Orionnnn/hub-stats huggingface dataset:
The Role of Computing Resources in Publishing Foundation Model Research
Authors: name: Yuexing Hao, name: Yue Huang, name: Haoran Zhang, name: Chenyang Zhao, name: Zhenwen Liang, name: Paul Pu Liang, name: Yue Zhao, name: Lichao Sun, name: Saleh Kalantari, name: Xiangliang Zhang, name: Marzyeh Ghassemi
Published: 2025-10-15 14:50:45

Cutting-edge research in Artificial Intelligence (AI) requires considerable
resources, including Graphics Processing Units (GPUs), data, and human
resources. In this paper, we evaluate of the relationship between these
resources and the scientific advancement of foundation models (FM). We reviewed
6517 FM papers published between 2022 to 2024, and surveyed 229 first-authors
to the impact of computing resources on scientific output. We find that
increased computing is correlated with national funding allocations and
citations, but our findings don't observe the strong correlations with research
environment (academic or industrial), domain, or study methodology. We advise
that individuals and institutions focus on creating shared and affordable
computing opportunities to lower the entry barrier for under-resourced
researchers. These steps can help expand participation in FM research, foster
diversity of ideas and contributors, and sustain innovation and progress in AI.
The data will be available at: https://mit-calc.csail.mit.edu/
Deflanderization for Game Dialogue: Balancing Character Authenticity
  with Task Execution in LLM-based NPCs
Authors: name: Pasin Buakhaw, name: Kun Kerdthaisong, name: Phuree Phenhiran, name: Pitikorn Khlaisamniang, name: Supasate Vorathammathorn, name: Piyalitt Ittichaiwong, name: Nutchanon Yongsatianchot
Published: 2025-10-15 14:17:23

The emergence of large language models (LLMs) has opened new opportunities
for cre- ating dynamic non-player characters (NPCs) in gaming environments,
enabling both func- tional task execution and persona-consistent dialogue
generation. In this paper, we (Tu_Character_lab) report our participation in
the Commonsense Persona-Grounded Dialogue Challenge (CPDC) 2025 Round 2, which
eval- uates agents across three tracks: task-oriented dialogue, context-aware
dialogue, and their integration. Our approach combines two complementary
strategies: (i) lightweight prompting techniques in the API track, including a
Deflanderization prompting method to suppress excessive role-play and improve
task fidelity, and (ii) fine-tuned large models in the GPU track, leveraging
Qwen3-14B with supervisedfinetuning (SFT) and Low-Rank Adaptation(LoRA). Our
best submissions ranked 2nd on Task 1, 2nd on Task 3 (API track), and 4th on
Task 3 (GPU track).
Hard2Verify: A Step-Level Verification Benchmark for Open-Ended Frontier
  Math
Authors: name: Shrey Pandit, name: Austin Xu, name: Xuan-Phi Nguyen, name: Yifei Ming, name: Caiming Xiong, name: Shafiq Joty
Published: 2025-10-15 16:50:54

Large language model (LLM)-based reasoning systems have recently achieved
gold medal-level performance in the IMO 2025 competition, writing mathematical
proofs where, to receive full credit, each step must be not only correct but
also sufficiently supported. To train LLM-based reasoners in such challenging,
open-ended settings, strong verifiers capable of catching step-level mistakes
are necessary prerequisites. We introduce Hard2Verify, a human-annotated,
step-level verification benchmark produced with over 500 hours of human labor.
Hard2Verify is designed to rigorously assess step-level verifiers at the
frontier: Verifiers must provide step-level annotations or identify the first
error in responses generated by frontier LLMs for very recent, challenging, and
open-ended math questions. We evaluate 29 generative critics and process reward
models, demonstrating that, beyond a few standouts, open-source verifiers lag
closed source models. We subsequently analyze what drives poor performance in
step-level verification, the impacts of scaling verifier compute, as well as
fundamental questions such as self-verification and verification-generation
dynamics.
Trace Anything: Representing Any Video in 4D via Trajectory Fields
Authors: name: Xinhang Liu, name: Yuxi Xiao, name: Donny Y. Chen, name: Jiashi Feng, name: Yu-Wing Tai, name: Chi-Keung Tang, name: Bingyi Kang
Published: 2025-10-15 17:59:04

Effective spatio-temporal representation is fundamental to modeling,
understanding, and predicting dynamics in videos. The atomic unit of a video,
the pixel, traces a continuous 3D trajectory over time, serving as the
primitive element of dynamics. Based on this principle, we propose representing
any video as a Trajectory Field: a dense mapping that assigns a continuous 3D
trajectory function of time to each pixel in every frame. With this
representation, we introduce Trace Anything, a neural network that predicts the
entire trajectory field in a single feed-forward pass. Specifically, for each
pixel in each frame, our model predicts a set of control points that
parameterizes a trajectory (i.e., a B-spline), yielding its 3D position at
arbitrary query time instants. We trained the Trace Anything model on
large-scale 4D data, including data from our new platform, and our experiments
demonstrate that: (i) Trace Anything achieves state-of-the-art performance on
our new benchmark for trajectory field estimation and performs competitively on
established point-tracking benchmarks; (ii) it offers significant efficiency
gains thanks to its one-pass paradigm, without requiring iterative optimization
or auxiliary estimators; and (iii) it exhibits emergent abilities, including
goal-conditioned manipulation, motion forecasting, and spatio-temporal fusion.
Project page: https://trace-anything.github.io/.
Generative Universal Verifier as Multimodal Meta-Reasoner
Authors: name: Xinchen Zhang, name: Xiaoying Zhang, name: Youbin Wu, name: Yanbin Cao, name: Renrui Zhang, name: Ruihang Chu, name: Ling Yang, name: Yujiu Yang
Published: 2025-10-15 17:59:24

We introduce Generative Universal Verifier, a novel concept and plugin
designed for next-generation multimodal reasoning in vision-language models and
unified multimodal models, providing the fundamental capability of reflection
and refinement on visual outcomes during the reasoning and generation process.
This work makes three main contributions: (1) We build ViVerBench, a
comprehensive benchmark spanning 16 categories of critical tasks for evaluating
visual outcomes in multimodal reasoning. Results show that existing VLMs
consistently underperform across these tasks, underscoring a substantial gap
from human-level capability in reliable visual verification. (2) We design two
automated pipelines to construct large-scale visual verification data and train
OmniVerifier-7B, the first omni-capable generative verifier trained for
universal visual verification and achieves notable gains on ViVerBench(+8.3).
Through training, we identify three atomic capabilities in visual verification
and demonstrate how they generalize and interact synergistically. (3) We
propose OmniVerifier-TTS, a sequential test-time scaling paradigm that
leverages the universal verifier to bridge image generation and editing within
unified models, enhancing the upper bound of generative ability through
iterative fine-grained optimization. Beyond generation, we extend universal
verifier to broader world-modeling interleaved reasoning scenarios.
Empirically, OmniVerifier-TTS achieves improvements on T2I-ReasonBench(+3.7),
and GenEval++(+4.3), outperforming existing parallel test-time scaling methods,
such as Best-of-N. By endowing multimodal reasoning with reliable visual
verification, OmniVerifier advances both reliable reflection during generation
and scalable test-time refinement, marking a step toward more trustworthy and
controllable next-generation reasoning systems.
PhysMaster: Mastering Physical Representation for Video Generation via
  Reinforcement Learning
Authors: name: Sihui Ji, name: Xi Chen, name: Xin Tao, name: Pengfei Wan, name: Hengshuang Zhao
Published: 2025-10-15 17:59:59

Video generation models nowadays are capable of generating visually realistic
videos, but often fail to adhere to physical laws, limiting their ability to
generate physically plausible videos and serve as ''world models''. To address
this issue, we propose PhysMaster, which captures physical knowledge as a
representation for guiding video generation models to enhance their
physics-awareness. Specifically, PhysMaster is based on the image-to-video task
where the model is expected to predict physically plausible dynamics from the
input image. Since the input image provides physical priors like relative
positions and potential interactions of objects in the scenario, we devise
PhysEncoder to encode physical information from it as an extra condition to
inject physical knowledge into the video generation process. The lack of proper
supervision on the model's physical performance beyond mere appearance
motivates PhysEncoder to apply reinforcement learning with human feedback to
physical representation learning, which leverages feedback from generation
models to optimize physical representations with Direct Preference Optimization
(DPO) in an end-to-end manner. PhysMaster provides a feasible solution for
improving physics-awareness of PhysEncoder and thus of video generation,
proving its ability on a simple proxy task and generalizability to wide-ranging
physical scenarios. This implies that our PhysMaster, which unifies solutions
for various physical processes via representation learning in the reinforcement
learning paradigm, can act as a generic and plug-in solution for physics-aware
video generation and broader applications.
Scaling LLM Multi-turn RL with End-to-end Summarization-based Context
  Management
Authors: name: Miao Lu, name: Weiwei Sun, name: Weihua Du, name: Zhan Ling, name: Xuesong Yao, name: Kang Liu, name: Jiecao Chen
Published: 2025-10-08 07:29:22

We study reinforcement learning (RL) fine-tuning of large language model
(LLM) agents for long-horizon multi-turn tool use, where context length quickly
becomes a fundamental bottleneck. Existing RL pipelines can suffer from
degraded instruction following, excessive rollout costs, and most importantly,
strict context limits. To address these challenges, we introduce
summarization-based context management to training. In specific, it
periodically compresses the tool using history by LLM-generated summaries that
retain task-relevant information to keep a compact context while enabling the
agent to scale beyond the fixed context window. Building on this formulation,
we derive a policy gradient representation that seamlessly enables standard LLM
RL infrastructures to optimize both tool-use behaviors as well as summarization
strategies in an end-to-end fashion. We instantiate this framework with
SUmmarization augmented Policy Optimization
(SUPO), an LLM RL algorithm that enables long-horizon training beyond
a fixed context limit. Experiments on interactive function calling and
searching tasks demonstrate that SUPO significantly improves the
success rate while maintaining the same or even lower working context length
compared to baselines. We also demonstrate that for complex searching tasks,
SUPO can further improve the evaluation performance when scaling
test-time maximum round of summarization beyond that of training time. Our
results establish summarization-based context management as a principled and
scalable approach for training RL agents beyond a fixed context length limit.
Scaling Long-Horizon LLM Agent via Context-Folding
Authors: name: Weiwei Sun, name: Miao Lu, name: Zhan Ling, name: Kang Liu, name: Xuesong Yao, name: Yiming Yang, name: Jiecao Chen
Published: 2025-10-13 22:00:58

Large language model (LLM) agents are fundamentally constrained by context
length on long-horizon tasks. We introduce Context-Folding, a framework that
empowers agents to actively manage their working context. An agent can
procedurally branch into a sub-trajectory to handle a subtask and then fold it
upon completion, collapsing the intermediate steps while retaining a concise
summary of the outcome. To make this behavior learnable, we develop an
end-to-end reinforcement learning framework FoldGRPO with specific process
rewards to encourage effective task decomposition and context management. On
complex long-horizon tasks (Deep Research and SWE), our folding agent matches
or outperforms the ReAct baselines while using an active context 10times
smaller and significantly outperforms models that rely on summarization-based
context management.
Kontinuous Kontext: Continuous Strength Control for Instruction-based
  Image Editing
Authors: name: Rishubh Parihar, name: Or Patashnik, name: Daniil Ostashev, name: R. Venkatesh Babu, name: Daniel Cohen-Or, name: Kuan-Chieh Wang
Published: 2025-10-09 17:51:03

Instruction-based image editing offers a powerful and intuitive way to
manipulate images through natural language. Yet, relying solely on text
instructions limits fine-grained control over the extent of edits. We introduce
Kontinuous Kontext, an instruction-driven editing model that provides a new
dimension of control over edit strength, enabling users to adjust edits
gradually from no change to a fully realized result in a smooth and continuous
manner. Kontinuous Kontext extends a state-of-the-art image editing model to
accept an additional input, a scalar edit strength which is then paired with
the edit instruction, enabling explicit control over the extent of the edit. To
inject this scalar information, we train a lightweight projector network that
maps the input scalar and the edit instruction to coefficients in the model's
modulation space. For training our model, we synthesize a diverse dataset of
image-edit-instruction-strength quadruplets using existing generative models,
followed by a filtering stage to ensure quality and consistency. Kontinuous
Kontext provides a unified approach for fine-grained control over edit strength
for instruction driven editing from subtle to strong across diverse operations
such as stylization, attribute, material, background, and shape changes,
without requiring attribute-specific training.
Why Do Transformers Fail to Forecast Time Series In-Context?
Authors: name: Yufa Zhou, name: Yixiao Wang, name: Surbhi Goel, name: Anru R. Zhang
Published: 2025-10-10 18:34:19

Time series forecasting (TSF) remains a challenging and largely unsolved
problem in machine learning, despite significant recent efforts leveraging
Large Language Models (LLMs), which predominantly rely on Transformer
architectures. Empirical evidence consistently shows that even powerful
Transformers often fail to outperform much simpler models, e.g., linear models,
on TSF tasks; however, a rigorous theoretical understanding of this phenomenon
remains limited. In this paper, we provide a theoretical analysis of
Transformers' limitations for TSF through the lens of In-Context Learning (ICL)
theory. Specifically, under AR(p) data, we establish that: (1) Linear
Self-Attention (LSA) models cannot achieve lower expected MSE than
classical linear models for in-context forecasting; (2) as the context length
approaches to infinity, LSA asymptotically recovers the optimal linear
predictor; and (3) under Chain-of-Thought (CoT) style inference, predictions
collapse to the mean exponentially. We empirically validate these findings
through carefully designed experiments. Our theory not only sheds light on
several previously underexplored phenomena but also offers practical insights
for designing more effective forecasting architectures. We hope our work
encourages the broader research community to revisit the fundamental
theoretical limitations of TSF and to critically evaluate the direct
application of increasingly sophisticated architectures without deeper
scrutiny.
The Geometry of Reasoning: Flowing Logics in Representation Space
Authors: name: Yufa Zhou, name: Yixiao Wang, name: Xunjian Yin, name: Shuyan Zhou, name: Anru R. Zhang
Published: 2025-10-10 18:44:00

We study how large language models (LLMs) ``think'' through their
representation space. We propose a novel geometric framework that models an
LLM's reasoning as flows -- embedding trajectories evolving where logic goes.
We disentangle logical structure from semantics by employing the same natural
deduction propositions with varied semantic carriers, allowing us to test
whether LLMs internalize logic beyond surface form. This perspective connects
reasoning with geometric quantities such as position, velocity, and curvature,
enabling formal analysis in representation and concept spaces. Our theory
establishes: (1) LLM reasoning corresponds to smooth flows in representation
space, and (2) logical statements act as local controllers of these flows'
velocities. Using learned representation proxies, we design controlled
experiments to visualize and quantify reasoning flows, providing empirical
validation of our theoretical framework. Our work serves as both a conceptual
foundation and practical tools for studying reasoning phenomenon, offering a
new lens for interpretability and formal analysis of LLMs' behavior.
Locket: Robust Feature-Locking Technique for Language Models
Authors: name: Lipeng He, name: Vasisht Duddu, name: N. Asokan
Published: 2025-10-14 03:35:59

Chatbot providers (e.g., OpenAI) rely on tiered subscription schemes to
generate revenue, offering basic models for free users, and advanced models for
paying subscribers. However, a finer-grained pay-to-unlock scheme for premium
features (e.g., math, coding) is thought to be more economically viable for the
providers. Such a scheme requires a feature-locking technique (FLoTE) which is
(i) effective in refusing locked features, (ii) utility-preserving for unlocked
features, (iii) robust against evasion or unauthorized credential sharing, and
(iv) scalable to multiple features and users. However, existing FLoTEs (e.g.,
password-locked models) are not robust or scalable. We present Locket, the
first robust and scalable FLoTE to enable pay-to-unlock schemes. Locket uses a
novel merging approach to attach adapters to an LLM for refusing unauthorized
features. Our comprehensive evaluation shows that Locket is effective (100%
refusal on locked features), utility-preserving (leq 7% utility degradation
in unlocked features), robust (leq 5% attack success rate), and scales to
multiple features and clients.
ContextGen: Contextual Layout Anchoring for Identity-Consistent
  Multi-Instance Generation
Authors: name: Ruihang Xu, name: Dewei Zhou, name: Fan Ma, name: Yi Yang
Published: 2025-10-13 04:21:19

Multi-instance image generation (MIG) remains a significant challenge for
modern diffusion models due to key limitations in achieving precise control
over object layout and preserving the identity of multiple distinct subjects.
To address these limitations, we introduce ContextGen, a novel Diffusion
Transformer framework for multi-instance generation that is guided by both
layout and reference images. Our approach integrates two key technical
contributions: a Contextual Layout Anchoring (CLA) mechanism that incorporates
the composite layout image into the generation context to robustly anchor the
objects in their desired positions, and Identity Consistency Attention (ICA),
an innovative attention mechanism that leverages contextual reference images to
ensure the identity consistency of multiple instances. Recognizing the lack of
large-scale, hierarchically-structured datasets for this task, we introduce
IMIG-100K, the first dataset with detailed layout and identity annotations.
Extensive experiments demonstrate that ContextGen sets a new state-of-the-art,
outperforming existing methods in control precision, identity fidelity, and
overall visual quality.
Mitigating the Noise Shift for Denoising Generative Models via Noise
  Awareness Guidance
Authors: name: Jincheng Zhong, name: Boyuan Jiang, name: Xin Tao, name: Pengfei Wan, name: Kun Gai, name: Mingsheng Long
Published: 2025-10-14 13:31:34

Existing denoising generative models rely on solving discretized reverse-time
SDEs or ODEs. In this paper, we identify a long-overlooked yet pervasive issue
in this family of models: a misalignment between the pre-defined noise level
and the actual noise level encoded in intermediate states during sampling. We
refer to this misalignment as noise shift. Through empirical analysis, we
demonstrate that noise shift is widespread in modern diffusion models and
exhibits a systematic bias, leading to sub-optimal generation due to both
out-of-distribution generalization and inaccurate denoising updates. To address
this problem, we propose Noise Awareness Guidance (NAG), a simple yet effective
correction method that explicitly steers sampling trajectories to remain
consistent with the pre-defined noise schedule. We further introduce a
classifier-free variant of NAG, which jointly trains a noise-conditional and a
noise-unconditional model via noise-condition dropout, thereby eliminating the
need for external classifiers. Extensive experiments, including ImageNet
generation and various supervised fine-tuning tasks, show that NAG consistently
mitigates noise shift and substantially improves the generation quality of
mainstream diffusion models.
Cautious Weight Decay
Authors: name: Lizhang Chen, name: Jonathan Li, name: Kaizhao Liang, name: Baiyu Su, name: Cong Xie, name: Nuo Wang Pierse, name: Chen Liang, name: Ni Lao, name: Qiang Liu
Published: 2025-10-14 11:32:55

We introduce Cautious Weight Decay (CWD), a one-line, optimizer-agnostic
modification that applies weight decay only to parameter coordinates whose
signs align with the optimizer update. Unlike standard decoupled decay, which
implicitly optimizes a regularized or constrained objective, CWD preserves the
original loss and admits a bilevel interpretation: it induces sliding-mode
behavior upon reaching the stationary manifold, allowing it to search for
locally Pareto-optimal stationary points of the unmodified objective. In
practice, CWD is a drop-in change for optimizers such as AdamW, Lion, and Muon,
requiring no new hyperparameters or additional tuning. For language model
pre-training and ImageNet classification, CWD consistently improves final loss
and accuracy at million- to billion-parameter scales.
Diffusion-Link: Diffusion Probabilistic Model for Bridging the
  Audio-Text Modality Gap
Authors: name: KiHyun Nam, name: Jongmin Choi, name: Hyeongkeun Lee, name: Jungwoo Heo, name: Joon Son Chung
Published: 2025-10-13 12:25:33

Contrastive audio-language pretraining yields powerful joint representations,
yet a persistent audio-text modality gap limits the benefits of coupling
multimodal encoders with large language models (LLMs). We present
Diffusion-Link, a diffusion-based modality-bridging module that generatively
maps audio embeddings into the text-embedding distribution. The module is
trained at the output embedding from the frozen multimodal encoder and
implemented as a lightweight network with three residual MLP blocks. To assess
the effect of Diffusion-Link on multimodal encoder-LLM coupling, we evaluate on
Automatic Audio Captioning (AAC); to our knowledge, this is the first
application of diffusion-based modality bridging to AAC. We report two results.
(1) Modality-gap analysis: on similarity and geometric criteria, Diffusion-Link
reduces the modality gap the most among prior diffusion-based methods and shows
a collective migration of audio embeddings toward the text distribution. (2)
Downstream AAC: attaching Diffusion-Link to the same multimodal LLM baseline
achieves state-of-the-art on AudioCaps in both zero-shot and fully supervised
captioning without external knowledge, with relative gains up to 52.5% and
7.5%, respectively. These findings show that closing the modality gap is
pivotal for effective coupling between multimodal encoders and LLMs, and
diffusion-based modality bridging offers a promising direction beyond
knowledge-retrieval-centric designs. Code will be released upon acceptance
https://github.com/DevKiHyun/Diffusion-Link