Orionnnn/hub-stats huggingface dataset:
VLM-FO1: Bridging the Gap Between High-Level Reasoning and Fine-Grained
  Perception in VLMs
Authors: name: Peng Liu, name: Haozhan Shen, name: Chunxin Fang, name: Zhicheng Sun, name: Jiajia Liao, name: Tiancheng Zhao
Published: 2025-09-30 08:10:56

Vision-Language Models (VLMs) excel at high-level scene understanding but
falter on fine-grained perception tasks requiring precise localization. This
failure stems from a fundamental mismatch, as generating exact numerical
coordinates is a challenging task for language-centric architectures. In this
paper, we introduce VLM-FO1, a novel framework that overcomes this limitation
by reframing object-centric perception from a brittle coordinate generation
problem into a robust feature retrieval task. Our method operates as a
plug-and-play module that integrates with any pre-trained VLM. It leverages a
Hybrid Fine-grained Region Encoder (HFRE), featuring a dual vision encoder, to
generate powerful region tokens rich in both semantic and spatial detail. A
token-based referencing system then enables the LLM to seamlessly reason about
and ground language in these specific visual regions. Experiments show that
VLM-FO1 achieves state-of-the-art performance across a diverse suite of
benchmarks, demonstrating exceptional capabilities in object grounding, region
generational understanding, and visual region reasoning. Crucially, our
two-stage training strategy ensures that these perception gains are achieved
without compromising the base model's general visual understanding
capabilities. VLM-FO1 establishes an effective and flexible paradigm for
building perception-aware VLMs, bridging the gap between high-level reasoning
and fine-grained visual grounding.
CurES: From Gradient Analysis to Efficient Curriculum Learning for
  Reasoning LLMs
Authors: name: Yongcheng Zeng, name: Zexu Sun, name: Bokai Ji, name: Erxue Min, name: Hengyi Cai, name: Shuaiqiang Wang, name: Dawei Yin, name: Haifeng Zhang, name: Xu Chen, name: Jun Wang
Published: 2025-10-01 15:41:27

Curriculum learning plays a crucial role in enhancing the training efficiency
of large language models (LLMs) on reasoning tasks. However, existing methods
often fail to adequately account for variations in prompt difficulty or rely on
simplistic filtering mechanisms to select prompt datasets within a narrow
criterion range, resulting in significant computational waste. In this work, we
approach the problem from the perspective of reinforcement learning gradient
optimization, offering a systematic and theoretical investigation into how to
improve the training efficiency of LLMs. We identify two key factors
influencing training efficiency: the selection of training prompts and the
allocation of rollout quantities across different prompts. Our theoretical
analysis reveals that the sampling distribution of prompts dictates the
convergence rate of gradient descent, while the allocation of the rollout
quantity influences the consistency and stability of overall gradient updates.
Based on these insights, we propose CurES, an efficient training method that
accelerates convergence and employs Bayesian posterior estimation to minimize
computational overhead. Experiments demonstrate that our CurES outperforms
Group Relative Policy Optimization (GRPO) by +3.30 points and
+4.82 points with 1.5B and 7B models, respectively. Additionally,
CurES exhibits faster convergence compared to baselines, including GRPO.
ReSWD: ReSTIR'd, not shaken. Combining Reservoir Sampling and Sliced
  Wasserstein Distance for Variance Reduction
Authors: name: Mark Boss, name: Andreas Engelhardt, name: Simon Donné, name: Varun Jampani
Published: 2025-10-01 16:01:17

Distribution matching is central to many vision and graphics tasks, where the
widely used Wasserstein distance is too costly to compute for high dimensional
distributions. The Sliced Wasserstein Distance (SWD) offers a scalable
alternative, yet its Monte Carlo estimator suffers from high variance,
resulting in noisy gradients and slow convergence. We introduce Reservoir SWD
(ReSWD), which integrates Weighted Reservoir Sampling into SWD to adaptively
retain informative projection directions in optimization steps, resulting in
stable gradients while remaining unbiased. Experiments on synthetic benchmarks
and real-world tasks such as color correction and diffusion guidance show that
ReSWD consistently outperforms standard SWD and other variance reduction
baselines. Project page: https://reservoirswd.github.io/
ACON: Optimizing Context Compression for Long-horizon LLM Agents
Authors: name: Minki Kang, name: Wei-Ning Chen, name: Dongge Han, name: Huseyin A. Inan, name: Lukas Wutschitz, name: Yanzhi Chen, name: Robert Sim, name: Saravan Rajmohan
Published: 2025-10-01 07:43:49

Large language models (LLMs) are increasingly deployed as agents in dynamic,
real-world environments, where success requires both reasoning and effective
tool use. A central challenge for agentic tasks is the growing context length,
as agents must accumulate long histories of actions and observations. This
expansion raises costs and reduces efficiency in long-horizon tasks, yet prior
work on context compression has mostly focused on single-step tasks or narrow
applications. We introduce Agent Context Optimization (ACON), a unified
framework that optimally compresses both environment observations and
interaction histories into concise yet informative condensations. ACON
leverages compression guideline optimization in natural language space: given
paired trajectories where full context succeeds but compressed context fails,
capable LLMs analyze the causes of failure, and the compression guideline is
updated accordingly. Furthermore, we propose distilling the optimized LLM
compressor into smaller models to reduce the overhead of the additional module.
Experiments on AppWorld, OfficeBench, and Multi-objective QA show that ACON
reduces memory usage by 26-54% (peak tokens) while largely preserving task
performance, preserves over 95% of accuracy when distilled into smaller
compressors, and enhances smaller LMs as long-horizon agents with up to 46%
performance improvement.
Eliciting Secret Knowledge from Language Models
Authors: name: Bartosz Cywiński, name: Emil Ryd, name: Rowan Wang, name: Senthooran Rajamanoharan, name: Neel Nanda, name: Arthur Conmy, name: Samuel Marks
Published: 2025-10-01 16:12:28

We study secret elicitation: discovering knowledge that an AI possesses but
does not explicitly verbalize. As a testbed, we train three families of large
language models (LLMs) to possess specific knowledge that they apply downstream
but deny knowing when asked directly. For example, in one setting, we train an
LLM to generate replies that are consistent with knowing the user is female,
while denying this knowledge when asked directly. We then design various
black-box and white-box secret elicitation techniques and evaluate them based
on whether they can help an LLM auditor successfully guess the secret
knowledge. Many of our techniques improve on simple baselines. Our most
effective techniques (performing best in 2/3 settings) are based on prefill
attacks, a black-box technique where the LLM reveals secret knowledge when
generating a completion from a predefined prefix. In our remaining setting,
white-box techniques based on logit lens and sparse autoencoders (SAEs) are
most effective. We release our models and code, establishing a public benchmark
for evaluating secret elicitation methods.
BroRL: Scaling Reinforcement Learning via Broadened Exploration
Authors: name: Jian Hu, name: Mingjie Liu, name: Ximing Lu, name: Fang Wu, name: Zaid Harchaoui, name: Shizhe Diao, name: Yejin Choi, name: Pavlo Molchanov, name: Jun Yang, name: Jan Kautz, name: Yi Dong
Published: 2025-10-01 17:59:02

Reinforcement Learning with Verifiable Rewards (RLVR) has emerged as a key
ingredient for unlocking complex reasoning capabilities in large language
models. Recent work ProRL has shown promise in scaling RL by increasing the
number of training steps. However, performance plateaus after thousands of
steps, with clear diminishing returns from allocating more computation to
additional training. In this work, we investigate a complementary paradigm for
scaling RL, BroR-Lincreasing the number of rollouts per example to hundreds to
exhaustively Broaden exploration, which yields continuous performance gains
beyond the saturation point observed in ProRL when scaling the number of
training steps. Our approach is motivated by a mass balance equation analysis
allowing us to characterize the rate of change in probability mass for correct
and incorrect tokens during the reinforcement process. We show that under a
one-step RL assumption, sampled rollout tokens always contribute to
correct-mass expansion, while unsampled tokens outside rollouts may lead to
gains or losses depending on their distribution and the net reward balance.
Importantly, as the number of rollouts per example N increases, the effect of
unsampled terms diminishes, ensuring overall correct-mass expansion. To
validate our theoretical analysis, we conduct simulations under more relaxed
conditions and find that a sufficiently large rollout size N-corresponding to
ample exploration-guarantees an increase in the probability mass of all correct
tokens. Empirically, BroRL revives models saturated after 3K ProRL training
steps and demonstrates robust, continuous improvement, achieving
state-of-the-art results for the 1.5B model across diverse benchmarks.
Making, not Taking, the Best of N
Authors: name: Ammar Khairi, name: Daniel D'souza, name: Marzieh Fadaee, name: Julia Kreutzer
Published: 2025-10-01 14:14:31

Obtaining high-quality generations in modern LLMs has largely been framed as
a selection problem: identifying a single winning generation from a diverse
pool of N samples, the Best-of-N (BoN). Yet, this approach is inherently
zero-sum, discarding diverse and potentially useful information from the pool.
Instead, we explore a collaborative setup, where all candidates can potentially
contribute to the final winning generation. To this end, we propose Fusion-of-N
(FusioN): a method that uses a general LLM judge to synthesize the most
informative elements of each sample into a single final answer. We compare
FusioN to BoN in two settings, (i) test-time scaling, where we sample and
aggregate from a single model at test-time (ii) synthetic data generation,
where we fuse samples from a pool of diverse teachers to improve a student
model. We extensively benchmark both setups across 11 languages, 3 diverse
tasks and varying model scales. Across the bench, FusioN consistently
outperforms BoN showing versatility and robustness both in test-time scaling
and in downstream gains from synthetic data generation. We also perform
extensive analysis on FusioN, where it shows surprising strengths and
robustness under challenging settings. These results show that we should shift
how we think about evaluating and utilizing LLM generations from a monolithic
measure of quality, to embracing their polylithic nature. This shift allows us
to integrate diverse strengths, unlock latent potential, and achieve
improvements that were previously inaccessible through selection alone.
Knapsack RL: Unlocking Exploration of LLMs via Optimizing Budget
  Allocation
Authors: name: Ziniu Li, name: Congliang Chen, name: Tianyun Yang, name: Tian Ding, name: Ruoyu Sun, name: Ge Zhang, name: Wenhao Huang, name: Zhi-Quan Luo
Published: 2025-09-30 06:41:57

Large Language Models (LLMs) can self-improve through reinforcement learning,
where they generate trajectories to explore and discover better solutions.
However, this exploration process is computationally expensive, often forcing
current methods to assign limited exploration budgets to each task. This
uniform allocation creates problematic edge cases: easy tasks consistently
succeed while difficult tasks consistently fail, both producing zero gradients
during training updates for the widely used Group Relative Policy Optimization
(GRPO). We address this problem from the lens of exploration budget allocation.
Viewing each task's exploration as an "item" with a distinct "value" and
"cost", we establish a connection to the classical knapsack problem. This
formulation allows us to derive an optimal assignment rule that adaptively
distributes resources based on the model's current learning status. When
applied to GRPO, our method increases the effective ratio of non-zero policy
gradients by 20-40% during training. Acting as a computational "free lunch",
our approach could reallocate exploration budgets from tasks where learning is
saturated to those where it is most impactful. This enables significantly
larger budgets (e.g., 93 rollouts) for especially challenging problems, which
would be computationally prohibitive under a uniform allocation. These
improvements translate to meaningful gains on mathematical reasoning
benchmarks, with average improvements of 2-4 points and peak gains of 9 points
on specific tasks. Notably, achieving comparable performance with traditional
homogeneous allocation would require about 2x the computational resources.
DeepSearch: Overcome the Bottleneck of Reinforcement Learning with
  Verifiable Rewards via Monte Carlo Tree Search
Authors: name: Fang Wu, name: Weihao Xuan, name: Heli Qi, name: Ximing Lu, name: Aaron Tu, name: Li Erran Li, name: Yejin Choi
Published: 2025-09-29 20:00:29

Although RLVR has become an essential component for developing advanced
reasoning skills in LLMs, contemporary studies have documented training
plateaus that emerge following thousands of optimization steps, demonstrating
notable decreases in performance gains despite increased computational
investment. This limitation stems from the sparse exploration patterns inherent
in current RLVR practices, where models rely on limited rollouts that often
miss critical reasoning paths and fail to provide systematic coverage of the
solution space. We present DeepSearch, a framework that integrates Monte Carlo
Tree Search directly into RLVR training. In contrast to existing methods that
rely on tree search only at inference, DeepSearch embeds structured search into
the training loop, enabling systematic exploration and fine-grained credit
assignment across reasoning steps. Through training-time exploration,
DeepSearch addresses the fundamental bottleneck of insufficient exploration,
which leads to diminishing performance improvements over prolonged training
steps. Our contributions include: (1) a global frontier selection strategy that
prioritizes promising nodes across the search tree, (2) selection with
entropy-based guidance that identifies confident paths for supervision, and (3)
adaptive replay buffer training with solution caching for efficiency.
Experiments on mathematical reasoning benchmarks show that DeepSearch achieves
62.95% average accuracy and establishes a new state-of-the-art for 1.5B
reasoning models - using 5.7x fewer GPU hours than extended training
approaches. These results highlight the importance of strategic exploration
over brute-force scaling and demonstrate the promise of algorithmic innovation
for advancing RLVR methodologies. DeepSearch establishes a new direction for
scaling reasoning capabilities through systematic search rather than prolonged
computation.
Infusing Theory of Mind into Socially Intelligent LLM Agents
Authors: name: EunJeong Hwang, name: Yuwei Yin, name: Giuseppe Carenini, name: Peter West, name: Vered Shwartz
Published: 2025-09-26 20:07:34

Theory of Mind (ToM)-an understanding of the mental states of others-is a key
aspect of human social intelligence, yet, chatbots and LLM-based social agents
do not typically integrate it. In this work, we demonstrate that LLMs that
explicitly use ToM get better at dialogue, achieving goals more effectively.
After showing that simply prompting models to generate mental states between
dialogue turns already provides significant benefit, we further introduce
ToMAgent (ToMA), a ToM-focused dialogue agent. ToMA is trained by pairing ToM
with dialogue lookahead to produce mental states that are maximally useful for
achieving dialogue goals. Experiments on the Sotopia interactive social
evaluation benchmark demonstrate the effectiveness of our method over a range
of baselines. Comprehensive analysis shows that ToMA exhibits more strategic,
goal-oriented reasoning behaviors, which enable long-horizon adaptation, while
maintaining better relationships with their partners. Our results suggest a
step forward in integrating ToM for building socially intelligent LLM agents.
Flash-Searcher: Fast and Effective Web Agents via DAG-Based Parallel
  Execution
Authors: name: Tianrui Qin, name: Qianben Chen, name: Sinuo Wang, name: He Xing, name: King Zhu, name: He Zhu, name: Dingfeng Shi, name: Xinxin Liu, name: Ge Zhang, name: Jiaheng Liu, name: Yuchen Eleanor Jiang, name: Xitong Gao, name: Wangchunshu Zhou
Published: 2025-09-29 17:39:30

Large language models (LLMs) have demonstrated remarkable capabilities in
complex reasoning tasks when equipped with external tools. However, current
frameworks predominantly rely on sequential processing, leading to inefficient
execution particularly for tasks requiring extensive tool interaction. This
paper introduces Flash-Searcher, a novel parallel agent reasoning framework
that fundamentally reimagines the execution paradigm from sequential chains to
directed acyclic graphs (DAGs). Flash-Searcher decomposes complex tasks into
subtasks with explicit dependencies, enabling concurrent execution of
independent reasoning paths while maintaining logical constraints. Through
dynamic workflow optimization, our framework continuously refines the execution
graph based on intermediate results, effectively integrating summary module.
Comprehensive evaluations across multiple benchmarks demonstrate that
Flash-Searcher consistently outperforms existing approaches. Specifically, it
achieves 67.7% accuracy on BrowseComp and 83% on xbench-DeepSearch, while
reducing agent execution steps by up to 35% compared to current frameworks.
Furthermore, when distilling this parallel reasoning pipeline into single
models, we observe substantial performance gains across diverse backbone
architectures, underscoring the generalizability of our methodology. Our work
thus represents a significant advance in agent architecture design, offering a
more scalable and efficient paradigm for complex reasoning tasks.
Boolean Satisfiability via Imitation Learning
Authors: name: Zewei Zhang, name: Huan Liu, name: Yuanhao Yu, name: Jun Chen, name: Xiangyu Xu
Published: 2025-09-29 19:09:37

We propose ImitSAT, a branching policy for conflict-driven clause learning
(CDCL) solvers based on imitation learning for the Boolean satisfiability
problem (SAT). Unlike previous methods that predict instance-level signals to
improve CDCL branching indirectly, or rely on reinforcement learning and
insufficient CDCL information to enhance branching, ImitSAT learns from expert
KeyTrace that collapses a full run into the sequence of surviving decisions.
Replaying a KeyTrace on the same instance is nearly conflict-free, providing
dense decision-level supervision and directly reducing propagations -- the
dominant contributor to wall-clock time. This prefix-conditioned supervision
enables ImitSAT to reproduce high-quality branches without exploration,
yielding faster convergence, stable training, and seamless integration into
CDCL. Extensive experiments demonstrate that ImitSAT reduces propagation counts
and runtime, outperforming state-of-the-art learned approaches. We released the
source code and trained model at https://github.com/zewei-Zhang/ImitSAT
An Empirical Study of Testing Practices in Open Source AI Agent
  Frameworks and Agentic Applications
Authors: name: Mohammed Mehedi Hasan, name: Hao Li, name: Emad Fallahzadeh, name: Gopi Krishnan Rajbahadur, name: Bram Adams, name: Ahmed E. Hassan
Published: 2025-09-23 16:02:09

Foundation model (FM)-based AI agents are rapidly gaining adoption across
diverse domains, but their inherent non-determinism and non-reproducibility
pose testing and quality assurance challenges. While recent benchmarks provide
task-level evaluations, there is limited understanding of how developers verify
the internal correctness of these agents during development.
  To address this gap, we conduct the first large-scale empirical study of
testing practices in the AI agent ecosystem, analyzing 39 open-source agent
frameworks and 439 agentic applications. We identify ten distinct testing
patterns and find that novel, agent-specific methods like DeepEval are seldom
used (around 1%), while traditional patterns like negative and membership
testing are widely adapted to manage FM uncertainty. By mapping these patterns
to canonical architectural components of agent frameworks and agentic
applications, we uncover a fundamental inversion of testing effort:
deterministic components like Resource Artifacts (tools) and Coordination
Artifacts (workflows) consume over 70% of testing effort, while the FM-based
Plan Body receives less than 5%. Crucially, this reveals a critical blind spot,
as the Trigger component (prompts) remains neglected, appearing in around 1% of
all tests.
  Our findings offer the first empirical testing baseline in FM-based agent
frameworks and agentic applications, revealing a rational but incomplete
adaptation to non-determinism. To address it, framework developers should
improve support for novel testing methods, application developers must adopt
prompt regression testing, and researchers should explore barriers to adoption.
Strengthening these practices is vital for building more robust and dependable
AI agents.
JoyAgent-JDGenie: Technical Report on the GAIA
Authors: name: Jiarun Liu, name: Shiyue Xu, name: Shangkun Liu, name: Yang Li, name: Wen Liu, name: Min Liu, name: Xiaoqing Zhou, name: Hanmin Wang, name: Shilin Jia, name: zhen Wang, name: Shaohua Tian, name: Hanhao Li, name: Junbo Zhang, name: Yongli Yu, name: Peng Cao, name: Haofen Wang
Published: 2025-10-01 04:41:58

Large Language Models are increasingly deployed as autonomous agents for
complex real-world tasks, yet existing systems often focus on isolated
improvements without a unifying design for robustness and adaptability. We
propose a generalist agent architecture that integrates three core components:
a collective multi-agent framework combining planning and execution agents with
critic model voting, a hierarchical memory system spanning working, semantic,
and procedural layers, and a refined tool suite for search, code execution, and
multimodal parsing. Evaluated on a comprehensive benchmark, our framework
consistently outperforms open-source baselines and approaches the performance
of proprietary systems. These results demonstrate the importance of
system-level integration and highlight a path toward scalable, resilient, and
adaptive AI assistants capable of operating across diverse domains and tasks.