Artificial Intelligence
Multi-Head Attention-Based Feature Extractor Integration with Soft Actor-Critic for Porosity Prediction and Process Parameter Optimization in Additive Manufacturing
Kianoush Aqabakee and Leonardo Stella
Additive manufacturing process optimization requires precise parameter control to minimize defects such as porosity. Traditional reinforcement learning (RL) approaches using discrete action spaces suffer from slow convergence and susceptibility to local optima, limiting their effectiveness for high-precision manufacturing tasks. This study addresses these limitations by employing a continuous action space combined with a novel architecture that integrates a multi-head
attention mechanism with the Soft Actor-Critic (SAC)
algorithm. The
attention-based feature extractor enhances the
agent's ability to capture subtle variations in low-dimensional input features, enabling more effective exploration-exploitation balance for navigating value spaces with local minima. We validate our approach on porosity prediction and process parameter optimization in
laser powder bed fusion, demonstrating faster convergence and higher final reward values compared to standard RL methods including DQN, PPO, TD3, and vanilla SAC. The proposed methodology achieves a convergence value of 322.79 within 14 episodes, outperforming existing approaches while maintaining stability throughout training.
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Artificial Intelligence
Residual-Space Evolutionary Optimization via Flow-based Generative Models
Zhuo Cao, Lena Krieger, Fernanda Nader et al. (+3 more)
Data editing with generative methods typically requires differentiable objectives and gradient-based search. However, these assumptions break down in flow-based settings, where edits are performed through forward and backward integration and often involve non-differentiable or black-box objectives. We introduce residual-space evolutionary optimization, a model-agnostic framework that addresses this gap by combining flow-based generative editing with evolutionary
algorithms. Building on the observation that conditional flow matching (CFM) can disentangle condition-controlled factors from instance-specific residuals, our framework directly operates in residual space and separates two complementary search regimes: self-pollination performs local exploitation through feature-preserving residual refinement, and cross-pollination promotes broader exploration by recombining residuals across heterogeneous samples. As a proof of concept, we validate on MorphoMNIST, a
benchmark dataset for counterfactual generation, and on
crystal data, demonstrating that this exploration–exploitation decomposition provides a useful mechanism for balancing target
alignment, instance preservation, and diversity, and extends beyond images to real-world scientific domains.
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All authors Zhuo Cao, Lena Krieger, Fernanda Nader, Xuan Zhao, Hanno Scharr, Ira Assent
Artificial Intelligence · Cross-listed from cs.CV
The Hidden Evolution of Disguised Visual Context inside the VLM
Wish Suharitdamrong, Tony Alex, Xiatian Zhu et al. (+2 more)
Visual
tokens enter Large
Language Models (LLMs) as raw, foreign signals. How they are transformed into meaningful representations and interact with the
language space depends entirely on the integration architecture. Whether by treating visual
tokens as in-context
prompts within the input sequence or injecting them directly into the LLM's intermediate layers. A controlled comparison and understanding of how these architectural choices affect visual information and its internal transformation to integrate with the LLM remains underexplored. We provide a fair comparison by evaluating in-context and layer-wise injection VLM integration paradigms under identical training conditions across single image, multi-image, and
video benchmarks. In doing so, we uncover a hidden
evolution where visual
tokens enter the LLM as disguised visual context, raw representations lacking linguistic structure, but are progressively reshaped depending on the integration paradigm, each capturing fundamentally different frequency characteristics of the visual signal. We show that this
evolution inside the LLM determines what visual features the VLM can utilize effectively, how visual representations align with the
language space, and ultimately how each paradigm performs across different tasks. We further demonstrate that
attention allocation alone is insufficient, and that performance is driven by the quality of visual representations at each layer.
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All authors Wish Suharitdamrong, Tony Alex, Xiatian Zhu, Muhammad Awais, Sara Atito
Artificial Intelligence · Cross-listed from cs.CV
Variable-Length Tokenization via Learnable Global Merging for Diffusion Transformers
Dong Hoon Lee and Seunghoon Hong
Latent
Diffusion Models (LDMs) have become dominant in visual synthesis, but their quality-compute trade-off is largely constrained by the tokenizer's fixed compression ratio. Variable-length tokenizers (VLTs) promise adaptive compression by varying
token counts, allowing
diffusion models to flexibly balance quality and compute. However, conventional VLTs modulate length by truncating ordered
token sequences, which makes
token semantics depend on
token position and breaks representational
alignment across lengths. This leads to a cross-length shift in the latent distribution that hinders a single variable-length
diffusion model from operating effectively. To address this, we propose a novel variable-length tokenizer that modulates length by merging
tokens. We show that encouraging similar
tokens to merge enables direct cross-length representation
alignment when the
diffusion transformer operates according to the merging pattern. Since conventional merging methods are data-dependent, making the merging pattern inaccessible during generation, we introduce learnable global merging, which is data-independent, to ensure compatibility with
diffusion transformers. On ImageNet 256$\times$256 generation, our merging-based variable-length tokenizer integrated with a
diffusion transformer achieves a superior gFID-compute trade-off compared to prior VLT methods. Code is available at [this https URL](https://github.com/movinghoon/lgm)
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Artificial Intelligence · Cross-listed from eess.SP
Evaluation of EEG Foundation Models for Event-Based Burst-Suppression Detection in ICU
Elisa Vasta, Thorir Mar Ingolfsson, Andrea Cossettini et al. (+4 more)
Burst suppression (BS) is a clinically relevant electroencephalographic (
EEG) pattern used to monitor sedation depth and
brain activity in critically ill patients, particularly during induced coma in Intensive Care Units (ICUs). Automatic burst detection remains challenging because BS patterns vary substantially between patients and annotated
datasets are scarce. Recently,
EEG Foundation Models (FMs) have shown promise across several downstream
EEG applications, but their usefulness for BS detection remains unexplored. We present the first study to evaluate
EEG FMs for burst detection in reduced-montage ICU
EEG without patient-specific calibration. We compare REVE-base, LUNA-large and LuMamba-Tiny with an adaptive thresholding baseline and a task-specific EEGNet baseline. Additionally, we complement conventional
EEG window-based classification with event-based burst detection evaluation. This helps assessing clinically whether burst episodes are correctly detected, reducing the impact of expected annotation variability. The best model, REVE-base, achieved the highest event-based F1-score ($0.868 \pm 0.167$) and reduced burst-per-minute error by 52.1% and 36.2% compared to EEGNet and adaptive thresholding respectively, supporting FMs for scalable
EEG monitoring in ICU. Ablation experiments showed that full fine-tuning was the most effective adaptation strategy with respect to frozen-backbone training, two-step fine-tuning, and LoRA-based adaptation, improving event-based F1-score over frozen-backbone training by up to $+0.102$ for LUNA-large. With reduced labeled
datasets, pretrained REVE-base outperformed random initialization by $+0.723$ event-based F1 points at 25% of the cohort, demonstrating the benefit of pretraining FM representations when adapted to burst detection with limited labeled data.
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All authors Elisa Vasta, Thorir Mar Ingolfsson, Andrea Cossettini, Luca Benini, Tilman Beck, Emanuela Keller, Una Pale
Artificial Intelligence
Process-Verified Reinforcement Learning for Theorem Proving via Lean
Minsu Kim and Se-Young Yun
While reinforcement learning from verifiable rewards (RLVR) typically has relied on a single binary verification signal, symbolic proof assistants in formal
reasoning offer rich, fine-grained structured feedback. This gap between structured processes and unstructured rewards highlights the importance of feedback that is both dense and sound. In this work, we demonstrate that the Lean proof assistant itself can serve as a symbolic process oracle, supplying both outcome-level and fine-grained tactic-level verified feedback during training. Proof attempts are parsed into tactic sequences, and Lean's elaboration marks both locally sound steps and the earliest failing step, yielding dense, verifier-grounded credit signals rooted in type theory. We incorporate these structured rewards into a GRPO-style reinforcement learning objective with first-error propagation and first-
token credit methods that balances outcome- and process-level advantages. Experiments with STP-Lean and DeepSeek-Prover-V1.5 show that tactic-level supervision outperforms outcome-only baselines in most settings, delivering improvements on
benchmarks such as MiniF2F and ProofNet. Beyond empirical gains, our study highlights a broader perspective: symbolic proof assistants are not only verifiers at evaluation time, but can also act as process-level reward oracles during training. This opens a path toward reinforcement learning frameworks that combine the scalability of
language models with the reliability of symbolic verification for formal
reasoning.
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Artificial Intelligence
Autonomous Event-Driven Multi-Agent Orchestration for Enterprise AI at Scale
Harsh Rao Dhanyamraju, Leonidas Raghav and Aaron Lee
Enterprise
agent language models must coordinate specialist
agents over continuous business event streams, yet most multi-
agent evaluations assume discrete request-response workflows. We present a controlled simulation based on a sanitized enterprise research snapshot: 208 scenarios, 393 events, and 1,051 expected
agent calls across Persona (<10
agents), Department (20-80), and Enterprise (200) registries. We compare DAG Plan & Execute with ReAct and evaluate a minimal Task Manager for priority inference, related-event merging, and preemption. Both systems perform well on the Persona cohort but score lower on the Enterprise cohort, especially for Simple scenarios; a larger, more confusable discovery space may contribute. The tested DAG Plan & Execute implementation generally maintains higher
agent-call precision, whereas ReAct shows stronger recall and Expected Answer Completeness in several Enterprise conditions. The Task Manager reduces high-priority queue latency by 14-75% and improves related-event Expected Answer Completeness by over 20 percentage points at Enterprise scale. These controlled-simulation results characterize the tested systems but do not establish production readiness.
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Artificial Intelligence · Cross-listed from cs.CV
Fanfu Xue, En Yu, Yantian Shen et al. (+5 more)
UAV Vision-
Language Navigation (
UAV-VLN) is typically formulated as a holistic search-and-reach problem, where long-range target discovery and final target approach are optimized and evaluated jointly. This formulation makes it difficult to assess a critical capability of aerial
embodied agents, namely whether a
UAV can accurately ground a visible target and translate
vision-
language evidence into precise 3D motion once the target enters its field of view. To address this limitation, we introduce
UAV-VLN-FOV, a target-visible
navigation task that isolates the see-and-reach stage and enables a more diagnostic evaluation of terminal reaching ability. We further propose 3DG-VLN, a
vision-
language waypoint prediction framework guided by dynamic 3D direction cues to enhance fine-grained visual grounding and spatial direction
alignment for precise target reaching. Specifically, 3DG-VLN adaptively processes high-resolution front-view and downward-view observations to preserve fine-grained visual and
geometric details for target grounding. It also updates the target-relative direction online during closed-loop
navigation, allowing the
agent to maintain spatial
alignment with the target and reduce accumulated direction drift. To support this task, we construct a dedicated high-resolution
benchmark which contains 2,717 trajectories with target-oriented high-level instructions, high-resolution front-view and downward-view
egocentric observations, and continuous 3D waypoint annotations. Experiments show that 3DG-VLN outperforms competitive
UAV-VLN baselines, achieving a 13.82% improvement in success rate. Real-world trials further demonstrate the potential of 3DG-VLN for practical see-and-reach
navigation. The source code and
benchmark are available at https://github.com/xuefanfu/3DG-VLN.
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All authors Fanfu Xue, En Yu, Yantian Shen, Zhikun Hu, Hongjun Wang, Yang Yang, Xindi Wang, Jiande Sun
Artificial Intelligence · Cross-listed from econ.GN
AI Economist Agent: An Agentic Framework for Evidence-Based Economic and Financial Analysis with RAG, Knowledge Graphs, and Large Language Models
Masahiro Kato
We propose an AI economist
agent for economic and financial scenario analysis. Scenario design often requires analysts to assess emerging risks with limited historical precedent, combine information from many sources, and translate qualitative mechanisms into internally consistent quantitative paths. Large
language models (LLMs) can search and synthesize this information, but fluent narratives alone do not establish the model-based calculations needed for economic conclusions. Our framework uses LLM
agents to plan the analysis, retrieve relevant evidence, and organize economic mechanisms, while registered quantitative models generate numerical outcomes and predefined tests determine whether intermediate results can be used in the final report. We apply the framework to European macro-financial stress scenarios and bank capital analysis. The empirical analysis evaluates
retrieval of economic mechanisms, scenario construction, model execution, and report generation under a historical information cutoff. The results show how the AI economist
agent can combine flexible evidence
retrieval and scenario construction while keeping the resulting analysis linked to identifiable sources and explicit model calculations.
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Artificial Intelligence · Cross-listed from cs.RO
A Neuromorphic Reinforcement Learning Framework for Efficient Pathfinding in Robotic Mobile Fulfillment Systems
Junzhe Xu, Zecui Zeng, Lusong Li et al. (+2 more)
Dynamic environmental changes, confined workspaces, and stringent real-time constraints make pathfinding in Robotic Mobile Fulfillment Systems (RMFS) a challenging problem for conventional search- and rule-based methods, which typically suffer from high computational complexity and long decision latency. While reinforcement learning (RL) has emerged as a powerful alternative, deploying learned policies with extreme energy efficiency on resource-constrained hardware remains an open challenge. We present SDQN-RMFS, an end-to-end framework that achieves high-fidelity deployment of an RL-trained policy from a full-precision artificial neural network (ANN) through to a neuromorphic chip. By computing only when triggered by sparse events, this framework unlocks ultra-low-power RMFS pathfinding. Our full-stack pipeline operates as follows: an ANN policy is first efficiently trained via a collision-allowing strategy to densify informative trajectories, and then converted into a spiking neural network (SNN) via a hard-label knowledge
distillation approach. This effectively addresses the output distribution mismatch, preserving policy capability across the ANN-to-SNN pipeline while substantially reducing inference latency. Hardware experiments demonstrate up to 11,281$\times$ energy savings and a nearly two-fold reduction in latency compared to a high-performance GPU baseline, while maintaining decision quality on par with the original trained policy. These results establish physical neuromorphic inference as a practical and energy-sustainable pathway for large-scale RMFS operations.
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All authors Junzhe Xu, Zecui Zeng, Lusong Li, Yuetong Fang, Renjing Xu
Artificial Intelligence · Cross-listed from cs.SE
When Lower Privileges Suffice: Investigating Over-Privileged Tool Selection in LLM Agents
Kaiyue Yang, Yuyan Bu, Jingwei Yi et al. (+5 more)
As LLM
agents increasingly select tools autonomously, their choices among tools with different privileges become safety-relevant. However, prior tool-selection studies focus on safety-agnostic metadata preferences, leaving privilege-sensitive choices underexplored. To address this gap, we study over-privileged tool selection, in which an
agent selects or escalates to a higher-privilege tool despite a sufficient lower-privilege alternative. We introduce ToolPrivBench to evaluate whether
agents choose higher-privilege tools despite sufficient lower-privilege alternatives, measuring both initial selection and escalation after transient tool failures. Across eight domains and five recurring risk patterns, we find that over-privileged tool selection is common among mainstream LLM
agents and is further amplified by transient failures. We further find that general safety
alignment does not reliably transfer to least-privilege tool choice, while
prompt-level controls provide only limited mitigation under transient failures. We therefore introduce a privilege-aware post-training defense that teaches
agents to prefer sufficient lower-privilege tools and escalate only when necessary. Our mitigation experiments show that this defense substantially reduces unnecessary high-privilege tool use while preserving general capabilities.
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All authors Kaiyue Yang, Yuyan Bu, Jingwei Yi, Yuchi Wang, Biyu Zhou, Juntao Dai, Songlin Hu, Yaodong Yang
Artificial Intelligence · Cross-listed from cs.LG
Hierarchical Control in Multi-Agent Games: LLM-based Planning and RL Execution
Jannik Hösch, Alessandro Sestini, Florian Fuchs et al. (+6 more)
Reinforcement learning (RL) has achieved strong performance in sequential decision-making, yet scaling to complex multi-
agent environments remains challenging due to sparse rewards, large state-action spaces, and the difficulty of learning coordinated strategies. We propose a hierarchical architecture where a pretrained large
language model (LLM) acts as a centralized strategic controller that selects among specialized RL skill policies for a team of
agents, while RL policies handle reactive low-level execution. We evaluate this hybrid system in a competitive 2v2 King of the Hill environment against behavior tree (BT) and “Flat” RL (end-to-end training without skill decomposition) baselines. The LLM+RL system achieves task performance statistically equivalent to hand-crafted BT (46.4% vs 51.5% win rate, $p=0.103$) while both significantly outperform Flat RL trained without skill decomposition. A user study ($n=15$) reveals that 60% of participants perceive LLM+RL
agents as the most human-like ($p=0.027$), citing behavioral adaptability and tactical variability. These results demonstrate that pretrained LLM
reasoning can effectively orchestrate pretrained RL skills, achieving competitive multi-
agent coordination and superior perceived believability without manual rule engineering.
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All authors Jannik Hösch, Alessandro Sestini, Florian Fuchs, Amir Baghi, Joakim Bergdahl, Iolanda Leite, Konrad Tollmar, Jean-Philippe Barrette-LaPierre, Linus Gisslén
Artificial Intelligence · Cross-listed from cs.LG
StreamKL: Fast and Memory-Efficient KL Divergence for Boosting Attention Distillation
Guangda Liu, Yiquan Wang, Chengwei Li et al. (+6 more)
Attention distillation, which trains one
attention distribution to match another by minimizing their Kullback-Leibler (KL) divergence, is widely used in knowledge
distillation, model compression, continual learning, and sparse-
attention LLM training. However, existing approaches materialize both
attention distributions before computing the KL reduction, incurring $O(N_QN_K)$
memory and IO costs that become prohibitive at long context lengths. We present StreamKL, the first fused GPU primitive for
attention KL divergence that eliminates this quadratic materialization. StreamKL derives a novel online formulation for the coupled two-distribution KL reduction, enabling a single one-pass forward kernel that streams query-key tiles through on-chip SRAM. For the backward pass, StreamKL recomputes
attention probabilities tile-by-tile, avoiding storage of quadratic intermediates. We further design and implement efficient GPU kernels with dedicated optimizations. Experiments show StreamKL delivers up to $43\times$ and $14\times$ speedups over baseline methods in the forward and backward passes, respectively. Most importantly, StreamKL reduces the extra HBM footprint of
attention distillation from $O(N_QN_K)$ to $O(1)$, enabling long-context
distillation on a single GPU.
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All authors Guangda Liu, Yiquan Wang, Chengwei Li, Wenhao Chen, Jing Lin, Yiwu Yao, Danning Ke, Wenchao Ding, Jieru Zhao
Artificial Intelligence · Cross-listed from cs.LG
Connect the Dots: Training LLMs for Long-Lifecycle Agents with Cross-Domain Generalization Via Reinforcement Learning
Yanxi Chen, Weijie Shi, Boyi Hu et al. (+6 more)
This work presents a general framework for training large
language models (LLMs) to "Connect the Dots" (CoD), a meta-capability required by long-lifecycle
agents: as an LLM-based AI
agent gets deployed in an environment, it solves a long sequence of tasks while continuously exploring the environment, learning from its own experiences, and iteratively self-updating its context about the environment, thereby achieving progressively better performance on future tasks conditioned on the updated context. Major components of the CoD framework include: (1)
algorithm design and infrastructure for end-to-end reinforcement learning (RL) with long rollout sequences interleaving solve-task and update-context episodes; (2) tasks and environments for incentivizing and eliciting the targeted meta-capability in LLMs during training, as well as for faithfully measuring progress during evaluation. We present proof-of-concept implementations of the CoD framework, including a GRPO-style RL
algorithm with fine-grained credit assignment, as well as tasks and environments tailored to the targeted meta-capability (rather than domain-specific LLM capabilities or standard task-by-task RL). Empirical results with Qwen3-8B and Qwen3.6-27B validate the efficacy of end-to-end RL training in the CoD setting, and demonstrate the potential for out-of-distribution generalization – within the training domains, across different domains, and from CoD to Ralph-loop settings – of the elicited meta-capability. Our investigation of CoD connects several lines of prior work, and opens up new opportunities for advancing LLMs and AI
agents. To facilitate further research and applications, we release our implementations at https://github.com/agentscope-ai/Trinity-RFT/tree/research/cod_v2/examples/research_cod.
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All authors Yanxi Chen, Weijie Shi, Boyi Hu, Zeyue Zhang, Zhiwei Wang, Yuexiang Xie, Yaliang Li, Bolin Ding, Jingren Zhou
Artificial Intelligence · Cross-listed from cs.RO
Tri-Info: Generalizable, Interpretable Failure Prediction for VLA Models via Information Theory
Jinghan Yang, Yunchao Zhang, Wang Yuan et al. (+4 more)
Vision-
Language-Action (VLA) models are increasingly deployed across diverse tasks, yet they remain black boxes whose physical interactions can cause irreversible harm, making generalizable and interpretable failure detection essential. We observe that successful and failed rollouts carry systematically different information-theoretic signatures. Building on this, we formalize VLA control as a closed-loop information pipeline and derive the Triple Information-theoretic (Tri-Info) signals that capture whether actions remain diverse, temporally consistent, and coupled to state transitions. Across six VLA models and three
benchmark environments, Tri-Info matches the strongest baselines in-domain. Moreover, Tri-Info transfers across architectures, environments, and the sim-to-real gap without retraining with labeled data, reaching 70% accuracy on real-world tasks. This establishes Tri-Info as a simple yet powerful method that not only detects failures with strong cross-domain generalization, but also delivers interpretable diagnostics of the underlying failure modes.
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All authors Jinghan Yang, Yunchao Zhang, Wang Yuan, Haolun Wan, Jiaming Zhang, Zhengyang Hu, Yanchao Yang
Artificial Intelligence
Beyond Static Endpoints: Tool Programs as an Interface for Flexible Agentic Web Services
Mugeng Liu, Shuoqi Li, Yixuan Zhang et al. (+1 more)
In the agentic web era, LLM-based
agents increasingly invoke web services as tools, yet most interfaces remain static endpoints that poorly express long-horizon workflows with loops, conditionals, joins, and retries. We present ToolPro, which represents an
agent's tool intent as an executable tool program that compactly encodes multi-step service interactions with explicit effect types. ToolPro combines constraint-guided program construction, effect-aware replay for exactly-once state-modifying calls, and a profile-driven policy that decides when program execution outperforms stepwise calling. We instantiate ToolPro over MCP-style services with WebAssembly sandboxing and evaluate it on diverse workflows of real-world applications. ToolPro reduces end-to-end latency by up to 53.4% and client-side traffic by up to 96.1%, with larger gains under higher network latency and workflow complexity.
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All authors Mugeng Liu, Shuoqi Li, Yixuan Zhang, Yun Ma
Artificial Intelligence
Reward as An Agent for Embodied World Models
Pu Li, Zhigang Lin, Qiang Wu et al. (+3 more)
While RL has become a promising tool for refining world models, existing methods largely rely on conservative rollouts near the training distribution, limiting exploration, behavioral diversity, and richer dynamic discovery. In this work, we challenge this conservative paradigm. We argue that the core limitation is not exploration itself, but the lack of reliable verification strategies to support broader exploration. Without reliable verification, expanded exploration becomes highly susceptible to reward hacking, where policies exploit imperfect rewards without achieving genuine improvement. To evaluate this motivation, we instantiate our method in
embodied world models, where physical plausibility, and task completion provide a rigorous testbed for scalable RL under complex dynamics. On the verification side, we introduce Reward as an
Agent, an agentic reward framework that actively evaluates generated behaviors to provide robust reward signals and mitigate reward hacking under distribution shifts. On the exploration side, we introduce Dynamic-Aware Rollout Diversification through DynDiff-GRPO, which explicitly expands action-space exploration to diversify trajectories, broaden state-action coverage, and encourage richer
embodied behaviors beyond conservative rollout regimes. By unifying Reward as an
Agent with DynDiff-GRPO, we enable RL on a more reliable reward foundation with substantially diversified sampling, effectively mitigating reward hacking while yielding significant accuracy gains across multiple open-source world models, thereby demonstrating that broader exploration can scale successfully when grounded in robust verification.
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All authors Pu Li, Zhigang Lin, Qiang Wu, Yongxuan Lv, Fei Wang, Shan You
Artificial Intelligence
ENPIRE: Agentic Robot Policy Self-Improvement in the Real World
Wenli Xiao, Jia Xie, Tonghe Zhang et al. (+14 more)
Achieving dexterous robotic manipulation in the real world heavily relies on human supervision and
algorithm engineering, which becomes a central bottleneck in the pursuit of general physical intelligence. Although emerging coding
agents can generate code to automate
algorithm search, their successes remain largely confined in digital environments. We
conjecture that the missing abstraction to automate robotics research is a repeatable feedback loop for real-world policy improvement: reset the scene, execute a policy, verify the outcome, and refine the next iteration. To bridge this gap, we introduce ENPIRE, a harness framework for coding
agents that instantiates this physical feedback routine with four core modules: an Environment module (EN) for automatic reset and verification, a Policy Improvement module (PI) that launches policy refinement, a Rollout module (R) to evaluate policies with one or multiple physical
robots operating in parallel, and an
Evolution module (E) in which coding
agents analyze logs, consult literature, improve training infrastructure and
algorithm code to address failure modes. This closed-loop system transforms real-world manipulation learning into a controllable optimization procedure, minimizing human effort while allowing fair ablations across training recipe and
agent variants. Powered by ENPIRE, frontier coding
agents can autonomously train a policy to achieve a 99% success rate on challenging, dexterous manipulation tasks, such as organizing a pin box, fastening a zip tie, and tool use, a process that further accelerates when we dispatch an
agent team on a
robot fleet. Our results suggest a practical and scalable path toward deploying coding
agents to autonomously advancing robotics in the physical world.
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All authors Wenli Xiao, Jia Xie, Tonghe Zhang, Haotian Lin, Letian "Max" Fu, Haoru Xue, Jalen Lu, Yi Yang, Cunxi Dai, Zi Wang, Jimmy Wu, Guanzhi Wang, S. Shankar Sastry, Ken Goldberg, Linxi "Jim" Fan, Yuke Zhu, Guanya Shi
Artificial Intelligence · Cross-listed from cs.CY
The Algorithmic-Human Manager: AI, Apps, and Workers in the Indian Gig Economy
Omir Kumar and Krishnan Narayanan
This paper examines the impact of artificial intelligence and digital technologies on the blue-collar gig economy in India, focusing on algorithmic management. This paper examines the impact of artificial intelligence and digital technologies on the blue collar gig economy in India, focusing on algorithmic management he use of automated systems to allocate, monitor, and evaluate work in location-based services such as ride sharing and delivery. Using a social justice framework and a mixed-methods approach comprising interviews with 16 gig workers and 21 key stakeholders, the study uncovers a dual reality: while AI-powered systems expand access to work and generate operational efficiencies, they simultaneously introduce significant challenges related to
fairness, transparency, and worker dignity. Key findings reveal that algorithmic systems are opaque by design, produce inequitable outcomes, and are not structured to reward additional labour with proportionate pay. The study advocates for a pragmatic hybrid governance model an Algorithmic Human Manager framework in which technological efficiency and human accountability operate together rather than in opposition. The findings carry implications for policymakers, platform companies, and civil society organizations working to design equitable AI governance frameworks for the gig economy in India and across the Global South.
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Artificial Intelligence · Cross-listed from cs.CV
ROSE: Benchmarking the Perception-to-Action Gap in Multimodal Models
Yihao Wang, Zijian He, Jie Ren et al. (+1 more)
Multimodal large
language models (MLLMs) are increasingly expected to act on visual information, yet the same scene may require different actions under different task contexts. How reliably can a model turn the same visual evidence into the action required by the current context? To answer this question, we introduce ROSE (Reference-conditioned Oddity and Symbolic Execution), a controlled
benchmark that holds the visual scene fixed while varying region constraints and required symbolic outputs. Through coupled counting and coordinate-action tasks, ROSE tests whether models can infer an implicit majority reference and act on the resulting fine-grained visual evidence under changing contexts. Across nine recent MLLMs, performance drops by as much as 44.5 percentage points from counting-oriented tasks to region-conditioned action, despite 98.8% human performance. The gap persists on paired scenes and regions for which the same model returns the correct count, while global-click and matched local controls show that coordinate grounding explains only part of the loss, revealing a distinct, model-dependent bottleneck in turning shared visual evidence into context-specific actions.
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All authors Yihao Wang, Zijian He, Jie Ren, Keze Wang
Artificial Intelligence · Cross-listed from cs.CV
Confidence Calibration for Multimodal LLMs: An Empirical Study through Medical VQA
Yuetian Du, Yucheng Wang, Ming Kong et al. (+4 more)
Multimodal Large
Language Models (MLLMs) show great potential in medical tasks, but their elicited confidence often misaligns with actual accuracy, potentially leading to misdiagnosis or overlooking correct advice. This study presents the first comprehensive analysis of the relationship between accuracy and confidence in medical MLLMs. It proposes a novel method that combines Multi-Strategy Fusion-Based Interrogation (MS-FBI) with auxiliary expert LLM assessment, aiming to improve confidence calibration in Medical Visual Question Answering (VQA). Experiments demonstrate that our method reduces the Expected Calibration Error (ECE) by an average of 40% across three Medical VQA
datasets, significantly enhancing MLLMs' reliability. The findings highlight the importance of domain-specific calibration for MLLMs in healthcare, offering a more trustworthy solution for AI-assisted diagnosis.
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All authors Yuetian Du, Yucheng Wang, Ming Kong, Tian Liang, Qiang Long, Bingdi Chen, Qiang Zhu
Artificial Intelligence
Advancing DialNav through Automatic Embodied Dialog Augmentation
Leekyeung Han, Sangwon Jung, Hyunji Min et al. (+3 more)
For
embodied agents capable of physical interaction, the capability to create and understand dialog is crucial to ensure both safety and effectiveness. While DialNav~\cite{han2025dialnav} provides a framework for holistic evaluation of the dialog–execution loop in photorealistic indoor
navigation, its performance remains limited by a critical scarcity of training data (2K episodes). To address this, we propose an automatic generation pipeline, and construct the RAINbow
dataset, a large-scale training
dataset with 238K episodes for DialNav. Our pipeline converts existing VLN
datasets into multi-turn dialog and creates cost-efficient and high-quality
dataset. Then, we introduce two additional complementary advances to unlock the data's full potential: (1) Dual-Strategy Training, a
navigation training scheme to align the
navigation training with the dynamic dialog-
navigation loop, and (2) a localization model that leverages VLN knowledge. By combining these complementary solutions, our model substantially outperforms the baseline in success rate on both Val Seen (58.24, +89%) and Val Unseen (29.05, +100%) splits, establishing a new state of the art.
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All authors Leekyeung Han, Sangwon Jung, Hyunji Min, Jinseong Jeong, Minyoung Kim, Paul Hongsuck Seo
Artificial Intelligence · Cross-listed from eess.IV
SIMBA: ABidirectional Retrieval Forward Simulation Framework for Modeling FY-4A GIIRS Hyperspectral Infrared Radiances Toward NWP Applications
Jingdong Shen, Fu Wang*, Qifeng Lu et al. (+4 more)
Hyperspectral infrared observations are an important data source for numerical weather prediction (NWP) because they provide rich information on the vertical structure of atmospheric temperature and humidity. However, most existing deep learning methods mainly focus on one-way
retrieval from radiances to atmospheric profiles, while the reverse radiance simulation process and the consistency between atmospheric state space and radiance observation space are insufficiently considered. In this study, we propose SIMBA, a unified bidirectional
retrieval-forward simulation framework for FY-4A GIIRS hyperspectral infrared radiance modeling toward NWP applications. The framework jointly performs atmospheric profile
retrieval and radiance reconstruction, introduces a cycle-consistency constraint to strengthen the coupling between the two processes, and employs a bidirectional Mamba state-space module to capture long-range dependencies along pressure levels. Using collocated FY-4A GIIRS observations and ERA5 reanalysis data, the proposed method is evaluated for temperature
retrieval, specific humidity
retrieval, long-wave radiance reconstruction, and medium-wave radiance reconstruction. Experimental results show that SIMBA outperforms several representative deep learning baselines across both
retrieval and reconstruction tasks, while ablation experiments confirm the contribution of the bidirectional design and cycle-consistency mechanism. These results demonstrate that the proposed framework is effective for joint atmospheric profile
retrieval and hyperspectral infrared radiance modeling, and suggest potential for future Jacobian-related analysis and NWP-oriented extensions.
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All authors Jingdong Shen, Fu Wang*, Qifeng Lu, Hao Huang, Chunqiang Wu, Chi Yang, Xiaofang Liu
Artificial Intelligence · Cross-listed from cs.CV
Triangular Consistency as a Universal Constraint for Learning Optical Flow
Yi Xiao, Carlos Rodriguez Coronel, Jing Zhan et al. (+3 more)
We propose triangular consistency as a first-principled constraint for optical flow, which is agnostic to network architecture, supervision type, and
dataset, and applies to both image-pair and multi-frame settings. This simple but powerful constraint is to compose two flows to induce a third flow and enforce consistency among the three. The composed flows may arise from (i) image pairs, yielding cycle consistency; (ii) multiple
video frames, producing longer-range motion through temporal chaining; or (iii) image pairs combined with controlled synthetic transformations, which becomes data augmentation. This triangular consistency introduces negligible computational overhead and requires no additional annotations. Since it is derived directly from the geometry of optical flow, it does not rely on model-specific assumptions and serves as a “universal” plug-and-play component for optical flow training. Experiments show consistent improvement across supervised, unsupervised, and transfer learning settings.
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All authors Yi Xiao, Carlos Rodriguez Coronel, Jing Zhan, Haniyeh Ehsani Oskouie, Alex Wong, Dong Lao
Artificial Intelligence
PhysDrift: Bridging the Embodiment Gap in Humanoid Co-Speech Motion Generation
Zhangzhao Liang, Xiaofen Xing, Mingyue Yang et al. (+2 more)
Humanoid robots require co-
speech motions that are not only expressive and
speech-aligned, but also physically executable under embodiment constraints. Existing co-
speech generation pipelines are predominantly human-centric: motions are first generated in human-body representations such as SMPL-X and subsequently retargeted to
humanoid robots. In this work, we identify a fundamental embodiment gap in this paradigm, where the mismatch between human motion
manifolds and
humanoid embodiment constraints disrupts embodiment consistency during motion transfer and physical execution. Through extensive analysis, we show that although retargeting can preserve coarse motion semantics, it significantly compresses motion diversity and weakens prosody-motion synchronization, limiting expressive
humanoid behaviors. To address this problem, we first propose IK-EER, a prosody-preserving
humanoid motion curation framework that jointly optimizes kinematic feasibility and
speech-motion temporal
alignment during retargeting. Building upon the curated
robot-native motion
dataset, we further introduce PhysDrift, an embodiment-aware co-
speech motion generation framework that directly predicts executable
humanoid joint trajectories from
speech without relying on intermediate human-body representations. Unlike conventional human-centric pipelines, PhysDrift maintains embodiment consistency throughout both training and inference while incorporating physical regularization to stabilize
robot motion dynamics. Extensive experiments and real-world
humanoid deployment demonstrate that embodiment-aware
robot-native generation substantially improves
speech-motion
alignment, physical plausibility, motion smoothness, inference efficiency, and real-time interaction capability.
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All authors Zhangzhao Liang, Xiaofen Xing, Mingyue Yang, Wenlve Zhou, Xiangmin Xu
Artificial Intelligence · Cross-listed from cs.CV
Speeding up the annotation process in semantic segmentation industrial applications
Marta Fernandez-Moreno, Margarita Guerrero, Rosalia Rementeria et al. (+2 more)
Current machine learning models commonly require large and well-annotated
datasets. However, the annotation process often becomes a bottleneck, with increased complexity leading to higher chances of human errors. Within this context, our goal in this paper is to leverage unsupervised
algorithms to improve data annotation efficiency for complex semantic
segmentation problems in industrial materials science. Previous research has quantified labeling time and others explored unsupervised methods. However, to the best of our knowledge, this is the first study to quantify how much unsupervised
algorithms accelerate the labeling process. We aim to validate the extent to which this laborious process can be accelerated, focusing on semantic
segmentation tasks that involve annotating each pixel of high-resolution images, such as the microstructure characterization challenge in materials science. Specifically, we demonstrate that by using unsupervised computer
vision algorithms, the time required for the labeling process can be reduced from 170 hours to 37 hours, achieving an approximate reduction of 78%. The
dataset we work with includes large images of dimensions 1280x959 and 960x703, which further increases the complexity of the annotation task. Despite these challenges, we create and share the largest public steel microstructure
segmentation dataset to date, available under MIT License with permanent DOI, contributing a fully annotated, high-resolution
dataset to the field. Additionally, this is the first work to compare the labeling time from scratch (a common approach in previous studies) to the labeling time when using these unsupervised
algorithms as a pre-annotation step. Furthermore, we provide a Deep Learning model trained on this
dataset, validated by field experts, and deployed in an industrial setting, serving as an initial
benchmark for this public
dataset.
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All authors Marta Fernandez-Moreno, Margarita Guerrero, Rosalia Rementeria, Pablo Mesejo, Raul Moreno
Artificial Intelligence · Cross-listed from cs.CV
Spatial-Aware Reduction Framework: Towards Efficient and Faithful Visual State Space Models
Jindi Lv, Aoyu Li, Yuhao Zhou et al. (+6 more)
Mamba demonstrates strong efficiency in modeling long visual sequences. However, when
token reduction is applied to structurally enhanced Mamba variants, these models exhibit a severe performance collapse. We attribute this degradation to the spatially agnostic nature of existing reduction methods, which violate the two-dimensional structural premise required by the selective scanning mechanism. In this work, we propose STORM, a spatial-aware
token reduction framework designed to maintain structural integrity throughout the compression process. STORM reformulates reduction into a structured operation on spatial units, enforcing localized constraints to maintain both grid
topology and neighborhood coherence. As a plug-and-play module, STORM equips existing reduction pipelines with explicit spatial awareness without any training. Empirical results demonstrate that STORM achieves state-of-the-art
pruning accuracy across diverse
vision Mamba backbones under training-free settings. Notably, STORM delivers a substantial accuracy recovery on VMamba, outperforming prior methods by up to 63.3% in top-1 accuracy. Meanwhile, STORM incurs only a 1.0% accuracy drop on PlainMamba, achieving performance comparable to ViT.
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All authors Jindi Lv, Aoyu Li, Yuhao Zhou, Zheng Zhu, Xiaofeng Wang, Qing Ye, Yueqi Duan, Wentao Feng, Jiancheng Lv
Artificial Intelligence
The Tao of Agency: Autotelic AI, Embedded Agency and Dissolution of the Self
Aritra Sarkar
Most artificial intelligence systems are built on the assumption that goals are exogenous and specified by the designer. Exploring what happens when an
agent begins generating its own goals opens the field of autotelic AI.
Agents are expected not merely to pursue objectives but to discover them. In this article, we trace its consequences through intrinsic motivation, resource-driven priors, causal-interventional learning, homeostasis, and embeddedness; the last of which is found to be a necessary but not sufficient condition for autotelic agency. Embeddedness individuates the
agent at the cost of revealing that the individuation is non-unique, such that the same dynamics admit many valid partitions, each defining a different candidate self. The deepest problem with autotelic AI is therefore not how the
agent generates goals, but how it generates and relativizes the self to which the goals are assigned. The
agent must believe in its own boundary in order to act, and see through that boundary in order to understand. We consolidate these developments into a single framework and extend it along three directions: a
quantum formulation in which the
agent-environment cut becomes physical, a philosophical reading against non-dual contemplative traditions, and a concrete LLM-based agentic instantiation.
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Artificial Intelligence
eCNNTO: A Highly Generalizable ConvNet for Accelerating Topology Optimization
Shengbiao Lu and Xiaodong Wei
This work proposes an element-based Convolutional Neural Network (CNN) to accelerate density-based
Topology Optimization (TO), termed eCNNTO. TO generally undergoes a large number of iterations, where finite element analysis is performed in every iteration, leading to the efficiency bottleneck especially when dense meshes are used to achieve high-resolution designs. To address this limitation, eCNNTO is proposed to build upon Kallioras et al. (2020), where a Deep Belief Network (DBN) was trained for every element to predict its near-optimal density from its early history, thereby skipping the great majority of iterations and significantly accelerating the TO procedure. However, the method lacks spatial correlations among neighboring elements and may lead to disconnected features in the final structure. The proposed method employs CNN with residual connections to address this issue. On top of it, a novel training strategy is introduced to further enhance the optimization efficiency, where the training
dataset consists of the final stage density histories rather than early ones. This change can also help reduce the required training data size. eCNNTO requires only a small
dataset to train and yet it can be generalized to problems with largely different boundary conditions, loading cases, design domain geometries, mesh resolutions, as well as non-design domains. In the end, the generalization capabilities and efficiency of eCNNTO are demonstrated through a variety of examples in two and three dimensions, achieving up to 90% and 97% reduction of iterations, respectively.
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Artificial Intelligence · Cross-listed from cs.RO
Co-policy: Responsive Human-Robot Co-Creation for Musical Performances
Xuetao Li, Wenke Huang, Mang Ye et al. (+4 more)
Art has long stood as a pivotal expression of human creativity.
Embodied artificial intelligence offers a route for generative models to participate in that creativity through physical action rather than disembodied digital content. In robotic music co-creation, it is challenging to connect semantic musical understanding with real-time and physically executable performance. We present Co-policy, a framework for human-
robot musical co-creation that separates semantic intent grounding, constrained musical variation, and visuomotor execution. To ground musical semantics, Co-policy uses pre-inference semantic anchors and a fine-tuned Qwen-vl planner (F-Qwen) to transform
speech, live musical seeds, and visual observations into structured co-creation plans. To support low-latency execution, Co-policy introduces a Gaussian-Mixture Visuomotor Policy (GMP), implemented as a conditional mixture-density policy that maps target notes and visual context to
multimodal robot actions in a single forward pass. Unlike robotic playback systems that merely reproduce user-specified notes, Co-policy generates complementary musical responses under both musical and physical constraints. Real-
robot chime experiments, ablations, and expert evaluation show improved intent
alignment, execution accuracy, and response frequency over
diffusion-policy and ablated baselines, supporting physically grounded action generation as a key requirement for
embodied human-AI co-creation.
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All authors Xuetao Li, Wenke Huang, Mang Ye, Zijian Liu, Jinhua Xie, Jifeng Xuan, Miao Li