Orionnnn/hub-stats huggingface dataset:
Exploitation Is All You Need... for Exploration
Authors: name: Micah Rentschler, name: Jesse Roberts
Published: 2025-08-02 09:42:59

Ensuring sufficient exploration is a central challenge when training
meta-reinforcement learning (meta-RL) agents to solve novel environments.
Conventional solutions to the exploration-exploitation dilemma inject explicit
incentives such as randomization, uncertainty bonuses, or intrinsic rewards to
encourage exploration. In this work, we hypothesize that an agent trained
solely to maximize a greedy (exploitation-only) objective can nonetheless
exhibit emergent exploratory behavior, provided three conditions are met: (1)
Recurring Environmental Structure, where the environment features repeatable
regularities that allow past experience to inform future choices; (2) Agent
Memory, enabling the agent to retain and utilize historical interaction data;
and (3) Long-Horizon Credit Assignment, where learning propagates returns over
a time frame sufficient for the delayed benefits of exploration to inform
current decisions. Through experiments in stochastic multi-armed bandits and
temporally extended gridworlds, we observe that, when both structure and memory
are present, a policy trained on a strictly greedy objective exhibits
information-seeking exploratory behavior. We further demonstrate, through
controlled ablations, that emergent exploration vanishes if either
environmental structure or agent memory is absent (Conditions 1 & 2).
Surprisingly, removing long-horizon credit assignment (Condition 3) does not
always prevent emergent exploration-a result we attribute to the
pseudo-Thompson Sampling effect. These findings suggest that, under the right
prerequisites, exploration and exploitation need not be treated as orthogonal
objectives but can emerge from a unified reward-maximization process.
AgentTTS: Large Language Model Agent for Test-time Compute-optimal
  Scaling Strategy in Complex Tasks
Authors: name: Fali Wang, name: Hui Liu, name: Zhenwei Dai, name: Jingying Zeng, name: Zhiwei Zhang, name: Zongyu Wu, name: Chen Luo, name: Zhen Li, name: Xianfeng Tang, name: Qi He, name: Suhang Wang
Published: 2025-07-26 19:21:18

Test-time scaling (TTS) enhances the performance of large language models
(LLMs) by allocating additional compute resources during inference. However,
existing research primarily investigates TTS in single-stage tasks; while many
real-world problems are multi-stage complex tasks, composed of a sequence of
heterogeneous subtasks with each subtask requires LLM of specific capability.
Therefore, we study a novel problem: the test-time compute-optimal scaling in
multi-stage complex tasks, aiming to select suitable models and allocate
budgets per subtask to maximize overall performance. TTS in multi-stage tasks
introduces two fundamental challenges: (i) The combinatorial search space of
model and budget allocations, combined with the high cost of inference, makes
brute-force search impractical. (ii) The optimal model and budget allocations
across subtasks are interdependent, increasing the complexity of the
compute-optimal search. To address this gap, we conduct extensive pilot
experiments on four tasks across six datasets, deriving three empirical
insights characterizing the behavior of LLMs in multi-stage complex tasks.
Informed by these insights, we propose AgentTTS, an LLM-agent-based framework
that autonomously searches for compute-optimal allocations through iterative
feedback-driven interactions with the execution environment. Experimental
results demonstrate that AgentTTS significantly outperforms traditional and
other LLM-based baselines in search efficiency, and shows improved robustness
to varying training set sizes and enhanced interpretability.
SitEmb-v1.5: Improved Context-Aware Dense Retrieval for Semantic
  Association and Long Story Comprehension
Authors: name: Junjie Wu, name: Jiangnan Li, name: Yuqing Li, name: Lemao Liu, name: Liyan Xu, name: Jiwei Li, name: Dit-Yan Yeung, name: Jie Zhou, name: Mo Yu
Published: 2025-08-03 23:59:31

Retrieval-augmented generation (RAG) over long documents typically involves
splitting the text into smaller chunks, which serve as the basic units for
retrieval. However, due to dependencies across the original document,
contextual information is often essential for accurately interpreting each
chunk. To address this, prior work has explored encoding longer context windows
to produce embeddings for longer chunks. Despite these efforts, gains in
retrieval and downstream tasks remain limited. This is because (1) longer
chunks strain the capacity of embedding models due to the increased amount of
information they must encode, and (2) many real-world applications still
require returning localized evidence due to constraints on model or human
bandwidth.
  We propose an alternative approach to this challenge by representing short
chunks in a way that is conditioned on a broader context window to enhance
retrieval performance -- i.e., situating a chunk's meaning within its context.
We further show that existing embedding models are not well-equipped to encode
such situated context effectively, and thus introduce a new training paradigm
and develop the situated embedding models (SitEmb). To evaluate our method, we
curate a book-plot retrieval dataset specifically designed to assess situated
retrieval capabilities. On this benchmark, our SitEmb-v1 model based on BGE-M3
substantially outperforms state-of-the-art embedding models, including several
with up to 7-8B parameters, with only 1B parameters. Our 8B SitEmb-v1.5 model
further improves performance by over 10% and shows strong results across
different languages and several downstream applications.
RoboMemory: A Brain-inspired Multi-memory Agentic Framework for Lifelong
  Learning in Physical Embodied Systems
Authors: name: Mingcong Lei, name: Honghao Cai, name: Zezhou Cui, name: Liangchen Tan, name: Junkun Hong, name: Gehan Hu, name: Shuangyu Zhu, name: Yimou Wu, name: Shaohan Jiang, name: Ge Wang, name: Zhen Li, name: Shuguang Cui, name: Yiming Zhao, name: Yatong Han
Published: 2025-08-02 15:39:42

We present RoboMemory, a brain-inspired multi-memory framework for lifelong
learning in physical embodied systems, addressing critical challenges in
real-world environments: continuous learning, multi-module memory latency, task
correlation capture, and infinite-loop mitigation in closed-loop planning.
Grounded in cognitive neuroscience, it integrates four core modules: the
Information Preprocessor (thalamus-like), the Lifelong Embodied Memory System
(hippocampus-like), the Closed-Loop Planning Module (prefrontal lobe-like), and
the Low-Level Executer (cerebellum-like) to enable long-term planning and
cumulative learning. The Lifelong Embodied Memory System, central to the
framework, alleviates inference speed issues in complex memory frameworks via
parallelized updates/retrieval across Spatial, Temporal, Episodic, and Semantic
submodules. It incorporates a dynamic Knowledge Graph (KG) and consistent
architectural design to enhance memory consistency and scalability. Evaluations
on EmbodiedBench show RoboMemory outperforms the open-source baseline
(Qwen2.5-VL-72B-Ins) by 25% in average success rate and surpasses the
closed-source State-of-the-Art (SOTA) (Claude3.5-Sonnet) by 5%, establishing
new SOTA. Ablation studies validate key components (critic, spatial memory,
long-term memory), while real-world deployment confirms its lifelong learning
capability with significantly improved success rates across repeated tasks.
RoboMemory alleviates high latency challenges with scalability, serving as a
foundational reference for integrating multi-modal memory systems in physical
robots.
Cyber-Zero: Training Cybersecurity Agents without Runtime
Authors: name: Terry Yue Zhuo, name: Dingmin Wang, name: Hantian Ding, name: Varun Kumar, name: Zijian Wang
Published: 2025-07-29 18:10:37

Large Language Models (LLMs) have achieved remarkable success in software
engineering tasks when trained with executable runtime environments,
particularly in resolving GitHub issues. However, such runtime environments are
often unavailable in other domains, especially cybersecurity, where challenge
configurations and execution contexts are ephemeral or restricted. We present
Cyber-Zero, the first runtime-free framework for synthesizing high-quality
agent trajectories to train cybersecurity LLMs. Cyber-Zero leverages publicly
available CTF writeups and employs persona-driven LLM simulation to
reverse-engineer runtime behaviors and generate realistic, long-horizon
interaction sequences without actual environments. Using trajectories
synthesized by Cyber-Zero, we train LLM-based agents that achieve up to 13.1%
absolute performance gains over baseline models on three prominent CTF
benchmarks: InterCode-CTF, NYU CTF Bench, and Cybench. Our best model,
Cyber-Zero-32B, establishes new state-of-the-art performance among open-weight
models, matching the capabilities of proprietary systems like DeepSeek-V3-0324
and Claude-3.5-Sonnet while offering superior cost-effectiveness, and
demonstrating that runtime-free trajectory synthesis can effectively
democratize the development of state-of-the-art cybersecurity agents.
Personalized Safety Alignment for Text-to-Image Diffusion Models
Authors: name: Yu Lei, name: Jinbin Bai, name: Qingyu Shi, name: Aosong Feng, name: Kaidong Yu
Published: 2025-08-02 02:23:20

Text-to-image diffusion models have revolutionized visual content generation,
but current safety mechanisms apply uniform standards that often fail to
account for individual user preferences. These models overlook the diverse
safety boundaries shaped by factors like age, mental health, and personal
beliefs. To address this, we propose Personalized Safety Alignment (PSA), a
framework that allows user-specific control over safety behaviors in generative
models. PSA integrates personalized user profiles into the diffusion process,
adjusting the model's behavior to match individual safety preferences while
preserving image quality. We introduce a new dataset, Sage, which captures
user-specific safety preferences and incorporates these profiles through a
cross-attention mechanism. Experiments show that PSA outperforms existing
methods in harmful content suppression and aligns generated content better with
user constraints, achieving higher Win Rate and Pass Rate scores. Our code,
data, and models are publicly available at
https://torpedo2648.github.io/PSAlign/.
VeOmni: Scaling Any Modality Model Training with Model-Centric
  Distributed Recipe Zoo
Authors: name: Qianli Ma, name: Yaowei Zheng, name: Zhelun Shi, name: Zhongkai Zhao, name: Bin Jia, name: Ziyue Huang, name: Zhiqi Lin, name: Youjie Li, name: Jiacheng Yang, name: Yanghua Peng, name: Zhi Zhang, name: Xin Liu
Published: 2025-08-04 11:33:04

Recent advances in large language models (LLMs) have driven impressive
progress in omni-modal understanding and generation. However, training
omni-modal LLMs remains a significant challenge due to the heterogeneous model
architectures required to process diverse modalities, necessitating
sophisticated system design for efficient large-scale training. Existing
frameworks typically entangle model definition with parallel logic, incurring
limited scalability and substantial engineering overhead for end-to-end
omni-modal training. % We present \veomni, a modular and efficient training
framework to accelerate the development of omni-modal LLMs. \veomni introduces
model-centric distributed recipes that decouples communication from
computation, enabling efficient 3D parallelism on omni-modal LLMs. \veomni also
features a flexible configuration interface supporting seamless integration of
new modalities with minimal code change. % Using \veomni, a omni-modal
mixture-of-experts (MoE) model with 30B parameters can be trained with over
2,800 tokens/sec/GPU throughput and scale to 160K context lengths via 3D
parallelism on 128 GPUs, showcasing its superior efficiency and scalability for
training large omni-modal LLMs.
CellForge: Agentic Design of Virtual Cell Models
Authors: name: Xiangru Tang, name: Zhuoyun Yu, name: Jiapeng Chen, name: Yan Cui, name: Daniel Shao, name: Weixu Wang, name: Fang Wu, name: Yuchen Zhuang, name: Wenqi Shi, name: Zhi Huang, name: Arman Cohan, name: Xihong Lin, name: Fabian Theis, name: Smita Krishnaswamy, name: Mark Gerstein
Published: 2025-08-04 10:43:31

Virtual cell modeling represents an emerging frontier at the intersection of
artificial intelligence and biology, aiming to predict quantities such as
responses to diverse perturbations quantitatively. However, autonomously
building computational models for virtual cells is challenging due to the
complexity of biological systems, the heterogeneity of data modalities, and the
need for domain-specific expertise across multiple disciplines. Here, we
introduce CellForge, an agentic system that leverages a multi-agent framework
that transforms presented biological datasets and research objectives directly
into optimized computational models for virtual cells. More specifically, given
only raw single-cell multi-omics data and task descriptions as input, CellForge
outputs both an optimized model architecture and executable code for training
virtual cell models and inference. The framework integrates three core modules:
Task Analysis for presented dataset characterization and relevant literature
retrieval, Method Design, where specialized agents collaboratively develop
optimized modeling strategies, and Experiment Execution for automated
generation of code. The agents in the Design module are separated into experts
with differing perspectives and a central moderator, and have to
collaboratively exchange solutions until they achieve a reasonable consensus.
We demonstrate CellForge's capabilities in single-cell perturbation prediction,
using six diverse datasets that encompass gene knockouts, drug treatments, and
cytokine stimulations across multiple modalities. CellForge consistently
outperforms task-specific state-of-the-art methods. Overall, CellForge
demonstrates how iterative interaction between LLM agents with differing
perspectives provides better solutions than directly addressing a modeling
challenge. Our code is publicly available at
https://github.com/gersteinlab/CellForge.
InstructVLA: Vision-Language-Action Instruction Tuning from
  Understanding to Manipulation
Authors: name: Shuai Yang, name: Hao Li, name: Yilun Chen, name: Bin Wang, name: Yang Tian, name: Tai Wang, name: Hanqing Wang, name: Feng Zhao, name: Yiyi Liao, name: Jiangmiao Pang
Published: 2025-07-23 13:57:06

To operate effectively in the real world, robots must integrate multimodal
reasoning with precise action generation. However, existing
vision-language-action (VLA) models often sacrifice one for the other, narrow
their abilities to task-specific manipulation data, and suffer catastrophic
forgetting of pre-trained vision-language capabilities. To bridge this gap, we
introduce InstructVLA, an end-to-end VLA model that preserves the flexible
reasoning of large vision-language models (VLMs) while delivering leading
manipulation performance. InstructVLA introduces a novel training paradigm,
Vision-Language-Action Instruction Tuning (VLA-IT), which employs multimodal
training with mixture-of-experts adaptation to jointly optimize textual
reasoning and action generation on both standard VLM corpora and a curated
650K-sample VLA-IT dataset. On in-domain SimplerEnv tasks, InstructVLA achieves
30.5% improvement over SpatialVLA. To evaluate generalization, we introduce
SimplerEnv-Instruct, an 80-task benchmark requiring closed-loop control and
high-level instruction understanding, where it outperforms a fine-tuned OpenVLA
by 92% and an action expert aided by GPT-4o by 29%. Additionally, InstructVLA
surpasses baseline VLMs on multimodal tasks and exhibits inference-time scaling
by leveraging textual reasoning to boost manipulation performance in both
simulated and real-world settings. These results demonstrate InstructVLA's
potential for bridging intuitive and steerable human-robot interaction with
efficient policy learning.
Llama-3.1-FoundationAI-SecurityLLM-8B-Instruct Technical Report
Authors: name: Sajana Weerawardhena, name: Paul Kassianik, name: Blaine Nelson, name: Baturay Saglam, name: Anu Vellore, name: Aman Priyanshu, name: Supriti Vijay, name: Massimo Aufiero, name: Arthur Goldblatt, name: Fraser Burch, name: Ed Li, name: Jianliang He, name: Dhruv Kedia, name: Kojin Oshiba, name: Zhouran Yang, name: Yaron Singer, name: Amin Karbasi
Published: 2025-08-01 20:25:57

Large language models (LLMs) have shown remarkable success across many
domains, yet their integration into cybersecurity applications remains limited
due to a lack of general-purpose cybersecurity data, representational
complexity, and safety and regulatory concerns. To address this gap, we
previously introduced Foundation-Sec-8B, a cybersecurity-focused LLM suitable
for fine-tuning on downstream tasks. That model, however, was not designed for
chat-style interactions or instruction-following. In this report, we release
Foundation-Sec-8B-Instruct: a model specifically trained for general-purpose
cybersecurity dialogue. Built on Foundation-Sec-8B, it combines domain-specific
knowledge with instruction-following, conversational capabilities, and
alignment with human preferences to produce high-quality, relevant responses.
Comprehensive evaluations show that Foundation-Sec-8B-Instruct outperforms
Llama 3.1-8B-Instruct on a range of cybersecurity tasks while matching its
instruction-following performance. It is also competitive with GPT-4o-mini on
cyber threat intelligence and instruction-following tasks. We envision
Foundation-Sec-8B-Instruct becoming an indispensable assistant in the daily
workflows of cybersecurity professionals. We release the model publicly at
https://huggingface.co/fdtn-ai/Foundation-Sec-8B-Instruct.
Trainable Dynamic Mask Sparse Attention
Authors: name: Jingze Shi, name: Yifan Wu, name: Bingheng Wu, name: Yiran Peng, name: Liangdong Wang, name: Guang Liu, name: Yuyu Luo
Published: 2025-08-04 07:05:15

In large language models, the demand for modeling long contexts is constantly
increasing, but the quadratic complexity of the standard self-attention
mechanism often becomes a bottleneck. Although existing sparse attention
mechanisms have improved efficiency, they may still encounter issues such as
static patterns or information loss. We introduce a trainable dynamic mask
sparse attention mechanism, Dynamic Mask Attention, which effectively utilizes
content-aware and position-aware sparsity. DMA achieves this through two key
innovations: First, it dynamically generates content-aware sparse masks from
value representations, enabling the model to identify and focus on critical
information adaptively. Second, it implements position-aware sparse attention
computation that effectively skips unnecessary calculation regions. This
dual-sparsity design allows the model to significantly reduce the computational
complexity of important information while retaining complete information,
achieving an excellent balance between information fidelity and computational
efficiency. We have verified the performance of DMA through comprehensive
experiments. Comparative studies show that DMA outperforms multi-head
attention, sliding window attention, multi-head latent attention, and native
sparse attention in terms of perplexity under Chinchilla Scaling Law settings.
Moreover, in challenging multi-query associative recall tasks, DMA also
demonstrates superior performance and efficiency compared to these methods.
Crucially, in the evaluation of a 1.7B parameter model, DMA significantly
outperforms multi-head attention in both standard benchmark performance and the
challenging needle-in-a-haystack task. These experimental results highlight its
capability to balance model efficiency and long-context modeling ability
effectively.
MCIF: Multimodal Crosslingual Instruction-Following Benchmark from
  Scientific Talks
Authors: name: Sara Papi, name: Maike Züfle, name: Marco Gaido, name: Beatrice Savoldi, name: Danni Liu, name: Ioannis Douros, name: Luisa Bentivogli, name: Jan Niehues
Published: 2025-07-25 19:00:51

Recent advances in large language models have catalyzed the development of
multimodal LLMs (MLLMs) that integrate text, speech, and vision within unified
frameworks. As MLLMs evolve from narrow, monolingual, task-specific systems to
general-purpose instruction-following models, a key frontier lies in evaluating
their multilingual and multimodal capabilities over both long and short
contexts. However, existing benchmarks fall short in evaluating these
dimensions jointly: they are often limited to English, mostly focus on one
single modality at a time, rely on short-form contexts, or lack human
annotations -- hindering comprehensive assessment of model performance across
languages, modalities, and task complexity. To address these gaps, we introduce
MCIF (Multimodal Crosslingual Instruction Following), the first multilingual
human-annotated benchmark based on scientific talks that is designed to
evaluate instruction-following in crosslingual, multimodal settings over both
short- and long-form inputs. MCIF spans three core modalities -- speech,
vision, and text -- and four diverse languages (English, German, Italian, and
Chinese), enabling a comprehensive evaluation of MLLMs' abilities to interpret
instructions across languages and combine them with multimodal contextual
information. MCIF is released under a CC-BY 4.0 license to encourage open
research and progress in MLLMs development.
Cognitive Kernel-Pro: A Framework for Deep Research Agents and Agent
  Foundation Models Training
Authors: name: Tianqing Fang, name: Zhisong Zhang, name: Xiaoyang Wang, name: Rui Wang, name: Can Qin, name: Yuxuan Wan, name: Jun-Yu Ma, name: Ce Zhang, name: Jiaqi Chen, name: Xiyun Li, name: Hongming Zhang, name: Haitao Mi, name: Dong Yu
Published: 2025-08-01 08:11:31

General AI Agents are increasingly recognized as foundational frameworks for
the next generation of artificial intelligence, enabling complex reasoning, web
interaction, coding, and autonomous research capabilities. However, current
agent systems are either closed-source or heavily reliant on a variety of paid
APIs and proprietary tools, limiting accessibility and reproducibility for the
research community. In this work, we present Cognitive Kernel-Pro, a
fully open-source and (to the maximum extent) free multi-module agent framework
designed to democratize the development and evaluation of advanced AI agents.
Within Cognitive Kernel-Pro, we systematically investigate the curation of
high-quality training data for Agent Foundation Models, focusing on the
construction of queries, trajectories, and verifiable answers across four key
domains: web, file, code, and general reasoning. Furthermore, we explore novel
strategies for agent test-time reflection and voting to enhance agent
robustness and performance. We evaluate Cognitive Kernel-Pro on GAIA, achieving
state-of-the-art results among open-source and free agents. Notably, our
8B-parameter open-source model surpasses previous leading systems such as
WebDancer and WebSailor, establishing a new performance standard for
accessible, high-capability AI agents. Code is available at
https://github.com/Tencent/CognitiveKernel-Pro