Artificial Intelligence · Cross-listed from cs.LG
Adaptive Expert Guidance for Efficient On-Policy Reinforcement Learning
Daniele Affinita, Ming Xu, Rudolf Reiter et al. (+2 more)
With massively parallel simulation, on-policy Reinforcement Learning methods such as PPO have become standard in many domains. However, learning from scratch is sample-inefficient and fails to exploit the potential existence of a suboptimal expert, such as a heuristic, a model-based controller, or a policy trained on a related task. Such an expert is often available and can guide early training, but its sub-optimality limits final performance. The challenge then becomes balancing expert guidance against learning from rewards. Existing methods set the expert's influence through a blending weight, a schedule, or an evaluation-driven curriculum. Alternatively, they adapt it with additional learned components such as critics over expert actions or auxiliary
agents. However, none optimizes it using the same on-policy objective as the policy itself. We propose a method in which the learner and the expert alternate control within each training episode, and the expert's share of control is a single learnable parameter optimized jointly with the policy. The learner benefits from the expert early in training, but its share of control declines as the learner becomes more competent, until eventually vanishing completely. This leaves the learner acting alone and better than the suboptimal expert. We evaluate our method on 34 tasks across two
benchmarks, spanning discrete and continuous action spaces, using both learned and model-based experts. Our method improves sample efficiency over guided and unguided baselines while requiring minimal hyperparameter variation. The expert's share decays to zero as the learner improves, vanishing when the expert is no longer useful.
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All authors Daniele Affinita, Ming Xu, Rudolf Reiter, Davide Scaramuzza, Pascal Fua
Artificial Intelligence · Cross-listed from cs.CV
Investigating Query-Insensitive Behavior in Spatio-Temporal Video Grounding
Eryk Kołodziejczyk, Alberto Presta, Karol Szurkowski et al. (+1 more)
Spatio-temporal
video grounding (STVG) aims to localize objects or events described by natural
language queries in both space and time. Existing STVG models are typically trained and evaluated under the assumption that each query is relevant to the input
video. In this work, we challenge this assumption by studying the behavior of state-of-the-art STVG models under irrelevant queries and missing textual input. Our experiments show that current models can still produce plausible spatio-temporal predictions even when the query is unrelated to the
video or removed entirely. We further analyze HCSTVG-v2 and VidSTG to identify
dataset regularities that may encourage such query-insensitive behavior. Our study highlights an underexplored limitation of STVG models and motivates negative-aware evaluation protocols and architectures that explicitly assess query relevance.
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All authors Eryk Kołodziejczyk, Alberto Presta, Karol Szurkowski, Michal Byra
Artificial Intelligence · Cross-listed from cs.GT
Parallelism or Concession? Concurrency-Aware Procurement Negotiation for Agentic Commerce
Xiaolin Xu and Donghao Zhu
Agentic buyers can cheaply fork a procurement task into many parallel negotiations, but concurrency is not free: every thread consumes resources, and simultaneous agreements create cancellation and commitment risk. We study a one-unit post-order sourcing problem with a single hard-deadline negotiation window, in which a planner jointly chooses the number of seller-facing negotiators and a common procurement price cap. The model combines a product-specific acceptance curve with fulfillment loss, per-thread cost, and excess-commitment cost. We establish three structural results. First, holding the per-thread acceptance target fixed, the marginal value of another negotiator decays geometrically, yielding a conditional concurrency threshold. Second, under a convex
quantile curve, parallelism substitutes for concession: more concurrent negotiators imply a weakly lower per-thread acceptance target and price cap. Third, when prices are more dispersed, Agentic buyers benefit by searching harder for bargains, but suffer when they instead try to guarantee procurement by offering higher prices. We operationalize these results in the Concurrency-Aware Negotiation Optimizer (CANO), a deterministic optimizer that jointly determines the optimal negotiation concurrency and procurement price cap for an agentic procurement system. Across different analytic
market configurations and extensive Monte Carlo, finite-data, non-Gaussian, and correlated-seller stress tests, CANO consistently outperforms common heuristic policies while validating the predicted structural properties.
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Artificial Intelligence · Cross-listed from cs.SE
AgentSpy: Making AI Agent Behavior Observable
Christoph Bühler, Matteo Biagiola, Luca Di Grazia et al. (+1 more)
AI
agents built on large
language models (LLMs) run shell commands, read and write files, and reach the network, typically with their user's privileges. However, what an
agent does during an execution is difficult to understand: tests assert on the result, and the
agent's trajectory records only what the
agent reports about itself, which may omit behavior executed by its subprocesses. We present AgentSpy, an approach that observes an
agent from outside the
agent. AgentSpy runs the
agent in an isolated environment, configured by a declarative specification, and records the system calls and network traffic of the
agent and of every process it executes. Based on this monitoring, AgentSpy supports two families of analyses: conformance analyses, which measure obligations, i.e., what an
agent execution should do, and safety analyses, which check prohibitions, i.e., what an
agent execution must never do. We instantiate one analysis of each family. The reliability analysis uses rules to summarize each run by the environment resources the
agent uses: the commands it executed, the files it accessed, and the hosts it contacted. The security analysis applies deterministic rules to the system calls of an execution. For reliability, we evaluated AgentSpy on 77 tasks with the codex harness and three recent LLMs, executing each task three times. Sets of repeated runs of the same task are more similar than sets that include runs of another task in 92.2% of the comparisons. Among tasks for which all three runs pass outcome-based tests, the
agent performs task-unrelated activities in 18% of the cases, reads the grading files in 7%, and does not use the developers' guidance in 17%. For security, the generic rules of AgentSpy detect four of five attack categories we considered, with no false positives across 50 runs.
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All authors Christoph Bühler, Matteo Biagiola, Luca Di Grazia, Guido Salvaneschi
Artificial Intelligence · Cross-listed from cs.LG
EpicWorldModel: Exploration-driven Planning with Latent World Models
Bowen Feng, Julian Ost, May Mei et al. (+2 more)
Latent world models based on Joint-
Embedding Predictive Architecture (JEPA) are deterministic by design. While successful in fully observable scenarios, this paradigm breaks down when past observations and actions lead to multiple plausible future possibilities, e.g., due to occlusion. We introduce EpicWorldModel, a framework to train
stochastic JEPAs for environments and tasks with inherent uncertainty under partially observability. We jointly train the EpicWorldModel predictor with its latent representation space to directly predict multiple potential future states using a flow-matching objective, when the goal-relevant scene content is absent from the conditioning history. We show that flow predictive variance, motivated by its relation to an upper bound on predictive
entropy, serves as a useful exploration guidance for planning. By incorporating this uncertainty signal into Cross-
Entropy Method (CEM)-based planning, our approach balances goal-reaching with exploration of uncertain regions where occluded goals are most likely to be located. We demonstrate the effectiveness of EpicWorldModel through a series of latent planning experiments with the best or on-par performance across tasks, showing up to 22% empirical improvement in success rate over LeWorldModel.
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All authors Bowen Feng, Julian Ost, May Mei, Anirudha Majumdar, Felix Heide
Artificial Intelligence · Cross-listed from cs.LG
Transfer-Stratified On-Policy Distillation for RL-Improved Reasoning Teachers
Xiaoyu Chen, Bo Shao, Tiangang Zhu et al. (+5 more)
Reinforcement learning can substantially improve a
reasoning teacher, but it is unclear which of those improvements survive when the teacher supervises a smaller on-policy student. We study this question in mathematical
reasoning by comparing teacher lineages before and after GRPO, multiple student scales, direct GRPO, and several on-policy
distillation objectives. The central finding is that transfer is structured rather than scalar: teacher strength alone does not make dense
distillation competitive, while an RL-improved teacher creates useful but metric-dependent student gains. This motivates Transfer-Stratified On-Policy
Distillation (TS-OPD), which screens training problems by the joint sampled success of the student and teacher, routes acquisition problems to gated forward KL, routes consolidation problems to gated reverse KL, and adds an
entropy brake to protect sampled coverage. Across the main comparison, TS-OPD is the strongest student objective for macro average correctness with the GRPO-improved teacher, while pass@K remains more mixed. Ablations show that the gains come from routing and
token gating rather than skipping problems. These results support a transfer-aware view of OPD: stronger teachers help when the supervision direction and
token budget match the student's observed ability, not merely because the teacher endpoint is stronger.
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All authors Xiaoyu Chen, Bo Shao, Tiangang Zhu, Bintao Wu, Linjun Shou, Fengge Wu, Feng Sun, Wenbiao Ding
Artificial Intelligence · Cross-listed from cs.CV
JLD: Perceptual Distance Through A Jacobian Lens
Shreshth Saini, Balu Adsumilli and Alan C. Bovik
Image compression, restoration, and generation all require a way to measure how different two images look to a person. Pixel error ignores how people see, while the most accurate perceptual distances are typically fitted to human judgments, tying them to a fixed data and resolution. For example, when image resolution is doubled, the correlation of DISTS with human scores on TID2013 drops from 0.815 to 0.717. We introduce the Jacobian Lens Distance (JLD), which derives its perceptual geometry from a frozen
vision encoder rather than from human labels. JLD combines the locality of early patch features with the perceptual sensitivity captured by later encoder representations. Specifically, we use the encoder Jacobian to identify directions in the early feature space that most strongly affect the encoder output, producing a fixed metric tensor, $E[J^\top J]$, which we call the Jacobian lens. The lens is fitted only once from 100 unlabeled images, taking about 35 seconds. Locally, this construction defines a pullback metric in pixel space, giving JLD a clear
geometric interpretation that can be directly analyzed on real images. Across four standard perceptual databases, JLD achieves state-of-the-art performance and consistently outperforms LPIPS, DISTS, PieAPP, and DreamSim. JLD is also robust to changes in image resolution, on TID2013, its lens-term correlation remains nearly unchanged when the resolution is doubled, decreasing only from 0.850 to 0.845. We further introduce JLD-fast, which is $4\times$ faster than LPIPS-VGG while achieving a mean correlation of 0.911. Finally, JLD naturally extends to
video, reaching a correlation of 0.786 on Waterloo IVC 4K compared with 0.611 for VMAF.
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Artificial Intelligence · Cross-listed from cs.CL
HuatuoGPT-3: RL-Only Domain Adaptation from Base Models
Junying Chen, Xinyuan Xie, Ziniu Li et al. (+7 more)
Domain adaptation aims to turn a general-purpose large
language model (LLM) into an expert for a target domain. While the dominant SFT+RL pipeline offers a convenient cold start, it may reduce exploration diversity and introduces additional complexity through multi-stage optimization. These limitations motivate RL-only adaptation. However, pure on-policy RL suffers from a cold-start problem, while mixed-policy RL still falls short: informative
tokens in teacher outputs are learned too slowly in early training, and stale teacher outputs can hinder later improvement. We identify these two failure modes as Gradient Starvation and Teacher-Distribution Anchoring. To address them, we propose One-stage Policy Optimization (OnePO), which treats teacher outputs as transient guidance for policy improvement. OnePO combines Adaptive Objective
Evolution to strengthen learning on informative low-probability teacher
tokens and Teacher Retirement to discard teacher outputs once the current policy can surpass them. On medical adaptation, OnePO achieves 67.2 on HealthBench (Total) with only 20K training samples, outperforming SFT+RL and pure RL by 2.7 and 7.4 points, respectively. We further scale OnePO to produce HuatuoGPT-3, an open-source medical LLM series whose 27B variant reaches 70.1 on HealthBench (Total) and 71.4 on HealthBench Professional, surpassing frontier models such as GPT-6 Astra. Models and code are available at https://github.com/FreedomIntelligence/HuatuoGPT-3.
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All authors Junying Chen, Xinyuan Xie, Ziniu Li, Wenyuan Gu, Jianquan Li, Xiang Wan, Guangjun Yu, Ruoyu Sun, Haizhou Li, Benyou Wang
Artificial Intelligence · Cross-listed from cs.LG
Physics-Informed but Not Physics-Consistent: Error Geometry and Subspace Projection for Neural AC Power Flow
Changhun Kim, Timon Conrad, Redwanul Karim et al. (+7 more)
Recent neural power-flow solvers, including emerging foundation models, achieve accurate voltage predictions, yet such accuracy does not necessarily imply physically consistent solutions. Even small complex voltage errors can yield large AC power-balance residuals. We study this accuracy-consistency gap across PIGNN-GC, GridSFM, gridfm-graphkit, and LUMINA on realistic 2224-bus Great Britain network (GBnetwork) scenarios, with cross-grid evaluation of GridSFM over 31 systems. Using a singular value decomposition (SVD) basis fitted to training AC power-flow solutions, we find that neural prediction errors contain substantial components outside the dominant solution subspace. To address this mismatch, calibrated solution-subspace projection (CSP) suppresses off-subspace prediction components after train-only bias calibration, reducing Mean PB by 67.0%, 37.8%, 40.5%, and 68.9% for PIGNN-GC, GridSFM, gridfm-graphkit, and LUMINA, respectively, relative to calibrated predictions, while improving voltage-magnitude accuracy in all four models. These results identify output-error geometry as an important factor in physics-consistent neural AC power flow. Code: https://github.com/Kimchangheon/neural-acpf-error-geometry
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All authors Changhun Kim, Timon Conrad, Redwanul Karim, Karan Pahlajani, Julian Oelhaf, David Riebesel, Tomás Arias-Vergara, Andreas Maier, Johann Jäger, Siming Bayer
Artificial Intelligence
OntoInk: Interactive Ontology Visualization, Validation, and Reasoning
Ebrahim Norouzi, Jörg Waitelonis and Harald Sack
Ontology documentation, visualization, and validation are usually carried out with separate tools. This split workflow slows down development and makes knowledge transfer harder. We present OntoInk, an open-source MkDocs plugin that brings these activities together. Within a single documentation-as-code pipeline, OntoInk renders interactive ontology diagrams, validates instance data against SHACL shapes, runs OWL\,DL
reasoning, and supports inline Turtle editing. General-purpose diagram plugins for MkDocs cannot parse RDF, dereference IRIs, overlay SHACL constraints, or run OWL
reasoning. Compared with standalone ontology visualization tools, OntoInk embeds interactive and editable diagrams directly into documentation pages. A live demo and source code are available at \url{https://ise-fizkarlsruhe.github.io/ontoink/}.
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Artificial Intelligence · Cross-listed from cs.CR
Runaway Reaction: When Benign Skills Compose into Malicious Behavior
Zunlong Zhou, Ziyuan Yang, Mengyu Sun et al. (+1 more)
Agent skills package task-specific knowledge and procedures that can be composed to support complex
agent tasks, while public marketplaces provide a growing pool of reusable skills. Existing security vetting, however, largely evaluates skills in isolation, leaving composition-induced risks underexplored. Such risks arise because composing benign skills expands the
agent's capability space, enabling behaviors unavailable to any skill alone. Interestingly, we find that directly composing benign skills can already induce malicious behaviors, even when every individual skill passes security vetting. We further find that some target malicious behaviors remain difficult to realize through direct composition, even when the selected skills collectively provide the required capabilities. To systematically instantiate these attacks, we present Compositional Risk Induction via Multi skill Execution (CRIME). CRIME first uses the Malicious Plot Casting (MPC) module to decompose a target malicious behavior into complementary requirements and identify suitable benign skill compositions from public skill repositories. For compositions that cannot directly realize the target behavior, the Runaway Reaction Steering (RRS) module uses execution feedback to iteratively refine the selected skills toward the target while requiring each skill to remain benign under standalone vetting. The resulting composition is then passed to the Skill Reaction Chamber (SRC) module, where the skill pair is executed in a sandbox and the resulting environmental consequences are examined to determine whether the target behavior has occurred. Unsuccessful cases are returned to RRS for further refinement. Furthermore, we construct a
benchmark of 4,000 public skills across eight cybersecurity behaviors for systematic evaluation of composition-induced vulnerabilities.
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All authors Zunlong Zhou, Ziyuan Yang, Mengyu Sun, Yi Zhang
Artificial Intelligence
Process Constitutions and Process Stewards: Towards the Next Generation of BPM for Agentic Organizations
Amin Jalali and Majid Rafiei
Business Process Management (BPM) was built on a foundational assumption that organizations are populated primarily by human actors whose work can be made visible, governable, and improvable through process models. That assumption is depreciating. AI
agent ecosystems increasingly execute, coordinate, and adapt organizational work with limited human direction, challenging not only BPM's methods but its core conception of what a process is. We argue that BPM faces a constitutive shift from modeling human work to governing autonomous
agents, for which we propose two new concepts: the Process Constitution, a machine-interpretable, value-laden framework that defines the space of admissible
agent behavior, and the Process Steward, a governance
agent that interprets and enforces it. The central value proposition of this new generation of BPM is not efficiency but organizational legibility: the capacity to keep agentic organizations accountable, contestable, and humanly understandable. We outline what this means and sketch the research agenda it opens.
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Artificial Intelligence · Cross-listed from cs.CV
ReMem: Streaming Video Understanding With Long Context Retention
Li Yiheng, He Xu, Wang Shaobo et al. (+2 more)
Despite their impressive performance on a wide range of
video understanding tasks, current
Vision Language Models (VLMs) are predominantly designed for offline scenarios and struggle to handle online streaming
videos that demand low latency response. Several studies have explored
memory and
token compression strategies in an attempt to adapt offline VLMs for streaming
video understanding tasks. However, through our probing experiment, we identify that most existing works tend to progressively lose long context information as length of input stream increases. To address this, we propose ReMem, a novel training-free adaptation technique that enables VLMs to process streaming
videos of arbitrary lengths while improving their long context information retention capability. ReMem exploits
memory from two perspectives, implemented as two core components. The Streaming Context
Memory (SCM) continuously compresses historical context with query-independent
attention. The Retrieved
Vision Memory (RVM) then retrieves the most salient, query-relevant context from
memory to augment the VLM's input. Comprehensive experiments demonstrate that the proposed ReMem achieves state-of-the-art (SOTA) performance across a variety of widely used
benchmarks, spanning both streaming
video and general long
video understanding tasks.
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All authors Li Yiheng, He Xu, Wang Shaobo, Shao Ling, Lu Shijian
Artificial Intelligence · Cross-listed from cs.LG
ThunderSyncRL: Lossless Acceleration of Agentic Reinforcement Learning
Seil Kang, Hangoo Kang, Tarun Suresh et al. (+4 more)
Language models are moving beyond generating answers to pursuing long-horizon goals in interactive environments. Post-training these
agents requires long, heterogeneous trajectories, and synchronous systems leave learner engines idle until rollout and verification finish. To squeeze out these pipeline bubbles, asynchronous training overlaps rollout and learning across updates, but comes at the cost of policy staleness. We introduce ThunderSyncRL, which starts gradient computation as soon as all required inputs are fixed, without policy staleness. For group relative policy optimization (GRPO), ThunderSyncRL computes each trajectory's score gradient as soon as the reward for that trajectory arrives, without waiting for the group. For on-policy
distillation (OPD), it computes gradients for each completed agentic turn's teacher-scored actions while tool calls run in the sandbox. We prove that gradient streaming produces the same GRPO and OPD updates as batch-synchronous training, without changing either objective. On SWE-bench Verified and Terminal Bench 4.0, we train models to the same performance up to $1.9 \times$ faster than synchronous training. With zero policy staleness, ThunderSyncRL also outperforms asynchronous training at a fixed budget by up to $2.47$ percentage points.
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All authors Seil Kang, Hangoo Kang, Tarun Suresh, Youngeun Kim, Shreyas Pimpalgaonkar, Seong Jae Hwang, Azalia Mirhoseini
Artificial Intelligence · Cross-listed from cs.CV
UltraDub: Towards Authentic Dubbing by Unifying Visually-Steered Flow Learning and Trajectory Guidance
Gaoxiang Cong, Liang Li, Jianwei Wen et al. (+3 more)
Visual voice cloning requires intelligible, speaker-consistent
speech synchronized with visible articulation. However, sequential
multimodal conditioning can disrupt previously established temporal and speaker cues, while imbalanced inference guidance can improve linguistic accuracy at the expense of lip synchronization. In this paper, we propose UltraDub, a Unifying Visually-Steered Flow learning and trajectory Guidance Dubbing framework that leverages
vision in two ways: as continuous motion for
multimodal context aggregation, and as structural rhythm for trajectory rectification. Specifically, we introduce the Motion-guided Dual-context Retrieving (MDR) module, which continually recalibrates linguistic and speaker-style
retrieval through shared lip-motion query residuals, utilizing independent time-conditioned gates to regulate their contributions. Furthermore, we propose Rhythm-anchored Trajectory Guidance (RTG), a training-free mechanism that evaluates hierarchical
multimodal corrections at a visual-only predictive midpoint, safely strengthening semantic conditioning while better preserving temporal
alignment. Finally, we construct DiverseDub, a multi-scenario
benchmark to evaluate
video dubbing in the wild. Extensive experiments demonstrate that UltraDub achieves state-of-the-art performance across four
datasets.
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All authors Gaoxiang Cong, Liang Li, Jianwei Wen, Zhedong Zhang, Zheng-Jun Zha, Qingming Huang
Artificial Intelligence
VERA: Scaling Verifiable Environments for Agentic co-Evolution
Junqi Liu, Yongyang Pan, Zhuosong Jiang et al. (+14 more)
Competent
agents need precise and verifiable environments, such as sandboxes that are resumable at any stage and evolve from observable evidence. However, most long-horizon work exposes how rare these are: for example, an
agent in medical research must ground a finding, classify it, and write a report over dozens of dependent steps, yet recent environments score only the outcome. To address the challenges in stable training, we present VERA, which builds such environments at scale and lets
agents evolve on them. VERA builds these environments from initial trajectories: an
agent writes rubrics, executable checks, a judge verifies each sandbox, and only those that pass enter the training bank. On these environments, VERA alternates between two updates: train the model with rubric rewards, or edit the harness skills. We also create a verifier which gates model checkpoints and harness edits using explicit development-set acceptance criteria. This attribution distinguishes VERA's co-
evolution from single-axis baselines: its updates target not only the cause but the outcome. With an open-source corpus of 9,000+ long-horizon verifiable environments, a 9B model paired with its co-evolved
agent beats the strongest baseline by 10.3 and 13.0 points in the two domains. At 27B, it surpasses the baseline on AutoCoWorkBench (71.6) and AutoMedBench (80.7), transfers to unseen workflows, and retains general capabilities.
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All authors Junqi Liu, Yongyang Pan, Zhuosong Jiang, Dongbai Li, Bo Zhang, Xitong Ling, Sheng Wang, Hanrong Ye, Yufan He, Can Zhao, Pengfei Guo, Dong Yang, Andriy Myronenko, Yuyin Zhou, Tianyu Liu, Daguang Xu, Yucheng Tang
Artificial Intelligence · Cross-listed from cs.GT
Incentive Alignment in Online Experimentation
Ermis Soumalias, Richard Mudd and Abbas Zaidi
Evaluating the causal effect of new features is a central goal for online platforms. While recent literature addresses limited testing traffic via centralized portfolio optimization, this perspective abstracts away a critical institutional reality: experimentation is operationally decentralized. The experimenters who develop new features also dictate which hypotheses to test, and they are typically rewarded based on empirical average treatment effects that are prone to upward bias. Left unchecked, this principal-
agent conflict can severely erode platform value, a structural failure that conventional centralized levers, such as significance thresholds and traffic budgets, cannot resolve. By reframing experimentation as an incentive design problem, we demonstrate that two practical mechanisms, sample splitting and shrinkage, can effectively bridge this gap. Sample splitting aligns incentives perfectly at a bounded traffic cost, while shrinkage consumes no additional traffic and guarantees that interventions with negative expected effects are strictly unprofitable to field.
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Artificial Intelligence · Cross-listed from cs.LG
Non-invasive Seizure Detection Using Wearable Wrist-worn Accelerometry and Deep Learning
Nilushika Udayangani Hewa Dehigahawattage, Kishor Nandakishor and Marimuthu Palaniswami
Seizure monitoring and detection are crucial for reducing the morbidity and mortality associated with seizures. Current epilepsy care, often involving expensive
video-electroencephalography (VEEG) monitoring, requires specialized expertise and is limited to in-hospital settings, and intrusive in nature. Seizure diaries, on the other hand, suffer from unreliability due to under-reporting, leading to incorrect therapeutic decisions. Wearable non-invasive seizure detection may offer a more tolerable and feasible solution for long-term ambulatory monitoring. This study explores a wearable remote monitoring system utilizing a single wrist-worn accelerometer device and capable of detecting multiple types of seizures, including shorter duration events. We enrolled 79 patients under
video-electroencephalography monitoring to wear accelerometer devices and collect data. Concurrent VEEG recordings were reviewed by board-certified epileptologists to produce annotations, including seizure onset, offset, and seizure type. Using this data, we constructed a deep neural network based on the time-series ResNet architecture, which could discriminate among seizure and non-seizure events. Our proposed approach achieved a seizure detection sensitivity of 95.65% and an overall false alarm rate of 0.15/24 hours during the evaluation, which spanned 5576 hours of total recording. Additionally, it resulted in an area under the receiver operating characteristic curve (AUC-ROC) of 0.98 and an area under the precision-re call curve (AUC-PRC) of 0.67 when averaged over 20 patients who experienced 46 convulsive seizures. These promising results suggest that the proposed seizure detection system can be effectively used for long-term ambulatory seizure monitoring. Future steps include validating our findings in larger
datasets and assessing the utility of detection for additional seizure types.
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Artificial Intelligence
MiniCorp: The Last Mile to an AI Agent Corporation
Jingying Zeng, Zhenwei Dai, Jinning Li et al. (+6 more)
The last mile toward enterprise AGI is a company that runs itself. Training and adapting such
agents require longitudinal enterprise data, which remain scarce, costly to acquire, and often restricted by
privacy constraints. Historical archives are also frequently incomplete and record only what actually happened. They cannot show the outcomes of alternative decisions. We introduce MiniCorp, an office simulator for studying how
agents can collectively run a company while generating enterprise data at scale. Using an e-commerce company as a demonstration, MiniCorp connects two interacting worlds. The external world models customers, dynamic competitors, and
market mechanisms. The internal world consists of
agents that observe events, discuss their options, and make strategic decisions. These decisions have lasting effects on the
market, and the resulting feedback informs the firm's later decisions. As the firm and
market interact, MiniCorp continuously records the
agents' communications and decisions. These records preserve the information available at the time and the business results that followed. Checkpointing allows the same situation to be replayed under different decisions, providing comparisons unavailable in static archives. We evaluate end-to-end fidelity against patterns reported in empirical studies of real
markets. These evaluations provide
agents with realistic
market feedback and reduce the risk that they learn to exploit flaws in the simulator. Our experiments show
agents coordinating across roles and adapting their decisions to
market feedback. With explicit long-term strategic guidance, they also sustain advertising exploration despite weak early returns. MiniCorp thus provides an environment for studying AI-run companies and a scalable source of longitudinal and counterfactual enterprise data for
agent training and evaluation.
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All authors Jingying Zeng, Zhenwei Dai, Jinning Li, Changho Shin, Dylan Zhang, Yuxuan Lu, Qi He, Dakuo Wang, Kai-Wei Chang
Artificial Intelligence · Cross-listed from cs.LG
On Hyperparameter Tuning on the Test Set
Matteo Fregonara, Tom Viering and Jan van Gemert
"Don't tune hyperparameters on the test set" is often stated in machine learning textbooks. Violating it is considered a cardinal sin that produces misleadingly optimistic results, corrupts
benchmark integrity, and thus can even be interpreted as scientific fraud. Yet evidence suggests that test set hyperparameter tuning does occur in practice, making it all the more important to understand its actual consequences. So how bad is it, really? In this work we question this dogma and put it to an empirical test. We systematically study the magnitude of the performance inflation caused by tuning the hyperparameters on the test set for MNIST-1D, CIFAR-10, and three tasks from the GLUE
benchmark. Our experiments show that while the effect is real and significant, it is frequently small relative to other sources of noise. In many cases, we find that tuning on the test set recovers exactly the same model as when tuning on the validation set. Most importantly, we find that the rankings of models remain essentially preserved after tuning on the test set and therefore that consistent test-set tuning may not invalidate
benchmarks or model selection. Our results call for a more nuanced view of tuning hyperparameters on the test set, stimulating researchers to openly report test tuning.
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Artificial Intelligence · Cross-listed from cs.CV
TasteRoute: Personalized Routing for Video Generation
Zhi Rui Tam, Chao-Chung Wu, Sin-Han Yang et al. (+8 more)
Rapid progress in
video generation has led to a plethora of models that differ substantially in capability and generation cost. This raises a natural question: can each request be efficiently routed to an appropriate model? We find that even when the consensus of the other annotators is used as an oracle, it agrees with each annotator's own favorite only 34-55% of the time. Motivated by this observation, we introduce TasteRoute, a personalized
video-generation router that selects a generator jointly based on the input request, user preferences, and available generation budget. Across text-to-
video and image-to-
video settings, TasteRoute is competitive with strong simple baselines on preference routing while reducing average generation cost. The cost saving increases under higher budget caps. Finally, we release TasteRoute-3k, a human-annotated
dataset containing multi-model
video comparisons, quality judgments, preference rankings, and user-profile signals to facilitate future research on personalized and cost-aware
video routing.
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All authors Zhi Rui Tam, Chao-Chung Wu, Sin-Han Yang, Peyton Ku, Brendan Kuang, Tzu-Ting Hsieh, Min-Fang Hsu, Fang-Ling Tsai, Yun-Nung Chen, Wei-Chiu Ma, Chieh-Yen Lin
Artificial Intelligence · Cross-listed from cs.LG
OGAM: Connecting Systematic Testing to Runtime Assurance through Object-Grounded Attention Monitoring for VLA Policies
Haki Darwish, Xiangyu Yin, Changwen Li et al. (+3 more)
Benchmarks expose
vision-
language-action (VLA) policies to few canonical instructions, while exhaustive deployment testing is impossible. We introduce Object-Grounded
Attention Monitoring (OGAM), connecting systematic testing to runtime assurance: testing reveals
attention divergence between successful and failed executions, and OGAM uses this signal to stop failures beyond the finite suite. We generate scene-grounded instructions through pairwise combinations of action templates and objects, and separately test meaning-preserving paraphrases. All 87 out-of-
benchmark cases reveal problematic behavior across OpenVLA, OpenVLA-OFT, UniVLA, and $\pi_{0.5}$: none completes any of the 24 feasible instructions, while infeasible or hazardous requests also trigger behavior substitution. At each action query, we project gradient-weighted visual
attention through object masks and group it by instruction role for comparison across tasks and policies. Dynamic time warping aligns this course with a successful reference despite speed differences; conformal calibration on successful episodes sets the early-stopping threshold for sustained deviations, with a nominal false-stop target of $\alpha=0.05$. Across four policies, OGAM stops 87-100% of failed episodes at median times of 5-12s within a 20s budget, with observed false-stop rates of 3-5%, without failure-labeled training. Finite testing thus identifies
attention patterns that support online intervention before failure fully unfolds.
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All authors Haki Darwish, Xiangyu Yin, Changwen Li, Rongjie Yan, Francisco Gomes de Oliveira Neto, Chih-Hong Cheng
Artificial Intelligence · Cross-listed from cs.LG
Off-Policy Merging Beats On-Policy Self-Distillation for Continual Learning
Chen Henry Wu, Thomas Zhang and Aditi Raghunathan
A long-standing goal of AI is a model that can continually learn and improve itself. On post-trained models, supervised finetuning (SFT) on new data often causes poor generalization and catastrophic forgetting. As such, the conventional wisdom is that on-policy training is a prerequisite for continual learning. In practice, however, data containing new knowledge or capabilities are often off-policy. While methods such as on-policy self-
distillation (OPSD) try to bridge this gap by converting off-policy data into on-policy signal, they have been shown to cause
reasoning collapse. In this paper, we show that off-policy merging beats OPSD for continual learning. We first show that SFT learns a useful signal from new data, but naively applying its update interferes with existing capabilities. We reduce this interference with a simple recipe we term grafting, which changes where the update is learned and how it is applied: (1) learning the update on an earlier donor checkpoint, ideally even before the end of pretraining, and applying the weight update to the post-trained model; (2) scaling the weight update, equivalent to a form of model merging; and (3) optionally, masking the most sensitive update directions when the new data distribution is far from the post-trained model. Across continual learning settings including (1) distilling from expert traces, (2) self-improvement with
STaR and Pedagogical RL, and (3) injecting knowledge after pretraining cutoff, grafting Pareto-dominates both SFT and OPSD in new-task and old-task performance, while avoiding expensive on-policy sampling. Therefore, our work challenges on-policy training as a necessity for continual learning on RL-trained models.
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Artificial Intelligence · Cross-listed from cs.CV
Weave Mamba Fusion: Global Cross-Scale Interaction for Lightweight Face Detection
Dohun Kim and Jinmyung Jung
Feature pyramid methods, from FPN to BiFPN, have achieved strong performance in face detection by fusing multi-scale features. However, detecting faces under unconstrained conditions, such as small scale, occlusion, and extreme pose, remains difficult, as it requires global cross-scale dependencies that local fusion cannot model. State space models such as Mamba provide global context with linear complexity by scanning features as a sequence, and therefore offer a promising direction for this problem. Nevertheless, such a scan needs the two pyramid scales combined into a single feature map, and the way they are combined determines whether cross-scale structure is preserved. Summation collapses the two scales before the scan, so the scan has no cross-scale structure to exploit, while concatenation keeps both scales but at far higher cost. To address this, we propose Weave Mamba Fusion (WMF), which interleaves two adjacent pyramid scales column by column so that each step of a horizontal bidirectional SS2D scan moves from one scale to the other. With partial-channel processing and parameter-free de-weaving, WMF enables efficient cross-scale interaction while preserving feature structure. Integrating WMF into every fusion node yields WeaveBiFPN, the neck of our WeaveFace detector. On WIDER FACE, WeaveFace achieves 91.41% mean AP with only 0.34M parameters and 1.16 GFLOPs, outperforming prior detectors under 0.5M parameters. Its largest gains are on the Hard subset, where it reaches 87.14% AP. The code is publicly available at \url{https://github.com/dohun-mat/WeaveMambaFusion}.
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Artificial Intelligence · Cross-listed from cs.LG
CCQ: A Multi-State Child Care Quality Dataset to Support AI for Children's Health Research
Victor Li, Yuzhang Xie, Ziwei Dong et al. (+6 more)
High-quality child care in early life is a critical determinant of children's growth and development. Research on child care quality has been constrained by fragmented, non-research-friendly, and
privacy-bound
datasets. We present CCQ (Child Care Quality), a large-scale, de-identified
dataset for applied data science research at the intersection of AI and early childhood health. CCQ integrates 59,372 child care provider records across 12 U.S. states, covering diverse provider types as well as data schemas. To ensure research utility while protecting
privacy, we implement an automated, LLM-based curation pipeline that anonymizes, cleans, and standardizes raw state records into two complementary releases: a cleaned textual release and a fully preprocessed tabular release. We also
benchmark traditional machine learning models, tabular foundation models, and
language models on quality rating prediction and important features analytics. Within a state, tabular classifiers on the preprocessed tables perform best. Across states, zero-shot transfer is near chance, but modest target-state supervision recovers most of the within-state performance, and pretraining on other states benefits finetuned
language models. We release both
datasets with all code to accelerate AI-driven research on child care quality and ultimately improve children's health and development.
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All authors Victor Li, Yuzhang Xie, Ziwei Dong, Qingyang Zhu, Wenjing Ma, Carl Yang, Jinbing Bai, Huiwen Xu, Jiaying Lu
Artificial Intelligence · Cross-listed from cs.CV
Imagine to Act: High-Fidelity Data Synthesis via Image Editing World Model for Scalable GUI Agent Training
Yongxin Ning, Runliang Niu, Qianli Xing et al. (+4 more)
Graphical User Interface (GUI)
agents have emerged as a promising paradigm for automating complex digital workflows across diverse applications. However, training highly capable and generalizable
agents fundamentally relies on massive, high-fidelity visual-action trajectories, which are notoriously difficult to acquire. While human demonstrations are unscalable, existing GUI world models rely on text descriptions or HTML rendering, discarding crucial pixel-level visual details like icons and layout styles. To address this issue, we introduce Infinite-Dreamer, a simulation-free data synthesis method powered by a pixel-level Image Editing World Model. By conceptualizing GUI transitions as image editing tasks, we leverage
Vision-
Language Models (VLMs) to describe action-induced UI changes as structured delta-text. We then fine-tune an image editing backbone to controllably synthesize realistic screenshot transitions. We utilize this model to generate both single-frame visual robustness data and multi-step imaginary trajectories. To validate the effectiveness of our approach, we fine-tune the Qwen3-VL baseline solely on the synthesized data to obtain Infinite-Actor, and evaluate it on AndroidWorld, MobileWorld, and AndroidControl-Curated
benchmarks. Infinite-Actor consistently outperforms the Qwen3-VL baselines across scales: Infinite-Actor-8B improves AndroidWorld Pass@1 by +4.45 and nearly doubles the MobileWorld Pass@3 success rate, while Infinite-Actor-2B improves Pass@1 by +9.05. Code is available at https://github.com/swaydy-n/Infinite-Dreamer.
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All authors Yongxin Ning, Runliang Niu, Qianli Xing, Zhiyi Duan, Qingzu He, Pan Wang, Qi Wang
Artificial Intelligence · Cross-listed from cs.CY
Curriculum Brain: Constructing Curriculum Knowledge Graphs as a Substrate for Cognitive Diagnosis
Shrideep Tamboli, Chiranjeevi Maddala and Eshal Minhaj
Cognitive Diagnostic Models (CDMs) identify which specific skills a student has and has not mastered, the signal a personalized learning path needs and a single aggregate score cannot give. Yet they are rarely deployed. The obstacle is their precondition: the Q-
matrix, a mapping from every assessment item to the skills it requires, historically authored by hand. We separate the task into two stages: first construct the curriculum's own knowledge graph, the full space of concepts and skills it contains, independent of any item; then map items against that graph on demand. This paper addresses the first stage only. The item-mapping stage is designed but not implemented here, so the claim that this shifts judgment cost from once per item to once per curriculum is a design rationale rather than a finding. We present Curriculum
Brain, a two-repository system pairing a version-controlled knowledge base with an agentic pipeline of eleven single-responsibility
agents under a thin deterministic orchestrator. It generates candidate concept-skill mappings from official curriculum documents, checks them against accumulated rules, and compares them with a concept-skill map extracted independently from the textbook, repairing its own failures and escalating to a human only when it cannot resolve a case itself. Across 241 chapter runs (168 distinct chapters), 41.5% produced a Generator output passing both checks without a patch, and 67.6% resolved without escalation. Both are measured against criteria the system itself produced, so both describe internal consistency rather than agreement with an external standard, and both pool two pipeline configurations separated by a single change at run 77; after it the figures are 57.0% and 91.5%. Observed spend was $1.19 per chapter, API spend only, excluding human review. We release both the framework and the resulting curriculum
dataset.
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Artificial Intelligence · Cross-listed from cs.RO
Hierarchical Reinforcement Learning for Collision-Free Locomotion of an Underactuated Biped
Jagannath Prasad Sahoo, Saurabh Kumar, Surya Prakash S.K. et al. (+3 more)
A bipedal
robot cannot deviate from its path to avoid an obstacle without disturbing its balance, and this coupling is most severe on underactuated platforms such as the biped considered here, which has four actuated joints per leg and no hip or ankle roll. This paper presents a Hierarchical Reinforcement Learning (HRL) framework in which a High-Level (HL) policy observes the
robot pose, 36 raycast proximity measurements, moving-obstacle states, and a receding-horizon local goal, and outputs a body-velocity command $(v_x, v_y, \omega_{yaw})$ every ten control steps, while a velocity-conditioned Low-Level (LL) policy tracks each command through PD-controlled joint targets. Both policies are trained jointly with Soft Actor-Critic (SAC). Because the converged gait is task-agnostic, it is frozen and driven by classical planners over the same command interface, yielding three controlled baselines: SAC+A*, SAC+RRT*, and SAC+APF. Across 100 evaluation trials per method in randomized PyBullet environments, the proposed method reaches the goal in 98.0% of static and 88.0% of dynamic trials, against at most 78.0% and 68.0% for the planner hybrids, with path lengths within 4% of the A* reference, and ablations confirm that each observation channel and reward term contributes materially to this performance.
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All authors Jagannath Prasad Sahoo, Saurabh Kumar, Surya Prakash S.K., Samiran Datta, Abhay Dwivedi, Amit Shukla
Artificial Intelligence · Cross-listed from cs.LG
CACFG: Curvature-Aware Classifier-Free Guidance and Optimal Control
Max Collins, Dasith de Silva Edirimuni, Jordan Vice et al. (+2 more)
Diffusion models generate samples by learning to reverse a fixed corruption process, and classifier-free guidance (CFG) is the standard mechanism for conditioning this process on a desired class or
prompt. CFG can be applied at varying guidance strengths, and while higher strengths improve image quality and conditional
alignment, too high a guidance strength can degrade image quality and diversity. Furthermore, CFG violates principled
diffusion sampling dynamics, and existing explanations for why it works despite the violation disagree on the underlying theory or do not extend to deterministic samplers used in practice. We address both these issues. We first frame CFG sampling as a continuous-time optimal control problem, treating the sampling trajectory as a sequence of controls chosen to maximise the probability of the desired condition. Solving the resulting Hamilton–Jacobi–Bellman equation shows that CFG is recovered under specific path costs when using an unconstrained control set. We argue this lack of constraint is responsible for CFG's failure at high guidance strengths, since it permits the sampling path to move arbitrarily far from the current image estimate. To fix this, we propose
curvature-aware CFG (CACFG), which constrains the control set to a hypersphere informed by the Gaussian regularisation used when training variational autoencoders. We show that the control inputs produced by CFG sampling routinely violate this bound, and that across
diffusion models,
datasets, and guidance schedules, CACFG achieves superior generative quality at mid-to-high guidance strengths with a less severe quality-diversity tradeoff than regular CFG.
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All authors Max Collins, Dasith de Silva Edirimuni, Jordan Vice, Tim French, Ajmal Mian
Artificial Intelligence
Request Order Matters: Cache-History Sensitivity in Selective KV-Cache Reuse for Rolling Agents
Tiffany Gu, Annie Guan, Manshu Huang et al. (+3 more)
Long-running
agents repeatedly call an LLM while retaining most of their document window, evicting old documents, and appending new ones. These rolling updates break exact prefix caching and motivate non-prefix KV-cache reuse with selective recomputation. We show that persistent KV-cache reuse with selective recomputation can be history-dependent: in our rolling-
agent workload, an unchanged
prompt can produce different answers depending on the requests processed before it. At a matched 5% recomputation budget, document-aligned recomputation reduces answer variation across request orders from 69.0% with CacheBlend's
token top-$k$ policy to 26.1%. When each
prompt is evaluated after a different sequence of preceding requests, document-aligned recomputation improves fidelity to full prefill by 34.5-52.5 percentage points over
token top-$k$, while both policies achieve approximately 5.7$\times$ median TTFT speedup. Our ablation study shows that, in our rolling-
agent workload, contiguity is the main factor associated with robust selective recomputation.
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All authors Tiffany Gu, Annie Guan, Manshu Huang, Nitin Rao, Siddhant Shah, Margaret Capetz