Orionnnn/hub-stats huggingface dataset:
TextQuests: How Good are LLMs at Text-Based Video Games?
Authors: name: Long Phan, name: Mantas Mazeika, name: Andy Zou, name: Dan Hendrycks
Published: 2025-07-31 16:22:55

Evaluating AI agents within complex, interactive environments that mirror
real-world challenges is critical for understanding their practical
capabilities. While existing agent benchmarks effectively assess skills like
tool use or performance on structured tasks, they often do not fully capture an
agent's ability to operate autonomously in exploratory environments that demand
sustained, self-directed reasoning over a long and growing context. To spur the
development of agents capable of more robust intrinsic reasoning over long
horizons, we introduce TextQuests, a benchmark based on the Infocom suite of
interactive fiction games. These text-based adventures, which can take human
players over 30 hours and require hundreds of precise actions to solve, serve
as an effective proxy for evaluating AI agents on focused, stateful tasks. The
benchmark is specifically designed to assess an LLM agent's capacity for
self-contained problem-solving by precluding the use of external tools, thereby
focusing on intrinsic long-context reasoning capabilities in an exploratory
environment characterized by the need for trial-and-error learning and
sustained problem-solving within a single interactive session. We release
TextQuests at https://textquests.ai.
Fact2Fiction: Targeted Poisoning Attack to Agentic Fact-checking System
Authors: name: Haorui He, name: Yupeng Li, name: Bin Benjamin Zhu, name: Dacheng Wen, name: Reynold Cheng, name: Francis C. M. Lau
Published: 2025-08-08 06:44:57

State-of-the-art fact-checking systems combat misinformation at scale by
employing autonomous LLM-based agents to decompose complex claims into smaller
sub-claims, verify each sub-claim individually, and aggregate the partial
results to produce verdicts with justifications (explanatory rationales for the
verdicts). The security of these systems is crucial, as compromised
fact-checkers, which tend to be easily underexplored, can amplify
misinformation. This work introduces Fact2Fiction, the first poisoning attack
framework targeting such agentic fact-checking systems. Fact2Fiction mirrors
the decomposition strategy and exploits system-generated justifications to
craft tailored malicious evidences that compromise sub-claim verification.
Extensive experiments demonstrate that Fact2Fiction achieves 8.9\%--21.2\%
higher attack success rates than state-of-the-art attacks across various
poisoning budgets. Fact2Fiction exposes security weaknesses in current
fact-checking systems and highlights the need for defensive countermeasures.
Compressing Chain-of-Thought in LLMs via Step Entropy
Authors: name: Zeju Li, name: Jianyuan Zhong, name: Ziyang Zheng, name: Xiangyu Wen, name: Zhijian Xu, name: Yingying Cheng, name: Fan Zhang, name: Qiang Xu
Published: 2025-08-05 11:48:18

Large Language Models (LLMs) using Chain-of-Thought (CoT) prompting excel at
complex reasoning but generate verbose thought processes with considerable
redundancy, leading to increased inference costs and reduced efficiency. We
introduce a novel CoT compression framework based on step entropy, a metric
that quantifies the informational contribution of individual reasoning steps to
identify redundancy. Through theoretical analysis and extensive empirical
validation on mathematical reasoning benchmarks, we demonstrate that steps with
low entropy are indeed highly redundant. Our experiments reveal that an
astonishing 80\% of low-entropy intermediate steps can be pruned with minor
degradation in the final answer accuracy across DeepSeek-R1-7B, 14B and
Qwen3-8B. This finding sharply contrasts with random or high-entropy pruning,
which severely impairs reasoning performance. Building on this, we propose a
novel two-stage training strategy combining Supervised Fine-Tuning (SFT) and
Group Relative Policy Optimization (GRPO) reinforcement learning. This approach
enables LLMs to autonomously learn to generate compressed COTs during inference
by strategically incorporating [SKIP] tokens. Our method significantly enhances
LLM inference efficiency while rigorously preserving accuracy, offering
profound implications for practical LLM deployment and a deeper understanding
of reasoning structures.
MoBE: Mixture-of-Basis-Experts for Compressing MoE-based LLMs
Authors: name: Xiaodong Chen, name: Mingming Ha, name: Zhenzhong Lan, name: Jing Zhang, name: Jianguo Li
Published: 2025-08-07 10:48:24

The Mixture-of-Experts (MoE) architecture has become a predominant paradigm
for scaling large language models (LLMs). Despite offering strong performance
and computational efficiency, large MoE-based LLMs like DeepSeek-V3-0324 and
Kimi-K2-Instruct present serious challenges due to substantial memory
requirements in deployment. While recent works have explored MoE compression to
address this issue, existing methods often suffer from considerable accuracy
drops (e.g., 7-14% relatively) even at modest compression rates. This paper
introduces a novel Mixture-of-Basis-Experts (MoBE) method that achieves model
compression while incurring minimal accuracy drops. Specifically, each up/gate
matrix in an expert is decomposed via a rank decomposition as W = AB, where
matrix A is unique to each expert. The relatively larger matrix B is further
re-parameterized as a linear combination of basis matrices {Bi} shared across
all experts within a given MoE layer. The factorization is learned by
minimizing the reconstruction error relative to the original weight matrices.
Experiments demonstrate that MoBE achieves notably lower accuracy drops
compared to prior works. For instance, MoBE can reduce the parameter counts of
Qwen3-235B-A22B-2507, DeepSeek-V3-0324 (671B) and Kimi-K2-Instruct (1T) by
24%-30% with only 1%-2% accuracy drop (about 2% drops when measured
relatively).
A Comprehensive Survey of Self-Evolving AI Agents: A New Paradigm
  Bridging Foundation Models and Lifelong Agentic Systems
Authors: name: Jinyuan Fang, name: Yanwen Peng, name: Xi Zhang, name: Yingxu Wang, name: Xinhao Yi, name: Guibin Zhang, name: Yi Xu, name: Bin Wu, name: Siwei Liu, name: Zihao Li, name: Zhaochun Ren, name: Nikos Aletras, name: Xi Wang, name: Han Zhou, name: Zaiqiao Meng
Published: 2025-08-10 16:07:32

Recent advances in large language models have sparked growing interest in AI
agents capable of solving complex, real-world tasks. However, most existing
agent systems rely on manually crafted configurations that remain static after
deployment, limiting their ability to adapt to dynamic and evolving
environments. To this end, recent research has explored agent evolution
techniques that aim to automatically enhance agent systems based on interaction
data and environmental feedback. This emerging direction lays the foundation
for self-evolving AI agents, which bridge the static capabilities of foundation
models with the continuous adaptability required by lifelong agentic systems.
In this survey, we provide a comprehensive review of existing techniques for
self-evolving agentic systems. Specifically, we first introduce a unified
conceptual framework that abstracts the feedback loop underlying the design of
self-evolving agentic systems. The framework highlights four key components:
System Inputs, Agent System, Environment, and Optimisers, serving as a
foundation for understanding and comparing different strategies. Based on this
framework, we systematically review a wide range of self-evolving techniques
that target different components of the agent system. We also investigate
domain-specific evolution strategies developed for specialised fields such as
biomedicine, programming, and finance, where optimisation objectives are
tightly coupled with domain constraints. In addition, we provide a dedicated
discussion on the evaluation, safety, and ethical considerations for
self-evolving agentic systems, which are critical to ensuring their
effectiveness and reliability. This survey aims to provide researchers and
practitioners with a systematic understanding of self-evolving AI agents,
laying the foundation for the development of more adaptive, autonomous, and
lifelong agentic systems.
Grove MoE: Towards Efficient and Superior MoE LLMs with Adjugate Experts
Authors: name: Haoyuan Wu, name: Haoxing Chen, name: Xiaodong Chen, name: Zhanchao Zhou, name: Tieyuan Chen, name: Yihong Zhuang, name: Guoshan Lu, name: Zenan Huang, name: Junbo Zhao, name: Lin Liu, name: Zhenzhong Lan, name: Bei Yu, name: Jianguo Li
Published: 2025-08-11 09:15:36

The Mixture of Experts (MoE) architecture is a cornerstone of modern
state-of-the-art (SOTA) large language models (LLMs). MoE models facilitate
scalability by enabling sparse parameter activation. However, traditional MoE
architecture uses homogeneous experts of a uniform size, activating a fixed
number of parameters irrespective of input complexity and thus limiting
computational efficiency. To overcome this limitation, we introduce Grove MoE,
a novel architecture incorporating experts of varying sizes, inspired by the
heterogeneous big.LITTLE CPU architecture. This architecture features novel
adjugate experts with a dynamic activation mechanism, enabling model capacity
expansion while maintaining manageable computational overhead. Building on this
architecture, we present GroveMoE-Base and GroveMoE-Inst, 33B-parameter LLMs
developed by applying an upcycling strategy to the Qwen3-30B-A3B-Base model
during mid-training and post-training. GroveMoE models dynamically activate
3.14-3.28B parameters based on token complexity and achieve performance
comparable to SOTA open-source models of similar or even larger size.
SONAR-LLM: Autoregressive Transformer that Thinks in Sentence Embeddings
  and Speaks in Tokens
Authors: name: Nikita Dragunov, name: Temurbek Rahmatullaev, name: Elizaveta Goncharova, name: Andrey Kuznetsov, name: Anton Razzhigaev
Published: 2025-08-07 12:03:44

The recently proposed Large Concept Model (LCM) generates text by predicting
a sequence of sentence-level embeddings and training with either mean-squared
error or diffusion objectives. We present SONAR-LLM, a decoder-only transformer
that "thinks" in the same continuous SONAR embedding space, yet is supervised
through token-level cross-entropy propagated via the frozen SONAR decoder. This
hybrid objective retains the semantic abstraction of LCM while eliminating its
diffusion sampler and restoring a likelihood-based training signal. Across
model sizes from 39M to 1.3B parameters, SONAR-LLM attains competitive
generation quality. We report scaling trends, ablations, benchmark results, and
release the complete training code and all pretrained checkpoints to foster
reproducibility and future research.
Speech-to-LaTeX: New Models and Datasets for Converting Spoken Equations
  and Sentences
Authors: name: Dmitrii Korzh, name: Dmitrii Tarasov, name: Artyom Iudin, name: Elvir Karimov, name: Matvey Skripkin, name: Nikita Kuzmin, name: Andrey Kuznetsov, name: Oleg Y. Rogov, name: Ivan Oseledets
Published: 2025-08-05 15:11:37

Conversion of spoken mathematical expressions is a challenging task that
involves transcribing speech into a strictly structured symbolic representation
while addressing the ambiguity inherent in the pronunciation of equations.
Although significant progress has been achieved in automatic speech recognition
(ASR) and language models (LM), the problem of converting spoken mathematics
into LaTeX remains underexplored. This task directly applies to educational and
research domains, such as lecture transcription or note creation. Based on ASR
post-correction, prior work requires 2 transcriptions, focuses only on isolated
equations, has a limited test set, and provides neither training data nor
multilingual coverage. To address these issues, we present the first fully
open-source large-scale dataset, comprising over 66,000 human-annotated audio
samples of mathematical equations and sentences in both English and Russian,
drawn from diverse scientific domains. In addition to the ASR post-correction
models and few-shot prompting, we apply audio language models, demonstrating
comparable character error rate (CER) results on the MathSpeech benchmark (28%
vs. 30%) for the equations conversion. In contrast, on the proposed
S2L-equations benchmark, our models outperform the MathSpeech model by a
substantial margin of more than 40 percentage points, even after accounting for
LaTeX formatting artifacts (27% vs. 64%). We establish the first benchmark for
mathematical sentence recognition (S2L-sentences) and achieve an equation CER
of 40%. This work lays the groundwork for future advances in multimodal AI,
with a particular focus on mathematical content recognition.
Part I: Tricks or Traps? A Deep Dive into RL for LLM Reasoning
Authors: name: Zihe Liu, name: Jiashun Liu, name: Yancheng He, name: Weixun Wang, name: Jiaheng Liu, name: Ling Pan, name: Xinyu Hu, name: Shaopan Xiong, name: Ju Huang, name: Jian Hu, name: Shengyi Huang, name: Siran Yang, name: Jiamang Wang, name: Wenbo Su, name: Bo Zheng
Published: 2025-08-11 17:39:45

Reinforcement learning for LLM reasoning has rapidly emerged as a prominent
research area, marked by a significant surge in related studies on both
algorithmic innovations and practical applications. Despite this progress,
several critical challenges remain, including the absence of standardized
guidelines for employing RL techniques and a fragmented understanding of their
underlying mechanisms. Additionally, inconsistent experimental settings,
variations in training data, and differences in model initialization have led
to conflicting conclusions, obscuring the key characteristics of these
techniques and creating confusion among practitioners when selecting
appropriate techniques. This paper systematically reviews widely adopted RL
techniques through rigorous reproductions and isolated evaluations within a
unified open-source framework. We analyze the internal mechanisms, applicable
scenarios, and core principles of each technique through fine-grained
experiments, including datasets of varying difficulty, model sizes, and
architectures. Based on these insights, we present clear guidelines for
selecting RL techniques tailored to specific setups, and provide a reliable
roadmap for practitioners navigating the RL for the LLM domain. Finally, we
reveal that a minimalist combination of two techniques can unlock the learning
capability of critic-free policies using vanilla PPO loss. The results
demonstrate that our simple combination consistently improves performance,
surpassing strategies like GRPO and DAPO.
Bifrost-1: Bridging Multimodal LLMs and Diffusion Models with
  Patch-level CLIP Latents
Authors: name: Han Lin, name: Jaemin Cho, name: Amir Zadeh, name: Chuan Li, name: Mohit Bansal
Published: 2025-08-08 02:38:47

There is growing interest in integrating high-fidelity visual synthesis
capabilities into large language models (LLMs) without compromising their
strong reasoning capabilities. Existing methods that directly train LLMs or
bridge LLMs and diffusion models usually suffer from costly training since the
backbone LLMs have not seen image representations during pretraining. We
present Bifrost-1, a unified framework that bridges pretrained multimodal LLMs
(MLLMs) and diffusion models using patch-level CLIP image embeddings as latent
variables, which are natively aligned with the MLLM's CLIP visual encoder.
These patch-level image embeddings are integrated into the diffusion model with
a lightweight adaptation of its ControlNet. To retain the original multimodal
reasoning capabilities of MLLMs, we equip the MLLM with a visual generation
branch initialized from the original MLLM parameters when predicting the
patch-level image embeddings. By seamlessly integrating pretrained MLLMs and
diffusion models with patch-level CLIP latents, our framework enables
high-fidelity controllable image generation with significant training
efficiency. Our experiments demonstrate that Bifrost-1 achieves comparable or
better performance than previous methods in terms of visual fidelity and
multimodal understanding, with substantially lower compute during training. We
also provide comprehensive ablation studies showing the effectiveness of our
design choices.
Shortcut Learning in Generalist Robot Policies: The Role of Dataset
  Diversity and Fragmentation
Authors: name: Youguang Xing, name: Xu Luo, name: Junlin Xie, name: Lianli Gao, name: Hengtao Shen, name: Jingkuan Song
Published: 2025-08-08 16:14:01

Generalist robot policies trained on large-scale datasets such as Open
X-Embodiment (OXE) demonstrate strong performance across a wide range of tasks.
However, they often struggle to generalize beyond the distribution of their
training data. In this paper, we investigate the underlying cause of this
limited generalization capability. We identify shortcut learning -- the
reliance on task-irrelevant features -- as a key impediment to generalization.
Through comprehensive theoretical and empirical analysis, we uncover two
primary contributors to shortcut learning: (1) limited diversity within
individual sub-datasets, and (2) significant distributional disparities across
sub-datasets, leading to dataset fragmentation. These issues arise from the
inherent structure of large-scale datasets like OXE, which are typically
composed of multiple sub-datasets collected independently across varied
environments and embodiments. Our findings provide critical insights into
dataset collection strategies that can reduce shortcut learning and enhance the
generalization ability of generalist robot policies. Moreover, in scenarios
where acquiring new large-scale data is impractical, we demonstrate that
carefully selected robotic data augmentation strategies can effectively reduce
shortcut learning in existing offline datasets, thereby improving
generalization capabilities of generalist robot policies, e.g., pi_0, in
both simulation and real-world environments. More information at
https://lucky-light-sun.github.io/proj/shortcut-learning-in-grps/.
Omni-Effects: Unified and Spatially-Controllable Visual Effects
  Generation
Authors: name: Fangyuan Mao, name: Aiming Hao, name: Jintao Chen, name: Dongxia Liu, name: Xiaokun Feng, name: Jiashu Zhu, name: Meiqi Wu, name: Chubin Chen, name: Jiahong Wu, name: Xiangxiang Chu
Published: 2025-08-11 13:41:24

Visual effects (VFX) are essential visual enhancements fundamental to modern
cinematic production. Although video generation models offer cost-efficient
solutions for VFX production, current methods are constrained by per-effect
LoRA training, which limits generation to single effects. This fundamental
limitation impedes applications that require spatially controllable composite
effects, i.e., the concurrent generation of multiple effects at designated
locations. However, integrating diverse effects into a unified framework faces
major challenges: interference from effect variations and spatial
uncontrollability during multi-VFX joint training. To tackle these challenges,
we propose Omni-Effects, a first unified framework capable of generating
prompt-guided effects and spatially controllable composite effects. The core of
our framework comprises two key innovations: (1) LoRA-based Mixture of Experts
(LoRA-MoE), which employs a group of expert LoRAs, integrating diverse effects
within a unified model while effectively mitigating cross-task interference.
(2) Spatial-Aware Prompt (SAP) incorporates spatial mask information into the
text token, enabling precise spatial control. Furthermore, we introduce an
Independent-Information Flow (IIF) module integrated within the SAP, isolating
the control signals corresponding to individual effects to prevent any unwanted
blending. To facilitate this research, we construct a comprehensive VFX dataset
Omni-VFX via a novel data collection pipeline combining image editing and
First-Last Frame-to-Video (FLF2V) synthesis, and introduce a dedicated VFX
evaluation framework for validating model performance. Extensive experiments
demonstrate that Omni-Effects achieves precise spatial control and diverse
effect generation, enabling users to specify both the category and location of
desired effects.
GLiClass: Generalist Lightweight Model for Sequence Classification Tasks
Authors: name: Ihor Stepanov, name: Mykhailo Shtopko, name: Dmytro Vodianytskyi, name: Oleksandr Lukashov, name: Alexander Yavorskyi, name: Mykyta Yaroshenko
Published: 2025-08-11 06:22:25

Classification is one of the most widespread tasks in AI applications,
serving often as the first step in filtering, sorting, and categorizing data.
Since modern AI systems must handle large volumes of input data and early
pipeline stages can propagate errors downstream, achieving high efficiency and
accuracy is critical. Moreover, classification requirements can change
dynamically based on user needs, necessitating models with strong zero-shot
capabilities. While generative LLMs have become mainstream for zero-shot
classification due to their versatility, they suffer from inconsistent
instruction following and computational inefficiency. Cross-encoders, commonly
used as rerankers in RAG pipelines, face a different bottleneck: they must
process text-label pairs sequentially, significantly reducing efficiency with
large label sets. Embedding-based approaches offer good efficiency but struggle
with complex scenarios involving logical and semantic constraints. We propose
GLiClass, a novel method that adapts the GLiNER architecture for sequence
classification tasks. Our approach achieves strong accuracy and efficiency
comparable to embedding-based methods, while maintaining the flexibility needed
for zero-shot and few-shot learning scenarios. Additionally, we adapted
proximal policy optimization (PPO) for multi-label text classification,
enabling training classifiers in data-sparse conditions or from human feedback.
Reinforcement Learning in Vision: A Survey
Authors: name: Weijia Wu, name: Chen Gao, name: Joya Chen, name: Kevin Qinghong Lin, name: Qingwei Meng, name: Yiming Zhang, name: Yuke Qiu, name: Hong Zhou, name: Mike Zheng Shou
Published: 2025-08-11 17:08:55

Recent advances at the intersection of reinforcement learning (RL) and visual
intelligence have enabled agents that not only perceive complex visual scenes
but also reason, generate, and act within them. This survey offers a critical
and up-to-date synthesis of the field. We first formalize visual RL problems
and trace the evolution of policy-optimization strategies from RLHF to
verifiable reward paradigms, and from Proximal Policy Optimization to Group
Relative Policy Optimization. We then organize more than 200 representative
works into four thematic pillars: multi-modal large language models, visual
generation, unified model frameworks, and vision-language-action models. For
each pillar we examine algorithmic design, reward engineering, benchmark
progress, and we distill trends such as curriculum-driven training,
preference-aligned diffusion, and unified reward modeling. Finally, we review
evaluation protocols spanning set-level fidelity, sample-level preference, and
state-level stability, and we identify open challenges that include sample
efficiency, generalization, and safe deployment. Our goal is to provide
researchers and practitioners with a coherent map of the rapidly expanding
landscape of visual RL and to highlight promising directions for future
inquiry. Resources are available at:
https://github.com/weijiawu/Awesome-Visual-Reinforcement-Learning.