Orionnnn/hub-stats huggingface dataset:
Compose Your Policies! Improving Diffusion-based or Flow-based Robot
  Policies via Test-time Distribution-level Composition
Authors: name: Jiahang Cao, name: Yize Huang, name: Hanzhong Guo, name: Rui Zhang, name: Mu Nan, name: Weijian Mai, name: Jiaxu Wang, name: Hao Cheng, name: Jingkai Sun, name: Gang Han, name: Wen Zhao, name: Qiang Zhang, name: Yijie Guo, name: Qihao Zheng, name: Chunfeng Song, name: Xiao Li, name: Ping Luo, name: Andrew F. Luo
Published: 2025-10-01 16:05:53

Diffusion-based models for robotic control, including vision-language-action
(VLA) and vision-action (VA) policies, have demonstrated significant
capabilities. Yet their advancement is constrained by the high cost of
acquiring large-scale interaction datasets. This work introduces an alternative
paradigm for enhancing policy performance without additional model training.
Perhaps surprisingly, we demonstrate that the composed policies can exceed the
performance of either parent policy. Our contribution is threefold. First, we
establish a theoretical foundation showing that the convex composition of
distributional scores from multiple diffusion models can yield a superior
one-step functional objective compared to any individual score. A
Gr\"onwall-type bound is then used to show that this single-step improvement
propagates through entire generation trajectories, leading to systemic
performance gains. Second, motivated by these results, we propose General
Policy Composition (GPC), a training-free method that enhances performance by
combining the distributional scores of multiple pre-trained policies via a
convex combination and test-time search. GPC is versatile, allowing for the
plug-and-play composition of heterogeneous policies, including VA and VLA
models, as well as those based on diffusion or flow-matching, irrespective of
their input visual modalities. Third, we provide extensive empirical
validation. Experiments on Robomimic, PushT, and RoboTwin benchmarks, alongside
real-world robotic evaluations, confirm that GPC consistently improves
performance and adaptability across a diverse set of tasks. Further analysis of
alternative composition operators and weighting strategies offers insights into
the mechanisms underlying the success of GPC. These results establish GPC as a
simple yet effective method for improving control performance by leveraging
existing policies.
FocusAgent: Simple Yet Effective Ways of Trimming the Large Context of
  Web Agents
Authors: name: Imene Kerboua, name: Sahar Omidi Shayegan, name: Megh Thakkar, name: Xing Han Lù, name: Léo Boisvert, name: Massimo Caccia, name: Jérémy Espinas, name: Alexandre Aussem, name: Véronique Eglin, name: Alexandre Lacoste
Published: 2025-10-03 17:41:30

Web agents powered by large language models (LLMs) must process lengthy web
page observations to complete user goals; these pages often exceed tens of
thousands of tokens. This saturates context limits and increases computational
cost processing; moreover, processing full pages exposes agents to security
risks such as prompt injection. Existing pruning strategies either discard
relevant content or retain irrelevant context, leading to suboptimal action
prediction. We introduce FocusAgent, a simple yet effective approach that
leverages a lightweight LLM retriever to extract the most relevant lines from
accessibility tree (AxTree) observations, guided by task goals. By pruning
noisy and irrelevant content, FocusAgent enables efficient reasoning while
reducing vulnerability to injection attacks. Experiments on WorkArena and
WebArena benchmarks show that FocusAgent matches the performance of strong
baselines, while reducing observation size by over 50%. Furthermore, a variant
of FocusAgent significantly reduces the success rate of prompt-injection
attacks, including banner and pop-up attacks, while maintaining task success
performance in attack-free settings. Our results highlight that targeted
LLM-based retrieval is a practical and robust strategy for building web agents
that are efficient, effective, and secure.
SurveyBench: How Well Can LLM(-Agents) Write Academic Surveys?
Authors: name: Zhaojun Sun, name: Xuzhou Zhu, name: Xuanhe Zhou, name: Xin Tong, name: Shuo Wang, name: Jie Fu, name: Guoliang Li, name: Zhiyuan Liu, name: Fan Wu
Published: 2025-10-03 15:49:09

Academic survey writing, which distills vast literature into a coherent and
insightful narrative, remains a labor-intensive and intellectually demanding
task. While recent approaches, such as general DeepResearch agents and
survey-specialized methods, can generate surveys automatically (a.k.a.
LLM4Survey), their outputs often fall short of human standards and there lacks
a rigorous, reader-aligned benchmark for thoroughly revealing their
deficiencies. To fill the gap, we propose a fine-grained, quiz-driven
evaluation framework SurveyBench, featuring (1) typical survey topics source
from recent 11,343 arXiv papers and corresponding 4,947 high-quality surveys;
(2) a multifaceted metric hierarchy that assesses the outline quality (e.g.,
coverage breadth, logical coherence), content quality (e.g., synthesis
granularity, clarity of insights), and non-textual richness; and (3) a
dual-mode evaluation protocol that includes content-based and quiz-based
answerability tests, explicitly aligned with readers' informational needs.
Results show SurveyBench effectively challenges existing LLM4Survey approaches
(e.g., on average 21% lower than human in content-based evaluation).
How Confident are Video Models? Empowering Video Models to Express their
  Uncertainty
Authors: name: Zhiting Mei, name: Ola Shorinwa, name: Anirudha Majumdar
Published: 2025-10-02 21:20:41

Generative video models demonstrate impressive text-to-video capabilities,
spurring widespread adoption in many real-world applications. However, like
large language models (LLMs), video generation models tend to hallucinate,
producing plausible videos even when they are factually wrong. Although
uncertainty quantification (UQ) of LLMs has been extensively studied in prior
work, no UQ method for video models exists, raising critical safety concerns.
To our knowledge, this paper represents the first work towards quantifying the
uncertainty of video models. We present a framework for uncertainty
quantification of generative video models, consisting of: (i) a metric for
evaluating the calibration of video models based on robust rank correlation
estimation with no stringent modeling assumptions; (ii) a black-box UQ method
for video models (termed S-QUBED), which leverages latent modeling to
rigorously decompose predictive uncertainty into its aleatoric and epistemic
components; and (iii) a UQ dataset to facilitate benchmarking calibration in
video models. By conditioning the generation task in the latent space, we
disentangle uncertainty arising due to vague task specifications from that
arising from lack of knowledge. Through extensive experiments on benchmark
video datasets, we demonstrate that S-QUBED computes calibrated total
uncertainty estimates that are negatively correlated with the task accuracy and
effectively computes the aleatoric and epistemic constituents.
Improving GUI Grounding with Explicit Position-to-Coordinate Mapping
Authors: name: Suyuchen Wang, name: Tianyu Zhang, name: Ahmed Masry, name: Christopher Pal, name: Spandana Gella, name: Bang Liu, name: Perouz Taslakian
Published: 2025-10-03 17:59:34

GUI grounding, the task of mapping natural-language instructions to pixel
coordinates, is crucial for autonomous agents, yet remains difficult for
current VLMs. The core bottleneck is reliable patch-to-pixel mapping, which
breaks when extrapolating to high-resolution displays unseen during training.
Current approaches generate coordinates as text tokens directly from visual
features, forcing the model to infer complex position-to-pixel mappings
implicitly; as a result, accuracy degrades and failures proliferate on new
resolutions. We address this with two complementary innovations. First, RULER
tokens serve as explicit coordinate markers, letting the model reference
positions similar to gridlines on a map and adjust rather than generate
coordinates from scratch. Second, Interleaved MRoPE (I-MRoPE) improves spatial
encoding by ensuring that width and height dimensions are represented equally,
addressing the asymmetry of standard positional schemes. Experiments on
ScreenSpot, ScreenSpot-V2, and ScreenSpot-Pro show consistent gains in
grounding accuracy, with the largest improvements on high-resolution
interfaces. By providing explicit spatial guidance rather than relying on
implicit learning, our approach enables more reliable GUI automation across
diverse resolutions and platforms.
Self-Improvement in Multimodal Large Language Models: A Survey
Authors: name: Shijian Deng, name: Kai Wang, name: Tianyu Yang, name: Harsh Singh, name: Yapeng Tian
Published: 2025-10-03 01:48:26

Recent advancements in self-improvement for Large Language Models (LLMs) have
efficiently enhanced model capabilities without significantly increasing costs,
particularly in terms of human effort. While this area is still relatively
young, its extension to the multimodal domain holds immense potential for
leveraging diverse data sources and developing more general self-improving
models. This survey is the first to provide a comprehensive overview of
self-improvement in Multimodal LLMs (MLLMs). We provide a structured overview
of the current literature and discuss methods from three perspectives: 1) data
collection, 2) data organization, and 3) model optimization, to facilitate the
further development of self-improvement in MLLMs. We also include commonly used
evaluations and downstream applications. Finally, we conclude by outlining open
challenges and future research directions.
Rethinking Thinking Tokens: LLMs as Improvement Operators
Authors: name: Lovish Madaan, name: Aniket Didolkar, name: Suchin Gururangan, name: John Quan, name: Ruan Silva, name: Ruslan Salakhutdinov, name: Manzil Zaheer, name: Sanjeev Arora, name: Anirudh Goyal
Published: 2025-10-01 17:08:59

Reasoning training incentivizes LLMs to produce long chains of thought (long
CoT), which among other things, allows them to explore solution strategies with
self-checking. This results in higher accuracy, but inflates context length,
token/compute cost, and answer latency. We ask: Can current models leverage
their metacognition to provide other combinations on this Pareto frontier,
e.g., better accuracy with lower context length and/or latency? Abstractly, we
view the model as an improvement operator on its own "thoughts" with a
continuum of possible strategies. We identify an interesting inference family
Parallel-Distill-Refine (PDR), which performs the following: (i) generate
diverse drafts in parallel; (ii) distill them into a bounded, textual
workspace; and (iii) refine conditioned on this workspace, producing an output
that seeds the next round. Importantly, context length (hence compute cost) is
controllable via degree of parallelism, and is no longer conflated with the
total number of generated tokens. We report PDR instantiations of current
models that give better accuracy than long CoT while incurring lower latency.
Setting degree of parallelism to 1 yields an interesting subcase, Sequential
Refinement (SR) (iteratively improve a single candidate answer) which provides
performance superior to long CoT. Success of such model orchestrations raises
the question whether further training could shift the Pareto frontier. To this
end, we train an 8B thinking model with Reinforcement Learning (RL) to make it
consistent with PDR as the inference method. On math tasks with verifiable
answers, iterative pipelines surpass single-pass baselines at matched
sequential budgets, with PDR delivering the largest gains (e.g., +11% on AIME
2024 and +9% on AIME 2025).
Fine-Grained Detection of Context-Grounded Hallucinations Using LLMs
Authors: name: Yehonatan Peisakhovsky, name: Zorik Gekhman, name: Yosi Mass, name: Liat Ein-Dor, name: Roi Reichart
Published: 2025-09-26 17:03:24

Context-grounded hallucinations are cases where model outputs contain
information not verifiable against the source text. We study the applicability
of LLMs for localizing such hallucinations, as a more practical alternative to
existing complex evaluation pipelines. In the absence of established benchmarks
for meta-evaluation of hallucinations localization, we construct one tailored
to LLMs, involving a challenging human annotation of over 1,000 examples. We
complement the benchmark with an LLM-based evaluation protocol, verifying its
quality in a human evaluation. Since existing representations of hallucinations
limit the types of errors that can be expressed, we propose a new
representation based on free-form textual descriptions, capturing the full
range of possible errors. We conduct a comprehensive study, evaluating four
large-scale LLMs, which highlights the benchmark's difficulty, as the best
model achieves an F1 score of only 0.67. Through careful analysis, we offer
insights into optimal prompting strategies for the task and identify the main
factors that make it challenging for LLMs: (1) a tendency to incorrectly flag
missing details as inconsistent, despite being instructed to check only facts
in the output; and (2) difficulty with outputs containing factually correct
information absent from the source - and thus not verifiable - due to alignment
with the model's parametric knowledge.
Aristotle: IMO-level Automated Theorem Proving
Authors: name: Tudor Achim, name: Alex Best, name: Kevin Der, name: Mathïs Fédérico, name: Sergei Gukov, name: Daniel Halpern-Leister, name: Kirsten Henningsgard, name: Yury Kudryashov, name: Alexander Meiburg, name: Martin Michelsen, name: Riley Patterson, name: Eric Rodriguez, name: Laura Scharff, name: Vikram Shanker, name: Vladmir Sicca, name: Hari Sowrirajan, name: Aidan Swope, name: Matyas Tamas, name: Vlad Tenev, name: Jonathan Thomm, name: Harold Williams, name: Lawrence Wu
Published: 2025-10-01 18:21:13

We introduce Aristotle, an AI system that combines formal verification with
informal reasoning, achieving gold-medal-equivalent performance on the 2025
International Mathematical Olympiad problems. Aristotle integrates three main
components: a Lean proof search system, an informal reasoning system that
generates and formalizes lemmas, and a dedicated geometry solver. Our system
demonstrates state-of-the-art performance with favorable scaling properties for
automated theorem proving.
Sparse Query Attention (SQA): A Computationally Efficient Attention
  Mechanism with Query Heads Reduction
Authors: name: Adam Filipek
Published: 2025-10-02 09:01:38

The Transformer architecture, underpinned by the Multi-Head Attention (MHA)
mechanism, has become the de facto standard for state-of-the-art models in
artificial intelligence. However, the quadratic computational complexity of MHA
with respect to sequence length presents a significant barrier to scaling,
particularly for applications involving long contexts. Prevailing solutions,
such as Multi-Query Attention (MQA) and Grouped-Query Attention (GQA), have
effectively addressed the memory bandwidth bottleneck that dominates
autoregressive inference latency by sharing Key and Value projections. While
highly successful, these methods do not reduce the fundamental number of
floating-point operations (FLOPs) required for the attention score computation,
which remains a critical bottleneck for training and full-sequence processing.
This paper introduces Sparse Query Attention (SQA), a novel attention
architecture that pursues an alternative and complementary optimization path.
Instead of reducing Key/Value heads, SQA reduces the number of Query heads.
This architectural modification directly decreases the computational complexity
of the attention mechanism by a factor proportional to the reduction in query
heads, thereby lowering the overall FLOPs. This work presents the theoretical
foundation of SQA, its mathematical formulation, and a family of architectural
variants. Empirical benchmarks on long sequences (32k-200k tokens) demonstrate
that SQA can achieve significant throughput improvements of up to 3x in
computation-bound scenarios such as model pre-training, fine-tuning, and
encoder-based tasks, with only a minimal impact on model quality in preliminary
smallscale experiments. SQA was discovered serendipitously during the
development of the upcoming Reactive Transformer architecture, suggesting its
potential as a powerful tool for building more efficient and scalable models
Controlled Generation for Private Synthetic Text
Authors: name: Zihao Zhao, name: Anjalie Field
Published: 2025-09-30 03:38:36

Text anonymization is essential for responsibly developing and deploying AI
in high-stakes domains such as healthcare, social services, and law. In this
work, we propose a novel methodology for privacy-preserving synthetic text
generation that leverages the principles of de-identification and the Hiding In
Plain Sight (HIPS) theory. Our approach introduces entity-aware control codes
to guide controllable generation using either in-context learning (ICL) or
prefix tuning. The ICL variant ensures privacy levels consistent with the
underlying de-identification system, while the prefix tuning variant
incorporates a custom masking strategy and loss function to support scalable,
high-quality generation. Experiments on legal and clinical datasets demonstrate
that our method achieves a strong balance between privacy protection and
utility, offering a practical and effective solution for synthetic text
generation in sensitive domains.
Think Right: Learning to Mitigate Under-Over Thinking via Adaptive,
  Attentive Compression
Authors: name: Joykirat Singh, name: Justin Chih-Yao Chen, name: Archiki Prasad, name: Elias Stengel-Eskin, name: Akshay Nambi, name: Mohit Bansal
Published: 2025-10-02 02:00:20

Recent thinking models solve complex reasoning tasks by scaling test-time
compute, but this scaling must be allocated in line with task difficulty. On
one hand, short reasoning (underthinking) leads to errors on harder problems
that require extended reasoning steps; but, excessively long reasoning
(overthinking) can be token-inefficient, generating unnecessary steps even
after reaching a correct intermediate solution. We refer to this as
under-adaptivity, where the model fails to modulate its response length
appropriately given problems of varying difficulty. To address under-adaptivity
and strike a balance between under- and overthinking, we propose TRAAC (Think
Right with Adaptive, Attentive Compression), an online post-training RL method
that leverages the model's self-attention over a long reasoning trajectory to
identify important steps and prune redundant ones. TRAAC also estimates
difficulty and incorporates it into training rewards, thereby learning to
allocate reasoning budget commensurate with example difficulty. Our approach
improves accuracy, reduces reasoning steps, and enables adaptive thinking
compared to base models and other RL baselines. Across a variety of tasks
(AIME, AMC, GPQA-D, BBEH), TRAAC (Qwen3-4B) achieves an average absolute
accuracy gain of 8.4% with a relative reduction in reasoning length of 36.8%
compared to the base model, and a 7.9% accuracy gain paired with a 29.4% length
drop compared to the best RL baseline. TRAAC also shows strong generalization:
although our models are trained on math datasets, they show accuracy and
efficiency gains on out-of-distribution non-math datasets like GPQA-D, BBEH,
and OptimalThinkingBench. Our analysis further verifies that TRAAC provides
fine-grained adjustments to thinking budget based on difficulty and that a
combination of task-difficulty calibration and attention-based compression
yields gains across diverse tasks.
Drawing Conclusions from Draws: Rethinking Preference Semantics in
  Arena-Style LLM Evaluation
Authors: name: Raphael Tang, name: Crystina Zhang, name: Wenyan Li, name: Carmen Lai, name: Pontus Stenetorp, name: Yao Lu
Published: 2025-10-02 17:59:41

In arena-style evaluation of large language models (LLMs), two LLMs respond
to a user query, and the user chooses the winning response or deems the
"battle" a draw, resulting in an adjustment to the ratings of both models. The
prevailing approach for modeling these rating dynamics is to view battles as
two-player game matches, as in chess, and apply the Elo rating system and its
derivatives. In this paper, we critically examine this paradigm. Specifically,
we question whether a draw genuinely means that the two models are equal and
hence whether their ratings should be equalized. Instead, we conjecture that
draws are more indicative of query difficulty: if the query is too easy, then
both models are more likely to succeed equally. On three real-world arena
datasets, we show that ignoring rating updates for draws yields a 1-3% relative
increase in battle outcome prediction accuracy (which includes draws) for all
four rating systems studied. Further analyses suggest that draws occur more for
queries rated as very easy and those as highly objective, with risk ratios of
1.37 and 1.35, respectively. We recommend future rating systems to reconsider
existing draw semantics and to account for query properties in rating updates.
One-Token Rollout: Guiding Supervised Fine-Tuning of LLMs with Policy
  Gradient
Authors: name: Rui Ming, name: Haoyuan Wu, name: Shoubo Hu, name: Zhuolun He, name: Bei Yu
Published: 2025-09-30 14:25:56

Supervised fine-tuning (SFT) is the predominant method for adapting large
language models (LLMs), yet it often struggles with generalization compared to
reinforcement learning (RL). In this work, we posit that this performance
disparity stems not just from the loss function, but from a more fundamental
difference: SFT learns from a fixed, pre-collected dataset, whereas RL utilizes
on-policy data sampled from the current policy. Building on this hypothesis, we
introduce one-token rollout (OTR), a novel fine-tuning algorithm that guides
SFT with the policy gradient method. OTR reframes the autoregressive learning
process by treating each token generation as a single-step reinforcement
learning trajectory. At each step, it performs a Monte Carlo ``rollout'' by
sampling multiple candidate tokens from the current policy's distribution. The
ground-truth token from the supervised data is then used to provide a reward
signal to these samples. Guided by policy gradient, our algorithm repurposes
static, off-policy supervised data into a dynamic, on-policy signal at the
token level, capturing the generalization benefits of on-policy learning while
bypassing the costly overhead of full sentence generation. Through extensive
experiments on a diverse suite of challenging benchmarks spanning mathematical
reasoning, code generation, and general domain reasoning, we demonstrate that
OTR consistently outperforms standard SFT. Our findings establish OTR as a
powerful and practical alternative for fine-tuning LLMs and provide compelling
evidence that the on-policy nature of data is a critical driver of
generalization, offering a promising new direction for fine-tuning LLMs.