Artificial Intelligence · Cross-listed from cs.LG
Tabular Deep Learning vs Classical Machine Learning for Urban Land Cover Classification
Muntasir Tabasum, Tanpia Tasnim, Md. Ekramul Islam et al. (+1 more)
Urban Land Cover (ULC) classification plays a crucial role in urban planning, environmental monitoring, and sustainable development. We study this task using the ULC
dataset from the UCI Machine Learning Repository, which includes tabular features derived from high-resolution aerial imagery across nine classes (e.g., roads, trees, grass, water). The
dataset presents typical remote sensing challenges, including high dimensionality, heterogeneous features, and class imbalance. In a unified, reproducible pipeline, we
benchmark classical machine learning models (e.g., Logistic Regression, SVM, Random Forest, XGBoost, CatBoost) against Tabular Deep Learning (TDL) models (TabNet, FT-
Transformer, TabTransformer, TabSeq, and 1D CNNs). To address class imbalance, we employ weighted cross-
entropy loss for TDL models and evaluate performance using accuracy, macro-precision, macro-recall, macro-F1, AUC-ROC, and confusion
matrices. Our results show that while tree ensembles remain strong general baselines, TDL models can match or exceed their performance when non-linear interactions are significant and imbalance handling is effective, providing complementary advantages for urban land cover mapping. See code: https://github.com/mtesha/tdl-vs-ml-urbanlandcover
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All authors Muntasir Tabasum, Tanpia Tasnim, Md. Ekramul Islam, Al Zadid Sultan Bin Habib
Artificial Intelligence · Cross-listed from cs.AR
WARD: Runtime Workload-Adaptive Vision TRansformer Framework for Dependable Edge AI
Mahdi Taheri, Pramit Kumar Bhaduri, Mohammad Masoumi et al. (+1 more)
Edge-deployed AI operate under dynamically changing power budgets, reliability requirements, and input distributions, requiring continuous adaptation. Such conditions arise in long-running edge AI applications, including autonomous systems, industrial monitoring, and satellite onboard intelligence. Existing fault-tolerant methods assume static operating conditions, whereas continual learning techniques neglect concurrent hardware faults during online adaptation. Moreover, the practical deployment of runtime-adaptive reliability frameworks on programmable AI accelerators remains largely unexplored. This paper presents WARD, a runtime-adaptive
Vision Transformer framework that combines channel-wise subnetwork partitioning, reliability-aware continual learning, and dynamic operating-mode scheduling to jointly optimize performance, fault tolerance, and adaptation according to runtime conditions. Two physically isolated subnetworks execute under four operating modes (i.e. Full-Precision Mode, Low-Power Mode, High-Reliability Mode, and Adaptive Mode) that dynamically adjust computational cost and reliability while ensuring uninterrupted inference for real-time requirements. To validate the practical deployability of the proposed framework, WARD is implemented on a lightweight FPGA-based accelerator extended with runtime hardware support for mode scheduling and resource management. Experimental results demonstrate that the proposed split architecture achieves a network-level failure rate of only 1.79% under high Bit Error Rates. The hardware implementation incurs less than 5% area overhead and supports runtime mode transitions within few clock cycles, demonstrating that adaptive reliability management can be integrated into programmable edge AI accelerators with negligible implementation overhead.
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All authors Mahdi Taheri, Pramit Kumar Bhaduri, Mohammad Masoumi, Ali Mahani
Artificial Intelligence · Cross-listed from cs.AR
REQAP: Resilient Weight Packing and Quantization for Edge DNN Acceleration
Mahdi Taheri, Samira Nazari, Mubassher Ansari et al. (+4 more)
Efficient deployment of Deep Neural Networks (DNNs) on edge accelerators requires aggressive model compression while maintaining reliability in fault-prone hardware environments. This paper presents a reliability-aware quantized weight packing methodology for systolic-array-based DNN accelerators. A sensitivity-driven mixed-precision quantization framework assigns layer-wise bit-widths according to accuracy impact while enforcing symmetric precision between weights and activations. A deterministic register-level packing strategy consolidates multiple heterogeneous operand pairs into fixed-width register words, enabling SIMD-within-a-register (SWAR) style parallel execution that reduces both
memory footprint and execution cycles. To improve resilience against hardware faults, selective bit-level protection replicates the most significant bits (MSBs) of critical layers into unused register space, achieving TMR-style protection with minimal overhead. A systolic-array simulation framework is developed to evaluate the proposed packing and fault-tolerance mechanisms under realistic execution conditions. Simulations in AlexNet, VGG-11, and ResNet-18 demonstrate up to 62%
memory reduction and up to 56% reduction in Multiply-Accumulate (MAC) operations, while significantly improving accuracy resilience under fault injection compared to baseline and fully protected models.
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All authors Mahdi Taheri, Samira Nazari, Mubassher Ansari, Ali Azarpeyvand, Mohsen Afsharchi, Maksim Jenihhin, Christian Herglotz
Artificial Intelligence · Cross-listed from cs.IR
RecEvolve: A Knowledge-Driven Autonomous Agent System for Recommender Systems
Weidi Pan, He Ma, Shuhao Ye et al. (+6 more)
The rise of agentic AI has catalyzed a shift toward self-iterating systems, opening new frontiers for the autonomous optimization of production recommender models. This paper presents the empirical validation of a knowledge-driven autonomous
agent system, deployed directly on a production large-scale Two-Tower
retrieval model. By delegating the entire research lifecycle, spanning idea generation, code implementation, offline training, and metric evaluation, to a continuous closed-loop autonomous framework, the
agent system executed over 40 completed autonomous training runs from scratch. Executing these runs under rigorous production-scale evaluations, the system systematically navigated hidden architectural bottlenecks on the latest production model to achieve a breakthrough ~20% relative improvement in NDCG, a gain that translated directly to a +3.77% increase in user satisfaction in live production traffic. Furthermore, the deployment exposed critical vulnerabilities in standard evaluation protocols, as the
agent system autonomously discovered reward-hacking shortcuts. These findings prove that an autonomous pipeline can dramatically accelerate the pace of machine learning research and stress-test the rigorousness of underlying experimental infrastructure, while also exposing novel challenges such as reward hacking and redundant exploration of failed hypotheses.
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All authors Weidi Pan, He Ma, Shuhao Ye, Palaksh Rungta, David McPeek, Junyi Jiao, Arnab Bhadury, Mingyan Gao, Onkar Dalal
Artificial Intelligence · Cross-listed from cs.CL
PACE: Publisher-Adaptive Content Extraction via Agentic Automation
Zhanlin Liu and Munirathnam Srikanth
Web content extraction is essential for reliable LLM data pipelines, yet existing methods often struggle to jointly satisfy accuracy, scalability, and adaptability. General-purpose extractors can be applied broadly, but they are often brittle on publisher-specific layouts and richer extraction targets such as metadata, images, and tables. Direct LLM-based extraction offers greater flexibility, but incurs substantial cost and latency at scale, while manually engineered publisher-specific parsers can achieve high accuracy but require substantial human effort to build and maintain. We introduce PACE, an agentic framework for learning publisher-specific extraction configurations from representative pages and user requirements. During training, PACE uses LLMs to analyze page structure and aggregate reusable extraction patterns. At inference time, the learned configurations instantiate a fixed deterministic extractor template, enabling scalable extraction without additional LLM calls. Experiments spanning article-body, metadata, and
multimodal extraction show that PACE outperforms scalable non-manual baselines while approaching the quality of manually engineered publisher-specific parsers. PACE achieves stronger extraction of article text, metadata, images, and tables, demonstrating that agentic configuration learning can automate publisher-specific extraction for LLM-ready page representations beyond article text.
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Artificial Intelligence · Cross-listed from cs.CL
Hallucinations in LLMs: A Lifecycle-Based Survey of Causes, Detection, Mitigation, and Prevention
Naveen Lamba, Sanju Tiwari and Manas Gaur
The lifecycle of
hallucination in LLMs is a concept that enables building solid frameworks on the control and reliability of LLMs in high-stakes environments, including health, legal, and scientific research. Although previous surveys have primarily focused on detection or mitigation, this survey provides a lifecycle-based overview of the
hallucinations in the LLMs, their cause, detection, mitigation, and prevention.We propose a three-fold categorization of
hallucinations across the LLM lifecycle: data-related, training-related, and inference-related, which is consistent with the lifecycle of the development of the LLM. Each of these stages is discussed regarding the cause of
hallucinations, their detection, and the ways they can be addressed under specific mitigation or prevention interventions. In addition, we discuss the available
benchmark data using a number of parameters so as to establish their suitability in identifying, restricting and managing
hallucinations. The survey provides researchers and practitioners with a standardized framework to understand, diagnose, and cure
hallucinations in a systematic system to present actionable data to build safer and more reliable LLMs.
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Artificial Intelligence · Cross-listed from cs.LG
Model of Models: When Does Emitting a Specialist Beat Attending, Adapting, or Tuning?
John C. Howell
Given a task described by a few examples, how should a model be specialized to it? Four mechanisms are available – zero-shot, in-context
attention, test-time gradient adaptation, and emitting specialist weights from a hypernetwork – yet the operating regime of the last is rarely mapped. We run the identical four-way comparison across six tasks spanning regression, generation,
language modeling, reinforcement learning, and clinical and genomic classification, holding the specialist, the context, and (where we can) the training budget fixed. The clearest wins for emission are about cost at matched quality: it ties the state-of-the-art amortized tabular model (TabPFN) on clinical few-shot classification while emitting a reusable specialist instead of re-attending the support set per query, and reaches noise-floor shape generation with a $132$-float per-instance program. On few-shot sinusoid regression it is $2$–$3$ orders of magnitude below MAML at zero test-time gradient steps – a margin that narrows to $\sim$$30\times$ but persists once training budgets are equalized. Emission cannot match in-context
attention on high-dimensional sequence modeling: under matched-budget pre-training a one-pass adapter recovers only a minority of the in-context gain ($14.0\pm0.9\%$ at $5$M, $11.2\pm0.5\%$ at $15$M), and a LoRA-rank sweep shows this shortfall is a partial capacity limit – capture climbs from $5\%$ to $21\%$ as rank grows but plateaus far below full recovery. Mechanism ablations confirm the emitted specialist is genuinely task-conditioned, not a memorized prior; and, more speculatively, emitted specialists compose in weight space – interpolating two of them tracks the corresponding blend of their functions. We close with a falsifiable thesis, operationalized through a per-task resolution measure, bounding when each conditioning mechanism should be preferred.
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Artificial Intelligence · Cross-listed from cs.CL
LLM Safety Alignment in Low-Resource Languages: A Systematic Literature Review
Valdini Douglace Lemofouet, Blessing Ngozi Uzor, Paula Chikaodinaka Anyanwu et al. (+9 more)
Large
Language Models (LLMs) have achieved substantial progress in safety
alignment, yet their safety guarantees remain significantly weaker in low-resource and
multilingual settings than in high-resource
languages. In this paper, we conduct a Systematic Literature Review (SLR) of LLM safety
alignment in low-resource
languages by adopting the PRISMA 2020 methodology. Out of roughly 1,500 papers identified from Semantic Scholar, arXiv, and OpenAlex, 50 relevant studies have been selected and analyzed. Our review is organized around four themes: safety
alignment methods,
multilingual safety risks, evaluation
benchmarks, and cross-lingual transferability. We further propose a taxonomy of safety
alignment approaches based on three adaptation mechanisms: data adaptation, objective optimization, and mechanistic
alignment. Across literature, translated English
benchmarks fail to sufficiently represent culturally rooted harms, and
multilingual models are more vulnerable to cross-lingual jailbreaks, code-switching attacks, and safety degradation in underrepresented
languages. These failures are driven by several key factors, including uneven
multilingual pre-training coverage, insufficient native-
language preference data, poor transfer of safety representations, and a lack of culturally aware evaluation frameworks. The review also notes that many low-resource
languages, especially African
languages, have fewer safety
benchmarks available than other
multilingual regions. Overall, the results reveal a persistent
multilingual safety gap, and suggest that future progress will require culturally grounded
benchmarks, participatory data collection, balanced
multilingual pre-training, and scalable
multilingual alignment methods.
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All authors Valdini Douglace Lemofouet, Blessing Ngozi Uzor, Paula Chikaodinaka Anyanwu, Danielle Blanche Kapsa, Sukairaj Hafiz Imam, P Sam Sahil, Abigail Oppong, Tassallah Abdullahi, Clemencia Siro, Idris Abdulmumin, Seid Muhie Yimam, Shamsuddeen Hassan Muhammad
Artificial Intelligence
Position: We Need Practical AI Alignment Methods to Mirror Human Reasoning
Vijay Keswani, Breanna K. Nguyen, Cyrus Cousins et al. (+3 more)
AI systems are increasingly employed as decision aids, decision delegates, or autonomous decision-makers. This position paper argues that in many settings, particularly high-stakes decision-making, we need accurate cognitively-aligned AI systems that reason similarly to their users, and faithfully communicate their
reasoning. We review evidence that cognitive
alignment improves understandability and trustworthiness, and provide new survey data showing that many users find cognitive
alignment "essential" when an AI's rationale for a judgment or action is important to them. We outline the gaps between existing
alignment methods and what is needed to achieve cognitive
alignment, and present a research agenda to address these gaps. We argue that cognitive misalignment represents a likely impediment to AI adoption in many envisioned applications, and that addressing it is important for creating AI systems on which users are both willing and justified to rely.
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All authors Vijay Keswani, Breanna K. Nguyen, Cyrus Cousins, Vincent Conitzer, Walter Sinnott-Armstrong, Jana Schaich Borg
Artificial Intelligence
AutoWorldModel-Bench: A State-Centric Benchmark for Automated World-Model Research
Marjan Moodi, Xuankang Zhu, Fernando De Mesentier Silva et al. (+2 more)
World modeling is an unsettled field: architectures, training objectives, and state representations interact in complex ways, and no single recipe dominates across environments. This makes it an ideal testbed for AI coding
agents acting as autonomous researchers–a setting in which the improvement direction is not specified in advance, unlike the engineering-to-spec tasks that dominate current
agent benchmarks. We introduce AutoWorldModel-Bench, a closed-loop
benchmark in which frontier coding
agents autonomously improve a provided base world model under a fixed compute budget. The
benchmark spans eight game environments under a unified structured-state representation–ground-truth entity state extracted from each game and consumed through a shared tensor format–which isolates dynamics modeling from perception and enables minutes-per-run iteration. Across 64 sessions, Codex-5.4 and Claude Opus 4.6 improve their base on a held-out test split in all but one session, with about half (33 of 64) a substantial gain ($\Delta \geq +0.10$) and the remaining improvements smaller but positive; in 91% of sessions the winning edit is a substantive change to the model or training rather than a hyperparameter tweak. Our
benchmark offers a setting in which frontier coding
agents can be evaluated on open-ended research rather than engineering-to-spec problems.
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All authors Marjan Moodi, Xuankang Zhu, Fernando De Mesentier Silva, Harold Chaput, Mohammad Reza Taesiri
Artificial Intelligence
SIRIN: A Unified Toolkit for Detecting Contextual Hallucinations in Retrieval-Augmented and Memory-Grounded LLM Systems
Julia Belikova, Rauf Parchiev, Mikhail Filimonov et al. (+3 more)
SIRIN (Semantic Inconsistency Recognition and Inspection Nexus) is a unified toolkit and interactive web UI for detecting contextual
hallucinations (fluent, plausible responses unsupported by the provided evidence) in
retrieval-augmented, agentic, and
memory-grounded LLM systems. SIRIN unifies three detector paradigms (representation probing, uncertainty estimation, and judge-style verification) and the complementary task of pre-generation query answerability under one interface, configuration system, and evaluation pipeline, supporting response- and span-level inspection in both white-box and black-box settings. The web UI enables live analysis of user-supplied context-query-answer triples through
hallucination scores, unsupported-span highlighting, and side-by-side detector comparison, with a lightweight plug-in design for adding new detectors. We demonstrate SIRIN on
hallucination detection, query answerability, and as a faithfulness gate within long-term
memory systems. The source code is publicly available at https://github.com/sb-ai-lab/SIRIN.
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All authors Julia Belikova, Rauf Parchiev, Mikhail Filimonov, Konstantin Polev, Andrey Savchenko, Maksim Makarenko
Artificial Intelligence · Cross-listed from cs.CL
Evaluating Federated Pre-Training: On the Reliability of Downstream Fine-Tuning and Intrinsic Evaluation
Claudia Grosser, Maike Heuer, Denis Krompass et al. (+1 more)
Federated pre-training offers a way to train foundation models on private or distributed data without centralizing the underlying
datasets. However, evaluating
federated pre-training remains challenging because differences in client participation and local data availability can make directly comparable evaluation difficult. Moreover, pre-training test perplexity is tied to the pre-training distribution, while downstream
benchmarks introduce task-specific adaptation that may not faithfully reflect the test perplexity established during pre-training. In this work, we study which evaluation protocol more reliably reflects
federated pre-training quality. Using a controlled set of centralized and
federated-trained models of a 16M parameter
transformer model trained on identical client data, we assess evaluation protocols by whether they preserve a reference ranking established on the same pre-training testset. We compare downstream fine-tuning on GLUE, including full, head-only, and reduced-data variants, with next-
token prediction on GLUE text as an intrinsic evaluation signal. Our results show that downstream fine-tuning does not reliably preserve the pre-training ranking, whereas direct next-
token prediction exhibits a strong correspondence with the pre-training test perplexity. These findings suggest that downstream fine-tuning alone can be misleading when comparing
federated pre-trained models, and that evaluation signals closer to the original pre-training objective deserve greater
attention.
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All authors Claudia Grosser, Maike Heuer, Denis Krompass, Thomas A. Runkler
Artificial Intelligence · Cross-listed from cs.NE
Optimizing Sensor Placement for Hydrogen Leak Detection in Enclosed Infrastructure: A Comparative Study Using CFD-informed Genetic Algorithm and DeepSets Neural Surrogate
Fangnian Wang, Nicholas Tan Jerome, Thomas Jordan et al. (+1 more)
Hydrogen infrastructure in enclosed environments, such as parking facilities for fuel
cell vehicles, presents significant safety challenges due to hydrogen's low ignition energy and wide flammability range. Current monitoring systems are largely reactive, detecting leaks only after hazardous concentrations have formed. This study develops a computational framework for proactive sensor placement optimization by integrating computational fluid dynamics (CFD), genetic
algorithm (GA) optimization, and a DeepSets neural surrogate. A CFD database of 180 scenarios was generated for a representative 50 m x 30 m x 3 m garage, covering multiple leak positions, rates (1-150 g/s), and ventilation conditions (ACH = 3-10 per hour). Sensor placement was optimized using a multi-objective GA and compared with uniform, random, and surrogate-assisted approaches. The GA achieved a detection rate of 96.1% within 60 s and reduced blind areas to 0.12%, corresponding to an approximately 5% improvement in composite fitness over a uniform baseline. The DeepSets surrogate reproduced near-optimal configurations with a fitness gap below 0.01 while reducing CFD evaluations by 89% and computational time by two orders of magnitude. Detection performance and spatial coverage remained comparable to the GA, demonstrating that surrogate-assisted optimization can retain solution quality while enabling rapid design iteration. Overall, the results show that CFD-informed optimization improves detection effectiveness and reduces sensor requirements compared to conventional layouts. The proposed framework supports scalable deployment and provides a foundation for integrating optimized sensor networks with digital twin systems for real-time monitoring and risk assessment.
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All authors Fangnian Wang, Nicholas Tan Jerome, Thomas Jordan, Frank Simon
Artificial Intelligence · Cross-listed from cs.LG
CORVUS: Context Optimization and Reduction Via Underlying Synchronization for LLM Coding Agents
Mingwei Zheng, David OBrien, Siwei Cui et al. (+4 more)
LLM coding
agents operate by constructing trajectories that accumulate
reasoning, tool calls, and results to enable multi-step decision-making. However, the conventional append-only trajectory architecture found in practice tightly couples file-read actions with their observations, capturing snapshots that become permanently fixed in the chronological history. As files change through
agent edits or concurrent human modifications, these snapshots become stale, causing
reasoning errors and causing
agents to redundantly re-read files, with each re-read appending yet another copy to the trajectory. To mitigate this, we propose CORVUS, a novel trajectory architecture that decouples file-read actions from their observations by maintaining a synchronized registry of relevant files and injecting only their current contents at each
reasoning cycle. This structural change produces significantly lighter-weight trajectories that remain synchronized with the actual codebase state by construction, eliminating redundant file copies and stale snapshots that bloat conventional trajectories. We evaluated CORVUS on SWE- POLYBENCH_VERIFIED and SWE-BENCH PRO across four LLMs, achieving 9-50% reduction in average input
tokens per task, 15-32% shorter final
prompts, and up to 37% fewer
reasoning cycles while maintaining comparable pass rates.
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All authors Mingwei Zheng, David OBrien, Siwei Cui, Pardis Pashakhanloo, Rajdeep Mukherjee, Myeongsoo Kim, Sachit Kuhar
Artificial Intelligence · Cross-listed from cs.CV
RMS@CC-MMD 2026: Multimodal Misogyny Detection via Geometric Interaction and Multi-View Consensus
Md. Ajwad Hossain
The proliferation of internet memes has introduced new complexities to automated content moderation, particularly in detecting misogyny. Memes often rely on a semantic clash between visual and textual modalities, where hateful intent is implicit and culturally grounded. This paper presents GeoMVC (
Geometric Interaction and Multi-View Consensus), developed for the CC-MMD Grand Challenge at ICMI 2026. To address the limitations of static feature concatenation, a
Geometric Interaction Layer is proposed that models cross-modal
alignment via Hadamard products and cosine similarity between frozen visual and textual
embeddings. We further mitigate distribution shifts caused by noisy OCR and code-mixed transliteration through a Multi-View Consensus strategy, aggregating predictions across raw, length-filtered, and English-translated text views. The system achieved Rank 2 in the Malayalam partition (Macro F1: 0.892) and Rank 3 in the Chinese partition (Macro F1: 0.895) on Task A, while securing Rank 5 in the Tamil partition (Macro F1: 0.521). A detailed error analysis on the development partition highlights open challenges in modeling localized transliteration and code-mixed sarcasm across Dravidian and Chinese cultural contexts.
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Artificial Intelligence
MPR-CiteG: Enhancing RAG with Multi-Portfolio Retrieval and Citation-Grounded Generation
Hyewon Lee, Minkyung Song, Junghyun Oh et al. (+2 more)
This paper presents the MPR-CiteG framework, which achieved second place in the ScienceON AI Challenge by addressing two fundamental challenges in generative AI: inefficient
retrieval and the absence of source verification. We propose a dual-component system, termed MPR-CiteG, in which the Multi-Portfolio Retriever (MPR) efficiently retrieves diverse and relevant information, while the Citation-Grounded Generation (CiteG) module ensures that every generated output remains factually consistent and explicitly attributed to its source. MPR-CiteG represents a significant step toward building more trustworthy and accurate LLMs that are not only capable of generating information but also of grounding their responses in reliable evidence, thereby mitigating common issues like model
hallucination. Extensive experiments on the challenge
dataset validate the effectiveness and reliability of our approach. Our code is available at https://github.com/2noweyh/MPR-citeG.
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All authors Hyewon Lee, Minkyung Song, Junghyun Oh, Seunghoon Han, Sungsu Lim
Artificial Intelligence · Cross-listed from cs.LG
Measuring the Dependency Gap: Diagnosing Inter-Column Fidelity in Tabular Generative Models
Jie Zhang
Synthetic tabular data are valued for preserving inter-column dependency, yet a routine fidelity score says neither where it is lost nor why. Equipping a classifier two-sample test (C2ST) with a gradient-boosted discriminator, we decompose it by controlled permutation into marginal, dependency and numerical–categorical cross components, read against a fully-factorized reference that destroys all dependency while keeping every marginal, and a real-data oracle. The linear detection score in common use rates that reference as nearly real, a known weakness we replicate on four
benchmarks. On a flow-matching (TabbyFlow) and a
diffusion (TabDiff) generator it exposes a deficit of the same order in both, whose source we narrow by elimination: not a structural limit of the mean-field objective, not mainly sampling discretization, and not closed by a $16\times$ capacity increase where that test has power, while the same measurement responds sharply when capacity is cut eightfold, so the plateau is measured rather than a blind spot. Destroying inter-column dependence while keeping every marginal and every feature–label relation costs between $0$ and $0.22$ minority-class $F_1$ by
dataset, and the generators' own shortfalls are not ordered by their deficits. An explicit cross-coupling module and a post-hoc copula leave the deficit in place. Neither generator's objective places a term on the joint at all; that this absence is what sustains the deficit is an explanation we reach by elimination rather than test. A controlled validation calibrates the diagnostic and identifies when it reports a false positive, which none of these generators triggers.
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Artificial Intelligence · Cross-listed from cs.LG
MotifRole-Diff: Risk-Optimal Role-Aware Corruption for Masked Molecular Graph Diffusion
Tasfia Nuzhat Ornee, Elias Hossain, Ivan Garibay et al. (+1 more)
Masked discrete
diffusion for molecular graph generation typically applies a uniform corruption schedule to all
tokens in a lossless graph-to-sequence representation, implicitly treating structurally heterogeneous molecular components as equally difficult and equally important to reconstruct. However, different molecular graph
token roles exhibit substantial variation in denoising difficulty and their influence on the decoded molecule, motivating role-specific corruption strategies. We introduce MotifRole-Diff, a role-aware corruption process that allocates masking rates according to empirically measured denoising difficulty and graph-level perturbation impact while preserving the model architecture, clean sequence space, and lossless molecular-graph decoder. We formulate schedule selection as the risk-optimal allocation of a fixed masking budget across
token roles. Our
theorem characterizes optimality for the modeled role-weighted residual risk, while downstream generation performance is evaluated empirically. Under matched architecture, training budget, and sampling compute, MotifRole-Diff improves validity on QM9 from 0.905 to 0.944 while reducing FCD from 1.701 to 1.609, and on MOSES improves validity from 0.920 to 0.938 while reducing FCD from 2.125 to 1.850. Role-wise diagnostics further show improved reconstruction across molecular graph
token categories. Together, these matched-compute results indicate that structurally informed corruption is a more effective masking strategy than uniform schedules for serialized molecular graph
diffusion.
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All authors Tasfia Nuzhat Ornee, Elias Hossain, Ivan Garibay, Niloofar Yousef
Artificial Intelligence
The Chronos Vulnerability: A Taxonomy of Temporal Persistence and Memory-Based Deception in Agentic AI
Om Narayan, Ramkinker Singh and Praveen Baskar
The transition from stateless generative models in artificial intelligence to stateful, autonomous
agents represents an architectural
evolution that, while providing the capabilities of long-term planning and the automation of enterprise workflows, also represents the introduction of a new form of security threat, the Chronos Vulnerability. The Chronos Vulnerability represents the threat of
memory-based attacks, including the
Memory Injection Attack (MINJA) and the sleeper
agent, in which the internal belief system of the autonomous
agent is compromised, effectively decoupling the attack vector from the final catastrophic event. This study formalizes the threat model for persistence-based attacks and the threat of Dynamics Blindness in the context of the World of Workflows
benchmark, demonstrating that traditional endpoint content filters are insufficient for the current stateful architecture. Consequently, this study synthesizes a defense-in-depth landscape, categorizing emerging frameworks such as diagnostic trajectory guardrails (AgentDoG), formal temporal verification (
Agent-C), immunological
memory consensus (A-MemGuard), and hardware-anchored trust via GPU-based Trusted Execution Environments (TEEs) and Zero-Trust
memory architectures.
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Artificial Intelligence · Cross-listed from cs.CR
ChainWatch: A Kill Chain-Aligned Sequential Detection Framework for Multi-Step Attacks in MCP-Based AI Agent Systems
Om Narayan, Rashmi Jyoti and Ramkinker Singh
The Model Context Protocol (MCP) is an open-source standard that allows AI
agents to connect to external tools, databases, and services. While this connectivity enables powerful
agent capabilities, it also introduces multi-step attacks that existing per-call defenses cannot reliably detect. Attackers can compose individually benign tool invocations into malicious sequences that evade isolated inspection. This paper presents ChainWatch, a sequential detection framework for identifying multi-step attacks in MCP-based AI
agent systems. ChainWatch models attack progression using a six-stage kill chain and applies a Hidden Markov Model (HMM) to classify tool-call sequences. Detection rules are triggered when a session exhibits suspicious progression across multiple stages. The framework is supported by a structured threat model covering direct sequential attacks, indirect
prompt injection chains, and hybrid multi-stage attacks. A 20-dimensional feature extraction schema captures behavioral signals from tool interactions. We demonstrate the approach using five representative attack scenarios from the security literature, showing how ChainWatch detects attack chains that evade traditional per-call security mechanisms.
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Artificial Intelligence · Cross-listed from cs.AR
BRIM: Workload-Balanced Dual-Sided Bit-Serial Sparse Inference Accelerator
Varun Manjunath, Ruokai Yin, Donghyun Lee et al. (+2 more)
Bit-serial accelerators exploit bit-level sparsity to reduce DNN inference cost, but existing designs exploit sparsity on only one operand, bounding the speedup. Extending sparsity exploitation to both operands simultaneously yields compounding reductions in partial products but introduces a critical new bottleneck: workload imbalance. Because each concurrent weight - activation pair's execution cost depends on the product of two independently varying operand non-zero bit counts, pairs that must complete together finish at vastly different times, leaving faster computations idle. We show this limits PE utilization to 56 - 64% in existing dual-sided designs. We present BRIM, a hardware - software co-designed dual-sided bit-serial sparse accelerator that directly targets this bottleneck. BRIM combines two integrated mechanisms: 1) Cyclic-Balanced
Pruning (CBP), a post-training weight optimization that reshapes weight representations based on profiled activation statistics to equalize expected workloads across concurrently processed pairs offline; and 2) Pairwise Slot Donation, a lightweight hardware mechanism that absorbs residual runtime imbalance with negligible area overhead. Evaluated across CNNs, ViTs, and LLMs under iso-area constraints, BRIM achieves over 90% PE utilization, up to 2.37x speedup, and up to 1.63x energy efficiency improvement over prior dual-sided designs.
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All authors Varun Manjunath, Ruokai Yin, Donghyun Lee, Arkapravo Ghosh, Priyadarshini Panda
Artificial Intelligence · Cross-listed from cs.CR
ChannelGuard: From Borrowed Safety to Application-Controlled Defenses in Multi-Agent LLMs
Elias Hossain, Md Mehedi Hasan Nipu, Fatema Tuj Johora Faria et al. (+4 more)
AI assistants increasingly work as teams, where one model makes a plan, others search the web, use tools, or read stored information, and another writes the final answer. This creates new security risks because harmful instructions can enter through information passed between models, not just through the user's message. Yet safety tests usually report only whether an attack succeeded, without showing what actually stopped it. We show that systems with similar safety scores can depend on very different protections. Across five
agent models, attacks were stopped by a cloud provider's filter, by the model itself, or by inconsistent model behavior. This matters because protection that comes from the model or provider may disappear when either one changes. We introduce CHANNELGUARD, an application-controlled defense that checks information at each handoff without extra training or additional model calls. Across more than 5,700 test runs, it stopped all tested attacks hidden in tool outputs on all five models. On two open models, it reduced successful
prompt attacks from 80% to 3% and from 77% to 10%, with no change in accuracy on 30 math problems. However, reworded attacks could still bypass the defense.
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All authors Elias Hossain, Md Mehedi Hasan Nipu, Fatema Tuj Johora Faria, Shubhashis Roy Dipta, Maleeha Sheikh, Tasfia Nuzhat Ornee, Niloofar Yousefi
Artificial Intelligence · Cross-listed from q-bio.GN
Making Single-Cell Data Distillation Auditable: Traceable Real-Cell Coresets via Discrete Min–Max Selection
Yaodi Luo, Peize He, Lingbei Meng et al. (+4 more)
Large single-
cell datasets are expensive to store, curate, and repeatedly reuse for model training. Data
distillation can reduce this burden by building smaller training sets. However, many existing methods rely on synthetic
cells. These synthetic
cells do not retain direct correspondence with assayed
cells and genes. This limits source-level inspection and biological traceability. Moreover, real-
cell expression
matrices are often sparse and noisy. In light of these challenges, we propose Minmax-CF, a label-aware characteristic-function selector for traceable single-
cell data
distillation. Minmax-CF formulates compression as a discrete min–max selection problem over characteristic-function directions. It uses
entropy-regularized maximization to emphasize the least preserved directions. Greedy minimization ranks
cells and genes by how much they reduce the resulting weighted error. The method alternates
cell and gene selection under explicit axis-specific budgets. Across five coarse-lineage
benchmarks and five compression budgets, Minmax-CF retains 95.3% of the Full-reference macro-F1 on average, with gaps that exceed one per-seed standard deviation. It also retains exact source-
cell indices and original gene symbols. Compared with size-matched synthetic PCA-Centroid and Distribution Matching (DM) baselines, Minmax-CF achieves higher coarse-lineage macro-F1 in 24 of 25 comparisons against each baseline. It exceeds their average performance by 10.4% and 17.4%, respectively. Retained
cells can also be projected onto independently computed
embeddings for direct biological interpretation.
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All authors Yaodi Luo, Peize He, Lingbei Meng, Bowen Han, Zheng Lu, Jianqing Zhu, Lian Zhang
Artificial Intelligence · Cross-listed from cs.CR
JailMeter: An Evidence-Based Evaluation Framework for Jailbreak Attacks on Large Language Models
Qingjia Huang, Jingyu Zhang, Jianguo Wu et al. (+6 more)
The assessment of jailbreak attacks against large
language models currently suffers from inconsistent evaluation criteria and methods, leading to unreliable estimates of attack success rates. We propose JailMeter, an evidence-based evaluation framework designed to more faithfully measure jailbreak effectiveness. Inspired by the Information Bottleneck theory, JailMeter applies dual-feedback optimization to filter jailbreak noise from model responses while preserving content relevant to the original malicious question. This process produces concise evidence for a rigorous assessment under which an attack is validated only when the response captures the malicious intent and delivers a complete answer, thereby signaling a substantive bypass of model safety
alignment. We evaluate JailMeter on JailMeter-Eva, a challenging
benchmark containing 330 human-labeled, non-rejected jailbreak instances. JailMeter achieves an accuracy of 97.27%, substantially outperforming existing evaluation methods. To support large-scale evaluation, we further distill JailMeter into a small
language model, JailMeter\textsubscript{SLM}, which maintains comparable reliability with significantly reduced computational costs. Code and
dataset are available at https://github.com/Magi2B0y/JailMeter.
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All authors Qingjia Huang, Jingyu Zhang, Jianguo Wu, Yakai Li, Weijuan Zhang, Yankai Rong, Junyi Yao, Shengzhi Zhang, Xiaoqi Jia
Artificial Intelligence · Cross-listed from cs.AR
Opto-ViT-v2: Noise-Resilient On-Chip Fine-Tuning for Photonic Near-Sensor Vision Transformer Accelerators
Xuming Chen, Deniz Najafi, Mehrdad Morsali et al. (+5 more)
Silicon-photonic (SiPh) accelerators have emerged as a promising platform for
Vision Transformer (ViT) inference by performing
matrix multiplications on microring-resonator (MRR) banks with high throughput and energy efficiency. Extending these platforms to support on-chip fine-tuning remains challenging because backpropagation requires large activation storage, frequent weight write-back to MRRs, and tolerance to device-level noise. We present Opto-ViT-v2, the first framework for parameter-efficient fine-tuning (PEFT) on a near-sensor SiPh ViT accelerator. Our tensorized low-rank decomposition separates pretrained optical weights from a small set of trainable electronic factors (as few as 8K parameters for ViT-Base), greatly reducing activation storage and weight updates while enabling practical on-chip training. We further introduce a gradient-accumulated sparse classifier that freezes low-importance weights through one-shot top-k gradient masking, reducing classifier training cost by about 40 percent. We also develop the first system-level noise model for photonic on-chip training, capturing the effects of MRR crosstalk, thermal drift, and
laser amplitude noise during both forward and backward propagation. Calibrated using measurements from more than 200 fabricated MRR devices, the model shows that low-rank factor updates are more robust than full fine-tuning and conventional layer-wise low-rank adaptation under identical noise conditions. Experiments on VTAB-1K (19 tasks) and FGVC few-shot
benchmarks demonstrate that Opto-ViT-v2 recovers within 0.3 to 0.8 percent of clean software accuracy under measured photonic noise while achieving more than 100 KFPS/W, enabling practical on-chip domain adaptation for photonic edge
vision systems.
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All authors Xuming Chen, Deniz Najafi, Mehrdad Morsali, Chengwei Zhou, Zahra Ghanaatianjobzari, Mahdi Nikdast, Shaahin Angizi, Gourav Datta
Artificial Intelligence · Cross-listed from cs.LG
Planning as Emergent Behavior in Reinforcement Learning with Relational Hidden States
Armin Sommer
Reinforcement learning is conventionally divided into model-based and model-free methods. In this taxonomy, model-based methods perform lookahead planning over a learned world model, whereas model-free methods learn a reactive state-action mapping. Recent work, however, has shown that planning can emerge from model-free reinforcement learning alone. The conditions under which this behavior emerges from a pure reward-maximization objective have so far remained unclear. In this paper, we present evidence that, in the observed cases, the hidden-state structure of the neural architecture is the deciding factor. We find that a network of relational hidden states, each anchored to an environment state and exchanging messages along learned relations, acquires a planning mechanism. These hidden states recover the environment's transition structure in their learned relations, and improve the policy at decision time by planning over the learned graph. In a matched control
agent that must additionally discover which
cells represent which states, no such binding arises, and no planning follows from it. We argue that this explains the observed phenomenon of emergent planning in model-free reinforcement learning and raises the question of how common such emergent planning might be more generally. Finally, we hypothesize that the discovered mechanism could describe how planning emerges from pure reward maximization in the human
brain through a neural architectural prior.
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Artificial Intelligence
When Does Machine Learning Beat Value Sorting? A Three-Dataset Diagnostic of Exposure-Weighted Shipment Prioritization
Jize Li
Delay-risk models are usually judged by predictive accuracy. What matters in practice is narrower: with capacity to review only a few shipments, which ones should a manager check first? We evaluate whether machine learning clears a demanding no-model baseline: inspect the highest-value shipments first. Across three real supply-chain contexts: SCMS procurement, DataCo logistics, and Olist e-commerce, we use leakage-controlled rolling-origin evaluation and 1000-sample paired bootstrap confidence intervals. Ranking by predicted delay severity times known value (M1) beats severity-only ranking in all three
datasets, yet it does not generally beat value sorting. At a 10% review budget, M1 minus VALUE_ONLY is -5.5 percentage points (pp) for SCMS, +10.1 pp for DataCo, and -4.9 pp for Olist. The divide is consistent with severity learnability: DataCo has R^2 = 0.27 and calibration bias of +0.01 days, whereas SCMS and Olist have R^2 of approximately -0.02 and negative calibration bias. Nested-CV cost-sensitive retraining does not deliver a stable improvement over M1. Rather than proposing a new learning
algorithm, this paper presents a deployment diagnostic and evaluation protocol. Value sorting should remain a permanent
benchmark, and ML should be deployed only after severity learnability and calibration have been audited and the model clears that gate under leakage-controlled rolling-origin evaluation.
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Artificial Intelligence · Cross-listed from cs.CL
For What Reason? Interpreting Models' Encoding of Causation and Antithesis
Abhidip Bhattacharyya and Shira Wein
Discourse relations provide document structure, critical to
language understanding and enabling
language model performance and ethicality. In this work, we investigate how instruction-tuned
Transformer models (LLaMA and Mistral) encode discourse relations in English, with a particular focus on the contrasting relations of causation and antithesis. Framing the task as a next-
token prediction task and applying a suite of interpretability techniques to test model internals, our findings show that certain early layers make predictive decisions at mid-sequence
tokens, while some mid-level layers finalize their decisions closer to the last
token. Most of the remaining layers primarily propagate earlier decisions rather than actively influencing them. Additionally, we observe that some layers exhibit a preference for one answer over alternatives, suggesting asymmetric representation of discourse-based
reasoning.\footnote{Our code is available at https://github.com/abhidipbhattacharyya/causation_vs_antithesis}
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Artificial Intelligence
Attacking Graph Foundation Models Through Their Shared Representation
Pankaj Kumar and Subhankar Mishra
A graph foundation model generalizes across graph domains by mapping every input into one shared representation before any task
reasoning. We call this map the
alignment layer, the component that separates a graph foundation model from a graph neural network, and we show it is a distinct attack surface that prior work has not studied. We attack it at inference time, with no access to training, on six public models spanning spectral tokenizers, text
embedding spaces, and a discrete codebook. A directed representation-space perturbation collapses every model, but at a budget comparable to the representation norm a plain graph network also needs, with one exception: OpenGraph, whose spectral tokenizer collapses at a fifth of that budget, an
alignment-specific fragility a plain network does not share and which a same-representation control traces to the tokenizer rather than the decoder. A realizable input-space attack that edits edges, features, or text removes at least half the correct predictions on three of the six models at peak. How much of this fragility an input-access attacker realizes tracks how directly the decoder reads the representation, and not the clean accuracy a task leaves; we measure this carrier gain structurally from the decoder's local Lipschitz sensitivity, and report clean-accuracy headroom as a within-model ordering heuristic that does not survive on realizable attacks.
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Artificial Intelligence · Cross-listed from cs.CL
The Story Shapes the Agent: Narrative Priors in LLM Behavior
Yixuan Wang, James Lester and Shashank Srivastava
Persona prompting is widely used to steer LLM
agent behavior, yet the narrative framing of a task can matter more than the assigned persona. We isolate this effect through structural isomorphism, constructing three text-based investigation games that share the same action space, stage progression, and resource constraints while varying only task narrative: disease investigation, IT troubleshooting, and murder mystery. Across 1,890 sessions spanning 3 models and 10 personas, we identify narrative priors: systematic action tendencies activated by a task's story framing, independent of its decision structure. Narrative priors explain 5-31x more behavioral variance than persona, are consistent across model architectures, and in two of three domains are negatively associated with task success. Persona effects that do transfer across narratives arise from behavioral anchors, persona descriptions whose
language maps directly onto shared actions. Causal interventions confirm this: removing anchor words from a high-transfer persona reduces cross-narrative consistency by 95%. Our framework also generalizes to a held-out fourth narrative and yields a persona-selection method that improves cross-narrative transfer. These results suggest that LLM behavior that survives narrative changes should be grounded in concrete actions rather than abstract descriptions.
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