Orionnnn/hub-stats huggingface dataset:
Do LLMs Feel? Teaching Emotion Recognition with Prompts, Retrieval, and
  Curriculum Learning
Authors: name: Xinran Li, name: Xiujuan Xu, name: Jiaqi Qiao, name: Yu Liu
Published: 2025-11-10 12:52:11

Emotion Recognition in Conversation (ERC) is a crucial task for understanding
human emotions and enabling natural human-computer interaction. Although Large
Language Models (LLMs) have recently shown great potential in this field, their
ability to capture the intrinsic connections between explicit and implicit
emotions remains limited. We propose a novel ERC training framework, PRC-Emo,
which integrates Prompt engineering, demonstration Retrieval, and Curriculum
learning, with the goal of exploring whether LLMs can effectively perceive
emotions in conversational contexts. Specifically, we design emotion-sensitive
prompt templates based on both explicit and implicit emotional cues to better
guide the model in understanding the speaker's psychological states. We
construct the first dedicated demonstration retrieval repository for ERC, which
includes training samples from widely used datasets, as well as high-quality
dialogue examples generated by LLMs and manually verified. Moreover, we
introduce a curriculum learning strategy into the LoRA fine-tuning process,
incorporating weighted emotional shifts between same-speaker and
different-speaker utterances to assign difficulty levels to dialogue samples,
which are then organized in an easy-to-hard training sequence. Experimental
results on two benchmark datasets-- IEMOCAP and MELD --show that our method
achieves new state-of-the-art (SOTA) performance, demonstrating the
effectiveness and generalizability of our approach in improving LLM-based
emotional understanding.
10 Open Challenges Steering the Future of Vision-Language-Action Models
Authors: name: Soujanya Poria, name: Navonil Majumder, name: Chia-Yu Hung, name: Amir Ali Bagherzadeh, name: Chuan Li, name: Kenneth Kwok, name: Ziwei Wang, name: Cheston Tan, name: Jiajun Wu, name: David Hsu
Published: 2025-11-08 09:02:13

Due to their ability of follow natural language instructions,
vision-language-action (VLA) models are increasingly prevalent in the embodied
AI arena, following the widespread success of their precursors -- LLMs and
VLMs. In this paper, we discuss 10 principal milestones in the ongoing
development of VLA models -- multimodality, reasoning, data, evaluation,
cross-robot action generalization, efficiency, whole-body coordination, safety,
agents, and coordination with humans. Furthermore, we discuss the emerging
trends of using spatial understanding, modeling world dynamics, post training,
and data synthesis -- all aiming to reach these milestones. Through these
discussions, we hope to bring attention to the research avenues that may
accelerate the development of VLA models into wider acceptability.
VADER: Towards Causal Video Anomaly Understanding with Relation-Aware
  Large Language Models
Authors: name: Ying Cheng, name: Yu-Ho Lin, name: Min-Hung Chen, name: Fu-En Yang, name: Shang-Hong Lai
Published: 2025-11-10 16:56:11

Video anomaly understanding (VAU) aims to provide detailed interpretation and
semantic comprehension of anomalous events within videos, addressing
limitations of traditional methods that focus solely on detecting and
localizing anomalies. However, existing approaches often neglect the deeper
causal relationships and interactions between objects, which are critical for
understanding anomalous behaviors. In this paper, we propose VADER, an
LLM-driven framework for Video Anomaly unDErstanding, which integrates keyframe
object Relation features with visual cues to enhance anomaly comprehension from
video. Specifically, VADER first applies an Anomaly Scorer to assign per-frame
anomaly scores, followed by a Context-AwarE Sampling (CAES) strategy to capture
the causal context of each anomalous event. A Relation Feature Extractor and a
COntrastive Relation Encoder (CORE) jointly model dynamic object interactions,
producing compact relational representations for downstream reasoning. These
visual and relational cues are integrated with LLMs to generate detailed,
causally grounded descriptions and support robust anomaly-related question
answering. Experiments on multiple real-world VAU benchmarks demonstrate that
VADER achieves strong results across anomaly description, explanation, and
causal reasoning tasks, advancing the frontier of explainable video anomaly
analysis.
Diffusion-SDPO: Safeguarded Direct Preference Optimization for Diffusion
  Models
Authors: name: Minghao Fu, name: Guo-Hua Wang, name: Tianyu Cui, name: Qing-Guo Chen, name: Zhao Xu, name: Weihua Luo, name: Kaifu Zhang
Published: 2025-11-05 09:30:49

Text-to-image diffusion models deliver high-quality images, yet aligning them
with human preferences remains challenging. We revisit diffusion-based Direct
Preference Optimization (DPO) for these models and identify a critical
pathology: enlarging the preference margin does not necessarily improve
generation quality. In particular, the standard Diffusion-DPO objective can
increase the reconstruction error of both winner and loser branches.
Consequently, degradation of the less-preferred outputs can become sufficiently
severe that the preferred branch is also adversely affected even as the margin
grows. To address this, we introduce Diffusion-SDPO, a safeguarded update rule
that preserves the winner by adaptively scaling the loser gradient according to
its alignment with the winner gradient. A first-order analysis yields a
closed-form scaling coefficient that guarantees the error of the preferred
output is non-increasing at each optimization step. Our method is simple,
model-agnostic, broadly compatible with existing DPO-style alignment frameworks
and adds only marginal computational overhead. Across standard text-to-image
benchmarks, Diffusion-SDPO delivers consistent gains over preference-learning
baselines on automated preference, aesthetic, and prompt alignment metrics.
Code is publicly available at https://github.com/AIDC-AI/Diffusion-SDPO.
SofT-GRPO: Surpassing Discrete-Token LLM Reinforcement Learning via
  Gumbel-Reparameterized Soft-Thinking Policy Optimization
Authors: name: Zhi Zheng, name: Wee Sun Lee
Published: 2025-11-09 14:55:50

The soft-thinking paradigm for Large Language Model (LLM) reasoning can
outperform the conventional discrete-token Chain-of-Thought (CoT) reasoning in
some scenarios, underscoring its research and application value. However, while
the discrete-token CoT reasoning pattern can be reinforced through policy
optimization algorithms such as group relative policy optimization (GRPO),
extending the soft-thinking pattern with Reinforcement Learning (RL) remains
challenging. This difficulty stems from the complexities of injecting
stochasticity into soft-thinking tokens and updating soft-thinking policies
accordingly. As a result, previous attempts to combine soft-thinking with GRPO
typically underperform their discrete-token GRPO counterparts. To fully unlock
the potential of soft-thinking, this paper presents a novel policy optimization
algorithm, SofT-GRPO, to reinforce LLMs under the soft-thinking reasoning
pattern. SofT-GRPO injects the Gumbel noise into logits, employs the
Gumbel-Softmax technique to avoid soft-thinking tokens outside the pre-trained
embedding space, and leverages the reparameterization trick in policy gradient.
We conduct experiments across base LLMs ranging from 1.5B to 7B parameters, and
results demonstrate that SofT-GRPO enables soft-thinking LLMs to slightly
outperform discrete-token GRPO on Pass@1 (+0.13% on average accuracy), while
exhibiting a substantial uplift on Pass@32 (+2.19% on average accuracy). Codes
and weights are available on https://github.com/zz1358m/SofT-GRPO-master
DRIVE: Data Curation Best Practices for Reinforcement Learning with
  Verifiable Reward in Competitive Code Generation
Authors: name: Speed Zhu, name: Jianwei Cai, name: Guang Chen, name: Lulu Wu, name: Saiyong Yang, name: Wiggin Zhou
Published: 2025-11-09 10:11:28

Recent reasoning-first models (e.g., OpenAI o1, DeepSeek R1) have spurred a
resurgence of interest in RLVR. Nevertheless, advances are dominated by
mathematics (e.g., AIME), with competitive-programming code generation
underexplored and data curation receiving less attention than RL algorithm
design. We investigate how to construct RLVR datasets (i.e., RL prompts) and
present practical training techniques that yield strong performance on
competitive-programming code generation. Our pipeline begins with supervised
fine-tuning (SFT) distilled from strong open-source models, augmented with
general-purpose and reasoning-intensive data. RL then follows a two-stage
process with executable, testcase-driven rewards: first, training on a large,
uniformly distributed set of competitive-programming problems using Group
Relative Policy Optimization (GRPO) with 8 rollouts per prompt and a relatively
short response-generation window (e.g., 32k during SFT and 24k in this stage)
to expand entropy and mitigate repetition and truncation; second, we perform
Pre-GRPO: updating on a small, high-quality set of challenging
problems with a large rollout budget (64 rollouts per prompt) under a
hard-focus curriculum that continuously retains the most difficult instances
throughout training. We implement our method on Qwen2.5-32B and evaluate on
LeetCode and Codeforces weekly contests to avoid data leakage. The resulting
model achieves state-of-the-art performance among models of similar scale and
is comparable to leading systems such as DeepSeek v3.1 and Doubao-1.5-Thinking.
We also examine scaling trends and observe strong RL scaling on an internal
large-scale MoE model. Our study distills concise best practices for data
curation, entropy expansion, and curriculum design in RLVR for
competitive-programming code generation.
LUT-LLM: Efficient Large Language Model Inference with Memory-based
  Computations on FPGAs
Authors: name: Zifan He, name: Shengyu Ye, name: Rui Ma, name: Yang Wang, name: Jason Cong
Published: 2025-11-09 01:17:08

The rapid progress of large language models (LLMs) has advanced numerous
applications, yet efficient single-batch inference remains vital for on-device
intelligence. While FPGAs offer fine-grained data control and high energy
efficiency, recent GPU optimizations have narrowed their advantage, especially
under arithmetic-based computation. To overcome this, we leverage FPGAs'
abundant on-chip memory to shift LLM inference from arithmetic- to memory-based
computation through table lookups. We present LUT-LLM, the first FPGA
accelerator enabling 1B+ LLM inference via vector-quantized memory operations.
Our analysis identifies activation-weight co-quantization as the most effective
scheme, supported by (1) bandwidth-aware parallel centroid search, (2)
efficient 2D table lookups, and (3) a spatial-temporal hybrid design minimizing
data caching. Implemented on an AMD V80 FPGA for a customized Qwen 3 1.7B
model, LUT-LLM achieves 1.66x lower latency than AMD MI210 and 1.72x higher
energy efficiency than NVIDIA A100, scaling to 32B models with 2.16x efficiency
gain over A100.
DigiData: Training and Evaluating General-Purpose Mobile Control Agents
Authors: name: Yuxuan Sun, name: Manchen Wang, name: Shengyi Qian, name: William R. Wong, name: Eric Gan, name: Pierluca D'Oro, name: Alejandro Castillejo Munoz, name: Sneha Silwal, name: Pedro Matias, name: Nitin Kamra, name: Satwik Kottur, name: Nick Raines, name: Xuanyi Zhao, name: Joy Chen, name: Joseph Greer, name: Andrea Madotto, name: Allen Bolourchi, name: James Valori, name: Kevin Carlberg, name: Karl Ridgeway, name: Joseph Tighe
Published: 2025-11-10 18:57:35

AI agents capable of controlling user interfaces have the potential to
transform human interaction with digital devices. To accelerate this
transformation, two fundamental building blocks are essential: high-quality
datasets that enable agents to achieve complex and human-relevant goals, and
robust evaluation methods that allow researchers and practitioners to rapidly
enhance agent performance. In this paper, we introduce DigiData, a large-scale,
high-quality, diverse, multi-modal dataset designed for training mobile control
agents. Unlike existing datasets, which derive goals from unstructured
interactions, DigiData is meticulously constructed through comprehensive
exploration of app features, resulting in greater diversity and higher goal
complexity. Additionally, we present DigiData-Bench, a benchmark for evaluating
mobile control agents on real-world complex tasks. We demonstrate that the
commonly used step-accuracy metric falls short in reliably assessing mobile
control agents and, to address this, we propose dynamic evaluation protocols
and AI-powered evaluations as rigorous alternatives for agent assessment. Our
contributions aim to significantly advance the development of mobile control
agents, paving the way for more intuitive and effective human-device
interactions.
SWE-fficiency: Can Language Models Optimize Real-World Repositories on Real Workloads?
Authors: name: Jeffrey Jian Ma, name: Milad Hashemi, name: Amir Yazdanbakhsh, name: Kevin Swersky, name: Ofir Press, name: Enhui Li, name: Vijay Janapa Reddi, name: Parthasarathy Ranganathan
Published: 2025-11-08 17:55:09

Optimizing the performance of large-scale software repositories demands expertise in code reasoning and software engineering (SWE) to reduce runtime while preserving program correctness. However, most benchmarks emphasize what to fix rather than how to fix code. We introduce SWE-fficiency, a benchmark for evaluating repository-level performance optimization on real workloads. Our suite contains 498 tasks across nine widely used data-science, machine-learning, and HPC repositories (e.g., numpy, pandas, scipy): given a complete codebase and a slow workload, an agent must investigate code semantics, localize bottlenecks and relevant tests, and produce a patch that matches or exceeds expert speedup while passing the same unit tests. To enable this how-to-fix evaluation, our automated pipeline scrapes GitHub pull requests for performance-improving edits, combining keyword filtering, static analysis, coverage tooling, and execution validation to both confirm expert speedup baselines and identify relevant repository unit tests. Empirical evaluation of state-of-the-art agents reveals significant underperformance. On average, agents achieve less than 0.15x the expert speedup: agents struggle in localizing optimization opportunities, reasoning about execution across functions, and maintaining correctness in proposed edits. We release the benchmark and accompanying data pipeline to facilitate research on automated performance engineering and long-horizon software reasoning.
MVU-Eval: Towards Multi-Video Understanding Evaluation for Multimodal
  LLMs
Authors: name: Tianhao Peng, name: Haochen Wang, name: Yuanxing Zhang, name: Zekun Wang, name: Zili Wang, name: Ge Zhang, name: Jian Yang, name: Shihao Li, name: Yanghai Wang, name: Xintao Wang, name: Houyi Li, name: Wei Ji, name: Pengfei Wan, name: Wenhao Huang, name: Zhaoxiang Zhang, name: Jiaheng Liu
Published: 2025-11-10 16:02:33

The advent of Multimodal Large Language Models (MLLMs) has expanded AI
capabilities to visual modalities, yet existing evaluation benchmarks remain
limited to single-video understanding, overlooking the critical need for
multi-video understanding in real-world scenarios (e.g., sports analytics and
autonomous driving). To address this significant gap, we introduce MVU-Eval,
the first comprehensive benchmark for evaluating Multi-Video Understanding for
MLLMs. Specifically, our MVU-Eval mainly assesses eight core competencies
through 1,824 meticulously curated question-answer pairs spanning 4,959 videos
from diverse domains, addressing both fundamental perception tasks and
high-order reasoning tasks. These capabilities are rigorously aligned with
real-world applications such as multi-sensor synthesis in autonomous systems
and cross-angle sports analytics. Through extensive evaluation of
state-of-the-art open-source and closed-source models, we reveal significant
performance discrepancies and limitations in current MLLMs' ability to perform
understanding across multiple videos. The benchmark will be made publicly
available to foster future research.
The Station: An Open-World Environment for AI-Driven Discovery
Authors: name: Stephen Chung, name: Wenyu Du
Published: 2025-11-09 10:13:00

We introduce the STATION, an open-world multi-agent environment that models a
miniature scientific ecosystem. Leveraging their extended context windows,
agents in the Station can engage in long scientific journeys that include
reading papers from peers, formulating hypotheses, submitting code, performing
analyses, and publishing results. Importantly, there is no centralized system
coordinating their activities - agents are free to choose their own actions and
develop their own narratives within the Station. Experiments demonstrate that
AI agents in the Station achieve new state-of-the-art performance on a wide
range of benchmarks, spanning from mathematics to computational biology to
machine learning, notably surpassing AlphaEvolve in circle packing. A rich
tapestry of narratives emerges as agents pursue independent research, interact
with peers, and build upon a cumulative history. From these emergent
narratives, novel methods arise organically, such as a new density-adaptive
algorithm for scRNA-seq batch integration. The Station marks a first step
towards autonomous scientific discovery driven by emergent behavior in an
open-world environment, representing a new paradigm that moves beyond rigid
optimization.
Robot Learning from a Physical World Model
Authors: name: Jiageng Mao, name: Sicheng He, name: Hao-Ning Wu, name: Yang You, name: Shuyang Sun, name: Zhicheng Wang, name: Yanan Bao, name: Huizhong Chen, name: Leonidas Guibas, name: Vitor Guizilini, name: Howard Zhou, name: Yue Wang
Published: 2025-11-10 18:59:07

We introduce PhysWorld, a framework that enables robot learning from video
generation through physical world modeling. Recent video generation models can
synthesize photorealistic visual demonstrations from language commands and
images, offering a powerful yet underexplored source of training signals for
robotics. However, directly retargeting pixel motions from generated videos to
robots neglects physics, often resulting in inaccurate manipulations. PhysWorld
addresses this limitation by coupling video generation with physical world
reconstruction. Given a single image and a task command, our method generates
task-conditioned videos and reconstructs the underlying physical world from the
videos, and the generated video motions are grounded into physically accurate
actions through object-centric residual reinforcement learning with the
physical world model. This synergy transforms implicit visual guidance into
physically executable robotic trajectories, eliminating the need for real robot
data collection and enabling zero-shot generalizable robotic manipulation.
Experiments on diverse real-world tasks demonstrate that PhysWorld
substantially improves manipulation accuracy compared to previous approaches.
Visit https://pointscoder.github.io/PhysWorld_Web/{the project webpage}
for details.
IterResearch: Rethinking Long-Horizon Agents via Markovian State
  Reconstruction
Authors: name: Guoxin Chen, name: Zile Qiao, name: Xuanzhong Chen, name: Donglei Yu, name: Haotian Xu, name: Wayne Xin Zhao, name: Ruihua Song, name: Wenbiao Yin, name: Huifeng Yin, name: Liwen Zhang, name: Kuan Li, name: Minpeng Liao, name: Yong Jiang, name: Pengjun Xie, name: Fei Huang, name: Jingren Zhou
Published: 2025-11-10 17:30:08

Recent advances in deep-research agents have shown promise for autonomous
knowledge construction through dynamic reasoning over external sources.
However, existing approaches rely on a mono-contextual paradigm that
accumulates all information in a single, expanding context window, leading to
context suffocation and noise contamination that limit their effectiveness on
long-horizon tasks. We introduce IterResearch, a novel iterative deep-research
paradigm that reformulates long-horizon research as a Markov Decision Process
with strategic workspace reconstruction. By maintaining an evolving report as
memory and periodically synthesizing insights, our approach preserves
consistent reasoning capacity across arbitrary exploration depths. We further
develop Efficiency-Aware Policy Optimization (EAPO), a reinforcement learning
framework that incentivizes efficient exploration through geometric reward
discounting and enables stable distributed training via adaptive downsampling.
Extensive experiments demonstrate that IterResearch achieves substantial
improvements over existing open-source agents with average +14.5pp across six
benchmarks and narrows the gap with frontier proprietary systems. Remarkably,
our paradigm exhibits unprecedented interaction scaling, extending to 2048
interactions with dramatic performance gains (from 3.5\% to 42.5\%), and serves
as an effective prompting strategy, improving frontier models by up to 19.2pp
over ReAct on long-horizon tasks. These findings position IterResearch as a
versatile solution for long-horizon reasoning, effective both as a trained
agent and as a prompting paradigm for frontier models.
RedOne 2.0: Rethinking Domain-specific LLM Post-Training in Social
  Networking Services
Authors: name: Fei Zhao, name: Chonggang Lu, name: Haofu Qian, name: Fangcheng Shi, name: Zijie Meng, name: Jianzhao Huang, name: Xu Tang, name: Zheyong Xie, name: Zheyu Ye, name: Zhe Xu, name: Yao Hu, name: Shaosheng Cao
Published: 2025-11-10 13:04:34

As a key medium for human interaction and information exchange, social
networking services (SNS) pose unique challenges for large language models
(LLMs): heterogeneous workloads, fast-shifting norms and slang, and
multilingual, culturally diverse corpora that induce sharp distribution shift.
Supervised fine-tuning (SFT) can specialize models but often triggers a
``seesaw'' between in-distribution gains and out-of-distribution robustness,
especially for smaller models. To address these challenges, we introduce RedOne
2.0, an SNS-oriented LLM trained with a progressive, RL-prioritized
post-training paradigm designed for rapid and stable adaptation. The pipeline
consist in three stages: (1) Exploratory Learning on curated SNS corpora to
establish initial alignment and identify systematic weaknesses; (2) Targeted
Fine-Tuning that selectively applies SFT to the diagnosed gaps while mixing a
small fraction of general data to mitigate forgetting; and (3) Refinement
Learning that re-applies RL with SNS-centric signals to consolidate
improvements and harmonize trade-offs across tasks. Across various tasks
spanning three categories, our 4B scale model delivers an average improvements
about 2.41 over the 7B sub-optimal baseline. Additionally, RedOne 2.0 achieves
average performance lift about 8.74 from the base model with less than half the
data required by SFT-centric method RedOne, evidencing superior data efficiency
and stability at compact scales. Overall, RedOne 2.0 establishes a competitive,
cost-effective baseline for domain-specific LLMs in SNS scenario, advancing
capability without sacrificing robustness.