Artificial Intelligence
HANSARD: A Reference Architecture for Forensic Readiness, Runtime Witnessing, and Graded Attribution in Autonomous Multi-Agent AI Systems
Christos Sardianos, Iliana Pla, Vasilis Efthymiou et al. (+4 more)
Autonomous multi-
agent systems nowadays act in finance, software supply chains, and security operations. Already, the first largely AI-orchestrated intrusion campaigns have been reported. Yet, when such a system causes harm, no method can robustly establish what happened, what caused it, or who is accountable. This is because provenance forensics works at the wrong abstraction, formal causality assumes the causal model, and
agent auditing trusts self-recording. The target failure mode is, thus, attribution laundering, i.e., spreading an act across redundant
agents until none is a but-for cause. Worse, the record is produced by the suspects, which comprises the assumption adopted throughout this work.
Agents may therefore anticipate the investigation and the part of logging infrastructure may itself collude. In this paper, HANSARD is proposed, a reference architecture treating accountability as a life-cycle property. First, a readiness profile sealed before operation bounds what later findings may claim. Second, capturing at five choke points beyond the
agents' reach makes omissions detectable, not only tampering. Third, a typed PROV-DM-aligned causal graph accrues as the system runs, and three indicators read it live to gate oversight without adjudicating. Fourth, post-incident replay yields contingent effects under the modified Halpern-Pearl definition, together with a compensation-set size. Finally, a synergy residual measures harm due to the combination rather than to individuals, making laundering visible. Cause, responsibility and accountability are then reported separately, each capped by an evidentiary tier, while a future research agenda is also provided.
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All authors Christos Sardianos, Iliana Pla, Vasilis Efthymiou, Iraklis Varlamis, Thomas Lagkas, Panagiotis Sarigiannidis, Georgios Th. Papadopoulos
Artificial Intelligence
ClawProBench: Trace-Aware Evaluation of AI Agents with Runtime Coverage and Frozen Workplace-Style Holdouts
YuanHang Xiao
Agent benchmarks often evaluate only final answers even when
agents run on stateful runtimes. We argue this under-specifies what is being evaluated: the proper unit is a declared model-plus-runtime configuration whose failures can occur in evidence acquisition, runtime routing, safety boundaries, or repeated execution. We present ClawProBench, a trace-aware
benchmark for runtime-native
agent evaluation instantiated on OpenClaw, a live
agent runtime with workspace tools and native surfaces for browsing,
memory, messaging, scheduling, skills, and subagents. ClawProBench defines two tracks: a 102-scenario full profile with live workspace and native-runtime routing tasks, and a frozen 68-scenario holdout with closed-world JSON output contracts for robust ranking. Trials are scored from execution traces via a safety-gated formula combining correctness, process quality, and efficiency, preserving failure evidence for audit. Our anonymous artifact includes
benchmark definitions, scoring code, manifests and sanitized traces. We evaluate 68 configurations on the full profile and 37 on holdout. The top safety-gated average trace score is 0.7671. Native-runtime tasks underperform workspace-live tasks (0.5238 vs. 0.6415). On holdout, pass@k-any outperforms strict three-trial pass (0.6638 vs. 0.2890), while full-profile and holdout rankings show weak
alignment (Spearman 0.1300). Rankings based purely on correctness differ substantially from process-aware, safety-gated and strict-pass views. Final-answer leaderboards may hide native-surface weaknesses, one-off successes and trace-local
agent failure modes.
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Artificial Intelligence
When Does AI for PDEs Yield Scientific Evidence?
Wenshuo Wang
Existing AI-for-PDE
benchmarks primarily assess models in terms of predictive or approximation accuracy. In physics research, however, AI outputs often serve as evidence for scientific claims. These two objectives are not equivalent: the former measures an output's agreement with a reference target or satisfaction of governing constraints; the latter asks whether, given a specified object of study, scientific claim, assumptions, and evidence standard, the output provides sufficient evidence for that claim. To bridge this gap, we extend a widely used PDE-simulation
benchmark and a comprehensive
benchmark for PDE inverse problems to enable, for the first time in AI for PDEs, evaluation of whether and to what extent model outputs support specified scientific claims. Our results show that numerical accuracy and evidential support can rank models differently, explain when and why they do so, and reveal that existing
benchmarks can favor methods whose outputs provide weaker support for the scientific claims of interest. Together, we formalize, empirically demonstrate, and explain this evaluation–use mismatch in AI for PDEs.
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Artificial Intelligence
Small Reasoning Models are Instruction Followers in Function Calling
Yalda Taheri, Mohammad Hassan Heydari, Erfan Naaman et al. (+1 more)
Function calling represents the core capability of agentic large
language models (LLMs). Existing research has focused on enhancing LLMs function-calling accuracy through fine-tuning, reinforcement learning (RL), and multi-
agent frameworks, particularly for native function-calling LLMs. This work demonstrates that LLMs achieve superior accuracy in function calling in instruction-following contexts (i.e., standard user-assistant interactions) rather than a tool calling context. We introduce Instruction-Followed Function Calling (IFFC), a novel framework that decouples function-calling logic from the primary LLM and delegates it to a dedicated smaller model operating within the instruction-following paradigm. Our method consistently outperforms both native function calling (NFC) and
prompt-based function calling (PFC) baselines, with particularly strong gains on
reasoning-oriented LLMs. Furthermore, we demonstrate that IFFC maintains robust performance under aggressive quantization, enabling efficient on-device deployment without significant accuracy degradation. This work establishes a new paradigm for reliable, resource-efficient function calling in edge-computing scenarios.
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All authors Yalda Taheri, Mohammad Hassan Heydari, Erfan Naaman, Afsaneh Fatemi
Artificial Intelligence · Cross-listed from cs.LG
Functional compatibility as a determinant of persistent neural learning
Hossein Javidnia
Neural networks can acquire new capabilities while damaging existing ones, but what determines whether new learning persists remains unclear. We identify functional compatibility, the extent to which incoming learning can coexist with behaviour that must be preserved, as an experimentally manipulable causal determinant of persistence. From identical neural states, we vary compatibility while matching unrestricted learning opportunity and imposing a common retention requirement. Persistent learning increases with compatibility across independent directions, convolutional and
transformer architectures,
vision and text, and a ten-seed replication. Learning rules and retention constraints determine how much compatible opportunity is retained, whereas nonlinear geometry limits the matched intervention at larger update norms. Functional compatibility therefore reframes stability-plasticity from preventing forgetting to determining which new learning can coexist with existing function and persist.
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Artificial Intelligence · Cross-listed from cs.RO
EMPIRE: Explicit Manipulation Planning as a Learnable Intermediate Representation for Egocentric Hand-Motion Forecasting
Wen Wang, Ruibing Hou, Hong Chang et al. (+2 more)
Forecasting dexterous hand motions from
egocentric observations is fundamental to intelligent interactive systems. Existing VLM-based methods typically map observations directly to future motions, overlooking the underlying manipulation process that governs hand-object interactions. Moreover, end-to-end optimization couples manipulation learning with motion synthesis, causing motion-generation gradients to interfere with the pre-learned manipulation-aware representations. To overcome these limitations, we propose EMPIRE, a two-stage framework that introduces Explicit Manipulation Planning as an Intermediate Representation for
Egocentric hand-motion
forecasting. Stage I: Learn to Plan. EMPIRE first learns explicit manipulation plans from
multimodal context to capture the progression of hand-object interactions. Stage II: Learn to Act. A motion generator synthesizes future bimanual hand motions conditioned on frozen planner representations, preventing motion-generation gradients from affecting manipulation planning. To support our method, we further construct EMPIRE-651K, a bimanual hand-motion
forecasting dataset comprising 650,910 training windows across 111 tasks, each paired with an explicit per-hand manipulation plan. Under identical training and evaluation protocols, EMPIRE achieves state-of-the-art
forecasting accuracy, with an MPJPE of 84.53 mm and a finger-relative error of 38.97mm. We release the code and
dataset at https://github.com/wangwen-banban/EMPIRE.
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All authors Wen Wang, Ruibing Hou, Hong Chang, Shiguang Shan, Xilin Chen
Artificial Intelligence
When Persona Simulations Are Informative: Graph-Structured Signals for Pluralistic Opinion Sensing
Taehyeon An, Jaehyeong Park and Donghyuk Shin
Persona-conditioned large
language models (LLMs) are increasingly used to simulate survey responses across diverse domains. However, apparent response variation can reflect unconditioned model priors or
token sampling noise rather than systematic persona conditioning. We argue that persona-conditioned variation is informative when semantically similar personas exhibit concordant response shifts. To operationalize this principle, we introduce Persona-Conditioned Informativeness (PCI), an unsupervised diagnostic metric that measures whether semantically similar personas deviate in concordant directions relative to item-level sample baselines. By modeling personas as a similarity graph, PCI uses Local Moran's I to quantify local spatial coherence and extract compact persona subsets without using construct labels. To evaluate PCI without external human
benchmarks, we test its ability to recover established latent value structure using the 57-item Portrait Values Questionnaire-Revised (PVQ-RR). Confirmatory factor analysis (CFA) shows that a PCI-selected 10% subset substantially improves overall construct recovery relative to response-stability and random selection. These findings support PCI as a principled internal diagnostic for screening synthetic respondents in survey pipelines.
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Artificial Intelligence · Cross-listed from cs.CL
Rank Reversal in Multilingual LLM Judges: A Label-Free Double-Centering Calibrator
Alhasan Mahmood, Samir Abdaljalil and Hasan Kurban
Multilingual LLM judges produce different evaluator-backbone rankings depending on the
prompt language: on an eight-
language Agent-as-a-Judge
benchmark, the top-ranked backbone alternates across English, Arabic, Chinese, Hindi, Japanese, Spanish, Turkish, and Swahili, and 7 of 15 backbone pairs show statistically significant pairwise rank reversal. We treat this as a measurement problem. The
multilingual judge score decomposes additively into task difficulty, backbone skill, and a
language-backbone interaction term, the last of which is recoverable without human labels by double-centering the
cell-mean score
matrix. We make this
estimator (Consensus-Based Calibration, CBC) explicit, give an $O(1/\sqrt{n})$ finite-sample concentration bound with variance constant $(1-\tfrac{1}{m})(1-\tfrac{1}{k})$, and show that it is unbiased even when task-
language interactions are present. Across 7{,}920 judge runs (6 backbones, 8
languages, 55 tasks, 3 frameworks), CBC raises held-out cross-task rank consistency $\tau$ from 0.650 to 0.902 and agrees with the held-out additive-model oracle in 100% of per-
language decisions versus 68.5% raw; these are consistency diagnostics, not human-grounded correctness measures. On a separately collected M-RewardBench panel (7
languages, 1{,}500 items per
language, 10{,}500
language-item instances, 5 evaluators), panel agreement with the public human gold preferences rises from 68.7% to 76.6% (gain 7.9 percentage points, 95% CI $[6.0, 9.9]$), our strongest external evidence of downstream usefulness. The
estimator is the standard two-way ANOVA interaction-recovery operation under sum-to-zero contrasts; our contribution is its application as a label-free post-hoc calibrator for
multilingual LLM judges, an explicit finite-sample concentration bound, and an unbiasedness result that holds even under task-
language misspecification.
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Artificial Intelligence
Think with Structured Grounding: Perceptual Reinforcement Learning for Chart and Visual-Tabular Understanding
Changjiang Jiang, Qiannian Zhao, Lei Xin et al. (+3 more)
Multimodal Large
Language Models (MLLMs) capable of thinking with images often rely on external tools for fine-grained perception. However, this reliance introduces significant inference latency and fails to effectively resolve the spatial-structural gap-a fundamental challenge in text-dense and structurally relational visuals (e.g., charts and visual tables) where strict relative spatial arrangements bind textual elements. Without external tools, standard MLLMs struggle with such fine-grained visual
reasoning tasks. To address these issues, we propose Think with Structured Grounding (TwSG), a novel fine-grained image perception framework designed to internalize complex images's tool-use capabilities within the model. TwSG distills the benefits of multi-step
reasoning and micro-cropping into a single efficient forward pass during inference. Specifically, we use an MLLM to identify key regions guided by ground-truth answers, and then
prompt a teacher model to generate high-quality visual question-answering (VQA) data. These fine-grained, region-based supervisory signals are subsequently distilled back into the full-image representation. Our training pipeline consists of two stages: (1) a cold-start supervised fine-tuning (SFT) phase using multi-turn data with focused area descriptions to foster complex
reasoning and error recovery; and (2) a reinforcement fine-tuning (RFT) phase driven by a novel process reward mechanism, TL-GRPO, which encourages strategic
reasoning. Extensive experiments across various MLLM architectures demonstrate that TwSG reduces inference latency while substantially improving accuracy and robustness, endowing models with native fine-grained region description and flexible
reasoning capabilities.
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All authors Changjiang Jiang, Qiannian Zhao, Lei Xin, Jinxiang Xie, Preslav Nakov, Zhuohan Xie
Artificial Intelligence
Where World Models Break: Natural-Input Failure Discovery
Zhanpeng Shi, Zi Liang, Rong Feng et al. (+3 more)
World models predict action-conditioned futures and serve as critical internal simulators for downstream planning and control. However, catastrophic prediction failures of world models could dangerously propagate through the control pipeline, as subsequent
agent or model training and decision-making depend heavily on the continuous environment
evolution forecasted by these world models. Existing evaluations overlook this systemic risk: by aggregating average errors over benign generations from general queries, they fail to stress-test the model against catastrophic collapses under rare or unobserved condition-action combinations. To bridge this gap, we formalize the natural-input failure discovery problem: under a finite query budget, finding environment-valid conditions and action prefixes that induce severe prediction risk, verifying whether these failures reproduce on fresh seeds, and testing their persistence under nearby valid edits. Discovering such critical failures is computationally challenging, as valid condition-action combinations explode exponentially, rendering exhaustive search or standard sampling infeasible given the high cost of noisy rollouts. To tackle this, we propose BasinLens, which exploits the underlying structure of valid inputs, where each coordinate possesses environment-defined semantic types and admissible domains, by pairing uncertainty-guided global search with typed local replacements. Across diverse
benchmarks and world-model families, BasinLens exposes reproducible and locally persistent failure modes that conventional evaluations fail to reveal, showing that average-case
benchmarks can mask important vulnerabilities in world-model-driven control.
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All authors Zhanpeng Shi, Zi Liang, Rong Feng, Shiqin Tang, Xuyang Chen, Hongzong Li
Artificial Intelligence · Cross-listed from cs.SD
Cross-Subject Generalization in Decoding Perceived Speech from Non-Invasive Brain Recordings
Aoke Zhang, Bo Wang, Xihong Wu et al. (+2 more)
Decoding perceived
speech from non-invasive
brain recordings has garnered significant
attention in recent years due to its wide range of potential applications. However, existing methods face considerable challenges in cross-subject decoding, primarily due to limited generalizability and the absence of explicit mechanisms for extracting subject-consistent information. These limitations result in high training costs and suboptimal decoding performance. To address these challenges, we propose an innovative Cross-Subject Perceived
Speech Decoding (CPSD) framework, which comprises two training stages: source model pre-training and personal specialization. In the source model pre-training stage, contrastive learning is employed to capture shared representations across multiple source subjects. Subsequently, personal specialization initializes the model for the target subject by extracting consistent components from the source model and fine-tuning it using target subject data. Additionally, we introduce the Positional Encoding-based Spatial
Attention (PESA) module, which remaps MEG/
EEG data into a standardized reference space, thereby enhancing cross-subject consistency and facilitating model training. We evaluate the proposed CPSD framework on three perceived
speech neural
datasets encompassing different modalities and
languages. The results demonstrate that our framework outperforms baseline methods by more than 6.8%, 15.4%, and 15.8% in Top-10 accuracy on the Armeni 2022, PKUEEG 2025, and Broderick 2018
datasets, respectively. Further analyses confirm the effectiveness, efficiency, and robustness of the proposed approach.
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All authors Aoke Zhang, Bo Wang, Xihong Wu, Heping Cheng, Jing Chen
Artificial Intelligence
LLMs for Survey Text Analysis - A Performance Comparison Between Humans and GPT-5 on Inductive Content Analysis
Leonardo Bergmann, Renata Gheorghiu, Ana Gvritishvili et al. (+3 more)
Large
language models (LLMs) are increasingly used to support text analysis in qualitative research, yet evidence on their performance in inductive content analysis remains limited. This study compares human and LLM-based inductive coding of open-ended survey responses from 903 answers across six variables from a European PhD student survey. Five human coders performed inductive content analysis following a standardized coding scheme, while an LLM (GPT-5.4) conducted the same task using an established prompting procedure. Agreement between human and LLM outputs was assessed using the Adjusted Rand Index (ARI). Results showed an
alignment between humans and the LLM, with ARI values of 0.61 for coding and 0.54 for theme generation. These values were close to the internal consistency of coding and theme results within humans (ARI = 0.68) and the LLM (ARI = 0.76). Agreement varied widely across variables, with low within-entity consistency consistently linked to low between-entity agreement, underscoring the role of data characteristics and individual performance in reliability. Overall, the findings suggest that LLMs can approximate human coding in this case-specific setting, particularly at the coding level, and may serve as a scalable support tool for inductive qualitative analysis.
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All authors Leonardo Bergmann, Renata Gheorghiu, Ana Gvritishvili, Alex Mican, Chris Stewart, Topias Tolonen-Weckström
Artificial Intelligence · Cross-listed from cs.CR
KONTOGRAPH: Verified Point-in-Time Feature Consistency and Amortised Explanation for Real-Time Anti-Money Laundering under a 200 ms Decision Budget
Ahmed Abolfadl
Regulation (EU) 2024/886 obliges European payment service providers to settle euro credit transfers in under ten seconds, around the clock. This removes both the overnight batch window in which anti-money-laundering (AML) analytics traditionally ran and the settlement delay that made recovery possible, forcing detection, explanation and decision inside a single-digit-second envelope. We present KONTOGRAPH, an end-to-end AML pipeline for the SEPA Instant rail built under a self-imposed 200 ms 99th-percentile budget, and report an empirical study on 1,562,860 simulated payments with injected typologies and deliberately incomplete labels. Three findings are of interest beyond the system itself. First, a temporal graph network with per-node
memory improves PR-AUC over a gradient-boosted tabular baseline from 0.0053 to 0.1717, a paired day-blocked bootstrap difference of +0.166 with 95% CI [0.105, 0.241]; per-node
memory alone more than doubles the score. Second, expressing each feature once and compiling it to three execution backends, with equivalence enforced by property-based tests that perturb the future, surfaced three point-in-time violations that code review had passed–each of which would have inflated reported performance. Third, and most consequential for practice, exporting the deployed tree ensemble to ONNX changed only $7.4 \times 10^{-8}$ in mean score yet altered 0.26% of decisions and inflated the alert volume by 12%, because 32-bit accumulation perturbs scores across a cost-optimal threshold of $3.98 \times 10^{-4}$. We argue that a serving-format conversion must be treated as a model change until measured, and that fidelity metrics for subgraph explainers can be vacuous when candidate neighbourhoods are small–a null result we report in full.
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Artificial Intelligence · Cross-listed from cs.CL
ProBel: Propaganda Detection with Techniques, Spans, and Explanations
Mohamed Bayan Kmainasi, Ali Ezzat Shahroor, Elisa Sartori et al. (+2 more)
Propaganda detection includes several related prediction levels, ranging from sentence-level decisions to technique classification and span identification. However, it remains unclear how supervision at these levels interacts when learned jointly across Arabic and English. We present ProBel, an Arabic and English resource that aligns binary labels, multi-label annotations over 23 propaganda techniques grouped into six coarse categories, technique-labeled spans, and reference explanations for the same news sentences. It includes a substantially larger English collection and supports matched binary, coarse-grained, multi-label, and span-level tasks in both
languages. We evaluate zero-shot prompting, task-specific fine-tuning, and joint training under a shared setup. A single bilingual multi-task model achieves the best overall performance and remains competitive across tasks and
languages. Cross-task analysis shows that transfer depends on the supervision level. Joint classification training preserves binary performance, whereas span-only training can weaken sentence-level prediction. Joint bilingual training yields the most stable results, while monolingual fine-tuning can reduce transfer to the other
language. We will release the data, code, and evaluation scripts.
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All authors Mohamed Bayan Kmainasi, Ali Ezzat Shahroor, Elisa Sartori, Giovanni Da San Martino, Firoj Alam
Artificial Intelligence · Cross-listed from cs.LG
Self-Supervised Graph Representation Learning for In-The-Wild Wearable and Smartphone based Emotion Recognition
Ioannis N. Ziogas, Leontios J. Hadjileontiadis, Ahsan H. Khandoker et al. (+1 more)
Wearable and smartphone-based
emotion recognition (WER) remains a challenging setting in affective computing, due to the notorious difficulty and bias associated with in-the-wild label collection. The high inter-and intra-subject emotional variability motivates us to explore WER modeling through graph node classification in a limited resources learning scheme powered by Self-Supervised Learning (SSL) graph masking augmentation tasks. We employ a subgraph sampling approach during training, utilizing labeled and unlabeled data, along with supervised, semi-supervised, and SSL mechanisms in a multi-task inductive graph neural network architecture. Our evaluations on K-EmoPhone through leave-one-group-out cross-validation in the binary arousal and valence tasks yield average accuracy gains of 4.3% and 7.8%, compared to the full resource setting, utilizing only 20% and 25% of the labels, respectively. Our model analysis sheds light on the relation of SSL graph augmentations to emotional arousal and valence and justifies the approach of SSL-driven subgraph training for in-the-wild WER.
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All authors Ioannis N. Ziogas, Leontios J. Hadjileontiadis, Ahsan H. Khandoker, Aamna Al Shehhi
Artificial Intelligence
WAM-OPD: Joint Video-Action Supervision for World Action Model Post-Training with On-Policy Distillation
Liuhaichen Yang, Zhengyang Zhong, Hanshang Zhu et al. (+13 more)
World Action Models (WAMs) generate both future
video and
robot actions, offering two connected outputs for post-training supervision. How can a pretrained WAM learn from a stronger Teacher on the histories it encounters during execution? We present WAM-OPD, which collects Student rollout histories and queries a Teacher for paired
video and action targets. The Student learns from both targets while retaining its one-step
video and action generation at deployment. Across 12 RoboTwin 2.0 tasks, WAM-OPD improves average success from 33.8% to 65.7%; across four real-
robot tasks, it improves average success from 51.4% to 64.6%. With the collected Student histories held fixed, joint
video-action supervision achieves the highest observed success on all three ablation tasks, while either modality alone also improves performance. A separate comparison with Teacher-generated histories finds task-dependent differences between the two history sources. These results demonstrate the value of paired
video-action supervision for improving WAM policies without increasing their deployed sampling budget.
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All authors Liuhaichen Yang, Zhengyang Zhong, Hanshang Zhu, Ningwei Bai, Qichen Yin, Zhi Han, Jiarui Qin, Zhuang Jiang, Chenchao Sheng, Hanbo Ma, Junkai Liu, Junkai Sun, Dongcheng Lyu, Bo Liu, Yi Dong, Zezhi Tang
Artificial Intelligence
Analyzing and Mitigating Cross-Lingual Degradation in Multilingual Medical VQA
Jingbo Wang, Sendong Zhao, Haochun Wang et al. (+2 more)
Medical visual question answering (VQA) is a crucial task in clinical AI, yet its evaluation has so far centered almost exclusively on English, limiting its relevance to linguistically diverse patients and clinicians. Recent
multilingual medical VQA
benchmarks show that large
vision-
language models (LVLMs) degrade in non-English
languages, but lack a fine-grained analysis of how cross-lingual variation affects the distinct capabilities that medical VQA requires. To this end, we construct a
multilingual medical VQA
benchmark over eight
languages, organized into four representative scenarios that isolate the core capabilities medical VQA requires. Evaluating five open- and closed-source LVLMs, we find that cross-lingual degradation is not uniform but highly scenario-dependent. We therefore propose MedVL-XLRepE, a training-free scenario-aware representation engineering method, leveraging LVLMs' superior English medical VQA capability to steer non-English representations toward their English counterparts at inference time. Across three LVLMs and eight
languages, MedVL-XLRepE consistently mitigates cross-lingual degradation, with gains of up to 6.33%.
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All authors Jingbo Wang, Sendong Zhao, Haochun Wang, Bing Qin, Ting Liu
Artificial Intelligence · Cross-listed from cs.CV
Pre-Decoding Acoustic Triage for Budgeted Vision-Language Captioning of Untrimmed Egocentric Video
Masoud Jalayer, Changyi Li and Yu Xiao
Automatically analyzing hours-long
egocentric video is increasingly essential for progress monitoring, quality control, and safety in logistics, construction, and manufacturing. Yet current pipelines that process short, fixed-size windows with a
vision-
language model (VLM) are prohibitively expensive because cost scales with the number of model calls. To reduce this cost, prior work proposes triage policies to select which windows merit a VLM invocation. However, these policies either sample uniformly or rank windows using visual features, which ironically requires the
video decoding that the budget constraints are meant to avoid. We propose audio-first triage: select windows using the lightest modality, scored before any
video frame is decoded, so the approach composes naturally with
token compression or quantization. The novelty lies in the objective, not the representation: rather than a per-frame sound-event detector, we train the selector to trigger once per action. This objective shift improves action coverage by 4.0-10.8 percentage points across all evaluated call rates, using frozen AudioSet-pretrained features without domain-specific sound-event labels. Using fewer than half of the available calls, the triage cuts 9-20% of VLM calls at matched coverage on EPIC-KITCHENS-100 (EK-100), surpasses uniform sampling through the mid-range on Ego4D over 247 clips, and outperforms two recent visual keyframe selectors. Code, the reference implementation and every results file this manuscript reads are at https://github.com/masjalayer/PreDecoding-AcousticTriage.
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Artificial Intelligence
Addressing the Selection Problem in Explainable AI
Claire Vlases and Katelyn Morrison
Explainable AI (XAI) research has produced a plethora of explanation techniques, yet user studies repeatedly show that available explanations are not effective in practice. We argue that, given the siloed nature of conventional XAI, users are struggling to select the appropriate XAI technique. Viewing XAI through a philosophical lens, we offer a formalization of what we call the selection problem: the systematic failure of XAI interfaces to bridge the gap between a user's natural-
language uncertainty and the explanation technique that resolves it. Following a logical premise-conclusion format, we show that conventional interfaces require users to translate their uncertainty into a technique selection, a challenging prerequisite to meet. We also propose a structural solution: a multi-
agent LLM orchestration tool that translates the user's query to the proper XAI explanation technique. We provide an example of how this structural solution could be instantiated to address the selection problem.
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Artificial Intelligence · Cross-listed from cs.LG
SANE: State Anomaly Neutralization for Stable Extreme-Context Delta-Rule Models
Qingwen Lin, Boyan Xu, Xiao Liu et al. (+2 more)
Delta-Rule recurrent models maintain a fixed-size state, enabling $O(1)$ inference
memory but potentially becoming unstable under extreme-context extrapolation. By tracking RWKV-7 over sequences of up to 100M
tokens, we empirically identify a distinct failure pattern: localized norm explosion atop a relatively sparse substrate, rather than global state saturation. Analysis of the recurrent update suggests that persistent decay keeps weakly updated entries small, whereas uneven injections allow a few channels to accumulate extreme values. Motivated by this diagnosis, we propose State Anomaly Neutralization (SANE), which applies adaptive $\tanh$ compression at chunk boundaries while preserving the intra-chunk parallel structure. Within a safe threshold range ($3 \le \alpha \le 5$), SANE matches the baseline on 11 short-context
reasoning benchmarks with no statistically significant degradation. After a 100M-
token prefix, which exceeds the training length by over $24{,}000\times$, SANE retains functional
reasoning ($33.46$–$35.56$) while the baseline encounters numerical overflow. In contrast, overly permissive thresholds ($\alpha \ge 8$) remain numerically stable but lose
reasoning capability entirely, showing that numerical stabilization alone does not guarantee functional
reasoning and revealing a capacity–stability trade-off in state compression.
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All authors Qingwen Lin, Boyan Xu, Xiao Liu, Zhifeng Hao, Ruichu Cai
Artificial Intelligence
Where Cognition Lives: Dissecting Emergent from Computed Function in a Minimal Complete Cognitive Architecture
Francisco M. Arrabal-Campos, Francisco G. Montoya, Alfredo Alcayde et al. (+1 more)
A cognitive architecture is more than the module that reasons: it must also decide how long to think and what deserves the effort. We built a minimal but complete system - a recurrent reasoner with adaptive halting, a homeostatic control field, and a value module - and asked of each part: does this function emerge from gradient descent, or must it be computed? Competence emerges. Stopping appears to emerge too, and to be worth more than everything decidable in advance, but that appearance is instrumentation: payoff at matched mean compute climbs from 0.467 (uniform) through 0.546 (difficulty) to 0.698 (ex-ante value), and the further climb to 0.921 (posterior self-observation) does not survive audit. PonderNet-style halting returns a halting-weighted mixture of hidden states while forced-depth baselines return one, and the
language head is trained on the mixture alone; equalizing the readout annihilates the apparent advantage of native execution (residual +0.000 [0.000, 0.000]). Value does not emerge: trained couplings capture zero of a payoff an explicit allocator captures completely (+0.151, routing correlation +0.79), so the second-order decisions that pay must be computed, at least where value is orthogonal to content, as here by construction. On a frozen LLM actuator the same instruments show self-consistency voting to be a measured bound (+0.0236 [+0.0150, +0.0326]) and inter-sample agreement nearly worthless as a stopping signal, its mass concentrating on wrong answers. Every null we assert carries a mechanism and a positive control, and the protocol is part of the contribution. Executing our own falsifiable prediction, value under commitment pays +0.1312 [+0.1124, +0.1502] in a cliff-cost family, some seven times the smooth-family estimate - not because the cliff shifts information ex ante, but because it multiplies the attainable range fivefold (5.1x [3.4, 8.2]).
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All authors Francisco M. Arrabal-Campos, Francisco G. Montoya, Alfredo Alcayde, Ignacio Fernández
Artificial Intelligence · Cross-listed from cs.CV
Multimodal examination answer data with expert-designed Outcome-Based Education rubrics for criterion-level assessment
Jahangir Alam SM, Md Khalid Syfullah, Saad Ahmed et al. (+4 more)
This data article describes a
multimodal collection of scanned examination answers paired with expert-designed Outcome-Based Education (OBE) grading metadata. The collection contains 485 answer submissions from 415 consenting students at four academic institutions. Eight faculty contributors supplied examination materials covering nine subjects and 12 distinct question templates. Each answer-level item links a scanned PDF to a randomized identifier, subject label, question, model answer, criterion definitions, performance-level descriptions, criterion marks, and a total mark. The 12 rubrics contain 47 criteria in total. The scans retain realistic academic content, including handwriting, printed text, equations, tables, code, figures, sketches, and diagrams. CamScanner, Adobe Scan, and conventional scanners contributed variation in illumination, contrast, orientation, compression, and resolution. Diverse handwriting, crossed-out work, revised calculations, and inserted corrections add further visual variability for robustness and generalization studies. Preparation involved heterogeneous-source consolidation, label and text standardization, score validation, identifier randomization, filename randomization, and JSON-to-PDF integrity checks. An answer-level audit confirmed 485 unique identifiers, 485 unique PDF filenames, agreement between each total mark and its criterion-mark sum, and scores within the applicable rubric maximum. The data can support rubric-aware automated evaluation,
multimodal document understanding, criterion-level feedback, score prediction, and
privacy-aware OBE assessment research. Access is restricted to research use and is available from the corresponding author upon reasonable request.
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All authors Jahangir Alam SM, Md Khalid Syfullah, Saad Ahmed, Munira Akter Mou, A K Z Rasel Rahman, A.K.M. Masudur Rahman, Mohammed Sowket Ali
Artificial Intelligence · Cross-listed from cs.CV
TransHands: Repurposing Human Pose Encoders as Hand Pose Encoders
Milo Piccioli, Gianluca Amprimo, Claudia Ferraris et al. (+1 more)
Lifting 3D hand poses from 2D
monocular representations remains challenging due to the limited availability of large-scale, diverse 3D-annotated hand
datasets, in contrast to the abundance of human body motion data. We address this limitation by transferring motion representations learned from large body pose corpora to the hand domain. We introduce TransHands, a backbone-agnostic transfer learning framework that enables pre-trained human motion encoders to be effectively adapted for 3D hand pose estimation from 2D pose inputs. Rather than training hand-specific biomechanical models from scratch, TransHands combines a two-stage training and fine-tuning strategy with a lightweight hand-specific input adaptation module that aligns hand kinematics with the representation space learned for full-body motion. We evaluate TransHands across four state-of-the-art motion modeling architectures, including
transformer-based, graph-based, and frequency- domain models. Results demonstrate that motion priors learned from body pose data transfer consistently across architectures, yielding consistent accuracy gains, strong cross-domain generalization, particularly in challenging
egocentric settings, and applicability for downstream tasks in real-world contexts.
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All authors Milo Piccioli, Gianluca Amprimo, Claudia Ferraris, Gabriella Olmo
Artificial Intelligence · Cross-listed from cs.CL
Register Shifts Break LLM Safety: A Bengali Benchmark with Culturally Grounded Harms
Naymul Islam, Nusrat Jahan Lia, Shubhashis Roy Dipta et al. (+2 more)
Bengali is the seventh-most-spoken
language globally, yet LLM safety evaluation remains overwhelmingly English-centric. We introduce BanglaSafe, a
benchmark of 879 Bengali
prompts combining 309 natively authored
prompts with 570 expert-reviewed
prompts, spanning 17 culturally grounded harm categories and five prompting conditions that vary
language, writing style, and authority framing. Evaluating 18 frontier LLMs, we find that over half of all responses are unsafe or partially unsafe (53.6%) while 14.7% contains strictly harmful content, and that the strongest observed effect is not the switch from English to Bengali but the choice of writing style within Bengali: the same harmful request phrased as a formal newspaper investigation succeeds 17 percentage points more often than the same request phrased as a casual message, with no
adversarial engineering involved. We further show that existing safety classifiers struggle to reliably evaluate Bengali content, with even frontier models failing on nearly half of all cases.
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All authors Naymul Islam, Nusrat Jahan Lia, Shubhashis Roy Dipta, Sabik Bin Sultan, Abdullah Khan Zehady
Artificial Intelligence
HERO: Human-profile Enhanced Retrieval Optimization Framework for Long-term Agent Memory
Yuanhua Lin, Yile Li, Zhiyuan Zhao et al. (+2 more)
Long-term
memory is crucial for personalized responses and long-horizon
agent interactions. Existing methods often rely on LLMs to compress or rewrite dialogue histories and use the transformed
memories as
retrieval evidence. Despite the progress in organizing fragmented contexts, two major drawbacks persist: (1) information loss from compression, which discards fine-grained but later useful details, and (2) semantic drift from rewriting, which erodes the original tone and situated context. In this work, we propose a novel Human-profile Enhanced
Retrieval Optimization framework for long-term
agent memory (HERO). Specifically, HERO converts the dialogue history into a traceable heterogeneous
memory graph that preserves raw dialogue text as evidence for
reasoning, thereby mitigating information loss. For
retrieval, HERO extracts initial anchors from the current query and incorporates human profiles via an iterative graph traversal; these anchors and profiles provide guidance signals that adaptively activate the most informative regions of the graph. Experiments on two
benchmark datasets show that HERO outperforms strong baselines on both factual and personalized
reasoning, while providing more faithful access to raw dialogue evidence.
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All authors Yuanhua Lin, Yile Li, Zhiyuan Zhao, Jing Shang, Jian Sun
Artificial Intelligence · Cross-listed from cs.RO
The Imitator Game: Benchmarking Robot Imitative Ability Beyond Action Prediction
Xunzhe Zhou, Yiyang Cai, Fengyi Wang et al. (+9 more)
Humans imitate at the level of intent: given a demonstration, we infer its goal and carry it out with whatever tools, objects, and layouts are at hand. Current
robot policies instead learn observation-to-action mappings from visual inputs and
language instructions, without explicitly inferring the demonstrated task. Learning from human
video thus remains largely trajectory-level: models can replay motions in near-identical scenes, but still struggle to imitate what the demonstrator intends rather than merely what they do. We introduce The Imitator Game, a four-level
benchmark (L0-L3) that progressively widens the gap between the human demonstration and the
robot's own scene, isolating where trajectory replay ceases to suffice and task understanding becomes necessary. We pair it with IG-10K, the largest environment-aligned paired human-
robot dataset to date and the only one instantiated across all four levels in both real and simulated settings (20,000+ paired episodes, 50+ tasks, 6 domains), and Imitator Arena, an open platform for blind A/B human evaluation. Across nine state-of-the-art models, performance is stable from L0 to L2 but collapses at L3, identifying functional substitution - achieving the same intent through a different object affordance - as the decisive barrier to intent-level imitation. Human-
video-conditioned models outperform caption-conditioned ones, yet every model falls below 13% zero-shot success on unseen tasks; fine-tuning IG-10K-pretrained models with only $10$ paired human-
robot demonstrations yields large gains that grow with pretraining scale. The project website and access to Imitator Arena are available at https://imitator-game.github.io.
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All authors Xunzhe Zhou, Yiyang Cai, Fengyi Wang, Ran Ju, Hanxiang Ren, Ruizhe Liu, Yu Zhang, Qian Luo, Feng Chen, Pei Zhou, Yi Ma, Yanchao Yang
Artificial Intelligence · Cross-listed from cs.CL
LLM Evaluation on Unseen Questions: Contextual Multidimensional IRT Model
Ergan Shang, Weijing Tang and Yinqiu He
Evaluation of large
language models (LLMs) increasingly requires predicting how a model will perform on new questions or tasks before collecting large amounts of new annotations. This problem is challenging because question difficulty, scenario, and underlying capability demands can vary substantially. Simple retrospective averages may confound model ability with item characteristics. In this paper, we study a model-based evaluation framework that combines multidimensional item response theory model with question contexts to predict LLM performance on unseen questions. The framework represents LLMs through latent capability profiles while using question content to inform item characteristics, allowing information to transfer beyond previously observed items. Empirically, we find that for within-scenario evaluation, incorporating question
embeddings improves prediction relative to model-free baselines, and that multidimensional latent structure provides a richer description of capability variation than unidimensional alternatives. At the same time, our results reveal an important limitation that the generalizability does not necessarily translate into reliable prediction under cross-scenario shift. These findings suggest that context-aware psychometric modeling is a promising direction for efficient and interpretable LLM evaluation, while also highlighting cross-scenario generalization as a central open challenge.
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Artificial Intelligence · Cross-listed from cs.SE
Learning from the Test: Self-Referential Differential Testing for Deep RL Agents
Junda He, Jieke Shi, Zhou Yang et al. (+2 more)
Deep Reinforcement Learning (DRL) has achieved significant success in complex decision-making problems. As DRL systems are increasingly deployed in real-world applications, ensuring their quality and reliability is paramount. Current works primarily focus on detecting safety-critical failures, often neglecting policy optimality, which can lead to reduced efficiency, user distrust, and economic losses. This oversight, compounded by the inherent "testing oracle problem" for optimality, leaves a significant gap in comprehensively evaluating DRL systems. To address this gap, we propose Delta (Differential Testing for DRL
Agents), a novel and comprehensive framework that automatically identifies both safety-critical and optimality bugs in DRL
agents. Delta employs a two-phase approach: (1) Safety Testing, where the
Agent Under Test (AUT) is evaluated for catastrophic failures while collecting data from its decision-making policy, and (2) Optimality Testing, where this collected data from the prior phase is used to train a challenger
agent via Offline Reinforcement Learning. Differential testing is then performed by comparing the challenger
agent against the AUT; instances where the challenger achieves higher cumulative rewards indicate optimality issues in the AUT. We demonstrate Delta's effectiveness across five environments. We investigate the effectiveness of three offline RL
algorithms (BC, BCQ, and CQL) in generating challenger
agents. Experimental results demonstrate that safety testing
datasets are valuable for training competent DRL
agents. Challenger
agents trained with BCQ proved most effective for identifying optimality issues within the framework of Delta. Across the five environments, Delta uncovered an average of 2,518 optimality issues, outperforming the baseline methods by 50.2%.
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All authors Junda He, Jieke Shi, Zhou Yang, Mingfei Cheng, David Lo
Artificial Intelligence · Cross-listed from cs.CV
OVIBench: Benchmarking Online Video Question Answering under Interruption
Naiming Liu, Zhiheng Wu, Shuning Wang et al. (+3 more)
Recent
vision language models (VLMs) have achieved strong progress in
video understanding. However, most existing
video QA research and
benchmarks still follow an offline, single-round paradigm, overlooking realistic interactions where users may interrupt the model during answer generation. To address this gap, we formulate the task of Online
Video Question Answering under Interruption and introduce OVIBench, the first standardized
benchmark for evaluating VLMs in this setting. OVIBench categorizes interruptions into three types: Cancellation, False Trigger, Correction and supports both open-ended and multiple-choice evaluations. To enable large-scale and reproducible testing, we develop an offline simulation protocol that reproduces interruption during generation under a unified temporal setup, together with a multi-dimensional metric suite for assessing interruption understanding and response generation. Experiments demonstrate that OVIBench effectively distinguishes models' interruption-handling abilities, especially in following correction requests. Finally, we construct a train set OVI-Train for interruption-aware fine-tuning. Models fine-tuned on this
dataset achieve significant gains on OVIBench, validating the effectiveness of our
benchmark and data design. OVIBench, OVI-Train, and the evaluation code will be released.
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All authors Naiming Liu, Zhiheng Wu, Shuning Wang, Tie Zhang, Bowen Liu, Tong Wang
Artificial Intelligence · Cross-listed from cs.CL
Length-Adaptive Decoding for Masked Diffusion Machine Translation
Yan Zhan, Mengkai Hou, Wanting Zhang et al. (+1 more)
Machine translation tests masked
diffusion language models (dLLMs) because every source
token must be rendered faithfully, while fixed canvas decoding must choose target length before denoising. Existing masked
diffusion decoding work mainly studies
token unmasking order, leaving this length decision under-explored despite its direct effect on coverage and redundancy. We introduce
Entropy-Valley (EV), a training-free length selector that scores candidate target canvases by mean predictive
entropy from all-mask forward passes and selects the canvas the backbone is most prepared to fill. Relative to a baseline using training corpus length statistics, EV recovers 64.9%, 65.3%, and 33.0% of the COMET-22 gain from reference target lengths on En$\to$Zh, Zh$\to$En, and En$\to$De. Our diagnostics show that denoising-friendly lengths need not match reference lengths. Evaluation by three translation experts supports the En$\leftrightarrow$Zh adequacy gains, with stronger evidence on Zh$\to$En. Compared with a LLaMA-3-8B autoregressive (AR) model trained on the same fine-tuning data, the EV system ties on En$\to$Zh and leads on Zh$\to$En; an oracle-length diagnostic further shows that, in this masked
diffusion MT setting, deciding which
tokens to reveal first matters less than how the target length is supplied.
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All authors Yan Zhan, Mengkai Hou, Wanting Zhang, Zhijun Gao