Artificial Intelligence · Cross-listed from cs.LG
The Surprising Effectiveness of Shared Memory in Looped Transformers
Giovanni Monea, Keshav Ramji, Yousef El-Kurdi et al. (+4 more)
Looped
Transformers apply the same layers several times per
token, adding compute to improve quality without more parameters. Each recursion, however, writes its own key-value cache, so
memory still grows with compute. Inference-time techniques can shrink this cache at a cost in quality. We pretrain looped
language models to share
memory: only the first recursion writes a cache, and later recursions read it while keeping a short window of their own. Surprisingly, we find that sharing
memory does not cost quality and instead improves it. At 150M-1B parameters, our Looped Prediction
Transformer (LPT) and its hybrid variant set a new quality-
memory frontier for looped models: with five recursions, the hybrid lowers validation perplexity on FineWeb-Edu by 1.12-1.82 relative to a same-size standard
Transformer while using 76-79% less context
memory. Through an extensive analysis, we investigate why
memory sharing helps. Shared and local
memory develop different representations, and later recursions attend mostly to the shared
memory, which also acts as a gradient highway to the first recursion.
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All authors Giovanni Monea, Keshav Ramji, Yousef El-Kurdi, Luis A. Lastras, Yoav Artzi, Nathan Godey, Ramón Fernandez Astudillo
Artificial Intelligence
THPL: A Vision-to-Language Decision Support Framework for Rainbow Trout Feeding Management in RAS
Meng Liang, Guanbo Feng, Haozhuang Chi et al. (+7 more)
In Recirculating Aquaculture Systems (RAS), precision feeding is critical for minimizing costs and improving fish welfare. However, existing methods lack cognitive
alignment between fish behaviors and management knowledge, impeding translation into executable, interpretable feeding decisions. To address this, we propose THPL, a generative feeding decision framework tailored for rainbow trout (Oncorhynchus mykiss) in RAS. First, Fishsort extracts trajectories to establish an Activity Coefficient (AC) quantifying feeding intensity. Second, a Hierarchical Behavior Encoder (HBE) models individual temporal progression and collective dynamics using Temporal and Set
Transformers, transforming trajectory tensors into dual-evidence representations of explicit physical and implicit soft
tokens. Finally, these
tokens are integrated with environmental parameters, metadata, and expert rules to fine-tune an LLM via LoRA, followed by counterfactual
multimodal Direct Preference Optimization (mDPO) to reinforce causal
reasoning. Results show that AC exhibits a statistically significant monotonic positive correlation with expert-annotated feeding intensity (Spearman $\rho = 0.925$, $p < 0.001$). Ablations indicate that decision accuracy improves from 33.33% (text-only baseline) to 93.33% with dual-evidence
tokens, confirming that continuous spatiotemporal
tokens provide necessary physical grounding for LLMs. Compared with standard LoRA, counterfactual mDPO elevates decision accuracy from 93.33% to 96.67%, advances METEOR from 58.10% to 85.30%, reduces Self-BLEU-2 from 58.79% to 52.88%, and increases Distinct-3 from 6.68% to 7.81%, suppressing templating and actuation biases while reinforcing causal consistency and operational safety. Overall, by integrating continuous kinematics with LLM
reasoning, this study provides a novel decision support paradigm for precision aquaculture.
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All authors Meng Liang, Guanbo Feng, Haozhuang Chi, Shilong Zhao, Zhixin Xiong, Yuhang He, Wenfeng Han, Tianhao Zhao, Zhihong Ma, Ying Liu
Artificial Intelligence · Cross-listed from cs.PL
Coco: An Agentic Copilot for the Hardware–Software Co-Design Lifecycle
Samuel Kushnir, Kavya Sreedhar, Yeshwanth Reddy Pogula et al. (+8 more)
Co-designing ML models and the accelerators that run them is an unusual
reasoning task: architects must draw confident, high-stakes conclusions about systems that do not yet exist, and the pace of both model
evolution and hardware cadence means the analysis burden grows every quarter. The evidence behind each decision–hundreds of gigabytes of fresh simulation sweeps over novel design points–is by construction absent from any LLM's pretraining corpus, and there is no external literature to retrieve; naive "chat-with-your-data" approaches hallucinate exactly where correctness matters most. We present Coco (Copilot for Codesign), an agentic platform deployed with TPU architects that accelerates the co-design lifecycle of setting up experiments, sweeping simulators, and deriving insights. Coco is built as four layers: (i) a datastore that automatically registers every simulation sweep into a normalized relational schema, so
agents ground every number in a SQL query rather than scraping heterogeneous files; (ii) a library of tools with typed APIs that
agents compose without human orchestration; (iii)
agents that encode recurring analysis workflows–most notably iso-execution analysis, which compares systems at matched execution configurations, including swept-but-dominated points off the Pareto frontier; and (iv) a platform UX whose navigation state doubles as
agent context. We report early deployment experience toward a reduction in time-to-simulation and time-to-insight, and argue that co-design is a distinct agentic domain: its data must be retrieved rather than memorized, its workflows are recurring but context-dependent, and expert adoption hinges on UX that balances IDE-style control with interactive exploration.
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All authors Samuel Kushnir, Kavya Sreedhar, Yeshwanth Reddy Pogula, Amir Yazdanbakhsh, Narges Shahidi, Ming Liu, Varun Gohil, Ravi Iyer, Parthasarathy Ranganathan, Christina Delimitrou, Suvinay Subramanian
Artificial Intelligence · Cross-listed from cs.CV
EviDent-CBCT: Evidence-Bottlenecked Report Generation from Dental CBCT under Non-Exhaustive Report Supervision
Ruiyang Hao, Zhi Qin Tan, Yulan He et al. (+2 more)
Dento-maxillofacial cone-beam CT (CBCT) reports may contain dozens of tooth-specific, anatomical, and spatial findings from a single 3D scan. Learning to generate such reports from limited clinical data is challenging because routine reports may not exhaustively document image findings, and a non-mention may reflect either absence or non-reporting. We present EviDent-CBCT, an evidence-bottlenecked framework designed for this incomplete supervision. An anatomy-aware network maps each CBCT scan to a discrete record of tooth-level, global, and tooth-IAC evidence. A dental-logic consistency projection reconciles incompatible evidence before a deterministic renderer and an image-blind local
language model generate the report using only this record. For tooth-level evidence, reliability-aware training uses eligible non-mentions as reduced-weight negatives, while unreported global and tooth-IAC labels remain unknown. A metal-sensitive input channel preserves intensity cues from dental materials. Across three validation runs, EviDent-CBCT achieves $0.666\pm0.006$ merged evidence set-F1 and $0.402\pm0.003$ RadFact-Lite-Dental logical-F1, versus $0.371\pm0.018$ for the strongest controlled direct baseline. In the ODIN 2026 challenge, it ranked second in automated evaluation and third in blinded clinical Arena comparison on the hidden test set. These results support the discrete evidence record as an effective and auditable interface for CBCT report generation.
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All authors Ruiyang Hao, Zhi Qin Tan, Yulan He, Owen Addison, Yunpeng Li
Artificial Intelligence · Cross-listed from cs.CR
Hop-Decayed Influence: New Vulnerabilities of Structural Auxiliary Indexing in GraphRAG Pipelines with LLM
Jisung Park, John Le and Heath Cooper
GraphRAG pipelines construct auxiliary structures during offline indexing–semantic summaries, hierarchical edges, and pre-computed scores–that determine how
retrieval is prioritised at query time. Prior attacks target only instance-level components (nodes, edges, triples), overlooking these schema-level structures. We formalise Auxiliary Schema-Level Entity as a novel attack surface and propose the 3S Framework (Semantics, Structure, Scoring) for its systematic exploitation. Our Hop-Decayed Influence (HDI) attack identifies high-impact targets through query-aware influence propagation and corrupts their auxiliary structures post-indexing. Across two
benchmarks (HotpotQA, 2WikiMultiHopQA) and two architectures (Microsoft GraphRAG, HippoRAG2), HDI achieves 88-94% attack success rate while modifying as few as 0.016% of auxiliary structures. Each modification affects up to 6.00 queries (Schema Leverage Ratio), demonstrating 1:N amplification unavailable to instance-level attacks. Manipulated structures evade perplexity and paraphrase defenses with over 99% evasion rate, as they remain linguistically coherent system-generated artifacts. These results reveal that auxiliary schema-level entities receive implicit trust without runtime validation, constituting a structural blind spot in current GraphRAG defenses. https://github.com/Jisung-Pacific/HDI-GraphRAG-Attack.
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Artificial Intelligence
Traversing the Satisfaction-Diversity Frontier in Text-to-Image Diffusion
Kevin Zhai, Siva Rajesh Kasa, Soumya Roy et al. (+2 more)
Text-to-image generation enables users to explore several images generated from the same
prompt. For these generated images to be useful, each one must reflect the user's preferences, measured by a learned reward, and differ visually from the others to maintain diversity. Existing methods are limited: they either address reward and diversity separately or combine them in one aggregate score, enabling high diversity to offset low rewards. In this paper, we address these limitations by formulating generation as satisficing: every image (candidate) must satisfy a reward floor and the batch of images must satisfy a diversity cutoff. The reward floor controls the balance between worst-candidate reward and batch diversity; we show that varying this floor defines a Pareto frontier. To traverse this frontier, we introduce SatisDive, a training-free inference-time method. SatisDive uses a batch-relative reward cutoff to distinguish lower- from higher-reward candidates, emphasizing reward improvement for candidates below the cutoff and diversity among candidates above it. On Pick-a-Pic, at matched DreamSim, SatisDive improves worst-candidate reward over FK steering by up to 0.43 with FLUX.1-dev as the base model and HPSv3 as the reward, and by up to 0.70 with SANA-1.6B as the base model and ImageReward as the reward. More broadly, across their overlapping DreamSim ranges, SatisDive's satisfaction-diversity curve Pareto-dominates FK steering's curve in each setting.
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All authors Kevin Zhai, Siva Rajesh Kasa, Soumya Roy, Sumit Negi, Mubarak Shah
Artificial Intelligence · Cross-listed from cs.NI
Network-in-the-Loop at Scale: GPU-Batched 5G Simulation for Massively Parallel Robot Learning
Zifan Zhang, Mingzhe Han, Kannan Athreya et al. (+1 more)
Massively parallel GPU simulators train multi-
robot policies in thousands of environments, and many fleets use private Fifth-Generation (5G) networks, where each
robot's delay depends on its teammates' traffic. Network-in-the-loop training places a simulated 5G network inside this loop. However, GPU
robot simulators reduce the network to an independent delay per message, while packet-level simulators run one scenario per CPU process and cannot keep pace with thousands of parallel environments. To bridge this gap, we present Isaac-Net, a GPU-batched 5G New
Radio (NR) module that advances the uplink of thousands of environments in lockstep with Isaac Lab physics. Isaac-Net simulates every slot, the 0.5~ms interval in which the base station decides which
robots transmit, for all environments at once. Extensive experiments confirm that its NR engine reproduces the median delay of ns-3 5G-LENA across loads, with a median delay 5–10% low on an unseen carrier and 9% high at 32
robots per environment in closed loop. The engine also reproduces the Age of Information (AoI), the age of each
robot's newest delivered report, while an independent delay per message leaves the AoI tail about three times too light. In a configuration validated against 5G-LENA, Isaac-Net keeps the network in the loop for about one million
robots on one GPU at 83% of the Isaac Lab rate without the network, measured under a random policy. Isaac-Net is open source at https://github.com/ZzZTripleZzZ/isaac-net
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All authors Zifan Zhang, Mingzhe Han, Kannan Athreya, Yuchen Liu
Artificial Intelligence · Cross-listed from cs.HC
Automating the Application of HCI Principles: Skills for On-Demand UI Construction, the Human-AI Space to Think, and the Future of HCI
Nathan Conklin, Miranda Capra and Chris North
Human-computer interaction (HCI) is in the middle of a transition: large
language models can now generate functional user interfaces (UIs) on demand from natural-
language task descriptions. A user explains what they are trying to accomplish, and the system materializes a working interface to support it. This capability already exists in systems such as Claude and ChatGPT and continues to grow in fidelity as the underlying models improve. The next step along this trajectory is to move from interfaces that are merely generated to interfaces that are generated well. We propose a framework in which the dialogue between user and artificial intelligence (AI) becomes a Space to Think: a shared, structured cognitive workspace in which task decomposition produces an on-demand user interface as an extension of the user's thinking rather than as a separate artifact. Within this paradigm, classical HCI design knowledge (Nielsen's heuristics, Norman's affordance prescriptions, Web Content Accessibility Guidelines (WCAG) success criteria, cognitive-load constraints, and mixed-initiative principles) is encoded as skills: machine-readable skill.md files that the generating
agent loads at runtime as software engineering tools. Skills turn HCI design knowledge into declarative, inspectable, version-controlled, and editable artifacts owned by the HCI community itself so that accessibility, learnability, and consistency become properties of a generative process rather than properties of a finished product. We outline a research agenda depicting a future where the HCI field transitions from today's design and knowledge heuristic checklist towards a future where the craft becomes machine-readable, executable, and open.
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Artificial Intelligence · Cross-listed from cs.LG
Lexicographic Multi-Objective On-Policy Distillation
Doseok Jang, Jon Ander Campos and Youran Qi
Reinforcement learning from verifiable rewards (RLVR) usually optimizes answer correctness, yet useful
language-model behavior also requires high-quality
reasoning and concise responses. Existing multi-reward post-training methods typically scalarize rewards or combine specialists without explicitly protecting a reward priority order. This is problematic when trade-offs are asymmetric: conciseness, for example, should not improve at the cost of correctness. We introduce Lexicographic Multi-Objective On-Policy
Distillation (LMOPD), a multi-teacher method for integrating reward-specialized policies under explicit priorities. For each student rollout, LMOPD selects the specialist for the first objective whose gate detects a deficiency, then locally projects its centered log-policy correction to remove components that oppose higher-priority specialists. We evaluate 30B-A3B mixture-of-experts
transformer models in two- and four-expert settings on three math
benchmarks, measuring retained specialist gains. With two experts, LMOPD's point estimates fully retain the accuracy and
reasoning-quality gains while acquiring $46.9\%$ of the conciseness gain. With four experts, it retains $\approx90\%$ of both the accuracy gain and
reasoning-correctness gain, compared to only $\approx57\%$ by the next best evaluated baseline. Matched four-expertablations show that lexicographic routing outperforms random routing and that projection further strengthens both top-priority capabilities. Across both scales, LMOPD preserves the highest-priority capabilities more effectively than the existing baselines we evaluate, demonstrating the value of explicit priorities for specialist integration.
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Artificial Intelligence
DeReAct: Decomposed Reasoning and Acting for Reliable AI Agents
Ajay Vohra, Tao Chen, Neeti Narayan et al. (+1 more)
ReAct-based
agents typically rely on a single LLM policy to propose actions, interact with the environment, and decide when a task is complete. This coupling makes action authorization and completion control difficult to enforce independently, allowing errors to propagate and unsupported completion claims to terminate execution. We introduce DeReAct, a modular
agent architecture that externalizes two gating policies: a Critic that validates proposed actions before execution, and a Context Manager that reconstructs an environment-supported State and certifies task completion. Across GAIA and SWE-bench Verified, DeReAct improves Pass@1 most for weaker
Brain models, with gains of 6.5–7.0 points for Qwen3-Coder-480B and 4.2–5.2 points for Claude Sonnet~4.5; gains diminish as
Brain capability increases. Trajectory and ablation analyses show that external gating is effective when targeted failures are sufficiently prevalent and the gating policy is itself sufficient. With Claude Opus~4.5, Pass@1 remains comparable to ReAct, while DeReAct produces more evidence-complete and constraint-satisfying trajectories, indicating that completion control can trade earlier termination for stronger grounding. Overall, DeReAct improves weaker
agents while retaining grounding benefits as models strengthen.
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All authors Ajay Vohra, Tao Chen, Neeti Narayan, Caron Zhang
Artificial Intelligence · Cross-listed from cs.CR
MIRROR: Multipath Quorum Integrity for LLM Multi-Agent Communication
Ryuichi Yamafuji Lun, Jingzhen Wang, Shreyas Kolte et al. (+1 more)
Inter-
agent communication is central to Large
Language Model Multi-
Agent Systems (LLM-MAS), but it introduces an underexplored vulnerability:
Agent-in-the-Middle (AiTM) attacks that manipulate messages in transit without compromising the
agents themselves. Prior work reports Attack Success Rates (ASR) approaching 100% on structured tasks. Existing defenses rely on semantic validation, which requires additional inference and can block benign outputs, or on transport-layer encryption, which does not help when an intermediary legitimately terminates TLS. We present MIRROR, a communication-layer integrity primitive that replicates a single canonicalized payload across k logical routes and accepts a message only when a strict majority of routes report the same digest. MIRROR uses unkeyed hashing and so authenticates nothing on its own, since an active on-path adversary can always recompute a digest over a payload it has modified. All integrity derives from the assumption that honest routes form a majority. The digest serves only to make witness routes constant-size and to bind the recovered payload to the quorum-agreed value under second-preimage resistance. We give the guarantee under a route-compromise bound alpha < 0.5, and extend it to correlated routes, where the quantity that matters is the size of the largest shared-failure group and not the route count. We further show that availability and integrity degrade at the same threshold: below alpha = 0.5, quorum-denial and message-dropping adversaries cannot block honest traffic. Across MMLU, HumanEval, and MBPP on two frameworks and four communication
topologies, and in a MetaGPT deployment against a production API, MIRROR reduces ASR to 0% below the threshold at 1x LLM
token cost. LLM-as-a-Judge costs 35x in the same deployment, and blocks up to 44.2% of benign outputs in the
topology sweep.
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All authors Ryuichi Yamafuji Lun, Jingzhen Wang, Shreyas Kolte, Ruiteng Li
Artificial Intelligence
A Multi Method Importance and Performance Efficiency Analysis of Topological Metrics for Natural Visibility Graph Based Cyber Attack Detection
Ali Melih Kanca and Ilker Turker
Natural Visibility Graph (NVG) based analysis characterizes network traffic through topological descriptors reflecting different structural properties. However, not all descriptors contribute equally to cyber-attack classification, and extracting a large metric set can increase computational cost. This study evaluates 21 NVG derived topological metrics and investigates whether a compact subset can preserve classification capability while improving computational efficiency. Four importance analysis methods SHAP, grouped Permutation Importance, Boruta, and Recursive Feature Elimination (RFE) are integrated through a Consensus Ranking strategy. Based on this ranking, Full21, Top15, Top10, Top7, Top5, and Top3 configurations are evaluated using the CICIDS2018
dataset, a CNN classifier, and stratified 5 fold cross validation. The three highest ranked metrics are avg_clustering_coeff_median, avg_clustering_coeff_std, and avg_clustering_coeff_mean. Top3 achieved the highest observed mean performance, with 97.148% accuracy, 97.055% weighted F1 score, and an MCC of 0.9675, compared with 95.999%, 95.521%, and 0.9549 for Full21, respectively. It also reduced total runtime from 14,961.39 s to 589.22 s (96.06%). These results indicate that importance guided metric reduction can provide a compact NVG representation with higher observed mean predictive performance and substantially lower computational cost under the evaluated setting.
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Artificial Intelligence
World Editing: Intervening on Executable Worlds at Increasing Depth
Max Ku, Nok-Kan Law, Yu-Chien Tang et al. (+15 more)
Interactive world models are increasingly capable of generating environments and acting within them, yet deliberately editing an existing executable world remains underexplored. We formulate world editing as intervening on an existing world while preserving properties that should remain unchanged, and introduce intervention depth as an axis describing how strongly an edit couples world entities, dynamics, and systems. We instantiate this capability through industry-grade game modding and introduce IGMWorld, together with IGMBench, a
benchmark of 110 tasks and over 1.1K executable state and behavioral criteria across Minecraft and Terraria. The tasks span property, entity, dynamics, and system interventions and are evaluated through deterministic executability, behavioral, preservation, and visual checks. Frontier coding
agents already exhibit substantial world-editing capability: the strongest configuration solves 78.2% of tasks under a strict task-level criterion, while criterion-level performance reaches 94.8%. Reliability generally decreases with intervention depth, and this pattern persists even among tasks with similar numbers of evaluation criteria. Most failed edits still build and load successfully, suggesting that the main difficulty is making the edited world behave as requested. Visual consistency remains a separate weakness, with all evaluated configurations below 50% joint visual pass rate. These results show that world editing is a distinct capability from world generation and interaction, and that executable games provide a practical testbed for studying it.
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All authors Max Ku, Nok-Kan Law, Yu-Chien Tang, Shih-Ying Yeh, Ping Nie, Andy Zheng, Tat Hei Lai, Fei-Yueh Chen, Nikko Yu, Wei-Chieh Sun, Suzy Huang, Chiao-Wei Hsu, Chih-Chuan Huang, Chak-Wing Mak, Ho Yin Sam Ng, Edisy Kin Wai Chan, Min-Hung Chen, Ho Kei Cheng
Artificial Intelligence
Choosing Before Acting: Comparative Value Estimation for Long-Horizon Tool-Use Agents
Yu Li, Zheng Zhang, Xin Liu et al. (+3 more)
Large
language models (LLMs) rely on long-horizon tool invocation sequences for complex tasks, where each invocation can alter the task state and condition subsequent decisions. In long-horizon tool use, final-outcome rewards provide weak credit assignment over long interaction traces. Step-level rewards can offer more targeted feedback, but obtaining reliable step supervision often requires human or LLM judgment, or additional rollouts to estimate the downstream effect of an intermediate decision. In this paper, we argue that effective tool-use
agents should estimate the long-horizon value of a possible next tool invocation before executing it. This objective requires comparative supervision over alternative invocations under the same context, while logged trajectories only contain the invocation that was actually taken. Therefore, we propose Comparative Inference for Tool-use
Agents (CITA). CITA trains a Comparative Inference Model (CIM) from paired signals that combine observed tool behavior, scalable supervision from a
Bayesian tool-graph simulator, and semantic judgments from LLM-based comparison. The resulting CIM learns to estimate how likely a possible next tool invocation is to support final task success under the current context. Across three tool-use
benchmarks and multiple backbone LLMs, CITA consistently improves Tool F1 and task success. Additional analysis shows that CIM learns accurate step-level value estimates for comparative tool choices.
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All authors Yu Li, Zheng Zhang, Xin Liu, Shengtian Yang, Guangfeng Cai, Lei Feng
Artificial Intelligence · Cross-listed from cs.LG
Slow-Fast Multi-Teacher On-Policy Distillation for Capability Preservation
Xiaofei Yin, Tong Chu, Jiyuan Fu et al. (+3 more)
Foundation
multimodal large
language models are designed to support a broad spectrum of capabilities across diverse domains. Multi-teacher on-policy
distillation (MOPD) provides an effective framework for consolidating domain-specific expertise into a single student model. However, MOPD training gradually drives the student away from its initialization model, and general capabilities decline as the displacement grows, resulting in capability interference. A direct remedy is constraining the student toward its initialization, but this suppresses the acquisition of domain expertise as well. We propose Slow-Fast Multi-Teacher On-Policy
Distillation (SF-MOPD), which couples a fast model, the current student updated directly by each teacher, with a slow model, an exponential moving average of the student. The slow model absorbs the learning signal gradually, serving as a moving capability reference that fuses the general foundation with confirmed domain expertise. For each teacher, SF-MOPD computes the teacher-induced update in log-probability space and removes only the component that pushes the fast model further away from the slow model, while retaining aligned and orthogonal components. Experiments across multiple model scales demonstrate that SF-MOPD effectively mitigates capability interference, enhances specialized
multimodal capabilities, and reduces the average degradation on general-capability
benchmarks, consistently outperforming vanilla MOPD.
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All authors Xiaofei Yin, Tong Chu, Jiyuan Fu, Jun Lan, Shuheng Zhou, Huijia Zhu
Artificial Intelligence · Cross-listed from cs.CV
DeskForge: Dense Supervision from Desktop Environments for Computer-Use Agents
A. Said Gurbuz, Ahmed Nassar, Sunghwan Hong et al. (+2 more)
Computer-use
agents need to reliably ground action targets in complex desktop scenes, where multiple applications, overlapping windows, and visually similar controls compete for
attention. Existing training data rarely pair such scenes with dense annotations or vary them in a controlled way. We introduce DeskForge, a controllable desktop environment that composes and explores real applications to generate large-scale supervision for computer-use
agents. It varies application states, content, window layout, appearance, and resolution, and fuses screenshots, accessibility trees, and window geometry into dense element annotations while recording the outcome of each executed action. Using this environment, we construct DeskForge-1M, a corpus of 1.2M annotated desktop observations containing 159.7M element instances. We fine-tune four
vision-
language models on 200K grounding examples drawn from DeskForge-1M. All four improve across held-out desktop conditions and on all five external GUI grounding
benchmarks; for Qwen3.5-4B, accuracy increases by 11.51 percentage points on ScreenSpot-Pro and 10.11 points on OSWorld-G. The gains also translate to long-horizon task completion: under a fixed planner, the fine-tuned action models solve more WebArena-Infinity and OpenApps tasks, with Qwen3.5-4B increasing from 31 to 50 of 119 tasks and from 3 to 15 of 100 tasks, respectively. These results show that controllable composition of real desktop environments provides a scalable source of supervision for improving both GUI grounding and long-horizon computer use. The framework code, the
dataset, and the fine-tuned model are available from the project page: https://saidgurbuz.github.io/deskforge/
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All authors A. Said Gurbuz, Ahmed Nassar, Sunghwan Hong, Marc Pollefeys, Peter W. J. Staar
Artificial Intelligence · Cross-listed from cs.SE
SimuVerity: Benchmarking Agents for Engineering-Grade Simulink Model Generation
Ruiqi Zhang, Jiahao Wang, Mingxuan Li et al. (+9 more)
Existing Simulink
benchmarks mainly evaluate whether generated models compile, execute, or resemble a reference model. These criteria do not establish whether a model satisfies its engineering requirements. We introduce SimuVerity, a
benchmark of 101 text-to-executable Simulink model-generation tasks across ten engineering domains. For each task, executable-system profiles ground the engineering specification and four families of native simulation scenarios. A hierarchical evaluator first checks artifact delivery, native executability, and engineering qualification, then scores qualified models across six dimensions covering accuracy, output quality, mechanistic fidelity, control and causal integrity, operating-domain robustness, and dynamic response. We evaluate six
agent systems with SimuVerity. The best system achieves an overall score of only 42.86. The results show that structural similarity is a poor proxy for engineering performance: capability bottlenecks arise both in producing qualified implementations and in satisfying multidimensional requirements after qualification. Meanwhile, some high-scoring models still exhibit severe visual-layout disorder. SimuVerity provides a systematic basis for assessing
agents' engineering capabilities and diagnosing failures in executable Simulink model generation.
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All authors Ruiqi Zhang, Jiahao Wang, Mingxuan Li, Haichen Luo, Chaoting Wang, Guoyu Mou, Keyu Lai, Hanchao Lv, Jiaxu Wang, Yibo Zheng, Aijun Yang, Xiaohua Wang
Artificial Intelligence
Keep It CALM: Analyzing the Limits of Global Unsafety in Text-to-Image Generation
NaHyeon Park, Minhyun Lee and Hyunjung Shim
Training-free safeguards for text-to-image generation often rely on a reusable safety signal, such as an unsafe direction or global toxic subspace, applied broadly across
prompts. We provide a controlled
geometric analysis of this global-unsafety assumption and reveal a consistent coverage-selectivity trade-off: compact unsafe subspaces fail to cover heterogeneous unsafe semantics, whereas broader aggregation increasingly distorts safety-adjacent benign
prompts. Motivated by this finding, we propose CALM (Counterfactual Adaptive Local Modulation), a training-free safeguard that replaces uniform global removal with
prompt-local counterfactual correction. Using matched unsafe-benign anchors, CALM routes each
prompt to active unsafe categories, minimally edits only violating
token representations toward the safe side, and suppresses positively aligned unsafe residual components. Across broad evaluation, CALM significantly improves unsafe content suppression while preserving benign utility, demonstrating that local counterfactual correction provides a more selective alternative to global unsafe signal removal.
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Artificial Intelligence · Cross-listed from cs.LG
$\Psi$-Resilience: Model-Free Feature Importance from 1D Topological Signals
Fabian Galis, Darian Onchis and Pedro Real Jurado
We introduce $\Psi$-Resilience, a model-free feature importance method that derives explanations directly from the data itself via 1D topological signals. Our method constructs a class-disagreement landscape by estimating class-conditional densities and taking their pointwise absolute difference along the feature axis. Then, the 0-dimensional persistence of this 1D signal defines a resilience functional that aggregates only those topological features that survive perturbations up to a robustness scale which is set by the user. This gives us a context-robust importance score that is inherently auditable via the underlying 1D landscapes and their persistence. We evaluate our method on both synthetic and real
datasets. On synthetic generators with specified ground-truth importance, $\Psi$-Resilience recovers the ranking of features with high fidelity, achieving Spearman rank correlations up to 0.8 and performing competitively with multiple feature importance methods, including SHAP and mutual information. On real
datasets with no known ground truth, our technique agrees with these methods, with correlations up to 0.9. These results show that $\Psi$-Resilience is a stable explanation method that enables rigorous, distribution-level auditing of feature importance without relying on a predictive model.
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Artificial Intelligence · Cross-listed from cs.CV
EditHero: A Benchmark for Long-Horizon Part-Level 3D Editing and Vibe Modeling
Ruihan Yu, Yu-Ju Tsai, Muyao Niu et al. (+9 more)
3D editing methods are usually tested on a single edit, yet an asset is built through a long sequence of revisions, each of which must implement the requested change while leaving everything else unchanged. We introduce EditHero, to our knowledge the first
benchmark for long-horizon, part-level 3D editing, with natural-
language instructions and target images for both geometry and texture. A deterministic assembly engine produces the exact target after every edit, and every sequence is reviewed by hand. We use EditHero to compare 2 opposite approaches to 3D editing. Non-agentic methods operate top down, regenerating the object from a learned 3D representation and inferring what to keep. In contrast, LLM/VLM
agents operate bottom up, editing through code that inspects the mesh and rewrites only the parts required by instructions. The non-agentic methods often miss the requested change and disturb regions that should stay fixed. Most LLMs follow instructions more closely, and all of them preserve the unedited parts better, but each of their edits takes minutes. We will release the engine and the edit sequences to support research on reliable iterative 3D editing.
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All authors Ruihan Yu, Yu-Ju Tsai, Muyao Niu, Runyi Li, Lian Fu, Hanqing Liu, Zheng-Hui Huang, Yonghao Yu, Sho Kuno, Ming-Hsuan Yang, Kaipeng Zhang, Zhixiang Wang
Artificial Intelligence · Cross-listed from cs.LG
Diffusion-Based Synthetic Data Pretraining for Enhancing Activity Recognition
E. Riveros, D. Vega-Oliveros, A. Soriano-Vargas et al. (+1 more)
Human activity recognition (HAR) is increasingly important for healthcare, well-being, and daily monitoring ap- plications, for which detecting alimentary activities such as eating and drinking can provide actionable insight into dietary habits and chronic disease management. HAR systems, however, often underperform on subtle and underrepresented classes, limiting their utility in real-world dietary monitoring. This work builds upon CABiGRU, a convolutional architecture with Bidirectional GRU layers, multi-head
attention, and residual connections, designed to capture discriminative temporal patterns from smart- watch accelerometer, gyroscope, and magnetometer data. To improve CaBiGRU's generalization and reduce underfitting in the minority class, we leverage synthetic sensor data windows using a
diffusion model and adopt a two-stage training strategy: pre-training CABiGRU on synthetic data, followed by fine-tuning on the real-world data. On the DEO (drinking/eating/other)
dataset, the proposed pipeline achieves a balanced accuracy of 90.6%, improving over a strong supervised baseline and showing the benefits of
diffusion-based synthetic pre-training for recognizing alimentary activities and representing a step forward dealing with unbalanced classes. These results suggest that combining
diffusion-generated data with targeted fine-tuning enhances robust recognition of dietary behaviors, supporting more reliable deployment in healthcare and nutrition-monitoring settings.
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All authors E. Riveros, D. Vega-Oliveros, A. Soriano-Vargas, A. Rocha
Artificial Intelligence
The AI Risk Observatory: What Can We Learn from AI Disclosures in Annual Reports About Societal Resilience?
Bart Jaworski and Andrew Sutton
Societal resilience research relies on access to useful and actionable data, which motivates our main research question: Can annual reports, processed at scale with LLMs, provide a useful signal about how companies disclose their response to AI? We test this by applying a reproducible two-stage classification pipeline to 9,821 annual reports from 1,362 UK listed companies (2020-2025, with partial 2026 data). We first validate the method against 474 human-annotated passages, finding high recall and moderate label-level agreement. We then report three empirical patterns: (i) between 2020 and 2025, the share of reports mentioning AI risk rose from 2.8% to 41.2%, while AI adoption disclosure also rose, from 13.8% to 45.2%, and named vendor mentions cluster around a small set of major providers led by Microsoft; (ii) disclosure varies substantially by Critical National Infrastructure sector and
market segment: AIM reports disclose AI risk at far lower rates than Main
Market reports, and sectors such as Energy and Data Infrastructure lag behind the rest in AI risk disclosure; and (iii) harm disclosures are near-absent (seven reports across the entire corpus). We develop a substantiveness classification to assess the quality of the disclosure and find that most AI risk disclosure is not substantive: in 2025, 41.2% of all reports mention AI as a risk, but only 4.3% contain AI risk disclosure we classify as substantive.
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Artificial Intelligence
Fast Models, Slow Evidence: A Paired and Self-Audited Evaluation of System-1 Decision Models for LLM Agent Harnesses
Jiawei Li
Agent harnesses make many small, typed decisions per task: which model to call, which tool to use, whether retrieved text is relevant, whether an input carries an injection. System-1 decision models answer such questions in a single forward pass with class probabilities, promising large cost and latency savings over LLM calls. We present a paired evaluation of an open-weight (Laya) and a hosted (Jev) System-1 model on 11
agent decision points built from 18 public sources: 7,283 base cases plus 6,640 robustness variants, with byte-identical inputs, paired tests, and cross-hardware and cross-day reproducibility checks. Jev is significantly more accurate on 9 of 11 decision points (+10.8 to +46.0 pp). Neither model beats chance on zero-shot model routing, and they tie on RAG relevance gating. Laya changes 30% of its answers when the option order is reversed and degrades sharply with many or similar candidates (31% at 50 nearest-neighbour tools, vs. 98% for Jev on items with a unique correct tool). We also audit our own pipeline. Three analysis errors and one design confound distorted headline deployment claims: an omitted pre-screen cost (reported 23.9% saving, actual 4.3%), gate accuracy reported as end-to-end quality (58% vs. 98%), in-sample thresholds (5% target, up to 17% held-out misses), and a "channel effect" on injection false positives that vanishes with channel-native content. Two other suspected confounds did not change the conclusions. All cases, raw outputs and analysis code are available at https://github.com/David-DL-Space/sys1-eval.
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Artificial Intelligence · Cross-listed from cs.CV
One Basis to Animate Them All: Gaussian Blendshape Distillation for Real-Time Avatars
Ramazan Fazylov, Stamatis Lefkimmiatis and Ivan Laptev
3D Gaussian avatars support fast rendering, however, their real-time animation is often challenged by the costly neural inference. We address this bottleneck and show that the animation of pretrained avatar models can be closely approximated by a linear combination of identity-independent blendshapes. Building on this finding, we introduce GALA (Gaussian Animation via Linear Approximation), a
distillation method that replaces per-frame heavy neural decoding with a shallow coefficient predictor and a linear blend. To improve fidelity and reduce
memory requirements, we propose to construct the basis using block-local PCA under a rendering-aware metric and a
memory budget. Our method learns a shallow MLP network to predict blendshape coefficients and applies to various animation architectures without retraining original models. We validate GALA by accelerating the inference of three distinct avatar models for 3D animation of facial expressions and full-bodies with clothing dynamics. Across these models, our
distillation generalizes to held-out identities and reduces CPU animation cost by up to three orders of magnitude while preserving most of the rendering quality. Excellent results of our method confirm the shared linear structure of learned avatar representations and enable highly efficient and accurate animation at frame rates reaching up to 60fps on mobile devices. Project page: https://ramazan793.github.io/gala/
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Artificial Intelligence · Cross-listed from cs.CL
KaliBench: A Fine-Grained Benchmark for Cybersecurity Tool Use on Kali Linux with Runtime-Free Verifiable Rewards
Pengfei Li, Naufal Suryanto, Sicheng Zhang et al. (+1 more)
LLMs are increasingly applied to cybersecurity workflows, where they are expected to translate analysts' intent into tool invocations. However, existing evaluations focus on knowledge-based assessments or end-to-end agentic tasks, and do not directly measure LLMs' ability to generate executable commands for real-world cybersecurity tools. This gap is critical because cybersecurity operations rely on strict command-line interfaces (CLIs), where minor syntax errors, incorrect flag–value bindings, or argument misordering can invalidate execution. We introduce KaliBench, a fine-grained
benchmark and
dataset for natural-
language–to–CLI translation on Kali Linux, comprising 8,504 query–command pairs spanning 1,642 tools across 23 capability dimensions and 5 security phases. KaliBench is constructed via a manuscript-grounded pipeline with deterministic canonicalization and alias-aware evaluation, enabling precise and reproducible assessment of tool selection and argument construction. To ensure both semantic correctness and practical executability, we develop a multi-stage verification pipeline that combines LLM-based validation, sandboxed terminal execution, and human-in-the-loop refinement. Building on these fine-grained, deterministic signals, KaliBench further enables runtime-free verifiable rewards for training. Across three evaluation modes and 24 configurations of general-purpose and security-focused open-weight models, no open-weight model exceeds 42% exact-command accuracy in the unrestricted setting, highlighting the difficulty of accurate CLI-based cybersecurity tool use without explicit tool hints. We further show that supervised fine-tuning and reinforcement learning with verifiable rewards derived from KaliBench significantly improve an 8B model and achieve performance comparable to a 685B MoE model.
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All authors Pengfei Li, Naufal Suryanto, Sicheng Zhang, Muzammal Naseer
Artificial Intelligence · Cross-listed from cs.RO
Reconstruct, Practice, Go Real: Guided Self-Improvement for Embodied Agents
Yen-Jen Wang, Haozhe Jiang, Shuying Deng et al. (+7 more)
Building reliable
robot capabilities across diverse tasks requires substantial human effort to develop and maintain skills, design rewards, and integrate perception with control. We present Reconstruct, Practice, Go Real (RPG), a framework for autonomous improvement of
robot execution systems without updating model weights. RPG identifies manipulation capabilities in an offline
dataset and constructs related practice tasks in simulation. During practice, RPG uses execution feedback, privileged simulator state, and available
dataset videos to diagnose failures. It develops new reusable symbolic skills, refines existing skills, and revises the system
prompt based on these diagnoses. Cross-task evaluation tests individual candidate changes and merged revisions before they are retained for reuse. At test time, a
multimodal LLM uses the resulting system
prompt and skill library to coordinate perception and
robot control. On held-out initializations of 22 manipulation tasks, RPG improves task success from 28.6% after the first practice round to 95.0% after 15 rounds, outperforming all evaluated baselines, including ASPIRE (75.5%) and CaP-
Agent0 powered by GPT-6 Astra Pro (60.0%). After a common calibration and hardware-adaptation procedure, the frozen system succeeds in all 30 physical trials, with ten trials on each of three tasks. Project Website: https://rpg-
robot.github.io/
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All authors Yen-Jen Wang, Haozhe Jiang, Shuying Deng, Haoru Xue, Weirui Ye, Rocky Duan, Nika Haghtalab, S. Shankar Sastry, Pieter Abbeel, Haozhi Qi
Artificial Intelligence
ScholarCatalyst: A Benchmark for Retrieving Papers That Inspire New Research
Sohyeon Kim, Yoonho Lee, Bo Liu et al. (+11 more)
What makes great scientists great? Even as AI systems start to make progress on open problems, scientists remain far ahead of them at sensing which prior idea, buried in an ever-growing archive of research, a new problem needs. To study this skill, we draw on researchers who know firsthand which earlier work advanced their completed projects, with papers serving as pointers to the ideas within. Using our automated pipeline that makes author annotation scalable, we build ScholarCatalyst by having 184 lead authors of 207 recent computer science papers label which candidates did or could have advanced their project, each with a detailed rationale. We introduce a
retrieval task with author-provided judgments: given an initial research question, retrieve these papers from only the literature available when the project began. Agentic search does no better than
embedding retrieval (0.42 vs. 0.48 Recall@20) despite calling that same retriever as a tool. Even an
agent built on Claude Fable 5.1, which may have seen the completed papers during training, reaches only 0.51 R@20. These results highlight the need for new training recipes that equip models with expert intuition for searching broad corpora. We envision ScholarCatalyst as a step toward scientific
agents that can take a half-formed idea and point to the prior research it needs.
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All authors Sohyeon Kim, Yoonho Lee, Bo Liu, Dayoon Ko, Rulin Shao, Seungone Kim, Graham Neubig, Pang Wei Koh, Aakanksha Chowdhery, Akari Asai, Omar Khattab, Yejin Choi, Gunhee Kim, Chelsea Finn
Artificial Intelligence · Cross-listed from cs.CV
SILSA: Sliding-Window Slice Latents for Topology-Preserving High-Resolution 3D Generation
Tianjiao Yu, Xinzhuo Li, Yifan Shen et al. (+4 more)
High-resolution 3D generation increasingly relies on voxel latents and multi-stage pipelines that first predict active structure and then synthesize local geometry. While effective, this design fragments continuous surfaces into many local
tokens, inflates generation cost, and often weakens topological consistency for thin or highly connected shapes. We introduce SILSA, a
topology-aware 3D generation framework that represents shapes with compact sliding-window slice latents. Instead of generating expensive voxel
tokens, SILSA uses a fixed set of overlapping slices along the three canonical axes, where each
token summarizes a local depth window to preserve cross-sectional continuity and support single-stage rectified-flow generation. A Slice VAE encodes oriented surface samples into multi-axis slice latents and reconstructs them with a sparse volumetric decoder, while a Volumetric Anchor
Lattice coordinates directional slice streams through a shared 3D workspace. To preserve structural correctness, we introduce slice-level
topology supervision that matches persistence diagrams and aligns Betti transitions across neighboring slices. Experiments show that SILSA improves structural fidelity while substantially reducing generation cost. SILSA improves PSNR by $8.7\%$, coverage by $5.96$ absolute points, and Betti error by $9.2\%$ over the strongest baseline, while using $70.0\%$ fewer
tokens than the next-most compact baseline and over $98\%$ fewer
tokens than sparse or hierarchical tokenizers, effectively reducing training
memory by $40.4\%$ and inference time by $58.5\%$. Qualitative results further show improved preservation of thin structures, repeated components, and long-range connectivity.
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All authors Tianjiao Yu, Xinzhuo Li, Yifan Shen, Ying Shen, Kiet A. Nguyen, Adheesh Sunil Juvekar, Ismini Lourentzou
Artificial Intelligence
VISTA: A Visual Harness for Reasoning in an Interactive World
Qiushi Han, Keya Hu, Linlu Qiu et al. (+2 more)
We show that
multimodal models possess strong
reasoning abilities and that an appropriate harness can unlock their potential to solve tasks across diverse interactive environments. We introduce VISTA, a visual harness that gives a general-purpose
multimodal model long-horizon
vision. VISTA allows the model to directly perceive the environment through visual observations and maintains a lossless visual
memory that preserves past observations in their original form. The model can actively retrieve these observations and reorganize its visual input as it reasons. On ARC-AGI-3, VISTA improves Claude Opus 5.0's Relative Human Action Efficiency score from 40.68 to a perfect 100.00, with the model completing all 25 public games using 57.4% fewer actions than first-time human participants. VISTA's simple design also allows it to extend naturally to diverse visual environments with minimal adaptation. Across three additional
benchmarks covering a diverse range of visual games and puzzles, it substantially outperforms baselines using the same underlying model with minimal harnesses. Our results highlight VISTA's potential as a general-purpose visual harness for advancing
multimodal agents in complex visual environments.
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All authors Qiushi Han, Keya Hu, Linlu Qiu, Cathy Wu, Kaiming He
Artificial Intelligence · Cross-listed from cs.LG
FERPO: Forward Entropy-Regularized Policy Optimization
Sebastian Sanokowski, Alireza Sarmadi and Majid Khadiv
Several state-of-the-art methods for online reinforcement learning in continuous control improve policies using action gradients of a learned critic. However, critics are typically trained to predict returns, and accurate value predictions do not necessarily yield accurate action derivatives, potentially leading to unreliable policy updates. We propose Forward
Entropy-Regularized Policy Optimization (FERPO), an on-policy maximum
entropy reinforcement learning
algorithm that performs policy improvement using critic values without differentiating the critic with respect to actions. FERPO derives an optimal target action distribution from a policy-improvement objective regularized by
entropy and Kullback-Leibler (KL) divergence. We then fit the actor to this target by minimizing a forward-KL objective, estimated using self-normalized importance sampling (SNIS) with actions drawn from the rollout policy. By limiting the target distribution's deviation from the rollout policy, the KL regularization helps keep these importance weights well behaved. In contrast to reverse-KL objectives, which can favor a subset of the target distribution's modes, the forward-KL objective encourages coverage of multiple high-value modes and thereby promotes exploration. Experiments and ablations on MuJoCo Playground and ManiSkill show competitive performance and sample-efficiency gains. Computational
benchmarks also demonstrate faster actor updates than Relative
Entropy Pathwise Policy Optimization (REPPO).
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