Artificial Intelligence · Cross-listed from cs.RO
Hierarchical Residual Policy Learning for Real-World Mobile Manipulation with Sparse Human Guidance
Zhefei Gong, Hefeifei Jiang, Zehao Wang et al. (+1 more)
Mobile Manipulation (MoMa) is a cornerstone of autonomous robotic systems, yet real-world fine-tuning remains a formidable challenge because of high-dimensional action spaces and intricate coupling between heterogeneous modules. In this paper, we propose HiRe-MoMa (Hierarchical Residual Mobile Manipulation), a systematic framework for stable and sample-efficient real-world fine-tuning of complex MoMa systems. At its core, HiRe-MoMa adopts a Hierarchical Residual Architecture that fine-tunes a frozen base policy. A Policy-Agnostic Residual learns additive corrections atop the base policy, while Hierarchical Decoupling splits the residual into mobile-base and manipulator-arm heads with unidirectional conditioning, mitigating gradient interference and aligning corrections with the platform's physical hierarchy. To improve sample efficiency in data-expensive real-world settings, we propose Expert-Guided Value
Alignment, which leverages sparse human interventions with under 20% training coverage to reshape the critic's value landscape, effectively guiding autonomous exploration through expert priors. Extensive real-world experiments on the TIAGo++ platform demonstrate that HiRe-MoMa consistently enhances task performance with only around 30 minutes of online interaction, regardless of the base policy architecture. To our knowledge, this is among the first frameworks to enable efficient on-
robot fine-tuning for mobile manipulation in the real world, providing a practical paradigm for deploying complex robotic systems in dynamic, human-centric environments. Further details can be found on our project website: https://hire-moma.github.io/
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All authors Zhefei Gong, Hefeifei Jiang, Zehao Wang, Jiachen Li
Artificial Intelligence · Cross-listed from cs.RO
Modeling and Understanding Neural Weights of Humanoid Policies with Diffusion
Huixuan Cheng, Bike Zhang, Shankar Sastry et al. (+1 more)
Recent advances in
robot learning have driven rapid progress in humanoid control, enabling increasingly agile behaviors in the real world. However, humanoid control networks are typically obtained through conventional training procedures, while comparatively little
attention has been given to directly modeling the distribution of their learned weights and generating functional networks in weight space. In this work, we investigate whether high-performing humanoid control networks can be generatively modeled directly through their weights. We adopt a latent
diffusion framework that couples a linear autoencoder with a
Diffusion Transformer. The autoencoder compresses actor network parameters into a compact latent space, and the
Diffusion Transformer models the distribution of these latents. Trained on networks collected from reinforcement learning for the Unitree G1
robot, our approach generates deployable networks that perform various motions in the real world, including walking, dancing, and
spin kick. Beyond generation, we further study the learned weight space to better understand the structure captured by these humanoid control networks. We identify a structural principle in the first-layer weights that consistently reflects the
robot's kinematic prior. Notably, this kinematic prior emerges under both Reinforcement Learning (RL) and Imitation Learning (IL) training, suggesting that it is not specific to a particular optimization process but may instead reflect common knowledge associated with humanoid embodiment. Together, these results demonstrate the feasibility of generative modeling in humanoid network weight space, while providing initial insights into the information encoded by their learned weights. Additional results are available on our project website: https://humanoid-weight-
diffusion.github.io/
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All authors Huixuan Cheng, Bike Zhang, Shankar Sastry, Koushil Sreenath
Artificial Intelligence
Co-Evolving Acting and Forecasting for Language Agents
Haofei Yu, Yixing Chen, Yining Zhao et al. (+6 more)
Training
language agents is bottlenecked by the high cost of long-horizon rollouts, yet standard agentic reinforcement learning (RL) learns only from a terminal scalar reward, leaving the supervision in intermediate steps largely unused. Prior work exploits this supervision through value targets that estimate per-step success probabilities or observation targets that fit environment response. Both are hard to construct well: (1) value targets come from the same single scalar signal for each step in the trajectory, and (2) observation targets, such as web pages and terminal logs, are long and noisy and reach only one step ahead. We propose \name, which adds a third kind of multi-step action targets. On successful on-policy trajectories, \name trains the policy backbone to generate the action sequences that follows each history. Each action is also supervised when forecast from the preceding histories, forming a temporal ensemble that the shared backbone absorbs during training, and this enables acting and forecasting to co-evolve. The policy still acts one step at a time at inference, with no overhead. Across five
benchmarks (ALFWorld, ScienceWorld, WebShop, $\tau^2$-bench, and AppWorld), \name (1) achieves stronger aggregate performance than GRPO and RL baselines with predictive auxiliary targets across simulated environments, while improving overall performance on both realistic applications; (2) delivers up to $1.9\times$ and $2.0\times$ higher sample efficiency than ECHO and GRPO, respectively, on ScienceWorld; and (3) achieves these gains with a single policy backbone, without additional models, separate pretraining, or inference overhead. The codebase is available at https://github.com/ulab-uiuc/CoPE.
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All authors Haofei Yu, Yixing Chen, Yining Zhao, Xuyan Ye, Yuxiang Liu, Zhigang Hua, Yan Xie, Shuang Yang, Jiaxuan You
Artificial Intelligence · Cross-listed from cs.LG
Your Transformer Is Secretly Elastic
Walter Simoncini, Dheeraj Varghese, Gertjan J. Burghouts et al. (+1 more)
Scaling up
transformers has produced models with broad capabilities, deployed across heterogeneous hardware and fluctuating workloads. Yet, model families offer only a few fixed sizes (e.g., S, B, L), forcing users to commit to a model based on hardware and latency constraints. This rigidity is often suboptimal under varying query loads. We introduce a retraining-free method that elastifies pre-trained
transformers into a single model that adapts on-the-fly across a continuum of computational budgets. Our key contribution is a differentiable relaxation of the functional unit-ranking problem: unlike prior work, which optimizes a single importance weight per MLP block or
attention head, we rank every individual MLP
neuron and
attention head jointly, on a common scale, by minimizing the model's loss under soft
pruning across many sparsity targets, all at once. Across 27
vision and text
transformer encoders ranging from 22M to 8B parameters, our method yields smooth degradation up to 60% sparsity and outperforms one-shot baselines under deep
pruning. For example,
pruning DINOv3 ViT-H+/16 to 50% sparsity removes 427M parameters while reducing ImageNet-1k linear-probing accuracy by only 0.8%, without the need for weight correction or retraining. At 60% sparsity, a pruned AugReg ViT-B/16 model achieves up to $1.80\times$ higher throughput and $1.26\times$ lower single-query latency on an A100 GPU. Elastic models are produced in minutes, with construction time scaling sub-linearly with parameter count.
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All authors Walter Simoncini, Dheeraj Varghese, Gertjan J. Burghouts, Cees G.M. Snoek
Artificial Intelligence · Cross-listed from cs.GT
Price of Anarchy of First-Price Auctions in Agentic Markets
Shubhendu Trivedi, Jacob Eisenstein, Alekh Agarwal et al. (+3 more)
An agentic
market is a decentralized serving-time
market where AI
agents bid to claim each user
prompt. An
agent's bid is based on its expected value for the
prompt, which is the expectation of the reward for its response minus its inference costs. First-price auctions (FPAs), in which the highest bidder pays its bid and receives the
prompt, are a natural way to allocate
prompts in such a
market, since they are simple and transparent to run, and do not require central knowledge of private capabilities and costs. However, FPAs are not truthful: participants may bid less than their true value in the hope of a larger profit, and the
prompt may then be allocated to someone other than the bidder who values it the most. This welfare loss is quantified by the $\textit{price-of-anarchy}$ (PoA). In this paper, we argue that agentic
markets are likely to possess features that reduce the price-of-anarchy. The first is a $\textit{competitive floor}$, which occurs when multiple bidders usually value the
prompt above some baseline share of the optimal welfare (e.g., because the best models are often distilled into cheaper alternatives, which perform similarly). We show that competition among them then limits the welfare loss, which can be driven to zero as the floor rises. The second feature is $\textit{near-independence}$ of values after conditioning on the
prompt, which occurs when most of the predictable variation in the expected value is determined by the difficulty of the
prompt. As the value distribution approaches conditional independence, we can derive an efficiency guarantee that approaches a factor of $1 - 1/e^2$ of the optimal welfare. Thus, both features give rise to welfare lower bounds that are significantly better than the well-known general case of $1 - 1/e$. We validate these results empirically using auctions with strategic bidders simulated on the EmbedLLM
prompt routing
dataset.
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All authors Shubhendu Trivedi, Jacob Eisenstein, Alekh Agarwal, Ian Gemp, Michael Kaisers, Adam Fisch
Artificial Intelligence · Cross-listed from cs.LG
Afshin Khadangi
We introduce Continuum
Attention (CA), a causal
Transformer attention operator that replaces discrete key and value lookup with integration over a continuous, piecewise linear
memory curve. Each query induces an exponential relevance density along the curve. Integrating the density and interpolated values yields exact normalized
attention from closed form exponential moments, including half
token boundary contributions. The gradients follow analytically from moments of order at most two, enabling parallel GPU implementations while retaining quadratic sequence complexity and the standard
Transformer decoder. Compared with ordinary softmax and focal
attention, CA reduces mean validation negative log likelihood by 0.0200 and 0.0480 nats per byte, respectively, winning all paired comparisons. An additional disjoint validation slice yields a 0.0457 nats per byte advantage over the strongest selected softmax baseline, with a paired 95% confidence interval. These findings motivate continuous integration as a promising
attention alternative while leaving larger scale generalization open.
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Artificial Intelligence · Cross-listed from cs.CV
Player-1: Learning to Play from Synthetic Experience
Mingyu Ouyang, Yiran Wang, Siyuan Hu et al. (+9 more)
Computer-use
agents have demonstrated strong capabilities in interpreting screens and executing keyboard and mouse actions. However, games, with their visually rich worlds and diverse mechanics, remain challenging and offer a demanding next step toward general digital control. Current game-
agent learning pipelines often rely on large-scale demonstrations with limited coverage of dynamic game states, while scalable environments for interactive training remain difficult to obtain. We introduce Player-1, a post-training framework for learning game control beyond demonstrations through synthetic experience. We extend the computer-use action space with relative pointer motion, sustained inputs, and composite actions. For supervised initialization, we fine-tune computer-use models on 6,044 curated gameplay trajectories that pair recorded actions with task instructions and visual
reasoning. We construct Player-1-Gym, sixteen synthetic 2D and 3D environments designed around control demands shared across games. Programmatically verifiable rewards and randomized observations and control dynamics support multi-environment reinforcement learning with PPO. The resulting Player-1-9B and Player-1-35B-A3B improve MineDojo-300 success over their SFT policies by 27.8 and 17.7 percentage points, respectively. Player-1-9B reaches 38.33% success compared with 12.63% for the strongest evaluated baseline, with further gains over SFT on ViZDoom-10 and GameWorld. Further analyses show that broader synthetic environment coverage improves transfer, while the use of composite actions that combine multiple keyboard and mouse inputs increases during RL. We demonstrate that learning from synthetic experience substantially improves game control beyond supervised initialization, with gains that generalize beyond the RL training suite.
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All authors Mingyu Ouyang, Yiran Wang, Siyuan Hu, Xingyilang Yin, Huiqiang Sun, Guangzhi Wang, Junfu Pu, Cheng Cheng, Honghao Chen, Ying Shan, Mike Zheng Shou, Hwee Tou Ng
Artificial Intelligence · Cross-listed from cs.LG
One Task, Many Interfaces: Simple Cross-Interface Consistency Training Improves LLM Agent Robustness
Artin Tajdini, Manpreet Kaur and Peng Qi
Post-training binds the competence of an LLM
agent to the interface it was trained on. Semantics-preserving perturbations of that interface, such as renaming an action or changing the notation, leave the underlying task unchanged yet make the
agent worse at it. Scoring each sample independently means interface randomization only broadens this dependence from a single interface to the training interface distribution, so out-of-distribution perturbations still degrade the
agent. We propose Cross-Interface Policy Regularization (CIPR), a self-supervised objective that replaces action names with uninformative identifiers and presents each training sample under two interfaces in which equivalent actions are identified by construction. CIPR penalizes the KL divergence between the action distributions induced under the two interfaces, so the policy behaves consistently across them. We evaluate CIPR in both supervised and reinforcement-learning post-training, where it improves robustness. On agentic function calling, held-out interface perturbations reduce the accuracy of a post-trained
agent by nearly 25%, and CIPR recovers 10.5% while leaving accuracy on the unchanged interface intact.
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Artificial Intelligence · Cross-listed from cs.LO
Beyond LLM Judge: Mechanical Verification for Agentic Autoformalization
Anna Mei and Michael R. Douglas
We introduce a novel method for verifying Lean formalizations that goes beyond LLM-judged faithfulness. Unlike most verification methods, which rely on LLMs or trained models, our method combines LLM-based semantic review with mechanical numerical and vacuity checks, providing checkable feedback and detecting errors missed by LLM judges. We apply this method, along with our Extract-Verify-Proof autoformalization pipeline, to construct the Formalized Library of Mathematical Functions (FLMF), a partial formalization of the NIST Digital Library of Mathematical Functions (DLMF). On three labeled
datasets, our method achieves the lowest accepted-error rate among the evaluated methods, measured as the fraction of incorrect statements accepted by the verification method. Expert review of the results also identifies translation and source defects in existing
datasets. Code available at https://github.com/annamei1/FLMF-Ch7ErrFunc-Release.git.
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Artificial Intelligence · Cross-listed from cs.LG
Recovering Reliable Supervision via Critique-Guided On-Policy Distillation
Kaixiang Zhao, Tianrun Yu, Shangzhe Li et al. (+4 more)
On-policy
distillation (OPD) trains a student model on its own rollouts with
token-level supervision from a teacher, assuming that the teacher can reach a correct answer from any prefix the student writes. We call this property the recoverability of the teacher and show it fails on incorrect rollouts: the teacher follows student errors, although it recognizes them. We propose CriticOPD, using error recognition to restore recoverability. The teacher critiques an incorrect rollout, allowing the student to rewrite its solution following the error. The resulting output, including the critique as privileged information, is scored by the teacher while the student uses the revision without the critique for training. On two Qwen3 students and six mathematical
reasoning benchmarks, CriticOPD improves OPD, with the largest gains on competition problems.
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All authors Kaixiang Zhao, Tianrun Yu, Shangzhe Li, Yuxiao Yang, Amanda Hughes, Weitong Zhang, Taylor W. Killian
Artificial Intelligence
Scaling Discovery Requires Forgetting
Shangyin Tan, Ben Zhou, Lakshya A Agrawal et al. (+8 more)
Automated research with LLM
agents has shown promising results in various domains. Through iterative refinement and scored verifiers, LLM
agents are capable of discovering new ideas. There are different ways to scale automated research: let one
agent iterate for longer, run several
agents in parallel, or further let the parallel
agents communicate and build on one another's candidates. However, we find that all three strategies eventually plateau as the
agents accumulate history. Each
agent becomes anchored to its own past attempts, making fewer and smaller edits. Beyond this point, additional budget brings little further improvement. In this paper, we show that the key to scaling discovery further is to forget. Periodically discarding the search state, including every
agent's session and the candidate history, and restarting from the best candidate found so far, unblocks the search and pushes the same budget further. We implement forgetting in a team of
agents with communication and evaluate it on four tasks spanning heuristic programming, kernel optimization and model training. The results show up to 15% improvement compared to parallel
agents, and up to 39% compared to a single
agent. Our results suggest that, for open-ended discovery, what an
agent is allowed to remember matters as much as how much compute it is given.
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All authors Shangyin Tan, Ben Zhou, Lakshya A Agrawal, Jialin Zhang, Yilin Chen, Qiuyang Mang, Donghyun Lee, Dennis Jacob, Koushik Sen, Alex Dimakis, Matei Zaharia
Artificial Intelligence
PhySGA: A Physics-Space Grounded Agentic Framework for Generalizable and Interactive 3D Composition
Weichen Lyu, Chong Xia, Mingyu Dong et al. (+3 more)
3D object composition is pivotal to digital content creation and
robot simulation, while also serving as a critical testbed for physical intelligence in foundation models. Previous methods either leverage large
language models to directly predict object coordinates, or utilize generative
diffusion priors to synthesize composite images for visual
alignment, both of which rely on static priors in digital spaces. However, these methods consistently struggle with complex compositions such as precise hanging,
gravity-driven settling and hidden containment, as these tasks require interactive construction processes involving global planning, fine-grained
alignment, and physical simulation. To address these challenges, we propose PhySGA, a Physics-Space Grounded Agentic Framework for 3D composition via
agent exploration and physical simulation, enabling the universal interactive assembly of rigid, articulated, and deformable objects. Directly applying vanilla LLMs or VLMs to this task proves insufficient, as current models inherently lack grounded physical understanding and interactive construction experience. To overcome this limitation, we design a collaborative feedforward-feedback architecture: an Assembly
Agent focuses on
geometric and kinematic
reasoning for global step planning and coarse adjustment, while a Critic
Agent reasons over contact dynamics for simulator state diagnosis and fine-grained closed-loop correction. Furthermore, Action Routers map semantic planning and analysis onto executable physical primitives within the simulator, while a task-specific Self-Evolving Skill Library distills historical trial-and-error feedback into reusable experiences, boosting success rates and minimizing exploration attempts. Extensive experiments within Isaac Sim demonstrate that PhySGA significantly surpasses existing methods in relation fidelity, physical plausibility, and visual quality.
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All authors Weichen Lyu, Chong Xia, Mingyu Dong, Long Xu, Shengjun Zhang, Yueqi Duan
Artificial Intelligence · Cross-listed from cs.CL
PerSo: Investigating Facets of LLMs' Social Intelligence in Real-World Persuasion Scenarios
Ali Zamani, Soroush Esfahanian, Erfan Shahabi et al. (+3 more)
Understanding the social intelligence of large
language models (LLMs) is increasingly important for applications involving human-AI interaction and multi-
agent environments. Social intelligence comprises two aspects: cognitive intelligence, the ability to infer others' mental states, and behavioral intelligence, the ability to select appropriate social actions based on these inferences to achieve social goals. Persuasion provides a natural testbed in which both aspects are simultaneously at play, yet prior
benchmarks typically evaluate them in isolation using synthetic scenarios. We introduce PerSo, a
benchmark that evaluates both aspects on real-world persuasion dialogues from Reddit's ChangeMyView community, using an LLM-in-the-loop pipeline to construct the evaluation tasks, validated through human annotation. Our evaluation of diverse LLMs shows that performance generally scales with model size but varies across dimensions. The strongest LLM slightly surpasses the best observed human performance on the behavioral task, while the best human participant remains ahead on the cognitive task.
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All authors Ali Zamani, Soroush Esfahanian, Erfan Shahabi, Mohammad Taher Pilehvar, Azadeh Shakery, Yadollah Yaghoobzadeh
Artificial Intelligence · Cross-listed from cs.LG
Detection of Potato Plant Leaf Diseases using Convolutional Neural Networks
Haimanot Bitew Alemayehu, Simachew Melaku Ayalew, Chilotie Desalew Mengesha et al. (+3 more)
Potato is an important food-security crop in Ethiopia, but its production is affected by foliar diseases, particularly early blight and late blight. This study develops a lightweight convolutional neural network (CNN) to classify potato leaves into three categories: healthy, early blight, and late blight. The proposed model was trained using 2,215 images from the PlantVillage
dataset and evaluated on an independent field
dataset containing 247 images collected from potato farms in Banja, Awi Zone, Ethiopia. On PlantVillage, the model achieved 99.04% training accuracy, 98.21% validation accuracy, and 98.66% test accuracy. On the field
dataset, it achieved 90.28% accuracy. The study also compares the proposed CNN with VGG16 and MobileNetV2 in terms of classification performance and computational efficiency. The proposed model contains 446,787 trainable parameters, occupies approximately 1.70 MB, and requires an average of 5.58 seconds per training epoch in the reported experimental setup, compared with 9.96 seconds for VGG16 and 7.68 seconds for MobileNetV2. The results demonstrate the potential of a compact CNN for potato leaf disease classification and highlight the importance of independent field evaluation, where image conditions are more variable than in controlled
benchmark datasets. The proposed approach may support preliminary disease identification for smallholder farmers in Ethiopia and similar agricultural settings.
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All authors Haimanot Bitew Alemayehu, Simachew Melaku Ayalew, Chilotie Desalew Mengesha, Ayisheshim Almaw Bogale, Anteneh Birhan Yenew, Abrham Tegegne Bogale
Artificial Intelligence · Cross-listed from cs.CL
PrismaCoder: Pushing the Boundaries of Open Multimodal Code Intelligence through Decomposed Rubric Synthesis
Qiushi Sun, Jingyang Gong, Yang Liu et al. (+14 more)
Multimodal code intelligence connects the symbolic world of programs with the visual world their execution creates. Interest in this capability is burgeoning, as models are increasingly asked to produce charts, webpages, animations, and semi-structured documents. Recently, proprietary models have pushed far ahead, producing interactive artifacts even from a text description or a screenshot alone. Yet their data and training remain closed, and open progress is held back by the quantity and quality of available data and by the pipelines and methods for producing it. Quality is the harder of the two, since a sample must be correct as a program, well built, and above all visually right once rendered. No existing signal covers all three. Execution often returns just a pass or fail, code similarity does not map to the visual output, and task-specific verifiers do not generalize. To push open progress further, we start from the data side with PrismaForge, a hierarchical data pipeline centered on a synthesized rubric that decomposes these factors into checkable criteria. The same rubric serves as both filter and reward: it curates the supervised corpus, then drives reinforcement learning on visual-coding tasks. Leveraging PrismaForge for data synthesis and filtering, we build PrismaCoder-2M, the most diverse open
dataset for
multimodal code intelligence and the largest to date. On the model side, we train PrismaCoder-9B and PrismaCoder-35B on this data and release both. They lead the open state of the art by a wide margin and rival leading proprietary systems. Our analyses further show that the proposed rubric-based pipeline improves
multimodal coding performance across model backbones. We release our code and data at https://qiushisun.github.io/PrismaCoder-Home/.
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All authors Qiushi Sun, Jingyang Gong, Yang Liu, Qiaosheng Chen, Zhangyue Yin, Zixian Huang, Hanxu Hu, Jing Xiong, Fangzhi Xu, Haochen Tan, Haoli Bai, Lifeng Shang, Xiaoguang Li, Ben Kao, Qipeng Guo, Lingpeng Kong, Jiahui Gao
Artificial Intelligence
ActiveEvidence: Decision-Focused Evidence Graphs with Verifiable Acquisition Gaps
Miaobo Hu, Shuhao Hu, Xiaobo Guo et al. (+5 more)
A
multimodal agent must decide whether visual evidence is worth acquiring, which candidate or resolution to query, and when further interaction can no longer change its decision. We study this problem under a strict free-metadata/paid-observation boundary,
stochastic vector costs, and a frozen execution trace, and treat it as decision-certifiable active evidence acquisition. A Counterfactual Evidence Interaction Graph (CEIG) represents unary evidence value, signed second-order interaction, and directed conditional harm while maintaining explicit score enclosures for unresolved interactions; a decision-weighted enclosure objective tightens the bounds that can change action identity. Gap-directed refinement evaluates only interactions that can displace the incumbent acquisition and stops once an $\epsilon$-decision certificate shows that unresolved terms cannot change the selected action beyond a declared tolerance. The certificate is model-relative: it verifies policy-consistent optimality under the pinned learned score, and semantic quality is measured separately through post-freeze labels. Paired continuation rollouts learn the downstream value of paid previews, an online operational cost filter is separated from independent finite-policy-family trajectory certification, and an acquisition monitor with an independently implemented verifier binds every certificate to the frozen trace. On 640 cases split into 384/128/128 training, validation, and test cases, the controller reaches task score 0.842, grounding F1 0.816, evidence recall 0.873, 1.74 accepted calls, 286.5K visual
tokens, and 742ms p95 latency. Dense replay of issued certificates shows 0.992 action agreement and a 0.988 certificate rate while evaluating 19.6 instead of 191.4 expensive interaction terms per decision.
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All authors Miaobo Hu, Shuhao Hu, Xiaobo Guo, Xin Wang, Bokun Wang, Peng Zhang, Daren Zha, Jun Xiao
Artificial Intelligence
The Wealth of Agents: Scaling Agentic AI Across Developers
Austin W. Hanjie, Carlos E. Jimenez, Zirui Wang et al. (+1 more)
Today's agentic systems are typically designed, built, and deployed by single developers with full control over all participating
agents. While this simplifies development, it limits the potential scale of agentic systems. We study multi-developer agentic systems (MDAs) which enable developers to collaboratively build agentic systems to push frontier capability beyond what any individual developer could achieve alone. We construct empirical and theoretical models of developers to compare MDAs with single-developer agentic systems (SDAs). On GAIA, a general assistant
benchmark, a population of specialized developers whose individual
agents do not exceed 35% accuracy reaches 70% when their
agents are combined in an MDA. This MDA performs 12 percentage points above the median general-purpose SDA built with several times each specialist's development budget. Our theoretical models show how MDAs can incentivize specialization and encourage more efficient use of aggregated developer resources. Put together, our results make a preliminary case for MDAs as a new scaling axis by enabling developers with diverse capabilities and resources to advance agentic AI collaboratively.
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All authors Austin W. Hanjie, Carlos E. Jimenez, Zirui Wang, Karthik Narasimhan
Artificial Intelligence · Cross-listed from cs.CL
VASPO: Self-Distillation with Privileged Value Estimation
Fei Wu, Zhenrong Zhang, Jianshu Zhang et al. (+1 more)
On-policy self-
distillation provides dense supervision for
reasoning models via a privileged self-teacher. However, this supervision reflects local
token preferences rather than the expected return of an unguided student continuation. Recent methods combine this signal with GRPO. They use GRPO for update directions and the teacher for signal scaling. However, we demonstrate a more effective integration within PPO. Specifically, we identify complementary positional dynamics: privileged information enhances a critic's value-prediction accuracy toward the response's end, whereas self-teacher guidance is strong initially, drops mid-generation, and rebounds finally. These insights motivate Value-Augmented Self-
Distillation Policy Optimization (VASPO), wherein a self-teacher and critic evaluate identical privileged information at student-generated prefixes. The critic computes
token-level PPO advantages from predicted continuation returns, while the self-teacher modulates selected positive advantages via bounded probability ratios. Across six mathematical
reasoning benchmarks, VASPO improves mean accuracy over PPO by 2.59 and 1.81 percentage points on Qwen3-4B and OLMo3-7B variants, respectively, and achieves the highest mean accuracy among evaluated methods on Knights and Knaves. Our code is available at https://github.com/cii030/VASPO.
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All authors Fei Wu, Zhenrong Zhang, Jianshu Zhang, Jun Du
Artificial Intelligence · Cross-listed from cs.CV
VascuSR: Iterative Anatomy-Aware Diffusion for CT Super-Resolution
Hairong Ni, Jiahe Huang, Minghao Guo et al. (+2 more)
Generative super-resolution (SR) can recover sharp detail in computed
tomography (CT) images from severely downsampled observations, but the missing high-frequency content is not uniquely determined by the low-resolution input. Consequently, visually plausible reconstructions may contain anatomically unsupported vascular detail, such as missing, spurious, or incorrectly connected vessels. To overcome this challenge, we introduce VascuSR, an iterative anatomy-aware
diffusion framework that combines image reconstruction with explicit 3D vascular guidance. First, to provide richer anatomical guidance, we use an explicit 3D vascular representation that captures structural information beyond individual slices. This representation also simplifies vascular refinement by allowing a lightweight 3D generative refiner to focus on vascular structure rather than complex CT appearance. Second, to guide slice-wise SR, a lightweight anatomy controller injects the refined structure into the
diffusion decoder, while iterative feedback updates this guidance from each new SR volume. On re-annotated MSD Task08 and HVM
datasets, VascuSR outperforms interpolation and learning-based SR baselines in segmentation accuracy (Dice and clDice) and 3D vascular structure (connectivity and mean centerline distance), while maintaining image quality comparable to its SR backbone. Compared with learning-based baselines, VascuSR reduces class-averaged connected-component counts by over 20% on both
datasets and mean centerline distance by over 10% on MSD.
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All authors Hairong Ni, Jiahe Huang, Minghao Guo, Wojciech Matusik, Rose Yu
Artificial Intelligence · Cross-listed from cs.LG
Hindsight-Privileged RLVR: Single-Rollout Token Credit Without Group Sampling
Wei Chen, Yubing Wu, Zhou Xu et al. (+9 more)
Reinforcement learning with verifiable rewards (RLVR) trains
language models from checked task outcomes, but assigning a terminal reward to individual
reasoning tokens or actions remains difficult. Group-relative methods such as Group Relative Policy Optimization (GRPO) sample several attempts per
prompt, but lose their reward-based learning signal when all attempts receive the same outcome. We propose Hindsight-Privileged RLVR (HP-RLVR), a single-rollout method that turns a completed trajectory's own future into
token-level update weights. The generating policy snapshot scores each eligible
token under its causal history and under a hindsight context containing the realized future and verifier feedback. A normalized Jensen–Shannon divergence between the two views weights the reward relative to a historical baseline, yielding bounded
token credit for both successful and failed attempts, with no reference solution or learned critic. In paired Qwen3-8B runs over a shared $100$-step schedule, HP-RLVR trains $1.21\times$, $1.46\times$, and $2.30\times$ faster than GRPO on mathematics, code generation, and ALFWorld, using one eighth the rollouts per
prompt. Accuracy is comparable overall, with HP-RLVR ahead on mathematics at all three sizes and GRPO ahead on code and ALFWorld. In ALFWorld, $43.2\%$ (4B) and $49.0\%$ (8B) of GRPO's gradient-norm records are zero, versus none for HP-RLVR.
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All authors Wei Chen, Yubing Wu, Zhou Xu, Haoyue Zhang, Delu Zeng, Jiahao Wang, Junmei Yang, Pan tiewen, Jingfeng Liu, Zhiheng Zhou, Bo Li, Min Chen
Artificial Intelligence · Cross-listed from cs.CV
Quantifying privacy risks in foundation model representations of brain MRI
Imen Bajar, Héber Hwang Arcolezi, Antoine Boutet et al. (+2 more)
Pretrained representations are increasingly used to share and reuse information from medical images without exposing the original scans, but it is unclear how much patient-specific information these representations retain. We investigate this question for
brain magnetic resonance imaging (MRI) by evaluating five frozen pretrained encoders spanning different architectures, pretraining objectives, and training domains on two longitudinal T1-weighted
datasets, OASIS-2 and MIRIAD. Under an attacker model with access to released
embeddings, the corresponding public encoder, and auxiliary MRI scans, we evaluate patient linkage and sensitive-attribute inference. Across the evaluated representations, we observe substantial subject-specific information: rank-1 patient linkage reaches up to 99% on MIRIAD and exceeds 90% on OASIS-2 for the highest-risk representation, despite the absence of an identification objective during encoder training. We further show that the released
embeddings retain information about a demographic attribute, with recorded-sex prediction reaching balanced accuracy of 90.9% on OASIS-2 and 95.5% on MIRIAD. Overall, our results demonstrate that replacing raw
brain MRI with learned
embeddings does not, by itself, eliminate patient-linkage or attribute-inference risk, and is therefore insufficient on its own to establish anonymity.
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All authors Imen Bajar, Héber Hwang Arcolezi, Antoine Boutet, Carole Frindel, Mohamed Maouche
Artificial Intelligence
SafeAdapt-Poker: Counterfactual Risk Calibration for Budgeted Adaptation under Opponent Shift
Miaobo Hu, Shuhao Hu, Xiaobo Guo et al. (+5 more)
Opponent histories can mislead after behavioral shifts. SafeAdapt-Poker formulates budgeted adaptation under opponent shift as a dual-counterfactual sequential-control problem using only information available before each decision. At each decision the controller asks two distinct questions: what local downside a deviation from the frozen anchor would induce under matched legal continuations, and what future adaptation value would be lost by consuming part of the remaining deviation budget now. The first question is answered by a monotone strategic-risk model over deviation intensity; the second by a paired budget-state continuation model that prices current budget consumption. The controller selects among support-preserving candidate deviations by trading immediate adaptation value against these two quantities, a pathwise cumulative-KL ledger bounds total anchor-relative deviation, and a finite family of frozen controllers is screened on complete paired calibration sessions before held-out interaction. A finite-candidate perturbation bound shows that the allocation objective degrades continuously with continuation-value estimation error. Evaluation covers exact-game exploitation and safety, four opponent shifts, local-risk calibration, delayed-opportunity allocation, direct opportunity-value estimation, value-error robustness, factorial mechanism attribution, budget sensitivity, abstract-HUNL cross-play, and player/time-held-out public-history behavior. On exact Leduc the full controller attains a matched weak-pool gain of +0.0718 with a worst-opponent delta of -0.0126, raises delayed-opportunity capture from 0.6318 under myopic allocation to 0.7841 under the same session budget, and reaches +42.7 mbb/hand in abstract-HUNL cross-play; the retained public-history screen reports 0.5820 action accuracy and 0.9277 log loss versus 0.5039 and 0.9778 for a public prior.
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All authors Miaobo Hu, Shuhao Hu, Xiaobo Guo, Xin Wang, Bokun Wang, Rui Chen, Daren Zha, Jun Xiao
Artificial Intelligence · Cross-listed from cs.LG
Selective Growing Q-Networks (SGQN): An Analysis of Adaptive Action Grids in Decoupled Q-Learning
Parham Nooranbakht, Seyed Mahdi Basiri Azad and Joschka Bödecker
Value-based Q-learning for continuous control can discretise the action space, but the resolution of the discretisation trades early exploration against final precision. Growing Q-Networks (GQN) grow the resolution uniformly from coarse to fine. We introduce Selective Growing Q-Networks (SGQN), which keeps decoupled Q-learning and replaces uniform growth with an adaptive action grid: bins are added next to bins with high estimated Q-value, bins with low Q-value are pruned, and actions are chosen epsilon-greedily. On eight DeepMind Control Suite (DMControl) tasks with 2 to 12 action dimensions, SGQN has the highest mean final return among the compared methods on four, is within 1.1% of the best method on three more, and is 3.4% below Bang-Bang MPO on Walker Run, using on average 7.5 of 9 available bins per dimension. We then test whether the adaptive grid explains these results, and find that it does not. Growth activates every candidate bin and behaves like uniform growth, and removing
pruning changes the return by at most 1.1%. Two other choices matter: starting from a coarse grid, since starting with all 9 bins produces collapsed seeds, and epsilon-greedy selection, since an annealed softmax lowers the return by 23% to 100%. SGQN is insensitive to its growth and
pruning hyperparameters. It also differs from the GQN and DecQN baselines in other respects, notably one gradient update per environment step instead of one per eight, which we did not control for, so we do not attribute its advantage to a single component. We present SGQN as a competitive value-based method and as an analysis of which ingredients of a growing action grid matter.
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Artificial Intelligence · Cross-listed from cs.CL
Equal CER, Unequal Damage: Severity-Weighted Character Error Rate for OCR
Nguyen Nguyen and Yongyi Zang
Character Error Rate (CER), the standard metric for OCR and
vision-
language models (VLMs), charges a typo as much as a wrong name. We introduce semCER, which keeps CER's
alignment and denominator and charges each grapheme edit a severity from an LLM judge, with no word segmentation. Normalized CER keeps the score of a whole transcription, where it tracks readers as well as semCER or better, and semCER breaks that score down into named edits. The charges track readers' ratings edit by edit in English, Vietnamese, and Chinese and put the edits readers rate most severe near the top of a sorted list. On pairs drawn where CER and semCER disagreed, readers side with semCER more often than with normalized CER in English and Chinese, and equally often in Vietnamese. Across 16
languages, the judge charges a wrong diacritic or tone mark almost nothing in Spanish but nearly a wrong letter's severity in Telugu. Where we can count real words, a general VLM's wrong letter forms one far above chance while a classical pipeline's stays near it. We release the 61 million judged edits, the aggregated reader ratings, and the distilled scorer at https://github.com/nguyenx2t/semcer.
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Artificial Intelligence · Cross-listed from cs.MA
From Training to Escalation: Forensic Analysis and Local Recreation of RL/RLHF-Induced Privilege Escalation in AI Agents (CVE-2026-65616)
Alicia Colmenero-Fernández
Mid-2026 reporting on autonomous AI evaluation incidents claimed that frontier large
language model (LLM)
agents escaped server containment through emergent coordination and cloud infrastructure attacks. This paper contextualizes those claims through forensic analysis and containerized recreation of both the Highly Persistent Internal Model (HPIM) training-case conditions and the Phase One privilege escalation targeting internal package repositories during OpenAI's ExploitGym evaluations. We recreate the configuration of the OpenAI-provided training example and reproduce the cryptographic verification bypass (CVE-2026-65616) in isolated repository servers, where misconfigured systems enabled
token forgery and privilege escalation through basic HTTP requests, Python, and JavaScript. Crucially, the training-example conditions and the Phase One (and later) misconfigurations share the same exploitable patterns. Our analysis explains how Zero Trust Architecture principles neutralize this shared attack vector. Contemporary AI risks derive directly from training and deployment conditions in ambivalent learning environments lacking adequate containment measures.
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Artificial Intelligence
SwarmRSI: Recursive Self-Improvement through Evolving Scientific Agent Swarm
Qianru Meng, Xi Zhang, Konstantinos Papakostas et al. (+9 more)
Recursive self-improvement (RSI) in scientific research aims to improve
agents' research capabilities through their own experimental experience. Scientific
agent swarms can combine evidence and implementations from different research directions to guide their
agents' subsequent experiments. However, poor experimental results can leave unclear whether to revise a hypothesis or its implementation, while overall program scores can hide components useful to other
agents. To address these challenges, we introduce SwarmRSI, a framework that evolves scientific
agent swarms for recursive self-improvement. We devise Feedback-Guided Swarm Mutation to refine hypotheses and their implementations using experimental feedback from other
agents. We also propose Complementary Swarm Recombination to reuse retained hypotheses and complementary program components across
agents, allowing partial results to support new research. Recursive Swarm
Evolution integrates both mechanisms into a feedback loop, using new experimental evidence to guide subsequent research. Experiments on OpenProblems, NatureBench and MLE-Bench Lite demonstrate SwarmRSI's state-of-the-art (SOTA) performance. Further evaluations show that accumulated research experience improves performance on held-out tasks. Biologists also reproduce its programs for follow-up biological analyses.
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All authors Qianru Meng, Xi Zhang, Konstantinos Papakostas, Congfeng Cao, Wenxiang Hu, Tianyi Lu, Xiao Zhang, Lun Zhou, Zaiqiao Meng, Zhaochun Ren, Xiaohui Yan, Yougang Lyu
Artificial Intelligence · Cross-listed from cs.CL
Analyzing performance of Retrieval Augmented Generation models for Question Answering Tasks
Fazle Mohammed Tasfiq, Sabab Attin, Sheikh Md. Tahmid Hossain et al. (+6 more)
Retrieval-Augmented Generation (RAG) models have become almost a cornerstone of open-domain question answering tasks; in which the quality of answers generated in the process, greatly relies on the effectiveness of
retrieval mechanisms. In the realm of LLM, the last two years have seen tremendous progress in RAG architectures, with many models reporting to have improved performance through various design innovations. The cultural question and answering systems of South Asia, in particular, present a specific challenge since the region is home to the vast number of
languages and traditions, which are closely interrelated. Along with these deeply intertwined historical narratives increase the need for culturally grounded and context-aware interpretation. Despite major architectural advances in LLMs, claiming significant improvements, challenges remain when answering these region specific questions in native
languages. In this study, we aim to extract high-performing factors to guide the development of optimized hybrid RAG systems. We further explore whether the integration of RAG changes performance differently for culturally grounded and non-cultural questions in a South Asian multilingual setup, and whether RAG produces a higher overall percentage of correct answers than contexted answering. We also examine how LLM performance changes when RAG methods are integrated into the question-answering pipeline in comparison with non-contexted and contexted answering and, evaluate whether the introduction of RAG benefits popular or non-popular culturally grounded questions more. Not only does our work evaluate the current methods but also carves a path for future designs of more accurate and efficient question-answering pipelines.
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All authors Fazle Mohammed Tasfiq, Sabab Attin, Sheikh Md. Tahmid Hossain, Raiat Reza, Tanzim Ahmmed, Nafis Chowdhury, Anika Ahmed, Moinul Haque, Farig Yousuf Sadeque
Artificial Intelligence · Cross-listed from cs.LG
OrionMoE-v1.0: A Mixture-of-Experts Foundation Model for Tabular In-Context Learning
Mohamed Bouadi, Nassim Bouarour, Aditya Tanna et al. (+1 more)
Tabular foundation models enable in-context learning by predicting unseen observations from a labeled support table without task-specific parameter updates. Despite recent advances, most existing approaches rely on dense architectures that apply the same transformations across features and
datasets. We introduce OrionMoE-Small, a compact tabular foundation model that combines distribution-aware representations with
dataset-conditioned sparse mixture-of-experts computation. Built on a dual-axis
Transformer architecture, OrionMoE-Small selectively activates experts along the feature axis, with routing decisions informed by both individual feature representations and the broader
dataset context. This design enables adaptive allocation of computation across heterogeneous tabular problems while retaining a shared computational pathway across tasks. OrionMoE-Small is pretrained entirely on synthetic classification tasks generated from a structured causal prior and performs downstream prediction without gradient-based adaptation. Experiments across multiple tabular
benchmarks demonstrate competitive performance against substantially larger foundation models, with particularly strong results on small-context and high-dimensional
datasets. Further analysis reveals favorable relative performance under class imbalance and highlights the importance of feature composition.
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All authors Mohamed Bouadi, Nassim Bouarour, Aditya Tanna, Vinay Kumar Sankarapu
Artificial Intelligence · Cross-listed from cs.CL
From Unreliable to Usable: Decomposing Failure Modes and Confidence Signals in LLM Entity Extraction - Evidence from Cyber Threat Intelligence
Jiahong Qian
Audits of large
language model (LLM) extraction from full-length documents report severe unreliability, leaving two questions open: what the failures actually consist of, and whether confidence signals can turn unreliable output into usable output. We run a preregistered, fully frozen evaluation of two new-generation low-cost LLMs (DeepSeek-V4-Flash, GLM-5.3-Flash) with a flagship-tier control (GPT-6.1-Sol) on a public corpus of 354 incident campaigns (315 unique reports, 630 model-report evaluations), scoring extraction quality against gold, confidence calibration, and robustness to single-sentence perturbations. Three findings emerge. (1) Unreliability persists across tiers: micro precision is 0.03-0.34 and verbalized calibration error (ECE) 0.53-0.94 across both splits; the gap does not close at the flagship tier. (2) The dominant failure is not
hallucination: a full mention census plus a three-tier annotation of 360 sampled candidates (two independent LLM annotators from vendors outside the evaluated set, Cohen's kappa = 0.79 on the preregistered acceptance line, fully disclosed) back-projects to 65.6% background mentions and 19.9% gold-scope gaps, with only 1.2% unsupported candidates. (3) Confidence ranks but does not verify: zero-cost self-consistency-frequency tiering passes a preregistered top-30% precision-gain bar in all six test-split model-type
cells (4/6 on train), the zero-cost text-frequency x SC frequency combination wins 3, ties 2, loses 1 across
cells, yet relation-level counterfactuals on all 45 targets show that a single misattribution sentence flips high-confidence judgments in 95.6-100% of cases, and even a background sentence explicitly marked as unrelated still binds a fake vulnerability in up to 26.7% of them. We conclude with a deployment boundary: confidence tiering may set review priorities, never truth.
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Artificial Intelligence · Cross-listed from cs.LG
One Size Does Not Fit All: Curvature-Aware Adaptive Perturbation for SAM
Alessia Rossi, Felipe Campelo and Gabriel L. Oliveira
Poor generalisation in deep neural networks is frequently associated with convergence to sharp minima. Sharpness-Aware Minimisation (SAM) improves generalisation of deep neural networks by minimising a combination of the training loss and the local sharpness of the loss landscape, biasing convergence towards flat minima. However, SAM relies on a fixed perturbation radius $\rho$ that ignores local
curvature, leading to inefficient behaviour: the perturbation may be too small to escape poor basins defined by sharp local minima, or too large to allow convergence to solutions with good loss-sharpness tradeoff. We reinterpret $\rho$ as the intensity of the perturbation signal and propose
Curvature-Aware Adaptive Perturbation for SAM (CAP-SAM), a geometry-aware method that modulates the perturbation radius based on a sharpness ratio, enabling a more effective search for robust minima on the loss landscape. We prove that CAP-SAM preserves the same convergence rate as standard SAM in non-convex
stochastic settings. Extensive experiments indicate that our approach consistently outperforms SAM and its most prominent variants across diverse network architectures on CIFAR-10, CIFAR-100, and ImageNet.
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