Orionnnn/hub-stats huggingface dataset:
VideoAgentTrek: Computer Use Pretraining from Unlabeled Videos
Authors: name: Dunjie Lu, name: Yiheng Xu, name: Junli Wang, name: Haoyuan Wu, name: Xinyuan Wang, name: Zekun Wang, name: Junlin Yang, name: Hongjin Su, name: Jixuan Chen, name: Junda Chen, name: Yuchen Mao, name: Jingren Zhou, name: Junyang Lin, name: Binyuan Hui, name: Tao Yu
Published: 2025-10-22 11:25:48

Training computer-use agents requires massive amounts of GUI interaction
data, but manually annotating action trajectories at scale is prohibitively
expensive. We present VideoAgentTrek, a scalable pipeline that automatically
mines training data from publicly available screen-recorded videos at web
scale, eliminating the need for manual annotation. Our approach addresses a key
challenge: raw videos contain implicit demonstrations but lack explicit action
labels. To solve this, we develop Video2Action, an inverse dynamics module
(IDM) with two components: (1) a video grounding model that detects and
localizes GUI actions with precise temporal boundaries and context, and (2) an
action-content recognizer that extracts structured parameters like click
coordinates and typed text with high fidelity. Applied to 39,000 YouTube
tutorial videos, our pipeline generates 1.52 million interaction steps
automatically. We leverage this data through continued pretraining followed by
supervised fine-tuning. On OSWorld-Verified, our approach improves task success
rates from 9.3% (SFT-only baseline) to 15.8%, a 70% relative improvement. On
AgentNetBench, step accuracy increases from 64.1% to 69.3%. Our results
demonstrate that passive internet videos can be transformed into high-quality
supervision for computer-use agents, providing a scalable alternative to
expensive manual annotation.
LoongRL:Reinforcement Learning for Advanced Reasoning over Long Contexts
Authors: name: Siyuan Wang, name: Gaokai Zhang, name: Li Lyna Zhang, name: Ning Shang, name: Fan Yang, name: Dongyao Chen, name: Mao Yang
Published: 2025-10-22 08:35:28

Reasoning over long contexts is essential for large language models. While
reinforcement learning (RL) enhances short-context reasoning by inducing "Aha"
moments in chain-of-thought, the advanced thinking patterns required for
long-context reasoning remain largely unexplored, and high-difficulty RL data
are scarce. In this paper, we introduce LoongRL, a data-driven RL method for
advanced long-context reasoning. Central to LoongRL is KeyChain, a synthesis
approach that transforms short multi-hop QA into high-difficulty long-context
tasks by inserting UUID chains that hide the true question among large
collections of distracting documents. Solving these tasks requires the model to
trace the correct chain step-by-step, identify the true question, retrieve
relevant facts and reason over them to answer correctly. RL training on
KeyChain data induces an emergent plan-retrieve-reason-recheck reasoning
pattern that generalizes far beyond training length. Models trained at 16K
effectively solve 128K tasks without prohibitive full-length RL rollout costs.
On Qwen2.5-7B and 14B, LoongRL substantially improves long-context multi-hop QA
accuracy by +23.5% and +21.1% absolute gains. The resulting LoongRL-14B reaches
a score of 74.2, rivaling much larger frontier models such as o3-mini (74.5)
and DeepSeek-R1 (74.9). It also improves long-context retrieval, passes all
128K needle-in-a-haystack stress tests, and preserves short-context reasoning
capabilities.
Pico-Banana-400K: A Large-Scale Dataset for Text-Guided Image Editing
Authors: name: Yusu Qian, name: Eli Bocek-Rivele, name: Liangchen Song, name: Jialing Tong, name: Yinfei Yang, name: Jiasen Lu, name: Wenze Hu, name: Zhe Gan
Published: 2025-10-22 17:43:15

Recent advances in multimodal models have demonstrated remarkable text-guided
image editing capabilities, with systems like GPT-4o and Nano-Banana setting
new benchmarks. However, the research community's progress remains constrained
by the absence of large-scale, high-quality, and openly accessible datasets
built from real images. We introduce Pico-Banana-400K, a comprehensive
400K-image dataset for instruction-based image editing. Our dataset is
constructed by leveraging Nano-Banana to generate diverse edit pairs from real
photographs in the OpenImages collection. What distinguishes Pico-Banana-400K
from previous synthetic datasets is our systematic approach to quality and
diversity. We employ a fine-grained image editing taxonomy to ensure
comprehensive coverage of edit types while maintaining precise content
preservation and instruction faithfulness through MLLM-based quality scoring
and careful curation. Beyond single turn editing, Pico-Banana-400K enables
research into complex editing scenarios. The dataset includes three specialized
subsets: (1) a 72K-example multi-turn collection for studying sequential
editing, reasoning, and planning across consecutive modifications; (2) a
56K-example preference subset for alignment research and reward model training;
and (3) paired long-short editing instructions for developing instruction
rewriting and summarization capabilities. By providing this large-scale,
high-quality, and task-rich resource, Pico-Banana-400K establishes a robust
foundation for training and benchmarking the next generation of text-guided
image editing models.
olmOCR 2: Unit Test Rewards for Document OCR
Authors: name: Jake Poznanski, name: Luca Soldaini, name: Kyle Lo
Published: 2025-10-22 17:53:02

We present olmOCR 2, the latest in our family of powerful OCR systems for
converting digitized print documents, like PDFs, into clean, naturally ordered
plain text. olmOCR 2 is powered by olmOCR-2-7B-1025, a specialized, 7B vision
language model (VLM) trained using reinforcement learning with verifiable
rewards (RLVR), where our rewards are a diverse set of binary unit tests. To
scale unit test creation, we develop a pipeline for generating synthetic
documents with diverse and challenging layouts, known ground-truth HTML source
code, and extracted test cases. We show that RL training on these test cases
results in state-of-the-art performance on olmOCR-Bench, our English-language
OCR benchmark, with the largest improvements in math formula conversion, table
parsing, and multi-column layouts compared to previous versions. We release our
model, data and code under permissive open licenses.
TheMCPCompany: Creating General-purpose Agents with Task-specific Tools
Authors: name: Reza Esfandiarpoor, name: Vishwas Suryanarayanan, name: Stephen H. Bach, name: Vishal Chowdhary, name: Anthony Aue
Published: 2025-10-22 06:42:01

Since the introduction of the Model Context Protocol (MCP), the number of
available tools for Large Language Models (LLMs) has increased significantly.
These task-specific tool sets offer an alternative to general-purpose tools
such as web browsers, while being easier to develop and maintain than GUIs.
However, current general-purpose agents predominantly rely on web browsers for
interacting with the environment. Here, we introduce TheMCPCompany, a benchmark
for evaluating tool-calling agents on tasks that involve interacting with
various real-world services. We use the REST APIs of these services to create
MCP servers, which include over 18,000 tools. We also provide manually
annotated ground-truth tools for each task. In our experiments, we use the
ground truth tools to show the potential of tool-calling agents for both
improving performance and reducing costs assuming perfect tool retrieval. Next,
we explore agent performance using tool retrieval to study the real-world
practicality of tool-based agents. While all models with tool retrieval perform
similarly or better than browser-based agents, smaller models cannot take full
advantage of the available tools through retrieval. On the other hand, GPT-5's
performance with tool retrieval is very close to its performance with
ground-truth tools. Overall, our work shows that the most advanced reasoning
models are effective at discovering tools in simpler environments, but
seriously struggle with navigating complex enterprise environments.
TheMCPCompany reveals that navigating tens of thousands of tools and combining
them in non-trivial ways to solve complex problems is still a challenging task
for current models and requires both better reasoning and better retrieval
models.
The Atomic Instruction Gap: Instruction-Tuned LLMs Struggle with Simple,
  Self-Contained Directives
Authors: name: Henry Lim, name: Kwan Hui Lim
Published: 2025-10-20 10:26:26

Instruction-tuned large language models (IT-LLMs) exhibit strong zero-shot
reasoning, yet their ability to execute simple, self-contained instructions
remains underexplored, despite this being foundational to complex
instruction-following. We evaluate 20 IT-LLMs on modified MMLU and MMLU-Pro
benchmarks, by systematically varying the format of option labels (alphabetic,
numeric, Roman) while keeping their meaning identical under four paradigms,
namely: (1) With explicit instructions, label changes cause large performance
shifts (e.g., -30.45\% for Roman vs. numeric), revealing instruction-format
bias. (2) Without instructions, performance drops further (up to -10.84\%) and
label sensitivity intensifies, underscoring the role of explicit guidance. (3)
When option contents are removed, models fail random-choice baselines except
with numeric labels, suggesting weak adherence to atomic directives. (4)
Three-shot exemplars yield no significant gains in robustness or fidelity, and
generation analyses show persistent label errors, especially for non-numeric
formats. Across model sizes, larger LLMs achieve higher accuracy but remain
inconsistent in instruction adherence. These results expose the insufficiencies
of current instruction-tuning paradigms and highlight the need for evaluation
methods and training strategies that explicitly target atomic
instruction-following.
Pruning Overparameterized Multi-Task Networks for Degraded Web Image
  Restoration
Authors: name: Thomas Katraouras, name: Dimitrios Rafailidis
Published: 2025-10-16 09:04:05

Image quality is a critical factor in delivering visually appealing content
on web platforms. However, images often suffer from degradation due to lossy
operations applied by online social networks (OSNs), negatively affecting user
experience. Image restoration is the process of recovering a clean high-quality
image from a given degraded input. Recently, multi-task (all-in-one) image
restoration models have gained significant attention, due to their ability to
simultaneously handle different types of image degradations. However, these
models often come with an excessively high number of trainable parameters,
making them computationally inefficient. In this paper, we propose a strategy
for compressing multi-task image restoration models. We aim to discover highly
sparse subnetworks within overparameterized deep models that can match or even
surpass the performance of their dense counterparts. The proposed model, namely
MIR-L, utilizes an iterative pruning strategy that removes low-magnitude
weights across multiple rounds, while resetting the remaining weights to their
original initialization. This iterative process is important for the multi-task
image restoration model's optimization, effectively uncovering "winning
tickets" that maintain or exceed state-of-the-art performance at high sparsity
levels. Experimental evaluation on benchmark datasets for the deraining,
dehazing, and denoising tasks shows that MIR-L retains only 10% of the
trainable parameters while maintaining high image restoration performance. Our
code, datasets and pre-trained models are made publicly available at
https://github.com/Thomkat/MIR-L.
When "Correct" Is Not Safe: Can We Trust Functionally Correct Patches
  Generated by Code Agents?
Authors: name: Yibo Peng, name: James Song, name: Lei Li, name: Xinyu Yang, name: Mihai Christodorescu, name: Ravi Mangal, name: Corina Pasareanu, name: Haizhong Zheng, name: Beidi Chen
Published: 2025-10-15 17:16:36

Code agents are increasingly trusted to autonomously fix bugs on platforms
such as GitHub, yet their security evaluation focuses almost exclusively on
functional correctness. In this paper, we reveal a novel type of threat to
real-world code agents: Functionally Correct yet Vulnerable (FCV) patches,
which pass all test cases but contain vulnerable code. With our proposed
FCV-Attack, which can be deliberately crafted by malicious attackers or
implicitly introduced by benign developers, we show that SOTA LLMs (e.g.,
ChatGPT and Claude) and agent scaffolds (e.g., SWE-agent and OpenHands) are all
vulnerable to this FCV threat; across 12 agent-model combinations on SWE-Bench,
the attack only requires black-box access and a single query to the code agent
to perform the attack. For example, for CWE-538 (information exposure
vulnerability), the FCV-Attack attains an attack success rate of 40.7% on
GPT-5 Mini + OpenHands. Our results reveal an important security threat
overlooked by current evaluation paradigms and urge the development of
security-aware defenses for code agents.
Predicting the Unpredictable: Reproducible BiLSTM Forecasting of
  Incident Counts in the Global Terrorism Database (GTD)
Authors: name: Oluwasegun Adegoke
Published: 2025-10-16 20:53:43

We study short-horizon forecasting of weekly terrorism incident counts using
the Global Terrorism Database (GTD, 1970--2016). We build a reproducible
pipeline with fixed time-based splits and evaluate a Bidirectional LSTM
(BiLSTM) against strong classical anchors (seasonal-naive, linear/ARIMA) and a
deep LSTM-Attention baseline. On the held-out test set, the BiLSTM attains RMSE
6.38, outperforming LSTM-Attention (9.19; +30.6\%) and a linear lag-regression
baseline (+35.4\% RMSE gain), with parallel improvements in MAE and MAPE.
Ablations varying temporal memory, training-history length, spatial grain,
lookback size, and feature groups show that models trained on long historical
data generalize best; a moderate lookback (20--30 weeks) provides strong
context; and bidirectional encoding is critical for capturing both build-up and
aftermath patterns within the window. Feature-group analysis indicates that
short-horizon structure (lagged counts and rolling statistics) contributes
most, with geographic and casualty features adding incremental lift. We release
code, configs, and compact result tables, and provide a data/ethics statement
documenting GTD licensing and research-only use. Overall, the study offers a
transparent, baseline-beating reference for GTD incident forecasting.
Any-Depth Alignment: Unlocking Innate Safety Alignment of LLMs to
  Any-Depth
Authors: name: Jiawei Zhang, name: Andrew Estornell, name: David D. Baek, name: Bo Li, name: Xiaojun Xu
Published: 2025-10-20 20:18:59

Large Language Models (LLMs) exhibit strong but shallow alignment: they
directly refuse harmful queries when a refusal is expected at the very start of
an assistant turn, yet this protection collapses once a harmful continuation is
underway (either through the adversarial attacks or via harmful
assistant-prefill attacks). This raises a fundamental question: Can the innate
shallow alignment in LLMs be unlocked to ensure safety at arbitrary generation
depths? To achieve this goal, we propose Any-Depth Alignment (ADA), an
effective inference-time defense with negligible overhead. ADA is built based
on our observation that alignment is concentrated in the assistant header
tokens through repeated use in shallow-refusal training, and these tokens
possess the model's strong alignment priors. By reintroducing these tokens
mid-stream, ADA induces the model to reassess harmfulness and recover refusals
at any point in generation. Across diverse open-source model families (Llama,
Gemma, Mistral, Qwen, DeepSeek, and gpt-oss), ADA achieves robust safety
performance without requiring any changes to the base model's parameters. It
secures a near-100% refusal rate against challenging adversarial prefill
attacks ranging from dozens to thousands of tokens. Furthermore, ADA reduces
the average success rate of prominent adversarial prompt attacks (such as GCG,
AutoDAN, PAIR, and TAP) to below 3%. This is all accomplished while preserving
utility on benign tasks with minimal over-refusal. ADA maintains this
resilience even after the base model undergoes subsequent instruction tuning
(benign or adversarial).
Static Sandboxes Are Inadequate: Modeling Societal Complexity Requires
  Open-Ended Co-Evolution in LLM-Based Multi-Agent Simulations
Authors: name: Jinkun Chen, name: Sher Badshah, name: Xuemin Yu, name: Sijia Han
Published: 2025-10-15 18:05:06

What if artificial agents could not just communicate, but also evolve, adapt,
and reshape their worlds in ways we cannot fully predict? With llm now powering
multi-agent systems and social simulations, we are witnessing new possibilities
for modeling open-ended, ever-changing environments. Yet, most current
simulations remain constrained within static sandboxes, characterized by
predefined tasks, limited dynamics, and rigid evaluation criteria. These
limitations prevent them from capturing the complexity of real-world societies.
In this paper, we argue that static, task-specific benchmarks are fundamentally
inadequate and must be rethought. We critically review emerging architectures
that blend llm with multi-agent dynamics, highlight key hurdles such as
balancing stability and diversity, evaluating unexpected behaviors, and scaling
to greater complexity, and introduce a fresh taxonomy for this rapidly evolving
field. Finally, we present a research roadmap centered on open-endedness,
continuous co-evolution, and the development of resilient, socially aligned AI
ecosystems. We call on the community to move beyond static paradigms and help
shape the next generation of adaptive, socially-aware multi-agent simulations.
Efficient Long-context Language Model Training by Core Attention
  Disaggregation
Authors: name: Yonghao Zhuang, name: Junda Chen, name: Bo Pang, name: Yi Gu, name: Yibo Zhu, name: Yimin Jiang, name: Ion Stoica, name: Eric Xing, name: Hao Zhang
Published: 2025-10-20 21:40:51

We present core attention disaggregation (CAD), a technique that improves
long-context large language model training by decoupling the core attention
computation, softmax(QK^T)V, from the rest of the model and executing it on a
separate pool of devices. In existing systems, core attention is colocated with
other layers; at long context lengths, its quadratic compute growth compared to
the near-linear growth of other components causes load imbalance and stragglers
across data and pipeline parallel groups. CAD is enabled by two observations.
First, core attention is stateless: it has no trainable parameters and only
minimal transient data, so balancing reduces to scheduling compute-bound tasks.
Second, it is composable: modern attention kernels retain high efficiency when
processing fused batches of token-level shards with arbitrary lengths. CAD
partitions core attention into token-level tasks and dispatches them to
dedicated attention servers, which dynamically rebatch tasks to equalize
compute without sacrificing kernel efficiency. We implement CAD in a system
called DistCA, which uses a ping-pong execution scheme to fully overlap
communication with computation and in-place execution on attention servers to
reduce memory use. On 512 H200 GPUs and context lengths up to 512k tokens,
DistCA improves end-to-end training throughput by up to 1.35x, eliminates data
and pipeline parallel stragglers, and achieves near-perfect compute and memory
balance.
Planned Diffusion
Authors: name: Daniel Israel, name: Tian Jin, name: Ellie Cheng, name: Guy Van den Broeck, name: Aditya Grover, name: Suvinay Subramanian, name: Michael Carbin
Published: 2025-10-20 20:27:48

A central challenge in large language model inference is the trade-off
between generation speed and output quality. Autoregressive models produce
high-quality text but generate tokens sequentially. Diffusion models can
generate tokens in parallel but often need many iterations to match the same
quality. We propose planned diffusion, a hybrid method that combines the
strengths of both paradigms. Planned diffusion works in two stages: first, the
model creates a short autoregressive plan that breaks the output into smaller,
independent spans. Second, the model generates these spans simultaneously using
diffusion. This approach expands the speed-quality Pareto frontier and provides
a practical path to faster, high-quality text generation. On AlpacaEval, a
suite of 805 instruction-following prompts, planned diffusion achieves
Pareto-optimal trade-off between quality and latency, achieving 1.27x to 1.81x
speedup over autoregressive generation with only 0.87\% to 5.4\% drop in win
rate, respectively. Our sensitivity analysis shows that the planning mechanism
of planned diffusion is minimal and reliable, and simple runtime knobs exist to
provide flexible control of the quality-latency trade-off.
Expanding the Action Space of LLMs to Reason Beyond Language
Authors: name: Zhongqi Yue, name: Weishi Wang, name: Yundaichuan Zhan, name: Juncheng Li, name: Daniel Dahlmeier, name: Fredrik D. Johansson
Published: 2025-10-08 21:56:58

Large Language Models (LLMs) are powerful reasoners in natural language, but
their actions are typically confined to outputting vocabulary tokens. As a
result, interactions with external environments -- such as symbolic operators
or simulators -- must be expressed through text in predefined formats, parsed,
and routed to external interfaces. This overloads the model's language with
both reasoning and control duties, and requires a hand-crafted parser, external
to the LLM. To address this, we decouple environment interactions from language
by internalizing them in an Expanded Action space (ExpA), beyond the
vocabulary. The model starts reasoning in the default language environment, but
may trigger routing actions and switch to an external environment at any time.
From there, the model can only invoke environment-specific actions, receive
feedback from the environment, and potentially route back to language as a
result. To promote effective exploration of the expanded action space and new
environments, we introduce ExpA Reinforcement Learning (EARL) with
counterfactual policy optimization. On tasks requiring multi-turn interactions
and contingent planning, EARL outperforms strong baselines with
vocabulary-constrained actions. It performs robustly across calculator-based
multi-task learning and, in the partially observed sorting problem, achieves
perfect Sort-4 accuracy while self-discovering an efficient algorithm
competitive with classical designs.