Orionnnn/hub-stats huggingface dataset:
ODYSSEY: Open-World Quadrupeds Exploration and Manipulation for
  Long-Horizon Tasks
Authors: name: Kaijun Wang, name: Liqin Lu, name: Mingyu Liu, name: Jianuo Jiang, name: Zeju Li, name: Bolin Zhang, name: Wancai Zheng, name: Xinyi Yu, name: Hao Chen, name: Chunhua Shen
Published: 2025-08-11 17:54:31

Language-guided long-horizon mobile manipulation has long been a grand
challenge in embodied semantic reasoning, generalizable manipulation, and
adaptive locomotion. Three fundamental limitations hinder progress: First,
although large language models have improved spatial reasoning and task
planning through semantic priors, existing implementations remain confined to
tabletop scenarios, failing to address the constrained perception and limited
actuation ranges of mobile platforms. Second, current manipulation strategies
exhibit insufficient generalization when confronted with the diverse object
configurations encountered in open-world environments. Third, while crucial for
practical deployment, the dual requirement of maintaining high platform
maneuverability alongside precise end-effector control in unstructured settings
remains understudied.
  In this work, we present ODYSSEY, a unified mobile manipulation framework for
agile quadruped robots equipped with manipulators, which seamlessly integrates
high-level task planning with low-level whole-body control. To address the
challenge of egocentric perception in language-conditioned tasks, we introduce
a hierarchical planner powered by a vision-language model, enabling
long-horizon instruction decomposition and precise action execution. At the
control level, our novel whole-body policy achieves robust coordination across
challenging terrains. We further present the first benchmark for long-horizon
mobile manipulation, evaluating diverse indoor and outdoor scenarios. Through
successful sim-to-real transfer, we demonstrate the system's generalization and
robustness in real-world deployments, underscoring the practicality of legged
manipulators in unstructured environments. Our work advances the feasibility of
generalized robotic assistants capable of complex, dynamic tasks. Our project
page: https://kaijwang.github.io/odyssey.github.io/
EgoTwin: Dreaming Body and View in First Person
Authors: name: Jingqiao Xiu, name: Fangzhou Hong, name: Yicong Li, name: Mengze Li, name: Wentao Wang, name: Sirui Han, name: Liang Pan, name: Ziwei Liu
Published: 2025-08-18 15:33:09

While exocentric video synthesis has achieved great progress, egocentric
video generation remains largely underexplored, which requires modeling
first-person view content along with camera motion patterns induced by the
wearer's body movements. To bridge this gap, we introduce a novel task of joint
egocentric video and human motion generation, characterized by two key
challenges: 1) Viewpoint Alignment: the camera trajectory in the generated
video must accurately align with the head trajectory derived from human motion;
2) Causal Interplay: the synthesized human motion must causally align with the
observed visual dynamics across adjacent video frames. To address these
challenges, we propose EgoTwin, a joint video-motion generation framework built
on the diffusion transformer architecture. Specifically, EgoTwin introduces a
head-centric motion representation that anchors the human motion to the head
joint and incorporates a cybernetics-inspired interaction mechanism that
explicitly captures the causal interplay between video and motion within
attention operations. For comprehensive evaluation, we curate a large-scale
real-world dataset of synchronized text-video-motion triplets and design novel
metrics to assess video-motion consistency. Extensive experiments demonstrate
the effectiveness of the EgoTwin framework.
Selective Contrastive Learning for Weakly Supervised Affordance
  Grounding
Authors: name: WonJun Moon, name: Hyun Seok Seong, name: Jae-Pil Heo
Published: 2025-08-11 11:49:37

Facilitating an entity's interaction with objects requires accurately
identifying parts that afford specific actions. Weakly supervised affordance
grounding (WSAG) seeks to imitate human learning from third-person
demonstrations, where humans intuitively grasp functional parts without needing
pixel-level annotations. To achieve this, grounding is typically learned using
a shared classifier across images from different perspectives, along with
distillation strategies incorporating part discovery process. However, since
affordance-relevant parts are not always easily distinguishable, models
primarily rely on classification, often focusing on common class-specific
patterns that are unrelated to affordance. To address this limitation, we move
beyond isolated part-level learning by introducing selective prototypical and
pixel contrastive objectives that adaptively learn affordance-relevant cues at
both the part and object levels, depending on the granularity of the available
information. Initially, we find the action-associated objects in both
egocentric (object-focused) and exocentric (third-person example) images by
leveraging CLIP. Then, by cross-referencing the discovered objects of
complementary views, we excavate the precise part-level affordance clues in
each perspective. By consistently learning to distinguish affordance-relevant
regions from affordance-irrelevant background context, our approach effectively
shifts activation from irrelevant areas toward meaningful affordance cues.
Experimental results demonstrate the effectiveness of our method. Codes are
available at github.com/hynnsk/SelectiveCL.
CRISP: Persistent Concept Unlearning via Sparse Autoencoders
Authors: name: Tomer Ashuach, name: Dana Arad, name: Aaron Mueller, name: Martin Tutek, name: Yonatan Belinkov
Published: 2025-08-19 09:01:22

As large language models (LLMs) are increasingly deployed in real-world
applications, the need to selectively remove unwanted knowledge while
preserving model utility has become paramount. Recent work has explored sparse
autoencoders (SAEs) to perform precise interventions on monosemantic features.
However, most SAE-based methods operate at inference time, which does not
create persistent changes in the model's parameters. Such interventions can be
bypassed or reversed by malicious actors with parameter access. We introduce
CRISP, a parameter-efficient method for persistent concept unlearning using
SAEs. CRISP automatically identifies salient SAE features across multiple
layers and suppresses their activations. We experiment with two LLMs and show
that our method outperforms prior approaches on safety-critical unlearning
tasks from the WMDP benchmark, successfully removing harmful knowledge while
preserving general and in-domain capabilities. Feature-level analysis reveals
that CRISP achieves semantically coherent separation between target and benign
concepts, allowing precise suppression of the target features.
Beyond Pass@1: Self-Play with Variational Problem Synthesis Sustains
  RLVR
Authors: name: Xiao Liang, name: Zhongzhi Li, name: Yeyun Gong, name: Yelong Shen, name: Ying Nian Wu, name: Zhijiang Guo, name: Weizhu Chen
Published: 2025-08-19 17:42:45

Reinforcement Learning with Verifiable Rewards (RLVR) has recently emerged as
a key paradigm for post-training Large Language Models (LLMs), particularly for
complex reasoning tasks. However, vanilla RLVR training has been shown to
improve Pass@1 performance at the expense of policy entropy, leading to reduced
generation diversity and limiting the Pass@k performance, which typically
represents the upper bound of LLM reasoning capability. In this paper, we
systematically analyze the policy's generation diversity from the perspective
of training problems and find that augmenting and updating training problems
helps mitigate entropy collapse during training. Based on these observations,
we propose an online Self-play with Variational problem Synthesis (SvS)
strategy for RLVR training, which uses the policy's correct solutions to
synthesize variational problems while ensuring their reference answers remain
identical to the originals. This self-improving strategy effectively maintains
policy entropy during training and substantially improves Pass@k compared with
standard RLVR, sustaining prolonged improvements and achieving absolute gains
of 18.3% and 22.8% in Pass@32 performance on the competition-level AIME24 and
AIME25 benchmarks. Experiments on 12 reasoning benchmarks across varying model
sizes from 3B to 32B consistently demonstrate the generalizability and
robustness of SvS.
End-to-End Agentic RAG System Training for Traceable Diagnostic
  Reasoning
Authors: name: Qiaoyu Zheng, name: Yuze Sun, name: Chaoyi Wu, name: Weike Zhao, name: Pengcheng Qiu, name: Yongguo Yu, name: Kun Sun, name: Yanfeng Wang, name: Ya Zhang, name: Weidi Xie
Published: 2025-08-21 17:42:47

Accurate diagnosis with medical large language models is hindered by
knowledge gaps and hallucinations. Retrieval and tool-augmented methods help,
but their impact is limited by weak use of external knowledge and poor
feedback-reasoning traceability. To address these challenges, We introduce
Deep-DxSearch, an agentic RAG system trained end-to-end with reinforcement
learning (RL) that enables steer tracebale retrieval-augmented reasoning for
medical diagnosis. In Deep-DxSearch, we first construct a large-scale medical
retrieval corpus comprising patient records and reliable medical knowledge
sources to support retrieval-aware reasoning across diagnostic scenarios. More
crutially, we frame the LLM as the core agent and the retrieval corpus as its
environment, using tailored rewards on format, retrieval, reasoning structure,
and diagnostic accuracy, thereby evolving the agentic RAG policy from
large-scale data through RL.
  Experiments demonstrate that our end-to-end agentic RL training framework
consistently outperforms prompt-engineering and training-free RAG approaches
across multiple data centers. After training, Deep-DxSearch achieves
substantial gains in diagnostic accuracy, surpassing strong diagnostic
baselines such as GPT-4o, DeepSeek-R1, and other medical-specific frameworks
for both common and rare disease diagnosis under in-distribution and
out-of-distribution settings. Moreover, ablation studies on reward design and
retrieval corpus components confirm their critical roles, underscoring the
uniqueness and effectiveness of our approach compared with traditional
implementations. Finally, case studies and interpretability analyses highlight
improvements in Deep-DxSearch's diagnostic policy, providing deeper insight
into its performance gains and supporting clinicians in delivering more
reliable and precise preliminary diagnoses. See
https://github.com/MAGIC-AI4Med/Deep-DxSearch.
InMind: Evaluating LLMs in Capturing and Applying Individual Human
  Reasoning Styles
Authors: name: Zizhen Li, name: Chuanhao Li, name: Yibin Wang, name: Qi Chen, name: Diping Song, name: Yukang Feng, name: Jianwen Sun, name: Jiaxin Ai, name: Fanrui Zhang, name: Mingzhu Sun, name: Kaipeng Zhang
Published: 2025-08-22 04:04:00

LLMs have shown strong performance on human-centric reasoning tasks. While
previous evaluations have explored whether LLMs can infer intentions or detect
deception, they often overlook the individualized reasoning styles that
influence how people interpret and act in social contexts. Social deduction
games (SDGs) provide a natural testbed for evaluating individualized reasoning
styles, where different players may adopt diverse but contextually valid
reasoning strategies under identical conditions. To address this, we introduce
InMind, a cognitively grounded evaluation framework designed to assess whether
LLMs can capture and apply personalized reasoning styles in SDGs. InMind
enhances structured gameplay data with round-level strategy traces and
post-game reflections, collected under both Observer and Participant modes. It
supports four cognitively motivated tasks that jointly evaluate both static
alignment and dynamic adaptation. As a case study, we apply InMind to the game
Avalon, evaluating 11 state-of-the-art LLMs. General-purpose LLMs, even GPT-4o
frequently rely on lexical cues, struggling to anchor reflections in temporal
gameplay or adapt to evolving strategies. In contrast, reasoning-enhanced LLMs
like DeepSeek-R1 exhibit early signs of style-sensitive reasoning. These
findings reveal key limitations in current LLMs' capacity for individualized,
adaptive reasoning, and position InMind as a step toward cognitively aligned
human-AI interaction.
AgentFly: Fine-tuning LLM Agents without Fine-tuning LLMs
Authors: name: Huichi Zhou, name: Yihang Chen, name: Siyuan Guo, name: Xue Yan, name: Kin Hei Lee, name: Zihan Wang, name: Ka Yiu Lee, name: Guchun Zhang, name: Kun Shao, name: Linyi Yang, name: Jun Wang
Published: 2025-08-22 07:25:30

In this paper, we introduce a novel learning paradigm for adaptive Large
Language Model (LLM) agents that eliminates the need for fine-tuning the
underlying LLMs. Existing approaches are often either rigid, relying on static,
handcrafted reflection workflows, or computationally intensive, requiring
gradient updates of LLM model parameters. In contrast, our method enables
low-cost continual adaptation via memory-based online reinforcement learning.
We formalise this as a Memory-augmented Markov Decision Process (M-MDP),
equipped with a neural case-selection policy to guide action decisions. Past
experiences are stored in an episodic memory, either differentiable or
non-parametric. The policy is continually updated based on environmental
feedback through a memory rewriting mechanism, whereas policy improvement is
achieved through efficient memory reading (retrieval). We instantiate our agent
model in the deep research setting, namely AgentFly, which attains top-1 on
GAIA validation (87.88% Pass@3) and 79.40% on the test set. It reaches
66.6% F1 and 80.4% PM on the DeepResearcher dataset, outperforming the
state-of-the-art training-based method, while case-based memory adds 4.7% to
9.6% absolute points on out-of-distribution tasks. Our approach offers a
scalable and efficient pathway for developing generalist LLM agents capable of
continuous, real-time learning without gradient updates, advancing machine
learning towards open-ended skill acquisition and deep research scenarios. The
code is available at https://github.com/Agent-on-the-Fly/AgentFly.
AgentScope 1.0: A Developer-Centric Framework for Building Agentic
  Applications
Authors: name: Dawei Gao, name: Zitao Li, name: Yuexiang Xie, name: Weirui Kuang, name: Liuyi Yao, name: Bingchen Qian, name: Zhijian Ma, name: Yue Cui, name: Haohao Luo, name: Shen Li, name: Lu Yi, name: Yi Yu, name: Shiqi He, name: Zhiling Luo, name: Wenmeng Zhou, name: Zhicheng Zhang, name: Xuguang He, name: Ziqian Chen, name: Weikai Liao, name: Farruh Isakulovich Kushnazarov, name: Yaliang Li, name: Bolin Ding, name: Jingren Zhou
Published: 2025-08-22 10:35:56

Driven by rapid advancements of Large Language Models (LLMs), agents are
empowered to combine intrinsic knowledge with dynamic tool use, greatly
enhancing their capacity to address real-world tasks. In line with such an
evolution, AgentScope introduces major improvements in a new version (1.0),
towards comprehensively supporting flexible and efficient tool-based
agent-environment interactions for building agentic applications. Specifically,
we abstract foundational components essential for agentic applications and
provide unified interfaces and extensible modules, enabling developers to
easily leverage the latest progress, such as new models and MCPs. Furthermore,
we ground agent behaviors in the ReAct paradigm and offer advanced agent-level
infrastructure based on a systematic asynchronous design, which enriches both
human-agent and agent-agent interaction patterns while improving execution
efficiency. Building on this foundation, we integrate several built-in agents
tailored to specific practical scenarios. AgentScope also includes robust
engineering support for developer-friendly experiences. We provide a scalable
evaluation module with a visual studio interface, making the development of
long-trajectory agentic applications more manageable and easier to trace. In
addition, AgentScope offers a runtime sandbox to ensure safe agent execution
and facilitates rapid deployment in production environments. With these
enhancements, AgentScope provides a practical foundation for building scalable,
adaptive, and effective agentic applications.
Do What? Teaching Vision-Language-Action Models to Reject the Impossible
Authors: name: Wen-Han Hsieh, name: Elvis Hsieh, name: Dantong Niu, name: Trevor Darrell, name: Roei Herzig, name: David M. Chan
Published: 2025-08-22 10:54:33

Recently, Vision-Language-Action (VLA) models have demonstrated strong
performance on a range of robotic tasks. These models rely on multimodal
inputs, with language instructions playing a crucial role -- not only in
predicting actions, but also in robustly interpreting user intent, even when
the requests are impossible to fulfill. In this work, we investigate how VLAs
can recognize, interpret, and respond to false-premise instructions: natural
language commands that reference objects or conditions absent from the
environment. We propose Instruct-Verify-and-Act (IVA), a unified framework that
(i) detects when an instruction cannot be executed due to a false premise, (ii)
engages in language-based clarification or correction, and (iii) grounds
plausible alternatives in perception and action. Towards this end, we construct
a large-scale instruction tuning setup with structured language prompts and
train a VLA model capable of handling both accurate and erroneous requests. Our
approach leverages a contextually augmented, semi-synthetic dataset containing
paired positive and false-premise instructions, enabling robust detection and
natural language correction. Our experiments show that IVA improves false
premise detection accuracy by 97.56% over baselines, while increasing
successful responses in false-premise scenarios by 50.78%.
Jailbreaking Commercial Black-Box LLMs with Explicitly Harmful Prompts
Authors: name: Chiyu Zhang, name: Lu Zhou, name: Xiaogang Xu, name: Jiafei Wu, name: Liming Fang, name: Zhe Liu
Published: 2025-08-14 06:46:56

Evaluating jailbreak attacks is challenging when prompts are not overtly
harmful or fail to induce harmful outputs. Unfortunately, many existing
red-teaming datasets contain such unsuitable prompts. To evaluate attacks
accurately, these datasets need to be assessed and cleaned for maliciousness.
However, existing malicious content detection methods rely on either manual
annotation, which is labor-intensive, or large language models (LLMs), which
have inconsistent accuracy in harmful types. To balance accuracy and
efficiency, we propose a hybrid evaluation framework named MDH (Malicious
content Detection based on LLMs with Human assistance) that combines LLM-based
annotation with minimal human oversight, and apply it to dataset cleaning and
detection of jailbroken responses. Furthermore, we find that well-crafted
developer messages can significantly boost jailbreak success, leading us to
propose two new strategies: D-Attack, which leverages context simulation, and
DH-CoT, which incorporates hijacked chains of thought. The Codes, datasets,
judgements, and detection results will be released in github repository:
https://github.com/AlienZhang1996/DH-CoT.
AetherCode: Evaluating LLMs' Ability to Win In Premier Programming
  Competitions
Authors: name: Zihan Wang, name: Jiaze Chen, name: Zhicheng Liu, name: Markus Mak, name: Yidi Du, name: Geonsik Moon, name: Luoqi Xu, name: Aaron Tua, name: Kunshuo Peng, name: Jiayi Lu, name: Mingfei Xia, name: Boqian Zou, name: Chenyang Ran, name: Guang Tian, name: Shoutai Zhu, name: Yeheng Duan, name: Zhenghui Kang, name: Zhenxing Lin, name: Shangshu Li, name: Qiang Luo, name: Qingshen Long, name: Zhiyong Chen, name: Yihan Xiao, name: Yurong Wu, name: Daoguang Zan, name: Yuyi Fu, name: Mingxuan Wang, name: Ming Ding
Published: 2025-08-22 14:04:55

Competitive programming has emerged as a critical benchmark for evaluating
the reasoning and coding capabilities of Large Language Models (LLMs). Despite
impressive progress on existing benchmarks, we argue that current evaluations
overstate model proficiency, masking a substantial gap between LLMs and elite
human programmers. This gap arises from two key limitations: insufficient
difficulty and scope of benchmark problems, and evaluation bias from
low-quality test cases. To address these shortcomings, we present AetherCode, a
new benchmark that draws problems from premier programming competitions such as
IOI and ICPC, offering broader coverage and higher difficulty. AetherCode
further incorporates comprehensive, expert-validated test suites built through
a hybrid of automated generation and human curation, ensuring rigorous and
reliable assessment. By combining challenging problem design with robust
evaluation, AetherCode provides a more faithful measure of LLM capabilities and
sets a new standard for future research in code reasoning.
TPLA: Tensor Parallel Latent Attention for Efficient Disaggregated
  Prefill \& Decode Inference
Authors: name: Xiaojuan Tang, name: Fanxu Meng, name: Pingzhi Tang, name: Yuxuan Wang, name: Di Yin, name: Xing Sun, name: Muhan Zhang
Published: 2025-08-21 15:25:40

Multi-Head Latent Attention (MLA), introduced in DeepSeek-V2, compresses
key-value states into a low-rank latent vector, caching only this vector to
reduce memory. In tensor parallelism (TP), however, attention heads are
computed across multiple devices, and each device must load the full cache,
eroding the advantage of MLA over Grouped Query Attention (GQA). We propose
Tensor-Parallel Latent Attention (TPLA): a scheme that partitions both the
latent representation and each head's input dimension across devices, performs
attention independently per shard, and then combines results with an
all-reduce. TPLA preserves the benefits of a compressed KV cache while
unlocking TP efficiency. Unlike Grouped Latent Attention (GLA), every head in
TPLA still leverages the full latent representation, maintaining stronger
representational capacity. TPLA is drop-in compatible with models pre-trained
using MLA: it supports MLA-style prefilling and enables efficient
tensor-parallel decoding without retraining. Applying simple orthogonal
transforms -- e.g., the Hadamard transform or PCA -- before TP slicing further
mitigates cross-shard interference, yielding minimal accuracy degradation. By
reducing the per-device KV cache for DeepSeek-V3 and Kimi-K2, we achieve 1.79x
and 1.93x speedups, respectively, at a 32K-token context length while
maintaining performance on commonsense and LongBench benchmarks. TPLA can be
implemented with FlashAttention-3, enabling practical end-to-end acceleration.