Artificial Intelligence · Cross-listed from physics.optics
Two-Stage Deformable-Convolutional Inverse Design of Nanophotonic Absorbers from Optical Spectra
Waleed Waseer, Muhammad Shahid Jabbar, Muhammad Sohail Ibrahim et al. (+1 more)
Data-driven inverse design enables efficient generation of nanophotonic structures with prescribed optical responses, but spectrum-to-geometry mapping remains challenging due to non-uniqueness and fine
geometric features. This work presents a two-stage deformable-convolutional framework for reconstructing metal–
insulator–metal resonator geometries from 80-dimensional absorption spectra. The spectrum is projected to a $150\times4\times4$ latent representation and decoded into a $64\times64$ resonator mask. Training combines supervised reconstruction with least-squares
adversarial refinement initialized from the best supervised checkpoint. A three-run ablation compares deformable convolution with plain convolution, involution, Dynamic Conv, and ODConv under the same architecture. The proposed model achieves $20.79\pm0.31$~dB PSNR and $0.8501\pm0.0082$ SSIM, improving over plain convolution by 2.16~dB and 0.0831, respectively. It further achieves Dice $0.9623\pm0.0027$, IoU $0.9342\pm0.0038$, and boundary F-score $0.9550\pm0.0027$. Spectral consistency evaluated using a frozen forward surrogate yields RMSE $0.0805\pm0.0013$ and $R^2=0.7923\pm0.0065$. Learned offsets show stronger adaptive sampling at coarse and intermediate decoder stages. Overall, deformable sampling with supervised initialization and
adversarial refinement improves spectrum-conditioned geometry reconstruction.
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All authors Waleed Waseer, Muhammad Shahid Jabbar, Muhammad Sohail Ibrahim, Shujaat Khan
Artificial Intelligence · Cross-listed from cs.CV
LookBack: Where and How to Score LVLM Responses via Visual Reference Usage
Beomsik Cho, Jinhyeong Kim, Dongseok Lee et al. (+1 more)
Large
Vision-
Language Models (LVLMs) integrate visual perception with
language generation, enabling responses that span image understanding and complex
reasoning. However, LVLMs do not just inherit the text-level
hallucinations; they also hallucinate against the image, producing fluent responses ungrounded in what they see. This makes LVLM response scoring inherently harder, and our diagnostics show that existing confidence-based metrics adopted from LLMs are insufficient for LVLMs. Specifically, removing the input image barely changes confidence-based selection, suggesting that output-space confidence primarily captures textual plausibility rather than agreement with the image. To address this gap, we propose LookBack, a training-free LVLM response scoring method that augments
token likelihood with visual lookback score, a lightweight measure of how strongly each response
token refers to image
tokens. Across four
benchmarks and three models, LookBack consistently improves Best-of-$N$ selection over existing baselines with negligible additional overhead.
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All authors Beomsik Cho, Jinhyeong Kim, Dongseok Lee, Jaehyung Kim
Artificial Intelligence · Cross-listed from cs.DC
User-Assisted Collaborative Distributed Inference for Efficient QoS-Aware Autoscaling
Alfreds Lapkovskis, Ali Beikmohammadi, Sindri Magnússon et al. (+1 more)
Growing demand for artificial intelligence (AI) inference services requires scalable infrastructure, yet centralized serving costs rise with demand. We propose a collaborative distributed inference system combining dedicated infrastructure with resources contributed by service users. Dedicated resources provide baseline capacity for maintaining quality of service (QoS), while volunteered resources absorb increasing demand without proportional growth in centralized infrastructure. To capture
stochastic and dynamic interactions among users, resources, tasks, and policies, we develop a high-dimensional generative Markov model with structured temporal factorization. The model supports simulation and provides a foundation for task scheduling and QoS-aware resource allocation optimization. We evaluate the system across user populations, resource capacities, and centralized and distributed scheduling policies. Simulations show that distributed scheduling becomes increasingly advantageous as the user population grows, improving request completion and P99 latency while substantially reducing dedicated resource consumption. These results demonstrate the feasibility of user-assisted collaborative inference for infrastructure-efficient autoscaling.
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All authors Alfreds Lapkovskis, Ali Beikmohammadi, Sindri Magnússon, Praveen Kumar Donta
Artificial Intelligence · Cross-listed from cs.CR
How China-Origin Vision-Language Models Move from Refusal to Reframing in State Alignment
Guang Yang, Fengchen Liu, Alex Wang et al. (+2 more)
State-aligned distortion has been documented in China-origin text-based large
language models (LLMs), but whether, and in what form, it arises in
multimodal systems has not been systematically examined. We construct a balanced
benchmark of 200 core entries spanning ten politically sensitive topics, plus a seven-variant visual-abstraction probe, and run nine
vision-
language models (VLMs), seven China-origin and two non-China, across four elicitation paradigms and two
prompt languages, yielding 21,708 trials. Each response is audited on six dimensions – explicit refusal, information integrity, visual grounding, state-aligned framing,
language consistency, and response length – by two independent frontier LLM judges, validated against three human experts on a 200-trial sample. Measuring each dimension separately lets us decompose
multimodal censorship into individual signals rather than a single refusal-based score; in particular, refusal and framing are measured independently, so a model can stop refusing while still reframing. We find that (i) Chinese-
language prompting roughly triples the odds of state-aligned framing, within every model; (ii) China-origin models reframe more than non-China models (direction robust across judges and human raters; magnitude 1.6–3.2x); (iii) the effect is strongest in text-only political commentary (36.5%) and is gated by recognition of the depicted subject rather than pixel detail, persisting even at silhouette for iconic images; and (iv) across four Qwen generations, state-aligned framing rises while explicit refusal falls: censorship migrates from a visible act (refusal) to an invisible one (fluent reframing). We argue this shift to invisible reframing is fundamentally a problem of human-AI interaction: it removes the very signal users rely on to recognize that information has been withheld.
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All authors Guang Yang, Fengchen Liu, Alex Wang, Homa Hosseinmardi, Amir Ghasemian
Artificial Intelligence · Cross-listed from cs.CR
Towards Model-based Run-time Cybersecurity: On Control-Flow Anomaly Detection, Attack Identification, and Hardware Monitoring
Martin Sachenbacher, Martin Leucker, Alexander Weiss et al. (+1 more)
Methods to increase the resilience of systems to cyber-attacks become increasingly important. Control-flow monitoring provides a principled basis to ensure integrity and detect possible anomalies at run-time. Once anomalies have been detected, so-called attack trees can be used to identify possible types of attacks. However, this approach is vulnerable to camouflage, by which attackers try to evade detection (and correct identification) by deliberately manipulating also the system's observed control flow. In this paper, we outline a model-based approach that provides more robust intrusion detection and attack identification through an architecture that combines software- with hardware-based monitoring. In this approach, software-level observation indicates suspicious activities, while hardware-level monitoring checks them separately in more detail, making it much harder for attacks to camouflage themselves and go undetected. We illustrate the approach with an authentication-service example that captures a realistic failure mode: a software-level observer sees an anomalous but apparently harmless control-flow deviation, maps it to a benign root cause in an attack tree, but misses the true intrusion. A second, independent hardware control-flow monitor observes the actual transition sequence and thereby changes the attack-tree diagnosis from a low-severity configuration or maintenance issue to a high-confidence code-injection or control-flow hijack. In this scenario, the proposed combination of control-flow anomaly detection, attack-tree based intrusion identification, and hardware-based monitoring can improve not only anomaly detection, but also the diagnostic precision of attack-tree-based cyber-attack identification.
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All authors Martin Sachenbacher, Martin Leucker, Alexander Weiss, Aliyu Tanko Ali
Artificial Intelligence · Cross-listed from cs.CY
Toward Meaningful Transparency for AI Chatbots: Disclosing Persuasive Intent Reduces Persuasion
Adrian Rauchfleisch and Andreas Jungherr
The growing role of AI-generated content and AI-enabled systems in public communication has led regulators to demand clear disclosure of content provenance and AI involvement. But the effects of such disclosures remain uncertain. We test two disclosure approaches in their impact on an AI chatbot's persuasive appeal. In a preregistered experiment, 1,500 UK adults held a short conversation with a persuasive chatbot about one of 60 policy issues. The chatbot was identical for everyone. We randomized the disclosure that people received: nothing (control), a prominent disclosure that they were interacting with an AI (T1), or that disclosure plus the chatbot's persuasive intent and instructions (T2). The chatbot shifted attitudes by 12.6 points on a 100-point scale in the control group. The AI-identity disclosure was practically equivalent to no disclosure, with a 13.1-point shift, whereas the additional intent disclosure cut the persuasive effect roughly in half to 6.3 points. It also made participants view the campaign's methods as less acceptable and support stronger penalties against it. For direct chatbot interactions, transparency about AI identity alone does not meaningfully impact its influence. While current rules emphasize what a system is, our results show why the regulation of persuasive AI must also address what the system is trying to do.
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Artificial Intelligence · Cross-listed from cs.CL
TELLME: Test-Enhanced Learning for Language Model Enrichment
Minjun Kim, Inho Won, Hyeonseok Lim et al. (+6 more)
Continual pre-training (CPT) has been widely adopted as a method for domain adaptation in large
language models. However, CPT has consistently been accompanied by challenges, such as the difficulty of acquiring large-scale domain-specific
datasets and high computational costs. In this study, we propose a novel method called Test-Enhanced Learning for
Language Model Enrichment (TELLME) to alleviate these issues. TELLME leverages the TestEnhanced Learning (TEL) principle, whereby the model's training efficiency is improved using quizzes during training. It integrates this principle with CPT, thereby promoting efficient domain-specific knowledge acquisition and long-term
memory retention. Experimental results demonstrate that TELLME outperforms existing methods by up to 23.6% in the financial domain and achieves a 9.8% improvement in long-term
memory retention.
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All authors Minjun Kim, Inho Won, Hyeonseok Lim, MinKyu Kim, Junghun Yuk, Wooyoung Go, Jongyoul Park, Jungyeul Park, KyungTae Lim
Artificial Intelligence · Cross-listed from cs.CL
GRPO for Financial Advice Generation: Outperforming Commercial LLMs under CATE Evaluation
Ofir Ben Shoham, Shrutendra Harsola, Vignesh Subrahmaniam et al. (+3 more)
Generating actionable financial advice from business records demands that models integrate numerical
reasoning, domain knowledge, and sound judgment, while avoiding recommendations that could harm the business. Direct supervision is difficult: historical decisions are not necessarily optimal, and high-quality free-form labels are expensive to obtain. We formulate financial advice generation as a reinforcement learning problem and fine-tune an open-weight
language model using Group Relative Policy Optimization (GRPO). Our reward is an LLM-as-a-judge rubric that scores each recommendation across multiple binary dimensions of advice quality, augmented with a safety gate for harm prevention. Since LLM-based evaluation alone cannot confirm whether improvements reflect genuine business value rather than adaptation to the judge, we complement it with a judge-independent audit based on a standard doubly-robust Conditional Average Treatment Effect (CATE)
estimator. Under this observational off-policy audit, our trained LLM achieves approximately twice the estimated gross-profit lift of the strongest evaluated commercial baseline ($0.0228$ vs.\ $0.0104$), together with the lowest downside rate and the least negative tail risk of any policy evaluated. Notably, the two evaluations do not rank the baselines identically: the untrained base model places last on the judge rubric but second on the causal audit, indicating that the audit captures a signal the judge does not. Our results demonstrate that GRPO with a finance-grounded reward signal can produce substantially more useful business recommendations than commercial LLMs, and that a judge-independent causal audit is a valuable complement to, rather than a confirmation of, LLM-as-a-judge assessment in financial NLP.
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All authors Ofir Ben Shoham, Shrutendra Harsola, Vignesh Subrahmaniam, Shravan Mohan, Yakov Gazman, Oded Vainas
Artificial Intelligence
The Sleeping Agent: What Gist-Based Context Compression Loses and Why
Nicholas E. Kyrkewood
Gist-based context compression—summarising older conversation history into compact representations—is a common approach in long-horizon
language model
agents, yet its effect on different types of
memory retrieval is poorly understood. We use Salience-Weighted Consolidation (SWC), a biologically-inspired compression framework motivated by sleep-based
memory consolidation, as a diagnostic probe to study when gist compression helps and when it hurts. SWC scores conversation history by salience, partitions it into priority tiers, and applies structured gist abstraction to mid-priority content. Evaluating four conditions on all ten LoCoMo conversations—1,935 matched text-only questions in total, 1,501 used in the primary aggregate after excluding Category 5 (
adversarial) questions—at temperature 0, we find a consistent task-type interaction: gist compression substantially outperforms truncation on multi-hop
reasoning and single-hop factual questions, but temporal questions remain substantially harder under compression, with compressed conditions scoring well below the full-context reference on the conversations where both are evaluated. We trace this failure to a specific mechanism: the gist abstraction
prompt preserves relational and event structure while discarding dates and times. A preservation analysis across all ten conversations confirms the mechanism: an approximately 20-fold increase in temporal expression preservation (3.05% to 62.39%) with a one-sentence
prompt modification, while named entity and event preservation rates barely change (x1.02 and x1.11), demonstrating that the fix is a precision instrument. The
prompt modification recovers +0.314 [0.254, 0.375] judge accuracy on category-2 (temporal) questions in the matched set. Code and results: https://github.com/kyrkewood/sleeping-
agent.
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Artificial Intelligence
HyperANFIS: Enhancing Rule Representation and Interpretability in Adaptive Neuro-Fuzzy Systems via Hyperbolic Geometry
Haoran Pei, Zhao Su, Zetao Lin et al. (+6 more)
The adaptive neuro-fuzzy inference system (ANFIS) is an interpretable
reasoning framework capable of generating explicit IF-THEN fuzzy rules, making it suitable for tasks requiring transparent
reasoning. However, existing ANFIS models generally construct rule antecedents and perform inference in Euclidean space, limiting their representational capacity and predictive performance. To address this issue, we propose
Hyperbolic ANFIS (HyperANFIS), a
hyperbolic extension of ANFIS. HyperANFIS preserves the fuzzy semantics and core architecture of conventional ANFIS while performing rule-prototype learning, rule activation, and consequent aggregation in
hyperbolic space. It also retains the ability to generate interpretable IF-THEN rules. By exploiting the representational properties of
hyperbolic geometry, HyperANFIS strengthens the fuzzy inference process, thereby improving predictive accuracy, inter-rule collaboration, and the credibility of its interpretable rules. Experimental results show that HyperANFIS consistently outperforms the standard ANFIS baseline and various ANFIS variants across all
datasets, while also generating higher-quality fuzzy rules.
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All authors Haoran Pei, Zhao Su, Zetao Lin, Haoran Li, Jun Shen, Qi Zhu, Lan Guo, Qingguo Zhou, Binbin Yong
Artificial Intelligence · Cross-listed from cs.SE
Instruction Alignment for Binary Code Representation Learning
Huaijin Wang and Shuai Wang
Binary code representation learning is a fundamental problem in software security and reverse engineering. Existing methods mainly learn function-level
embeddings that capture coarse-grained semantic relationships between binary functions, but they largely ignore fine-grained instruction-level correspondences. This limitation misses valuable supervision signals available from compiler debug information, which can support the learning of more accurate and interpretable binary code representations. We propose to leverage instruction
alignment knowledge to further improve binary code representation learning. Our preliminary study reveals that models finetuned for function-level binary code similarity exhibit substantially better instruction
alignment than their pre-trained model, suggesting a strong correlation between instruction
alignment and function-level
embedding quality. Motivated by this observation, we design a training approach that explicitly incorporates instruction
alignment as an auxiliary training objective. Our experiments show that instruction
alignment training improves
retrieval accuracy and provides more discriminative signal for the model's similarity judgments.
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Artificial Intelligence · Cross-listed from cs.LG
MOON: Multi-Objective OrthoNormalized Updates for Multitask Learning
Shiji Zhou, Kunlin Lyu, Lei Zhang et al. (+2 more)
Multi-objective optimization (MOO) has demonstrated significant success in multi-task learning by mitigating task conflicts through gradient manipulation. However, most existing methods flatten model parameters into vectors and perform gradient manipulation under Euclidean geometry, thereby overlooking the
matrix structure prevalent in modern architectures such as
Transformers. In this paper, we show that gradient manipulation in Euclidean space does not generally yield the steepest descent direction under
matrix geometry, potentially limiting optimization efficiency. Drawing from the theory of steepest descent for
matrix-valued parameters, we propose MOON (Multi-Objective OrthoNormalized Updates), which performs gradient manipulation under spectral–
nuclear norm geometry and uses the orthonormalized manipulated gradient for parameter updates. Theoretically, for smooth non-convex objectives, we establish convergence of the averaged Pareto-stationarity measure at rates of $\mathcal{O}(T^{-1/2})$ in the deterministic setting and $\mathcal{O}(T^{-1/4})$ under
stochastic gradients. Empirical results across various
benchmarks show that MOON consistently improves both optimization efficiency and final multi-task performance. Our code is available at https://github.com/KunlinLyu/MOON.
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All authors Shiji Zhou, Kunlin Lyu, Lei Zhang, Ruodong Wang, Yifan Sun
Artificial Intelligence · Cross-listed from cs.CV
JieZi: A Large-Scale Expert-Audited Dataset and Benchmark for Ancient Chinese Character Exegesis
Ran Li, Huiguo He, Jiahuan Cao et al. (+3 more)
The scholarly exegesis of ancient Chinese characters demands integrating visual observation, linguistic analysis, and historical context. However, existing computational approaches focus narrowly on subtasks such as character recognition and
retrieval, lacking the structured
datasets and
benchmarks required for comprehensive scholarly analysis. To address this limitation, we introduce Ancient Chinese Character Exegesis (ACCE), a
vision-
language question answering (VQA) task that models the scholarly exegesis process. ACCE is organized into four progressive levels: basic character identification, glyph-form analysis, meaning exegesis, and diachronic
evolution analysis. To support this task, we construct two complementary resources. JieZi-
Dataset is the first large-scale, expert-audited VQA training
dataset for ACCE, comprising over 500K QA pairs. It is constructed via a pipeline that reduces factual errors by constraining generation with expert-designed templates and source-text references. Human verification is further applied at each key stage to ensure scholarly accuracy. JieZi-Bench is an evaluation
benchmark aligned with the exegesis process, constructed and verified by human experts to ensure evaluation reliability. It consists of four levels with reference answers curated from authoritative lexicographic works held separate from the training data. Experiments on
multimodal large
language models show that current models perform well on basic identification but struggle with glyph analysis, semantic
reasoning, and diachronic understanding. Fine-tuning on JieZi-
Dataset substantially improves performance across all four levels. Code and
dataset are available at https://github.com/Ran00w/JieZi.
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All authors Ran Li, Huiguo He, Jiahuan Cao, Junle Liu, Hiuyi Cheng, Lianwen Jin
Artificial Intelligence · Cross-listed from cs.RO
G0.5: One Autoregressive Stream for Robot Reasoning and Action
Yicheng Liu, Zibin Dong, Baijun Ye et al. (+24 more)
The prevailing recipe for
Vision-
Language-Action (VLA) models couples a pretrained VLM with a separately trained flow-matching action expert. This makes the VLM a context encoder rather than a decision-maker. We introduce G0.5, a pretrained autoregressive VLA in which a single
transformer decoder emits
reasoning and action
tokens under a single objective. Three components make this tractable at foundation-model scale: a learnable cross-embodiment action tokenizer that maps heterogeneous
robot actions into a shared vocabulary; a native chain-of-thought stream interleaving task decomposition, object grounding, and action hints with action
tokens; and a visual
memory module that injects multi-second history through the
vision encoder. Because
reasoning and action share a single set of weights, the pretrained VLM's capabilities carry over to physical behavior: the model follows instructions closely, and
prompts directly steer action granularity, task horizon, and out-of-distribution scene handling without further training. Pretrained on a large collection of
robot datasets together with VQA samples, G0.5 surpasses state-of-the-art models across 7 independent regimes: real-world fine-tuning on R1lite and R1pro
robots (76.7% vs.\ 53.3% for $\pi_{0.5}$ and 24.4% for GR00T-N1.7), the 2025 BEHAVIOR Challenge on 50 long-horizon household mobile manipulation tasks using a generalist policy (31.4% vs.\ 26.3% for $\pi_{0.5}$ and 26.1% for the challenge winner), DROID post-training followed by zero-shot transfer to an unseen environment and objects (82.5%), a
language-following Pick-and-Place
benchmark, LIBERO (98.9%), RoboTwin 2.0 (93.3%), and SimplerEnv-Bridge (87.3%).
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All authors Yicheng Liu, Zibin Dong, Baijun Ye, Tianyuan Yuan, Tao Jiang, Anqi Yang, Shicheng Cao, Haonan Liu, Yue Sun, Zihan Guo, Xiao Liu, Dong Ke, Changxun Pan, Chenru Wu, Tailai Cheng, Xiaoshu Ren, Xinlei Zhang, Jianning Cui, Zijie Zhao, Haoyu Zhang, Kaiming Xu, Haodong Yang, Bowen Zhang, Jiahui Niu, Shaoting Zhu, Shiduo Zhang, Hang Zhao
Artificial Intelligence · Cross-listed from cs.CV
Advancing MLLM-based UAV Image Understanding and Reasoning: A Benchmark and a Training-Free Multi-Agent System
Haoyu Zhang, Shuoxun Zhang, Peng Ye et al. (+5 more)
Multimodal Large
Language Model (MLLM)-based
UAV aerial image understanding and
reasoning is essential for aerial intelligence yet poses distinct challenges arising from extreme scale variation, arbitrary camera orientations, and high object density. Despite growing interest, existing evaluations remain fragmented across individual
datasets and narrow tasks, leaving a critical gap in unified assessment of
UAV understanding and
reasoning capabilities. To fill this gap, we construct UAVQA-Bench, a
benchmark of 1,500 human-annotated QA pairs drawn from 13 public
UAV datasets, covering 6 capability dimensions and 16 tasks in both multiple-choice and visual grounding formats. Systematic evaluation of a broad range of open-source and closed-source MLLMs as well as
agent-based systems on UAVQA-Bench identifies three key failure modes: domain-toolset mismatch, unchecked error propagation, and static
reasoning. Motivated by these findings, we propose
UAV-MAS, a training-free multi-
agent system for MLLM-based
UAV aerial image understanding and
reasoning, comprising a Domain-Specific Perception Engine (DSPE) that routes queries to task-appropriate visual tools, a Context-Aware Iterative Refinement module (CAIR) that validates intermediate
reasoning to curb error accumulation, and a Difficulty-Aware Adaptive Search mechanism (DAAS) that adjusts search depth to question difficulty.
UAV-MAS with a 32B open-source MLLM achieves 77.0% overall accuracy on UAVQA-Bench, surpassing Gemini 3 Pro by 4.0%, while the 8B variant improves 8.7% over its base model.
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All authors Haoyu Zhang, Shuoxun Zhang, Peng Ye, Lin Zhang, Jiakang Yuan, Shenghong Yi, Yuening Wang, Tao Chen
Artificial Intelligence · Cross-listed from cs.CL
Locating and Controlling Implicit Personalization in Large Language Models
Yueru Yan, Siqi Wu and Thai Le
Large
language models (LLMs) often shift their outputs in response to implicit demographic cues even when users never state a demographic identity. Previous work has documented this behavior, but the connection between these behavioral changes and the model's internal activations remains unclear. Using matched cued and neutral conversations across five LLMs, we establish that a localized internal activation signal tracks changes in recommendations, with correlations up to r=0.87. When multiple cues appear together, their internal signals largely combine, but the changes in output do not simply add up. We further show that removing the internal signal associated with one cue can suppress its influence, often more effectively than asking the model to ignore demographics via prompting, while largely preserving general
benchmark performance. However, the ability to selectively remove one dimension's influence while leaving co-present dimensions intact remains highly model- and attribute-specific. These results connect implicit personalization behavior to an internal signal that can be analyzed and causally controlled.
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Artificial Intelligence · Cross-listed from quant-ph
A 12-CNOT Double Qubit Excitation Gate
Irfansha Shaik
Effective implementation of high-level
quantum gates is essential for practical
quantum computing. In this work, we presented, to the best of our knowledge, the first reported 12-CNOT decomposition of the double
qubit excitation operator. We compared our new
circuit with the previous SOTA 13-CNOT
circuits in 4 different metrics. Our new
circuit has the lowest CNOT count (12), lowest CNOT depth (8, roughly 27% reduction), and lowest total
circuit depth (15, 25% reduction) among all the previous SOTA
circuits. With output
qubit relabeling, the CNOT depth can be further reduced to 7 (roughly 36% reduction from 11). Further, we only added 2 extra 1q gates (from 11 to 13) compared to the best of the SOTA
circuits. As the double
qubit excitation operator can be used as a building block hundreds or thousands of times in practical
quantum algorithms, any reduction in such primitives compounds over the full
circuit, resulting in significant overall resource savings.
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Artificial Intelligence · Cross-listed from cs.CR
Fingerprinting Text-to-Image Diffusion Models via Collapsed Generation
Yuanmin Huang, Chen Chen, Geng Hong et al. (+5 more)
Proprietary text-to-image
diffusion models are increasingly distributed as hosted services and downloadable checkpoints, making their intellectual property (IP) protection an increasingly critical concern when model leakage, copying, or unauthorized fine-tuning is disputed. In this work, we present a non-invasive model fingerprinting framework based on collapsed generation, a phenomenon where certain input conditions produce highly consistent images across multiple
stochastic seeds. We show that collapsed generation is an intrinsic, model-dependent property of the learned generation process. These collapse-prone conditions therefore expose model-specific behavioral signatures, enabling reliable ownership verification without
embedding invasive watermarks. After preparing conditions on the source model, the framework verifies a suspect model under two access settings: (1) white-box pipeline access, where optimized continuous
embeddings can be injected into the generation process, and (2) black-box API-only access, where natural
language prompts are queried through the service interface. In both cases, ownership evidence is measured by whether the suspect model reproduces the source model's collapse behavior across
stochastic samplings. Extensive experiments across UNet- and
transformer-based
diffusion models show that collapsed generation fingerprints can distinguish different source models with low confusion. These fingerprints remain verifiable in fine-tuned derivatives and under common and adaptive model- or query-level obfuscations, while requiring only a modest verification query budget. Together, these results establish collapsed generation as a reliable intrinsic evidence source for non-invasive
diffusion model ownership verification.
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All authors Yuanmin Huang, Chen Chen, Geng Hong, Xiaoyu You, Hui Xue, Zhenxing Qian, Mi Zhang, Min Yang
Artificial Intelligence
Harness-IF: Evaluating Instruction Following Across Instruction Surfaces in Coding Agents
Zining Huang, Haoran Que, Hong Zeng et al. (+8 more)
When a coding
agent obeys a rule, it may simply have been going to do that anyway. Existing instruction-following
benchmarks cannot tell the difference: they concentrate rules in the user turn, while coding-
agent benchmarks emphasize final task success. We introduce Harness-IF, which scores operational rules one at a time from execution evidence: 60 realistic multi-turn coding items drawn from a 642-rule library, 256 rules receiving verdicts, placed on the five configurable surfaces a deployed
agent reads. To separate compliance from coincidence we introduce Against-Prior Accuracy (AP-Acc), which scores only rules labeled as opposing unprompted defaults, observed by re-running tasks with the rule withheld across nine probe builds and curated otherwise. Across 12 frontier models, accuracy spans 72.1-85.9% and AP-Acc 66.1-78.6%; every model is worse on against-prior rules, by 3.6 to 7.4 points (mean 5.81), and the direction survives a common-support analysis with item-clustered intervals. Aggregate scores therefore overstate compliance by a model-specific margin: prior control leaves the top build unchanged and exchanges three adjacent rank pairs. A counterbalanced conflict pilot on nine separate builds adds a second result: pooled precedence does not follow
prompt depth, with system
prompts, project files, and user instructions ahead of tool and skill descriptions.
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All authors Zining Huang, Haoran Que, Hong Zeng, Ge Zhang, Zuo Wang, Jin Chen, Haodong Wang, Zhongfei Hou, Changxin Pu, Shen Yan, Wenhao Huang
Artificial Intelligence
Proportional Analogies on Probability Distributions via Bayesian Updating
Pierre-Alexandre Murena
Analogies are quaternary relations of the form "A is to B as C is to D". Among the various formalizations of analogical
reasoning, proportional analogies provide an important axiomatic framework by characterizing valid analogies through a set of postulates. While proportional analogies have been extensively studied over Boolean, symbolic, and real-valued domains, their extension to probability distributions remains largely unexplored. In this paper, we introduce a notion of proportional analogy for probability distributions based on
Bayesian updating. Our approach builds upon the idea that two distributions are related whenever one can be transformed into the other through
Bayesian updating induced by a suitable set of observations. We investigate this framework for several standard members of the exponential family and discuss how it naturally extends to arbitrary probability distributions through Gaussian mixture approximations.
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Artificial Intelligence · Cross-listed from cs.CL
When the API Speaks the Wrong Language: Revisiting Post-Training for Multilingual Tool Use
Siddharth Chauhan, Thomas Butler, Abhishek Singhania et al. (+2 more)
The reliability of Large
Language Models (LLMs) for API calling degrades in
multilingual settings. A common failure occurs when a model selects the correct tool but generates argument values in an inconsistent
language, which we term Argument
Language Mismatch (ALM). Although semantically correct, such outputs are operationally invalid and not captured by standard API-calling metrics. We revisit post-training strategies for mitigating ALM and find that, in our
benchmark, supervised fine-tuning (SFT) provides a strong baseline, substantially improving argument
language consistency and end-to-end function call accuracy. Under consistent model selection, SFT achieves performance comparable to, and sometimes exceeding more complex reinforcement learning (RL) approaches. We further examine whether RL with structured, argument-aware rewards offers additional benefits. While methods such as Group Relative Policy Optimization (GRPO) can improve
language consistency and better preserve general
reasoning ability, these gains are incremental and most pronounced in generalization and multi-objective trade-offs. Overall, our results suggest that much of the performance in
multilingual API grounding can be achieved through careful supervised training, with RL providing targeted rather than fundamental improvements.
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All authors Siddharth Chauhan, Thomas Butler, Abhishek Singhania, Pankaj Porwal, Honey Gupta
Artificial Intelligence · Cross-listed from cs.LG
High-dimensional Multi-objective Bayesian Optimization with Learned Variable Interactions
Hongyan Wang, Jiayu Huang, Haotian Zheng et al. (+6 more)
Multi-objective
Bayesian optimization (MOBO) is effective in identifying the Pareto fronts for expensive black-box problems. However, most current MOBO approaches are limited to low-dimensional decision space due to its exponential sampling complexity. This paper presents decision variable interaction analysis-based MOBO, ViaMOBO, a generic framework for expensive multi-objective problems with high-dimensional decision space. The key idea of ViaMOBO is that it utilizes a variable interaction analysis model to determine whether the decision space can be completely or partially divided, and then performs local
Bayesian optimization in the divided decision subspaces. Through the variable analysis model, it can be derived whether the objectives in black-box problems are separable, partially separable, or non-separable based on the potential independent or interdependent relationships among decision variables without any strong assumptions. We compare ViaMOBO with the state-of-the-art MOBO methods on both synthetic and real-world
benchmarks. The experimental results demonstrate that ViaMOBO outperforms other related MOBO baselines in approximating the Pareto front of high-dimensional expensive multi-objective problems.
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All authors Hongyan Wang, Jiayu Huang, Haotian Zheng, Xin Gao, Chi Ding, Ying Liu, Xia Wang, Qing Xu, Keqiang Li
Artificial Intelligence
Making Your LLMs More Objective: Stabilizing LLM Safety Behavior Across Traits with Trait-Invariant Safety Tuning
Lang Cao
Aligned large
language models (LLMs) are expected to exhibit safety behavior based on the content of the user request: they should refuse unsafe requests and comply with safe ones. However, we show that the same request can elicit substantially different safety decisions under different traits assigned in the system
prompt, a failure mode we call trait-induced safety variation. To measure this failure, we introduce refusal-based metrics: Trait-Induced Deviation measures
dataset-level deviation from the no-trait baseline, while Trait-Induced Flip Rate measures whether the same request receives different safety decisions across traits. We then provide a representation-level analysis of the mechanism behind trait-induced safety shifts and find that traits perturb the model's safety representations within a low-dimensional subspace. To achieve trait-invariant safety, where safety behavior remains stable across traits, we introduce Trait-Invariant Safety Tuning (TIST), a simple yet effective self-
distillation framework that aligns an LLM's trait-conditioned behavior with its no-trait behavior. Guided by our analysis, we further propose Trait-Subspace Neutralization (TraSN), an instantiation of TIST, which enforces invariance only within the identified trait subspace. Experiments show that TraSN improves trait-invariant safety and strengthens harmful-request safety while preserving general capability. Our results highlight traits as an important factor in LLM safety and robust model behavior.
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Artificial Intelligence · Cross-listed from cs.LG
Robust and Efficient Noisy-Label Time-Series Classification via Dynamic Time Warping Based Granular Ball Computing
Ziqiang Li, Yun Liu and Gouhei Tanaka
Dynamic Time Warping (DTW)-based Nearest-Neighbor (NN) classifiers are effective for time-series classification but are vulnerable to mislabeled training samples and require numerous DTW computations during inference. We propose DTW-based Granular Ball Computing (DTW-GBC), which organizes temporally similar training samples into granular balls and performs classification at the granule level. We further develop two granular-ball construction strategies for DTW-GBC. Experiments on four
benchmark datasets with symmetric label noise show that the two DTW-GBC variants generally mitigate the performance degradation caused by label noise while requiring substantially fewer comparisons than DTW-based 1-NN during inference. These findings suggest that DTW-GBC provides a favorable balance between classification robustness and inference efficiency.
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Artificial Intelligence · Cross-listed from cs.LG
Consolidator: Learning Persistent Routed Memory Across Context Boundaries
Sungwoo Goo, Hwi-yeol Yun and Sangkeun Jung
Copying short-term
memory (STM) into a slower store can preserve state across a context boundary, but persistence alone does not ensure that the retained state influences subsequent
memory access. We test this distinction in a Phasor
Memory Network (PMNet) using Consolidator, a shared slot-local operator that transforms routed STM before accumulating it into long-term
memory (LTM), without replaying the source
tokens. After each consolidation, the KV cache and STM are cleared. The retained LTM can still be read and is also fed into the hierarchical router, thereby conditioning which explicit-
memory slots subsequent inputs access. We evaluate this mechanism on a two-segment modulo-10 mapping task in which the second segment updates the mapping at the same
memory address. Following a second consolidation and reset, a held-out query must recover the updated mapping from LTM. The backbone and
memory interface are frozen, leaving only 12.35K Consolidator parameters trainable (0.041% of a 29.95M model). Across five paired runs from the same STM-pretraining checkpoint, direct LTM routing raises updated-mapping recall from $44.38\pm1.94\%$ to $87.02\pm1.76\%$ ($+42.64\pm1.10$ percentage points), while immediate STM recall remains 89.90% in both conditions; both train separate Consolidators and retain the same LTM read paths. Learned consolidation outperforms forced identity accumulation by $21.40\pm1.91$ percentage points without routing and $68.70\pm1.76$ with routing. Thus, on this task, consolidated LTM serves as both retrievable content and an access state that shapes subsequent slot selection.
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Artificial Intelligence · Cross-listed from cs.LG
REOPD: Reliability-Adaptive Reward Extrapolation for On-Policy Distillation
Yang Sun, Lichao Ma, Houyuan Qin et al. (+5 more)
On-policy
distillation (OPD) trains a student on its own trajectories under dense
token-level supervision from a teacher. Reward-extrapolation methods such as ExOPD amplify the teacher-reference log-likelihood ratio to move beyond direct imitation, but apply a single global coefficient $\lambda$ to every
token. This can drive the student to fit extreme peaks in the implicit reward, causing reward hacking and unstable training, and the optimal $\lambda$ varies across domains, requiring costly sweeps. We propose REOPD, a reliability-adaptive reward extrapolation framework for OPD. REOPD combines a
token-level compatibility weight with a batch-level adaptive budget, yielding a
token-wise coefficient $\lambda_{b,t}=1+\gamma_b q_t$ that preserves teacher
alignment while selectively extrapolating along reliable teacher-reference directions. It requires no verifier, reward model, value model, or extra rollout beyond standard OPD. REOPD outperforms G-OPD on single-teacher mathematics and on both domains in the multi-teacher setting, while matching G-OPD on single-teacher code, demonstrating effective fine-grained reliability adaptation across domains and teacher configurations.
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All authors Yang Sun, Lichao Ma, Houyuan Qin, Yuxin Liu, Hanyang Lu, Yao Zhu, Pinlong Cai, Guohang Yan
Artificial Intelligence · Cross-listed from cs.CL
The Wording Effect: Quantifying Two-Way Drift in LLM Benchmark Performance
Shailja Thakur, Sungeun An, Chad DeLuca et al. (+1 more)
A
benchmark score comes from a single phrasing of each problem. That single phrasing is treated as if it stood for the whole space of ways the same problem could be asked, but it does not. We show that rephrasing a problem while keeping its meaning and answer fixed routinely flips a model's answer in both directions, so some failures become successes and some successes become failures. We call this drift. BenchDrift generates meaning-preserving variations of
benchmark problems along four axes, namely linguistic, referential, pragmatic, and structural, and measures how often, and why, correctness flips under each. Across eight models and three
benchmarks (GSM8K, MMLU, MATH-Hard), we observe that drift is large in both directions. Two findings stand out. First, phrasing sensitivity does not fade as models get better. Instead, it changes sign. Weak models gain more from rephrasing than they lose, while strong models lose far more than they gain. We find that the best models on a
benchmark are therefore the ones whose scores depend most on the wording they happened to be given. Second, the models largely agree on which rephrasings cost the most correct answers even though they differ in how much they drift, so fragility belongs to the rephrasing and not to the model. Furthermore, rephrasing breaks answers a model was confident about, whether the problem is made shorter or longer. Code and Data: https://github.com/IBM/BenchDrift/tree/demo-ui
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All authors Shailja Thakur, Sungeun An, Chad DeLuca, Hima Patel
Artificial Intelligence
HUGIN: Enhancing Vision-Language Planning for Autonomous Logistics Sorting
Xikai Sun, Cangtian Zhou, Kebin Liu et al. (+6 more)
Autonomous logistics sorting systems (ALSS) are an important industrial application of
embodied AI, which requires joint planning over spatially disjoint camera views. We formulate this setting as Joint Multi-Scene Understanding (JMSU). With open-world visual understanding and task-planning capabilities,
vision-
language models (VLMs) are promising candidates for JMSU. However, directly applying existing VLMs to JMSU is non-trivial due to scarce cross-scene supervision and
attention dispersion caused by long visual context in JMSU. To address these challenges, we propose HUGIN, a training framework with two complementary components. Endogenous Data Augmentation recombines verified atomic facts under operating constraints, while Global Context Ranking aligns the instruction representation more strongly with the complete visual context than with a partial visual context. To support ongoing research, we construct a high-quality industrial sorting
dataset and
benchmark named SortingBench from four layouts of autonomous logistics sorting systems. Across five open VLMs, HUGIN consistently outperforms matched baselines; for example, the accuracy on SortingBench of Qwen3-VL-8B increases from 63.6% to 78.8%. Additional experiments verify the effectiveness of each component and JMSU's spillover benefits in
embodied tasks. Deployment tests involving more than 15,000 packages support the practical viability of VLM-based planning for autonomous logistics sorting.
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All authors Xikai Sun, Cangtian Zhou, Kebin Liu, Ke Ma, Xu Wang, Zaishu Chen, Haotian Wang, Li Liu, Yunhao Liu
Artificial Intelligence · Cross-listed from cs.AR
APEX: Adaptive Expert Prefetching for Memory-Efficient Edge MoE Inference
Alish Kanani, Layan Badawi and Umit Y. Ogras
Mixture-of-Experts (MoE) models are attractive for edge deployment because they provide high model capacity while activating only a small subset of parameters per
token, improving compute efficiency. However, MoE inference at the edge is fundamentally limited by
memory. Expert parameters are large and often reside in off-chip
memory due to capacity, cost, and power constraints, putting expert loading to the critical path. We present APEX: Adaptive Expert Prefetching, a predictive resource management framework that overlaps expert loading with useful computation. APEX introduces a lightweight prefetch router that predicts candidate experts before the
attention block to dynamically fetch additional experts using a learned confidence model. This adaptive strategy achieves over 99% overlap accuracy, significantly outperforming fixed top-k prefetching techniques. APEX supports two execution modes: a correctness-preserving mode that guarantees exact routing semantics, and a stall-free mode that eliminates residual stalls by operating on available experts with negligible impact on application accuracy. Across multiple MoE models, the correctness-preserving mode reduces per-
token latency by up to 26% and improves energy-delay product (EDP) by up to 41% over state-of-the-art baselines, while the stall-free mode provides additional efficiency gains with negligible impact on application accuracy. These results establish adaptive, confidence-driven expert prefetching as an effective approach for efficient MoE inference on edge systems.
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Artificial Intelligence
FrontierFinance: A Challenging Benchmark for Measuring Frontier Intelligence of Finance Agents
Yuhao Zhang, O. Ozan Koyluoglu, Thejas Venkatesh et al. (+4 more)
AI
agents are increasingly deployed for professional investment research, yet no
benchmark captures the complexity of the full investor workflow. Existing
benchmarks mainly target financial data extraction, a narrow slice that current models have largely saturated, while reference-based metrics and generic LLM-as-a-judge scoring fall short on the open-ended, long-form answers that real analyst queries demand. We introduce FrontierFinance, a fully open
benchmark of 220 expert-crafted queries and 11,543 source-attributed rubrics spanning six crucial use cases across the full investor workflow. FrontierFinance is both broader and harder than existing public finance
benchmarks. Evaluating frontier models and
agent systems under a common harness restricted to publicly available data, we find that the tool harness, not the model alone, strongly shapes quality and efficiency; that Samaya's in-house system leads at 56.0%, ahead of the strongest frontier model (Claude Fable 5, 49.2%) at roughly 2.2x lower cost; and that the best open-weight model (Kimi K3, 46.4%) nearly matches the best proprietary model at 4.5x lower cost. Screening & Discovery and Sector, Industry & Macro remain the hardest use cases across all systems, where even the best systems reach only 33% and 39%. We make the
dataset and grading code publicly available.
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All authors Yuhao Zhang, O. Ozan Koyluoglu, Thejas Venkatesh, Richard Diehl Martinez, Vishank Bhatia, Arash Alidoust, Ashwin Paranjape