Orionnnn/hub-stats huggingface dataset:
Routing Manifold Alignment Improves Generalization of Mixture-of-Experts
  LLMs
Authors: name: Zhongyang Li, name: Ziyue Li, name: Tianyi Zhou
Published: 2025-11-10 18:59:53

Sparse Mixture-of-Experts (MoE) have been widely adopted in recent large
language models since it can efficiently scale up the model capability without
increasing the inference cost. However, evaluations on broad downstream tasks
reveal a consistent suboptimality of the routers in existing MoE LLMs, which
results in a severe performance gap (e.g., 10-20% in accuracy) to the optimal
routing. In this paper, we show that aligning the manifold of routing weights
with that of task embedding can effectively reduce the gap and improve MoE
LLMs' generalization performance. Our method, "Routing Manifold Alignment
(RoMA)", introduces an additional manifold regularization term in the
post-training objective and only requires lightweight finetuning of routers
(with other parameters frozen). Specifically, the regularization encourages the
routing weights of each sample to be close to those of its successful neighbors
(whose routing weights lead to correct answers) in a task embedding space.
Consequently, samples targeting similar tasks will share similar expert choices
across layers. Building such bindings between tasks and experts over different
samples is essential to achieve better generalization. Moreover, RoMA
demonstrates the advantage of unifying the task understanding (by embedding
models) with solution generation (by MoE LLMs). In experiments, we finetune
routers in OLMoE, DeepSeekMoE, and Qwen3-MoE using RoMA. Evaluations on diverse
benchmarks and extensive comparisons with baselines show the substantial
improvement brought by RoMA.
DIMO: Diverse 3D Motion Generation for Arbitrary Objects
Authors: name: Linzhan Mou, name: Jiahui Lei, name: Chen Wang, name: Lingjie Liu, name: Kostas Daniilidis
Published: 2025-11-10 18:56:49

We present DIMO, a generative approach capable of generating diverse 3D
motions for arbitrary objects from a single image. The core idea of our work is
to leverage the rich priors in well-trained video models to extract the common
motion patterns and then embed them into a shared low-dimensional latent space.
Specifically, we first generate multiple videos of the same object with diverse
motions. We then embed each motion into a latent vector and train a shared
motion decoder to learn the distribution of motions represented by a structured
and compact motion representation, i.e., neural key point trajectories. The
canonical 3D Gaussians are then driven by these key points and fused to model
the geometry and appearance. During inference time with learned latent space,
we can instantly sample diverse 3D motions in a single-forward pass and support
several interesting applications including 3D motion interpolation and
language-guided motion generation. Our project page is available at
https://linzhanm.github.io/dimo.
HaluMem: Evaluating Hallucinations in Memory Systems of Agents
Authors: name: Ding Chen, name: Simin Niu, name: Kehang Li, name: Peng Liu, name: Xiangping Zheng, name: Bo Tang, name: Xinchi Li, name: Feiyu Xiong, name: Zhiyu Li
Published: 2025-11-05 14:37:34

Memory systems are key components that enable AI systems such as LLMs and AI
agents to achieve long-term learning and sustained interaction. However, during
memory storage and retrieval, these systems frequently exhibit memory
hallucinations, including fabrication, errors, conflicts, and omissions.
Existing evaluations of memory hallucinations are primarily end-to-end question
answering, which makes it difficult to localize the operational stage within
the memory system where hallucinations arise. To address this, we introduce the
Hallucination in Memory Benchmark (HaluMem), the first operation level
hallucination evaluation benchmark tailored to memory systems. HaluMem defines
three evaluation tasks (memory extraction, memory updating, and memory question
answering) to comprehensively reveal hallucination behaviors across different
operational stages of interaction. To support evaluation, we construct
user-centric, multi-turn human-AI interaction datasets, HaluMem-Medium and
HaluMem-Long. Both include about 15k memory points and 3.5k multi-type
questions. The average dialogue length per user reaches 1.5k and 2.6k turns,
with context lengths exceeding 1M tokens, enabling evaluation of hallucinations
across different context scales and task complexities. Empirical studies based
on HaluMem show that existing memory systems tend to generate and accumulate
hallucinations during the extraction and updating stages, which subsequently
propagate errors to the question answering stage. Future research should focus
on developing interpretable and constrained memory operation mechanisms that
systematically suppress hallucinations and improve memory reliability.
RLoop: An Self-Improving Framework for Reinforcement Learning with
  Iterative Policy Initialization
Authors: name: Zeng Zhiyuan, name: Jiashuo Liu, name: Zhangyue Yin, name: Ge Zhang, name: Wenhao Huang, name: Xipeng Qiu
Published: 2025-11-06 11:27:16

While Reinforcement Learning for Verifiable Rewards (RLVR) is powerful for
training large reasoning models, its training dynamics harbor a critical
challenge: RL overfitting, where models gain training rewards but lose
generalization. Our analysis reveals this is driven by policy
over-specialization and catastrophic forgetting of diverse solutions generated
during training. Standard optimization discards this valuable inter-step policy
diversity. To address this, we introduce RLoop, a self-improving framework
built on iterative policy initialization. RLoop transforms the standard
training process into a virtuous cycle: it first uses RL to explore the
solution space from a given policy, then filters the successful trajectories to
create an expert dataset. This dataset is used via Rejection-sampling
Fine-Tuning (RFT) to refine the initial policy, creating a superior starting
point for the next iteration. This loop of exploration and exploitation via
iterative re-initialization effectively converts transient policy variations
into robust performance gains. Our experiments show RLoop mitigates forgetting
and substantially improves generalization, boosting average accuracy by 9% and
pass@32 by over 15% compared to vanilla RL.
Real-Time Reasoning Agents in Evolving Environments
Authors: name: Yule Wen, name: Yixin Ye, name: Yanzhe Zhang, name: Diyi Yang, name: Hao Zhu
Published: 2025-11-07 00:51:02

Agents in the real world must make not only logical but also timely
judgments. This requires continuous awareness of the dynamic environment:
hazards emerge, opportunities arise, and other agents act, while the agent's
reasoning is still unfolding. Despite advances in language model reasoning,
existing approaches fail to account for this dynamic nature. We introduce
real-time reasoning as a new problem formulation for agents in evolving
environments and build Real-Time Reasoning Gym to demonstrate it. We study two
paradigms for deploying language models in agents: (1) reactive agents, which
employ language models with bounded reasoning computation for rapid responses,
and (2) planning agents, which allow extended reasoning computation for complex
problems. Our experiments show that even state-of-the-art models struggle with
making logical and timely judgments in either paradigm. To address this
limitation, we propose AgileThinker, which simultaneously engages both
reasoning paradigms. AgileThinker consistently outperforms agents engaging only
one reasoning paradigm as the task difficulty and time pressure rise,
effectively balancing reasoning depth and response latency. Our work
establishes real-time reasoning as a critical testbed for developing practical
agents and provides a foundation for research in temporally constrained AI
systems, highlighting a path toward real-time capable agents.
VeriCoT: Neuro-symbolic Chain-of-Thought Validation via Logical
  Consistency Checks
Authors: name: Yu Feng, name: Nathaniel Weir, name: Kaj Bostrom, name: Sam Bayless, name: Darion Cassel, name: Sapana Chaudhary, name: Benjamin Kiesl-Reiter, name: Huzefa Rangwala
Published: 2025-11-06 18:50:08

LLMs can perform multi-step reasoning through Chain-of-Thought (CoT), but
they cannot reliably verify their own logic. Even when they reach correct
answers, the underlying reasoning may be flawed, undermining trust in
high-stakes scenarios. To mitigate this issue, we introduce VeriCoT, a
neuro-symbolic method that extracts and verifies formal logical arguments from
CoT reasoning. VeriCoT formalizes each CoT reasoning step into first-order
logic and identifies premises that ground the argument in source context,
commonsense knowledge, or prior reasoning steps. The symbolic representation
enables automated solvers to verify logical validity while the NL premises
allow humans and systems to identify ungrounded or fallacious reasoning steps.
Experiments on the ProofWriter, LegalBench, and BioASQ datasets show VeriCoT
effectively identifies flawed reasoning, and serves as a strong predictor of
final answer correctness. We also leverage VeriCoT's verification signal for
(1) inference-time self-reflection, (2) supervised fine-tuning (SFT) on
VeriCoT-distilled datasets and (3) preference fine-tuning (PFT) with direct
preference optimization (DPO) using verification-based pairwise rewards,
further improving reasoning validity and accuracy.
HAFixAgent: History-Aware Automated Program Repair Agent
Authors: name: Yu Shi, name: Hao Li, name: Bram Adams, name: Ahmed E. Hassan
Published: 2025-11-02 18:45:34

Automated program repair (APR) has recently shifted toward large language
models and agent-based systems, yet most systems rely on local snapshot
context, overlooking repository history. Prior work shows that repository
history helps repair single-line bugs, since the last commit touching the buggy
line is often the bug-introducing one. In this paper, we investigate whether
repository history can also improve agentic APR systems at scale, especially
for complex multi-hunk bugs. We present HAFixAgent, a History-Aware Bug-Fixing
Agent that injects blame-derived repository heuristics into its repair loop. A
preliminary study of all 854 real-world bugs from Defects4J motivates our
design, showing that bug-relevant history is both widely available and highly
concentrated. Empirical comparison of HAFixAgent with two state-of-the-art
baselines shows: (1) Effectiveness: HAFixAgent significantly improves over the
agent-based baseline (by 212.3%) and the multi-hunk baseline (by 29.9%). (2)
Efficiency: history does not significantly increase agent steps and keeps token
costs comparable, with notably lower median costs for complex
multi-file-multi-hunk bugs. (3) Practicality: combining different historical
heuristics repairs more bugs, offering a clear cost-benefit trade-off.
HAFixAgent offers a practical recipe for history-aware agentic APR: ground the
agent in version control history, prioritize diff-based historical context, and
integrate complementary heuristics when needed.
CritiCal: Can Critique Help LLM Uncertainty or Confidence Calibration?
Authors: name: Qing Zong, name: Jiayu Liu, name: Tianshi Zheng, name: Chunyang Li, name: Baixuan Xu, name: Haochen Shi, name: Weiqi Wang, name: Zhaowei Wang, name: Chunkit Chan, name: Yangqiu Song
Published: 2025-10-28 15:16:06

Accurate confidence calibration in Large Language Models (LLMs) is critical
for safe use in high-stakes domains, where clear verbalized confidence enhances
user trust. Traditional methods that mimic reference confidence expressions
often fail to capture the reasoning needed for accurate confidence assessment.
We propose natural language critiques as a solution, ideally suited for
confidence calibration, as precise gold confidence labels are hard to obtain
and often require multiple generations. This paper studies how natural language
critiques can enhance verbalized confidence, addressing: (1) What to critique:
uncertainty (question-focused) or confidence (answer-specific)? Analysis shows
confidence suits multiple-choice tasks, while uncertainty excels in open-ended
scenarios. (2) How to critique: self-critique or critique calibration training?
We propose Self-Critique, enabling LLMs to critique and optimize their
confidence beyond mere accuracy, and CritiCal, a novel Critique Calibration
training method that leverages natural language critiques to improve confidence
calibration, moving beyond direct numerical optimization. Experiments show that
CritiCal significantly outperforms Self-Critique and other competitive
baselines, even surpassing its teacher model, GPT-4o, in complex reasoning
tasks. CritiCal also shows robust generalization in out-of-distribution
settings, advancing LLM's reliability.
Too Good to be Bad: On the Failure of LLMs to Role-Play Villains
Authors: name: Zihao Yi, name: Qingxuan Jiang, name: Ruotian Ma, name: Xingyu Chen, name: Qu Yang, name: Mengru Wang, name: Fanghua Ye, name: Ying Shen, name: Zhaopeng Tu, name: Xiaolong Li, name: Linus
Published: 2025-11-07 03:50:52

Large Language Models (LLMs) are increasingly tasked with creative
generation, including the simulation of fictional characters. However, their
ability to portray non-prosocial, antagonistic personas remains largely
unexamined. We hypothesize that the safety alignment of modern LLMs creates a
fundamental conflict with the task of authentically role-playing morally
ambiguous or villainous characters. To investigate this, we introduce the Moral
RolePlay benchmark, a new dataset featuring a four-level moral alignment scale
and a balanced test set for rigorous evaluation. We task state-of-the-art LLMs
with role-playing characters from moral paragons to pure villains. Our
large-scale evaluation reveals a consistent, monotonic decline in role-playing
fidelity as character morality decreases. We find that models struggle most
with traits directly antithetical to safety principles, such as ``Deceitful''
and ``Manipulative'', often substituting nuanced malevolence with superficial
aggression. Furthermore, we demonstrate that general chatbot proficiency is a
poor predictor of villain role-playing ability, with highly safety-aligned
models performing particularly poorly. Our work provides the first systematic
evidence of this critical limitation, highlighting a key tension between model
safety and creative fidelity. Our benchmark and findings pave the way for
developing more nuanced, context-aware alignment methods.
Visual Spatial Tuning
Authors: name: Rui Yang, name: Ziyu Zhu, name: Yanwei Li, name: Jingjia Huang, name: Shen Yan, name: Siyuan Zhou, name: Zhe Liu, name: Xiangtai Li, name: Shuangye Li, name: Wenqian Wang, name: Yi Lin, name: Hengshuang Zhao
Published: 2025-11-07 18:59:16

Capturing spatial relationships from visual inputs is a cornerstone of
human-like general intelligence. Several previous studies have tried to enhance
the spatial awareness of Vision-Language Models (VLMs) by adding extra expert
encoders, which brings extra overhead and usually harms general capabilities.
To enhance the spatial ability in general architectures, we introduce Visual
Spatial Tuning (VST), a comprehensive framework to cultivate VLMs with
human-like visuospatial abilities, from spatial perception to reasoning. We
first attempt to enhance spatial perception in VLMs by constructing a
large-scale dataset termed VST-P, which comprises 4.1 million samples spanning
19 skills across single views, multiple images, and videos. Then, we present
VST-R, a curated dataset with 135K samples that instruct models to reason in
space. In particular, we adopt a progressive training pipeline: supervised
fine-tuning to build foundational spatial knowledge, followed by reinforcement
learning to further improve spatial reasoning abilities. Without the
side-effect to general capabilities, the proposed VST consistently achieves
state-of-the-art results on several spatial benchmarks, including 34.8% on
MMSI-Bench and 61.2% on VSIBench. It turns out that the
Vision-Language-Action models can be significantly enhanced with the proposed
spatial tuning paradigm, paving the way for more physically grounded AI.
Jailbreaking in the Haystack
Authors: name: Rishi Rajesh Shah, name: Chen Henry Wu, name: Shashwat Saxena, name: Ziqian Zhong, name: Alexander Robey, name: Aditi Raghunathan
Published: 2025-11-05 01:12:50

Recent advances in long-context language models (LMs) have enabled
million-token inputs, expanding their capabilities across complex tasks like
computer-use agents. Yet, the safety implications of these extended contexts
remain unclear. To bridge this gap, we introduce NINJA (short for
Needle-in-haystack jailbreak attack), a method that jailbreaks aligned LMs by
appending benign, model-generated content to harmful user goals. Critical to
our method is the observation that the position of harmful goals play an
important role in safety. Experiments on standard safety benchmark, HarmBench,
show that NINJA significantly increases attack success rates across
state-of-the-art open and proprietary models, including LLaMA, Qwen, Mistral,
and Gemini. Unlike prior jailbreaking methods, our approach is low-resource,
transferable, and less detectable. Moreover, we show that NINJA is
compute-optimal -- under a fixed compute budget, increasing context length can
outperform increasing the number of trials in best-of-N jailbreak. These
findings reveal that even benign long contexts -- when crafted with careful
goal positioning -- introduce fundamental vulnerabilities in modern LMs.
Towards Mitigating Hallucinations in Large Vision-Language Models by
  Refining Textual Embeddings
Authors: name: Aakriti Agrawal, name: Gouthaman KV, name: Rohith Aralikatti, name: Gauri Jagatap, name: Jiaxin Yuan, name: Vijay Kamarshi, name: Andrea Fanelli, name: Furong Huang
Published: 2025-11-07 06:39:54

In this work, we identify an inherent bias in prevailing LVLM architectures
toward the language modality, largely resulting from the common practice of
simply appending visual embeddings to the input text sequence. To address this,
we propose a simple yet effective method that refines textual embeddings by
integrating average-pooled visual features. Our approach demonstrably improves
visual grounding and significantly reduces hallucinations on established
benchmarks. While average pooling offers a straightforward, robust, and
efficient means of incorporating visual information, we believe that more
sophisticated fusion methods could further enhance visual grounding and
cross-modal alignment. Given that the primary focus of this work is to
highlight the modality imbalance and its impact on hallucinations -- and to
show that refining textual embeddings with visual information mitigates this
issue -- we leave exploration of advanced fusion strategies for future work.
Dense Motion Captioning
Authors: name: Shiyao Xu, name: Benedetta Liberatori, name: Gül Varol, name: Paolo Rota
Published: 2025-11-07 15:55:10

Recent advances in 3D human motion and language integration have primarily
focused on text-to-motion generation, leaving the task of motion understanding
relatively unexplored. We introduce Dense Motion Captioning, a novel task that
aims to temporally localize and caption actions within 3D human motion
sequences. Current datasets fall short in providing detailed temporal
annotations and predominantly consist of short sequences featuring few actions.
To overcome these limitations, we present the Complex Motion Dataset (CompMo),
the first large-scale dataset featuring richly annotated, complex motion
sequences with precise temporal boundaries. Built through a carefully designed
data generation pipeline, CompMo includes 60,000 motion sequences, each
composed of multiple actions ranging from at least two to ten, accurately
annotated with their temporal extents. We further present DEMO, a model that
integrates a large language model with a simple motion adapter, trained to
generate dense, temporally grounded captions. Our experiments show that DEMO
substantially outperforms existing methods on CompMo as well as on adapted
benchmarks, establishing a robust baseline for future research in 3D motion
understanding and captioning.
DeepEyesV2: Toward Agentic Multimodal Model
Authors: name: Jack Hong, name: Chenxiao Zhao, name: ChengLin Zhu, name: Weiheng Lu, name: Guohai Xu, name: Xing Yu
Published: 2025-11-07 14:31:20

Agentic multimodal models should not only comprehend text and images, but
also actively invoke external tools, such as code execution environments and
web search, and integrate these operations into reasoning. In this work, we
introduce DeepEyesV2 and explore how to build an agentic multimodal model from
the perspectives of data construction, training methods, and model evaluation.
We observe that direct reinforcement learning alone fails to induce robust
tool-use behavior. This phenomenon motivates a two-stage training pipeline: a
cold-start stage to establish tool-use patterns, and reinforcement learning
stage to further refine tool invocation. We curate a diverse, moderately
challenging training dataset, specifically including examples where tool use is
beneficial. We further introduce RealX-Bench, a comprehensive benchmark
designed to evaluate real-world multimodal reasoning, which inherently requires
the integration of multiple capabilities, including perception, search, and
reasoning. We evaluate DeepEyesV2 on RealX-Bench and other representative
benchmarks, demonstrating its effectiveness across real-world understanding,
mathematical reasoning, and search-intensive tasks. Moreover, DeepEyesV2
exhibits task-adaptive tool invocation, tending to use image operations for
perception tasks and numerical computations for reasoning tasks. Reinforcement
learning further enables complex tool combinations and allows model to
selectively invoke tools based on context. We hope our study can provide
guidance for community in developing agentic multimodal models.
EVTAR: End-to-End Try on with Additional Unpaired Visual Reference
Authors: name: Liuzhuozheng Li, name: Yue Gong, name: Shanyuan Liu, name: Bo Cheng, name: Yuhang Ma, name: Liebucha Wu, name: Dengyang Jiang, name: Zanyi Wang, name: Dawei Leng, name: Yuhui Yin
Published: 2025-11-02 14:32:31

We propose EVTAR, an End-to-End Virtual Try-on model with Additional
Reference, that directly fits the target garment onto the person image while
incorporating reference images to enhance try-on accuracy. Most existing
virtual try-on approaches rely on complex inputs such as agnostic person
images, human pose, densepose, or body keypoints, making them labor-intensive
and impractical for real-world applications. In contrast, EVTAR adopts a
two-stage training strategy, enabling simple inference with only the source
image and the target garment inputs. Our model generates try-on results without
masks, densepose, or segmentation maps. Moreover, EVTAR leverages additional
reference images of different individuals wearing the same clothes to preserve
garment texture and fine-grained details better. This mechanism is analogous to
how humans consider reference models when choosing outfits, thereby simulating
a more realistic and high-quality dressing effect. We enrich the training data
with supplementary references and unpaired person images to support these
capabilities. We evaluate EVTAR on two widely used benchmarks and diverse
tasks, and the results consistently validate the effectiveness of our approach.