.
Taming Modality Entanglement in Continual Audio-Visual Segmentation
Authors: name: Yuyang Hong, name: Qi Yang, name: Tao Zhang, name: Zili Wang, name: Zhaojin Fu, name: Kun Ding, name: Bin Fan, name: Shiming Xiang
Published: 2025-10-20 07:23:36

Recently, significant progress has been made in multi-modal continual
learning, aiming to learn new tasks sequentially in multi-modal settings while
preserving performance on previously learned ones. However, existing methods
mainly focus on coarse-grained tasks, with limitations in addressing modality
entanglement in fine-grained continual learning settings. To bridge this gap,
we introduce a novel Continual Audio-Visual Segmentation (CAVS) task, aiming to
continuously segment new classes guided by audio. Through comprehensive
analysis, two critical challenges are identified: 1) multi-modal semantic
drift, where a sounding objects is labeled as background in sequential tasks;
2) co-occurrence confusion, where frequent co-occurring classes tend to be
confused. In this work, a Collision-based Multi-modal Rehearsal (CMR) framework
is designed to address these challenges. Specifically, for multi-modal semantic
drift, a Multi-modal Sample Selection (MSS) strategy is proposed to select
samples with high modal consistency for rehearsal. Meanwhile, for co-occurence
confusion, a Collision-based Sample Rehearsal (CSR) mechanism is designed,
allowing for the increase of rehearsal sample frequency of those confusable
classes during training process. Moreover, we construct three audio-visual
incremental scenarios to verify effectiveness of our method. Comprehensive
experiments demonstrate that our method significantly outperforms single-modal
continual learning methods.