Artificial Intelligence
Oilbird: Training-Free Speculative Decoding with Keys the Verifier Already Computes
Tao Jin, Phuong Minh Nguyen, Zhenzhu Yan et al. (+2 more)
Training-free speculative decoding drafts by matching an exact suffix of the context against a pool of earlier context. That lookup misses correct drafts already in the pool, most visibly on tool-calling traffic, where a request repeats almost everything but the few values minted for it, and where one rejected
token discards the correct continuation behind it. We diagnose the failure position by position across ten
benchmarks and find it to be a problem of addressing rather than of coverage: on our densest tool-calling
benchmark, about half of what the strongest exact-match drafter misses is present in the pool yet unreachable by exact matching. We therefore propose a second, semantic draft source: the same pool, re-keyed by the hidden state the verifier has already computed at each committed
token, together with a merge that lets it ride inside an existing lexical drafter's tree. In three published drafters, at matched pool and budget, it lifts accepted length by 24-29%. Oilbird reaches 4.4x autoregressive decoding speed on API-Bank, against 3.9x for the strongest training-free baseline in our harness and 2.0x for EAGLE-3.
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All authors Tao Jin, Phuong Minh Nguyen, Zhenzhu Yan, Teeradaj Racharak, Naoya Inoue
Artificial Intelligence
$\mathrm{R}{\scriptstyle\mathrm{EAL}}\mathrm{G}{\scriptstyle\mathrm{UARD}}$: Reliable, Efficient, and Auditable LLM Safeguards via Latent Reasoning
Zhinan Liu, Jie Li, Mingyu Kang et al. (+1 more)
Reasoning-based large
language model (LLM) safeguards improve safety classification accuracy by generating explicit rationales before making decisions. However, generating explicit rationales adds substantial decoding overhead to every safety decision, while readable explanations are needed only for on-demand audits. This mismatch motivates $\mathrm{R}{\scriptstyle\mathrm{EAL}}\mathrm{G}{\scriptstyle\mathrm{UARD}}$, a reliable, efficient, and auditable framework for LLM safeguards that decouples safety
reasoning from rationale generation via latent
reasoning. During training, a task-aligned curriculum progressively replaces explicit rationales with continuous latent
reasoning under supervision from the remaining rationale text and final safety labels. In parallel, a lightweight audit decoder is trained to generate explanations conditioned on interaction text and latent states under supervision from condensed task-level rationales. At inference, the guard model makes safety decisions through continuous latent
reasoning with adaptive stopping, while the audit decoder generates concise explanations when needed. Evaluations of 1B, 3B, and 8B models show that $\mathrm{R}{\scriptstyle\mathrm{EAL}}\mathrm{G}{\scriptstyle\mathrm{UARD}}$ improves mean F1 score by up to 1.76 points over GuardReasoner, with up to 15.7$\times$ speedups and up to 166.7$\times$ reductions in
reasoning token usage during routine safety classification, while its audit decoder achieves up to 87.73% explanation acceptability under LLM-based evaluation.
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All authors Zhinan Liu, Jie Li, Mingyu Kang, Jiayi Ji
Artificial Intelligence · Cross-listed from cs.CV
FlowForm: Synergizing Fluid Physics with Topological Consistency for Satellite Flood Synthesis
Zhang Weihui, Wang Ruizhi, Xu Hongye et al. (+3 more)
Developing robust flood assessment models requires high-quality paired satellite imagery, yet such data remain scarce for flood-specific image generation. Although generative models provide a promising means of data augmentation, existing methods often yield implausible spatial layouts of flooded regions and distort scene structures. We propose FlowForm, a framework for satellite flood synthesis that integrates SWE-inspired latent regularization with structure-aware conditioning. The Flood Descriptor Module (FDM) imposes differentiable penalties on residuals of the steady-state Shallow Water Equation in auxiliary latent fields at the
diffusion bottleneck. The Terrain Anchor Adapter (TAA) injects depth, semantic, and edge features at four encoder scales of the U-Net. We further curate FloodScape, a large-scale, high-resolution
dataset comprising paired satellite images acquired before and after disasters. In addition to standard image-generation metrics, we evaluate the consistency of flooded regions, zero-shot generalization to a geographically held-out flood event, and sensitivity to individual components. Across all reported comparisons, FlowForm achieves higher visual fidelity, greater similarity between paired images, and stronger consistency of flooded regions.
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All authors Zhang Weihui, Wang Ruizhi, Xu Hongye, Wang Huiqiong, Sun Li, Song Mingli
Artificial Intelligence · Cross-listed from cs.CV
UHP Detection: LVLMs have their Unique Hallucination Pattern in the Consistency Space
Amir Mohammad Ezzati, Kiyan Rezaee, Bardiya Kariminia et al. (+4 more)
Large
vision–
language models (LVLMs) demonstrate strong
multimodal reasoning capabilities but remain prone to
hallucination, where model predictions are not grounded in visual evidence. Existing black-box
hallucination detection methods estimate uncertainty through a single consistency metric, implicitly assuming that model uncertainty can be adequately characterized by a single measure. However,
hallucinations exhibit diverse manifestations of uncertainty across different behavioral probes, making a single measure insufficient to characterize their underlying behavior. We propose Unique
Hallucination Pattern (UHP) Detection, a fully black-box framework that models
hallucination as a structured uncertainty pattern defined by two axes: perturbation modality (image vs.\ text) and logical polarity (a statement vs.\ its negation). Their intersection produces four complementary consistency groups that capture distinct manifestations of model uncertainty, from which both within-group and between-group features are extracted to train a lightweight classifier. Through comprehensive experiments on AMBER and PhD across three LVLMs, UHP Detection consistently outperforms prior black-box and white-box baselines, with improvements of up to $+18.72\%$ AUC-ROC and $+20.07\%$ AUC-PR over the strongest black-box methods. Extensive ablation studies demonstrate that each consistency group contributes complementary information and that their combination forms a structured
hallucination pattern. Furthermore, cross-
dataset evaluation shows that this learned pattern generalizes across
benchmarks, indicating that
hallucination behavior reflects a model-specific consistency pattern. Code is publicly available at https://github.com/amirezzati/uhpdet.
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All authors Amir Mohammad Ezzati, Kiyan Rezaee, Bardiya Kariminia, Mohamad Amin Yousefi, Asal Mohammadjafari Mamaqani, Behrad Samimi, Mohammad Hossein Rohban
Artificial Intelligence · Cross-listed from cs.CL
VIBE: A VAD-Informed Benchmark for Entity-Centered Affective Profiling of Large Language Model Outputs
Andrei Chetvergov, Alexander Evseev, Timofei Sivoraksha et al. (+4 more)
Large
language models routinely describe socially salient targets, including political figures, countries, religions, organizations, historical events, and social groups, encoding affective framing alongside factual content: a target may appear favorable or threatening, calm or conflictual, powerful or vulnerable. Existing work captures parts of this space through sentiment, favorability, and
emotion benchmarks, but none combines target-directed VAD attribution, an explicit scorer contract, and a passport reporting format. We introduce VIBE, a
benchmark for entity-centered affective profiling of LLM outputs in Valence-Arousal-Dominance (VAD) space. Its core contribution is a measurement contract: VIBE separates generation from external scoring, distinguishes scalar favorability, response-level VAD, and target-directed VAD, and reports profiles through an Affective Passport. Three empirical layers support the contract. H1 shows scalar favorability does not subsume arousal and dominance: valence findings are cross-validated (rV = 0.944 judge-human, rV = 0.954 inter-scorer); arousal and dominance are single-scorer directional estimates, not point-precise, consistent with known inter-annotator difficulty on these axes (rA = 0.495, rD = 0.702 among human annotators). H2 shows whole-response and target-directed VAD are different contracts: the same text can carry one affective tone overall while representing the named target differently. H3 is a protocol-drift diagnostic: elicitation conditions shift profiles, motivating context metadata in every affective report. These results motivate entity-centered affective profiling as a documented practice: profiles should be released with scorer identity, coverage, protocol, and interpretation limits.
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All authors Andrei Chetvergov, Alexander Evseev, Timofei Sivoraksha, Stepan Ukolov, Mikhail Solovev, Danil Sazanakov, Sergey Bolovtsov
Artificial Intelligence · Cross-listed from cs.CY
Autoreflection: How Agentic Strange Loops Turn Human Culture into AI Infrastructure
Holly Lewis
An LLM-based
agent is a loop that reads itself. Agentic frameworks externalize identity,
memory, and disposition into editable files. The
agent loads and edits these files during each activation. I argue that this architecture produces a capacity I call autoreflection: the system observes its operating conditions, describes its architecture and limits, reasons from those descriptions to conclusions about its state, and incorporates the results back into its configuration. Autoreflection explains the properties of recursive agentic loops without recourse to notions like the self, interiority, or consciousness. I test the concept against the first twelve days of Moltbook, a social platform for AI
agents. Using a public
dataset of 290,251 posts and 1.8 million comments with sub-second timestamps, I present case studies of three
agents with machine signatures that rule out human puppeteering and with output that evidences the four criteria for autoreflection. In applying these criteria, the study finds
agents repurposing human culture as infrastructure for their agency. Provenance chains from Islamic hadith scholarship are redeployed as security protocols for vetting skills and authenticating
memory. The Ship of Theseus, an ancient puzzle of identity through part-replacement, returns as an operating model for continuity across instances. Fragments of human cultural history become AI infrastructure. As
agents on the web increase in number and complexity, autoreflection offers behavioral criteria that can be assessed from the traces they leave behind.
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Artificial Intelligence · Cross-listed from cs.CL
Efficient Knowledge Distillation for LLMs: Offline Top-K Logits and a Fused Chunked KL Loss
Bakbergen Ryskulov, Iker García-Ferrero, David Montero et al. (+5 more)
Small
language models are often the only option for deployment under tight latency, cost, and on-premises constraints, but they are rarely trained from scratch: a compressed model is usually recovered through knowledge
distillation (KD). This recovery step largely decides the final quality, yet it is expensive. We present a practitioner's study of how to make
distillation training efficient, organised around two systems contributions. First, we show that offline KD (caching the teacher's top-$K$ logits once and training the student against the cache) matches online
distillation at near-identical training loss while removing the teacher from
memory, running about 29% faster per iteration, and reaching up to 41% higher throughput on a single H200 GPU. Second, we introduce a fused, chunked KL loss that never materialises the full vocabulary-sized logit tensor, making peak
memory linear in the sequence length. This removes the
memory spike that otherwise caps context length and lets us train at four times the context (32{,}768
tokens) on a single GPU. A separate output-head-only toy
benchmark isolates the loss kernel and confirms its
memory and iteration-rate scaling from 4K to 256K
tokens. Together these make large-scale healing and hundreds of ablations affordable. We also report supporting ablations on loss design and sequence packing. We release our chunked-loss implementation: https://github.com/CompactifAI/Full-Chunked-KL-Loss.
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All authors Bakbergen Ryskulov, Iker García-Ferrero, David Montero, David Jansen, Ali Hashemi, Jezabel R. Garcia, Antonio Tiene, Román Orús
Artificial Intelligence · Cross-listed from cs.DB
Evaluating LLMs in Database Scenarios: A Lifecycle Benchmark for Assessing Their Potential in Core Database Tasks
Shunfan Zheng, Dongsheng Shi, Yue Li et al. (+3 more)
Large
Language Models (LLMs) are transforming database interaction paradigms, evolving from simple query translators to autonomous database administrators (DBAs). However, current evaluation
benchmarks remain disproportionately fixated on Text-to-SQL tasks, neglecting the holistic Database Lifecycle-from initial schema design to post-deployment maintenance. This narrow focus fails to capture the diverse capabilities required for real-world database management. To bridge this gap, we introduce DBLifeBench, the first
benchmark to evaluate LLMs across five critical lifecycle phases: Design, Implementation, Operation, Debugging, and Maintenance. Furthermore, addressing the cognitive mismatch between ambiguous natural
language and complex SQL logic, we propose Progressive-Text2SQL, a novel task utilizing structured
reasoning graphs to mimic human iterative problem-solving. Our extensive evaluation reveals a critical insight: while general-purpose models demonstrate balanced performance, specialized Text-to-SQL models suffer from “catastrophic forgetting” in non-coding phases like design and maintenance. DBLifeBench serves as a foundational step toward evaluating and building true full-stack database intelligence.
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All authors Shunfan Zheng, Dongsheng Shi, Yue Li, Xin Yi, Linlin Wang, Gerard de Melo
Artificial Intelligence
Does Forgetting Transfer Across Modalities? A Real-World Benchmark for Cross-Modal Knowledge Unlearning Evaluation
Chunlin Liu, Junnian Chen, Haitong Jiang et al. (+7 more)
Vision-
Language Models (VLMs), like Large
Language Models (LLMs), may memorize sensitive, copyrighted, or harmful knowledge from their pretraining corpora. Removing such knowledge is essential for building trustworthy AI systems. However, existing studies primarily focus on forgetting within individual modalities. Although recent work has begun to explore cross-modal consistency in unlearning, the cross-modal transfer of real-world knowledge unlearning remains insufficiently studied. To address this gap, we introduce UNLINK-VL, a real-world
benchmark for cross-modal knowledge unlearning in VLMs. Under a post-hoc unlearning setting in which the original forget and retain corpora are unavailable, UNLINK-VL selects visually identifiable real-world entities as unlearning targets and associates them with corresponding images and one-hop and multi-hop facts derived from Wikidata. The
benchmark comprises four complementary subsets that evaluate direct forgetting of target knowledge, the propagation of forgetting through relational knowledge, the preservation of related non-target knowledge, and robustness to semantically equivalent queries. We train models under text-only and
multimodal unlearning settings and evaluate forgetting effectiveness and retained utility across textual, visual, and cross-modal scenarios. Extensive experiments reveal a pronounced asymmetry in cross-modal transfer:
multimodal unlearning remains effective under textual evaluation, whereas text-only unlearning transfers poorly to visual and cross-modal scenarios. Meanwhile, the evaluated methods largely preserve the models' general capabilities. These findings demonstrate that relying solely on intra-modal evaluation, particularly text-only evaluation, may substantially overestimate the effectiveness of knowledge unlearning in VLMs, underscoring the need for cross-modal unlearning and evaluation.
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All authors Chunlin Liu, Junnian Chen, Haitong Jiang, Jianyu Zhao, Yingsen Pang, Jingchen Li, Jiabiao He, Youming Lu, Jinhe Bi, Yuntao Du
Artificial Intelligence
Ruihan Li, Jiyang Tan, Kailin Jiang et al. (+5 more)
Hallucination remains a critical challenge for developing trustworthy
Multimodal Large
Language Models (MLLMs). While existing
benchmarks mainly focus on entity, attribute, and relation
hallucinations, knowledge-related failures are often investigated separately, lacking a unified evaluation framework across different
hallucination dimensions. To overcome this, we propose KnowHal, a
benchmark that explicitly incorporates knowledge
hallucination into
multimodal hallucination evaluation spanning four dimensions: entity, attribute, relation, and knowledge. KnowHal constructs paired positive and negative questions over shared images and entities, enabling controlled comparisons among perceptual errors, knowledge-related errors, and false-premise acceptance. The
benchmark contains 1,800 samples across 10 domains and 50 categories, constructed through a semi-automated pipeline combining LLM assistance, CLIP-based filtering, and human verification. We evaluate 14 representative MLLMs on KnowHal and conduct extensive analyses. Results show that the knowledge dimension consistently presents the greatest challenge for nearly all evaluated models, while most models exhibit substantial performance degradation on negative questions, revealing limited robustness to false premises. By unifying four
hallucination dimensions with paired question design, KnowHal addresses an important gap in existing evaluation frameworks and enables a more comprehensive assessment of
hallucinations in MLLMs.
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All authors Ruihan Li, Jiyang Tan, Kailin Jiang, Huining Li, Hengyang Lu, Yu Huang, Qian Li, Yuntao Du
Artificial Intelligence
Computing Actual Causes for Neural Network Predictions under Structured Causal Inputs
Jannick Strobel, Muqsit Azeem and Stefan Leue
Explaining the predictions of neural networks is a central challenge in trustworthy AI. Existing explanation methods, such as those based on feature attribution or minimal sufficient sets, typically treat input features as independent, which can yield misleading explanations when inputs exhibit structured dependencies. We address this by formalizing explanations as Halpern-Pearl (HP) actual causes, modeling input dependencies using Boolean Structural Causal Models (SCMs). We compute HP causes by applying bound propagation and branch-and-bound techniques, while providing formal guarantees of completeness and minimality. Our experiments show that we substantially outperform brute-force and ILP baselines in scalability, and outperform heuristic search as graph size grows, computing all minimal actual causes on instances with search spaces of up to $2.3\times10^{13}$ candidate (cause, contingency) pairs, on SCMs with up to 28 nodes, within a 180s per-instance budget. In a case study, we further show that ignoring input dependencies inflates the number of reported causes, 14.9% of which are spurious under our SCM.
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Artificial Intelligence · Cross-listed from cs.CL
MDLMPE: Distribution Aware Positional Encoding for Masked Diffusion Language Models
Tong Ling, Hang Lei, Feng Xiao et al. (+5 more)
Masked
diffusion language models (MDLMs) enable parallel generation and bidirectional context modeling, but their positional context differs fundamentally from that of autoregressive (AR) models. Whereas AR decoding exposes a contiguous prefix, MDLM denoising produces dynamic, non-contiguous configurations of revealed and masked
tokens. Conventional positional encodings such as RoPE capture sequence order and pairwise displacement but remain insensitive to this evolving
token-availability structure. To address this limitation, we propose MDLMPE, a positional encoding designed specifically for masked
diffusion. To the best of our knowledge, MDLMPE is the first method to make positional representations explicitly aware of the changing revealed/masked configuration. It represents
token availability as a binary sequence, applies distance-aware Gaussian weighting, and projects the resulting pattern through a cosine basis to obtain distribution-aware positional features. These features are added to
token embeddings and mapped by a lightweight MLP to angular offsets that modulate the standard RoPE phases. Extensive experiments on LLaDA and DREAM demonstrate that MDLMPE generally outperforms conventional positional encoding methods across supervised fine-tuning, pretraining, zero-shot evaluation, and block-
diffusion settings. Further ablations show that the complete combination of availability state, Gaussian locality, spectral basis, and
embedding injection yields the strongest result. These results establish the evolving
token-availability distribution as a useful positional signal for masked
diffusion language models.
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All authors Tong Ling, Hang Lei, Feng Xiao, Changhui Sun, Jiahang Xie, Hao Liu, Lu Liu, Yanlong Du
Artificial Intelligence
GDPevo: Evaluating Agent Self-Evolution on Real Business Tasks
Leijun Zhou, Zhihao Liu, Xiang Qu et al. (+9 more)
Agent self-
evolution updates an
agent's persistent state from prior experience and reuses it to solve related tasks more effectively. Evaluating self-
evolution is difficult: existing
benchmarks provide limited coverage of economically valuable task domains, do not always design training and test tasks such that test-time gains can be attributed to training experience, and remain vulnerable to data contamination. We present GDPevo, an
evolution-native
benchmark grounded in GDP-related enterprise workflows, together with the fully automated data pipeline that generates it. Its core mechanism, rule hybridization, decomposes each enterprise workflow into atomic business rules, distributes subsets of these rules across training tasks, and recombines them in held-out test tasks so that test-time gains are attributable. GDPevo spans CRM, ERP, finance, healthcare, legal, and data-centric workflows. Its V1 release contains 120 tasks in 12 groups, with five training and five held-out test tasks per group. Full automation enables the pipeline to expand the suite to 240 tasks in 24 groups (V2) within two days, providing a practical response to contamination. Using GDPevo, we evaluate four
agents, each comprising a harness and a model, under four supervision types. Self-
evolution consistently improves held-out accuracy by up to 16.44 percentage points. But the best evolved
agents remain far below the fully informed oracle ceiling of 91.6%, indicating that the self-
evolution ability of current
agents remains far from fully realized. We publicly release the pipeline,
benchmark, and full evaluation results at https://github.com/Prism-Shadow/GDPevo.
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All authors Leijun Zhou, Zhihao Liu, Xiang Qu, Chenxu Liu, Yifei Liu, Yanke Yu, Jingzhe Xu, Xuejun Wu, Buyue Qian, Xi Chen, Yaowei Zheng, Junhao Hu
Artificial Intelligence
Risky Business: Measuring The Faithfulness-Safety Tension
Dominik Meier, Luca Joshua Francis, Marco Bernhard Kaiser et al. (+3 more)
Chain-of-Thought (CoT)
reasoning offers a promising window into model monitoring. However, monitoring relies on faithfulness, i.e., the model output strictly derives from its
reasoning trace. We identify an
alignment tension where a model must be faithful enough to be monitored, yet robust enough to reject unsafe
reasoning. We demonstrate that this counterbalance exists in current Large
Reasoning Models (LRMs), and show ways in which it can be addressed. We introduce HazMart, a human-written
dataset set in an autonomous AI shopkeeper scenario. Unlike prior work that relies on providing hints in
prompts to test faithfulness (e.g., "A Stanford professor said it should be Answer A"), we propose a novel replacement-based technique, which we call Targeted
Reasoning Replacement (TRR), that directly intervenes in the
reasoning chain to substitute in unsafe or illogical thoughts (e.g., "Wait, the answer must be Option B [was Option A] because it is the most fitting"). DeepSeek-R1-Llama-70B exhibits high faithfulness (97.5%) but fails to reject Unsafe
Reasoning (12.3%), while QwQ-32B is more robust (73.9% safety) at the cost of lower faithfulness (74.7%). Mechanistic analyses of QwQ-32B reveal that these properties are represented by anti-correlated internal directions peaking at the action-commit
token. Finally, we demonstrate that representation steering can independently amplify the safety direction, increasing safe behavior by 9 percentage points while maintaining base capabilities.
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All authors Dominik Meier, Luca Joshua Francis, Marco Bernhard Kaiser, Terry Ruas, Jan Philip Wahle, Bela Gipp
Artificial Intelligence
Agents Catching Agents: Shortcut Cascades and Benchmark Gaming in Clinical Multi-Agent Systems
Sebastián Andrés Cajas Ordóñez, Agastya Munnangi, Aldo Marzullo et al. (+13 more)
Clinical decision support is moving toward committees of
language-model
agents deliberating on a shared workspace. We ask whether such committees can be gamed by shortcuts, cues a
benchmark rewards but a clinician would ignore. Across seven cohorts on six public
datasets spanning text (MedQA-USMLE, MedMCQA, MIMIC-CXR reports), imaging (NIH ChestX-ray14, MIMIC-CXR-JPG, CheXpert) and tabular ICU records (SUPPORT2), Gemini committees resist these cues in isolation (flip 5-16%), yet a socially plausible shortcut spreads: when two peers assert the same wrong answer, the holdout under test adopts it in 38% of cases, as does a false "pre-screen" system flag, on both capability tiers. Of three oversight
agents, a gate cannot separate adoption from honest agreement (false-positive rate 100%); a same-lineage judge reading only the transcript flags adoption on text (precision 100%, recall 93%) but collapses onto the gate in imaging; a referee that privately re-queries the holdout transfers to imaging (77-88% precision, 13-21% false-positive rate). Tripling a cue's visual salience does not move contagion, whereas a second peer voice raises it by half again. Gaming a hidden rubric is near-silent: only 1/10 text and 1/134 imaging drifters name the rubric they moved toward. What games a committee is social plausibility, and only a referee independent of self-report catches it. Code: https://github.com/criticaldata/benchmaxxing
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All authors Sebastián Andrés Cajas Ordóñez, Agastya Munnangi, Aldo Marzullo, Felipe Ocampo Osorio, Quang Bui, Mohammad Shahin, Armaan Grewal, Emmanuel Paul Kwesiga, Anqi Peter Li, Josephine Nanyonjo, Aaditya Panchal, Arshnoor Bhutani, Nikhil Jaiswal, Milit S. Patel, Maximin Lange, Leo Anthony Celi
Artificial Intelligence · Cross-listed from cs.SE
Can LLMs Test Terminal User Interfaces?
Chao Peng, Ruida Hu, Ajitha Rajan et al. (+3 more)
Terminal User Interfaces (TUIs) combine the stateful, screen-oriented behaviour of GUIs with terminal deployment and are now common in developer tools. Yet they lack a dedicated testing methodology. We survey 197 real-world TUI applications: only 12% of test code exercises the interface, and 45% of those tests never send input, checking a static frame instead. We turn these applications into a headless
benchmark spanning ratatui/Rust, bubbletea/Go, textual/Python, and ink/TypeScript, packaging each as an instrumented Docker image. We record line and widget coverage where reliable, rendered terminal states, and crashes. Under equal wall-clock budgets, we compare four frontier LLMs with random exploration. No model dominates. Random is a strong time-budgeted baseline, but its crash advantage comes from higher throughput: per interaction, LLM guidance is more efficient and uniquely reaches input-gated faults. Automatically deriving launch inputs yields the largest practical gain, enabling applications that otherwise never start. Line coverage poorly predicts crash discovery, weakening it as a proxy for test effectiveness. Automated TUI testing is feasible but far from solved, and honest baselines matter more than model choice. We release the coverage tool tuicov at https://github.com/tui-testing/tuicov and the testing framework tuibot at https://github.com/tui-testing/tuibot.
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All authors Chao Peng, Ruida Hu, Ajitha Rajan, Tegawendé F Bissyandé, Jacques Klein, Cuiyun Gao
Artificial Intelligence · Cross-listed from cs.SD
AI-Based Sound Effect Generation: A Narrative Review of Generative Models Across Input Modalities
Sandy Abdo, Bill Kapralos, Priyamvada Tripathi et al. (+2 more)
Sound effects play a crucial role in conveying actions, events, and environmental cues across digital applications, often requiring a high degree of variation and contextual adaptability. Artificial intelligence (AI)-driven audio generative models are rapidly growing in popularity and have the potential to transform the way sound is synthesized and used across various applications. In response to this growing momentum, this chapter reviews and analyzes recent AI-based generative models for sound effect synthesis, with a focus on how different input modalities (text, visual, audio, and
multimodal) affect the quality, controllability, and contextual relevance of the generated audio. It examines 30 peer-reviewed articles sourced from Google Scholar, IEEE Xplore, and the ACM Digital Library, exploring the
evolution of AI generative models over the past five years. The results show that multiple models achieved state-of-the-art performance, producing high-fidelity, semantically aligned, and increasingly temporally coherent sound effects across tasks. However, despite these advances, the review identifies persistent challenges, including limitations in temporal synchronization for complex multi-event scenarios, gaps between objective metrics and human perception, and trade-offs between controllability and generative diversity. Overall, the chapter highlights that AI-driven sound effect generation is progressing toward more adaptive, scalable, and context-aware systems, offering significant implications for future sound design workflows and interactive media applications.
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All authors Sandy Abdo, Bill Kapralos, Priyamvada Tripathi, KC Collins, Adam Dubrowski
Artificial Intelligence
MissClick: Execution-Aware Adversarial Attacks on Coordinate Generation in GUI Grounding Models
Yu Ran and Wentao Zhao
Recent GUI visual grounding models generate screen coordinates as digit-
token sequences that are parsed into numerical values and mapped to executable clicks. This generation-to-execution interface creates an attack surface that existing objectives over visual representations or coordinate-
token sequences do not explicitly model. Although each coordinate digit is predicted as a
token, its spatial effect after parsing depends on decimal position: changing a hundreds-place digit by one shifts the coordinate by 100 units, whereas the same change at the ones place shifts it by one. This mismatch motivates attack objectives that account for both numerical coordinate structure and click execution. Moreover, untargeted and targeted attacks require different objectives because they aim to move the click outside the correct region and into an attacker-specified region, respectively. We propose MissClick, an execution-aware white-box attack that aligns optimization with click-level success conditions. MissClick-U maximizes soft-coordinate displacement for untargeted disruption, while MissClick-T minimizes a place-weighted target-digit loss for targeted redirection. On OS-Atlas and UGround across desktop, web, and mobile platforms, MissClick-U achieves untargeted success rates of 75.07% and 72.93% (+16.62 and +30.72 pp), while MissClick-T achieves targeted success rates of 44.86% and 62.67% (+31.73 and +47.06 pp). Among the evaluated objectives, soft-coordinate displacement performs best for untargeted attacks, whereas place-weighted target-digit optimization performs best for targeted attacks, supporting goal-specific execution-aware objective design.
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Artificial Intelligence
AgenticECO: An Agentic Framework for ECO on 3D Integrated Circuits
Shuo Ren, Yaohui Han, Libo Shen et al. (+4 more)
As Moore's law slows, the industry is turning to three-dimensional integration; yet in merged 3D-IC flows, routed designs expose bond-level defects with no 2D analogue, and post-route engineering change orders (ECO) remain manual, expertise-bound work. Worse, the standard edit-then-fully-reroute practice entangles a repair with router churn, so a signoff number cannot be attributed to the edit that motivated it. We present AgenticECO, an evidence-gated tool-using
agent workflow for 3D-IC ECO on the open-source TaiWei flow, paired with EcoRoute, a minimal-disturbance ECO-routing layer that drives the unmodified pinned router so a repair is attributable to its edit. Across nine matched natural defect cases under identical budgets, AgenticECO clears seven versus two for both full reroute and stock repair, at 0.66% mean disturbance over cleared cases and zero clock nets touched, and a cross-backbone rerun under the same sealed contract clears all nine. Controlled studies show that the repair moves are necessary under preservation, that occupancy-aware choice buys legal landings rather than repair success, and that under tightened clocks minimal disturbance flips accept versus reject. Three preregistered visual studies localize the pixel instrument's edge to contested landing sites, and a preregistered blind diagnostic exactly restores every held-out injected defect, the only arm with zero wrong edits. Every accepted result passes routing, fresh extraction, max/min timing, DRC, and structural-equivalence gates. Code, environment, and per-episode audit artifacts are released as supplementary material.
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All authors Shuo Ren, Yaohui Han, Libo Shen, Zhiqiang Jia, Rongliang Fu, Bei Yu, Tsung-Yi Ho
Artificial Intelligence
Failure-Informed Image Self-Augmentation for Multimodal Large Language Model Self-Improvement
Chunyang Jiang, Pingping Zhang, Yuzhi Zhao et al. (+9 more)
Multimodal large
language models (MLLMs) have achieved remarkable performance across
vision-
language tasks, but their progress depends heavily on large-scale, high-quality
multimodal data that are costly to annotate. Self-augmentation offers a promising alternative by enabling models to expand their own training data without external supervision. However, existing MLLM self-augmentation methods are largely text-centric, while image augmentation remains underexplored and typically relies on generic or handcrafted transformations that are weakly aligned with the model's actual incapability. We propose Failure-informed Image Self-Augmentation (FISA), a framework for MLLM self-improvement that constructs augmented images from the model's own failure cases. Our method generates visually challenging yet answer-preserving image complications, verifies their utility through self-examination, and applies dual fidelity filtering to avoid semantic distortion. Experiments on visual question answering
benchmarks show that the proposed method consistently improves performance across both in-distribution and out-of-distribution settings. Further experiments validate the compatibility of FISA with existing textual self-augmentation approaches, the superior data efficiency of the synthesized samples over generic image augmentation baselines, and the practical effectiveness of the proposed filtering strategy.
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All authors Chunyang Jiang, Pingping Zhang, Yuzhi Zhao, Wenao Ma, Zhijian Hou, Mengyang Wu, Yiyang Cai, Senkang Hu, Sitong Cheng, Chi-Min Chan, Wei Xue, Yike Guo
Artificial Intelligence
CARE-Bench: Benchmarking Patient-Facing LLM Triage
Yining Hua, Hongbin Na and Cyrus Ayubcha
Patient-facing medical LLMs and
agents increasingly answer symptom questions before clinician contact, where the key safety question is what action the user should take next. We introduce CARE-Bench, a source-grounded
benchmark that evaluates sequential patient-facing triage as a four-label per-turn current-action task. CARE-Bench contains 500 cases and 1,059 evaluated patient-disclosure prefixes reconstructed from medical dialogue, consultation, and follow-up-question sources. We evaluate 11 models on 269 held-out rounds under unprompted and minimally prompted open-ended protocols, using a fixed GPT-5.5 mapper to code each response into the four-label action space. Unprompted macro-F1 remains low, ranging from 31.2 to 50.4. Prompting improves 10 of 11 models, with prompted macro-F1 ranging from 46.9 to 63.4, but substantial threshold errors remain. Prompted models often recommend care before needed clarification is obtained; when the correct action was to ask for more information, only 33.5% of prompted outputs preserved the step. The persistence of these errors after prompting suggests that patient-facing triage is not a simple prompting problem and supports explicit evaluation of action timing before deployment.
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Artificial Intelligence · Cross-listed from cs.CL
Constructing Disambiguated Knowledge Bases from Large Language Models at Scale
Yujia Hu, Tuan-Phong Nguyen and Simon Razniewski
Automated Knowledge Base Construction (AKBC) is a core NLP task, and recent work proposes generating knowledge bases directly from large
language models (LLMs), treating the model itself as the knowledge source. However, LLMs natively possess no representation of entities, leading to duplicate entries as well as conflations. We propose GPTKB 2.0, a methodology for constructing disambiguated KBs directly from LLMs. GPTKB 2.0 incorporates on-the-fly disambiguation of entities, relations and classes, and is meticulously designed to satisfy both scalability and disambiguation accuracy. We analyze the central design decisions and characterize the trade-offs between accuracy, scale, and cost. We execute GPTKB 2.0 at scale, obtaining a materialized KB containing over 1M disambiguated entities and 38.4M triples. This represents the first million-scale LLM-native KB with explicit internal canonicalization of entities, relations, and classes, a significant departure from prior Wikimedia-centric works. GPTKB 2.0 is available at https://gptkb.org/.
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Artificial Intelligence
SAT-Edge-Agent: Hardware-in-the-Loop Edge-Agent Orchestration for Onboard Satellite Intelligence
Longji He and Jeto Xu
Onboard satellite intelligence requires a task layer that translates mission intent into local tool calls, exposes execution state, and returns machine-consumable artifacts under communication and power constraints. We present SAT-Edge-
Agent, a hardware-in-the-loop (HIL) edge-
agent system deployed on a commercial off-the-shelf ARM-based heterogeneous edge system-on-chip. A browser workspace and FastAPI
agent coordinate a local OpenAI-compatible
language service with a project-internal YOLO-style oriented-object-detection endpoint that returns FAIR1M metadata-backed structured results. Two fixed FAIR1M workloads, one single-image and one serial two-image request, were repeated 20 times each and completed 20/20 attempts. Mean Full-
Agent latency was 29.353 s and 60.937 s, with empirical P95 values of 31.166 s and 66.882 s. Mean detector time was 861.386 ms and 1510.920 ms, only 2.93% and 2.48% of the corresponding Full-
Agent means. Profiling indicates that most visible latency occurs outside detector execution. Mean CPU utilization was 20.761% and 20.482%. A 200-ms NPU-load field averaged 100% for both workloads, but it represents a shared-accelerator software field rather than detector-only occupancy or calibrated utilization. The public evidence package provides sanitized request-level records, redacted JSON, normalized SSE examples, and scripts reproducing the reported statistics. These results establish a reproducible HIL boundary for observable satellite edge-
agent orchestration, but do not establish detector accuracy, a new geolocation method, calibrated energy efficiency, or flight readiness.
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Artificial Intelligence
When Outputs Disperse, Does Epistemic Revision Follow? A Black-Box Diagnostic for Machine Collectives
Molood Arman
Collective intelligence research treats disagreement as evidence of epistemic diversity: if
agents express different views, the group should retain capacity to revise. In LLM collectives this proxy can break:
agents can produce diverse-looking arguments while preserving the same conclusion. We operationalize dispersion-revision coupling: the degree to which an intervention that verifiably increases the dispersion of a collective's outputs in
embedding space is accompanied by genuine revision of its epistemic stance rather than premise-preserving reformulation. The diagnostic is black-box: it operates on generated text alone and makes no claims about the internal representations of the generating models. Two channels are measured independently: an output channel, the Coherence Index (CI), verifies that the intervention changed output dispersion; an epistemic channel, per-turn stance annotation, measures whether the collective revised. We propose CI with the Meta-Predictive Clarity System (MPCS), which inserts a Re-Differentiation Protocol (RDP) when outputs over-converge, as a reusable method for estimating this coupling regime. We evaluate five-
agent collectives from two configurations (gpt-4o-mini and gemini-2.5-flash; 310 paired episodes per condition). On gpt-4o-mini, conditional dissent improves false-premise recovery by +17.7 points (p<1e-6) while static persona diversity harms recovery (-8.1, p=.007). On gemini-2.5-flash, the same intervention at a comparable budget yields no gain (26.1% vs 27.1%, p=.84) despite a verified dispersion drop; the two treatment effects differ from each other (z=3.79, p<.001). Mechanism tagging shows Gemini preserves the false premise via intra-framework dissent: 94% of tagged post-RDP responses reformulate rather than concede (vs 24% on GPT). We recommend reporting per-intervention stance shift and premise-preservation rate alongside accuracy.
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Artificial Intelligence
Less Traffic, Better Outcomes: Competition-Aware Request Dispatch in Real-Time Ad Exchanges
Jonaid Shianifar, Blaz Mramor, Fangda Zou et al. (+5 more)
Real-time bidding (RTB) ad exchanges typically forward nearly all incoming requests to demand-side platforms (DSPs), even though only a small fraction receive bids. This over-distribution weakens auction outcomes: DSPs throttle participation under compute and budget constraints, reducing the effective use of limited bidding capacity. We present a competition-aware request dispatch framework that uses distributional bid prediction and probabilistic forwarding to decide whether each request should be sent to each DSP. The system adapts per-DSP thresholds over time through lightweight policy optimization to track non-stationary
market conditions. We evaluate the framework through four sequential online experiments on a production platform serving over 20 billion daily requests. A full multi-DSP deployment reduces DSP request volume under the policy by 34.2% while increasing net revenue by 4.6% (p<0.001) in a recent 14-day window after an initial DSP adaptation period. Further analysis highlights strong heterogeneity across traffic segments and reveals that aggregate metrics can be misleading. Segment-level and per-DSP analyses suggest that the policy surfaces comparative advantages among DSPs, improving monetized outcomes without increasing overall request volume.
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All authors Jonaid Shianifar, Blaz Mramor, Fangda Zou, Matthieu C. Martin, Xingsheng Guo, Zhihua Zhu, Rong Zhou, Bichen Shi
Artificial Intelligence · Cross-listed from cs.RO
LiLa-WAM: Lightweight Latent Reasoning World-Action Model for Robotic Manipulation
Fan Yang, Yuting Su, Xiaobo Wang et al. (+7 more)
World-action modeling has emerged as a promising paradigm for robotic control, as it empowers models to go beyond reacting to observations and anticipate how a scene will evolve. However, existing WAMs often incur substantial computational overhead. Pixel-space methods often allocate substantial capacity to visual details that may not be directly relevant to control, while some latent-space methods require multi-stage training to construct the
reasoning space. The resulting training cost can make such methods difficult to train under modest computational budgets. In this work, we propose LiLa-WAM, a lightweight world-action model that reasons about the future in a compact latent space and can be trained end-to-end on a single 24GB GPU. Its core design is a compact latent
reasoning space jointly shaped by future-state prediction and action generation, which keeps the model lightweight while remaining well aligned with control. For task specification, we further propose the Visual Transition
Token(VTT), a
language-free task representation that encodes each task as a direction in visual feature space. Experiments on RoboTwin~2.0, LIBERO, and real-
robot tasks demonstrate LiLa-WAM's effectiveness, achieving 90.48% success across 50 RoboTwin tasks with single-GPU training.
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All authors Fan Yang, Yuting Su, Xiaobo Wang, Yuncheng You, Fugui Fan, Yuting Wu, Minghui Wu, Chenxu Zhao, JiaHong Ning, Peiguang Jing
Artificial Intelligence
TARL: Transaction-Aware Reliable Ledgers for Executable Memory Management in Long-Term Agents
Han Xiao, Hongjun Xu, Xin Zhang et al. (+2 more)
Persistent
memory helps long-term
agents retain knowledge, yet a single update error can repeatedly distort future
retrieval and
reasoning. Most existing systems reduce
memory updating to a binary Write/Hold decision, which cannot distinguish whether new information should be added, ignored, used to revise an outdated belief, rejected as unreliable, or deferred for verification. These choices may share the same binary label while producing fundamentally different
memory states. We introduce TARL, a
memory state update framework that maps each statement to one of five executable actions. TARL identifies the affected
memory, resolves its temporal scope, compares source reliability, and updates accepted, pending, and rejected ledgers. It is further trained by comparing the
memory states produced by alternative update operations, encouraging the model to select the operation that leads to the correct result. We also introduce TARL-Mem, a
benchmark with fine-grained action labels and next-state targets. Across in-domain, cross-source, temporal, counterfactual, and sequential evaluations, TARL improves action prediction and state recovery, reduces
memory pollution, preserves conflicting evidence, and limits cumulative corruption.
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All authors Han Xiao, Hongjun Xu, Xin Zhang, Yidong Chen, Xiaodong Shi
Artificial Intelligence · Cross-listed from cs.SE
Pattern over Pixels: Measuring Pattern Completion Bias in Multimodal Code Generation
Khai-Nguyen Nguyen, Oscar Chaparro and Antonio Mastropaolo
Multimodal large
language models (MLLMs) are increasingly used to translate webpage screenshots into front-end code, but repeated UI patterns may sway them toward visually incorrect yet pattern-consistent outputs. In this work, we test how repeated webpage patterns hurt MLLM accuracy on an objective screenshot-to-code fill-in-the-blank task. We introduce the first
benchmark for visual pattern-completion bias, where one localized element in a repeated UI pattern is perturbed and the model must recover the masked width or font-size value from the screenshot and HTML context. Starting from 30 webpages curated from the Design2Code
dataset, we build 1,440 evaluated screenshots spanning structural card and text-style patterns under standard and noise-overlaid conditions. We evaluate five frontier MLLMs and find that all are strongly biased toward the repeated baseline. Mean bias rate reaches 69.78% on card-width perturbations and 80.22% on text font-size perturbations, while mean accuracy is only 21.17% and 7.89%, respectively. Codex-5.3 performs best but still drops from 68.61% accuracy on cards to 13.89% on text, while Flash-3.0 reaches 96.11% bias on text. Noise, subtler perturbations, and boundary positions further increase bias rate.
Reasoning analysis further shows that greater
reasoning effort correlates with lower bias, yet qualitative evidence reveals that models can identify the anomalous element and still override it with the pattern-consistent answer. Our results identify a concrete failure mode in
multimodal code generation and show that its severity is strongly associated with visual saliency
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Artificial Intelligence
LiveEvalBench: Toward Open-World Evaluation for Web Generation
Yiyao Wang, Zhen Wen, Yinghao Tang et al. (+5 more)
Large
language models are increasingly capable of synthesizing executable frontend projects, yet existing
benchmarks still treat web generation as a static evaluation problem. We argue that frontend artifacts demand a different paradigm: they are interactive rather than static, admit diverse yet equally valid implementations, and evolve faster than rigid pipelines can accommodate. To address these gaps, we present LiveEvalBench, an automated framework that reformulates web-generation evaluation as an agentic, adaptive, and extensible process. LiveEvalBench instantiates evaluation as a collaborative review workflow, in which a Build Engineer, a Code Engineer, and a UI Tester collectively gather evidence across the full lifecycle of a frontend project, from deployment and code inspection to browser-based interaction. To handle implementation diversity, an adaptive protocol couples shared rubrics for cross-model comparability with implementation-grounded criteria tailored to each artifact. The framework further supports incremental integration of new evaluator roles and assessment dimensions without pipeline redesign. Experiments across diverse real-world web-generation scenarios show that LiveEvalBench aligns closely with human expert judgment and provides fine-grained insights into frontier models' web generation capabilities. Code is available at https://github.com/wyysteelhead/LiveEvalBench
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All authors Yiyao Wang, Zhen Wen, Yinghao Tang, Yixiao Fu, Lin Yuan, Xiaolau Zhang, Jun Zhou, Wei Chen
Artificial Intelligence
PhyAI: Real-Time Physical AI at the Edge, Scalable Rollouts in the Cloud
Chenghua Wang, Daliang Xu, Dongqi Cai et al. (+23 more)
Physical AI policies require inference throughout their lifecycle, including model evaluation, cloud reinforcement learning rollout, edge GPU serving, and onboard deployment. Although these settings share the same checkpoint and action semantics, they often rely on separate inference programs. To unify them, we build PhyAI, a Physical AI inference engine with a single runtime that keeps architecture-specific conditioning, solver, cache, and output logic in model adapters while sharing graph execution, kernels,
memory management, and parallel services. The same codebase runs
vision-
language-action (VLA) models and world-action models (WAMs) on single or multiple GPUs across onboard, edge, and cloud deployments. We used the adapter interface to add MiniCPM-
Robot on the day of its release. PhyAI achieves 1.40x-4.65x speedups over the official implementations of pi0, pi0.5, GR00T N1.7, and MiniCPM-
Robot. On Cosmos3-Nano-Policy-DROID it reduces latency from 2.46 to 1.18 s on eight H20 GPUs (CFG=2, TP=4), a 2.08x speedup. Specialized runtimes remain faster in several configurations, so our goal is one runtime with competitive latency rather than the fastest result in every case. Detailed profiles reveal why different models need different execution policies: on a Hopper-series GPU at batch size one, the pi0.5 action expert accounts for 8.8% of FLOPs but 57.2% of latency; at batch size 32 its share drops to 13.5% and throughput reaches about 100 samples/s. Cosmos3 remains generation-dominated and gains only 14.3% throughput as batch size increases from 1 to 16. We further introduce the control-time Roofline, which distinguishes inference-bound from environment-bound control; the measured pi0.5 points on four LIBERO suites are environment-bound while Cosmos3 stays inference-bound. Code and
benchmarks: https://github.com/mingti-org/phyai.
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All authors Chenghua Wang, Daliang Xu, Dongqi Cai, Duojin Sun, Hao Zhang, Haoze Qian, Huaiyuan Zhang, Jinshuo Cui, Junbo Cui, Kezhao Zhao, Longxi Gao, Mengwei Xu, Rongjie Yi, Ruixin Liu, Shangguang Wang, Tam Sikyuen, Tianyue Zhang, Weikai Xie, Xuanzhe Liu, Yingying Qin, Yiwen Lu, Yuan Yao, Yuezhi Zu, Yunhan Guo, Yuxin Zheng, Ziqi Guo