Machine Learning · Cross-listed from cs.CY
Two Wrongs, No Right: Auditing Social-Desirability Bias in LLM Annotators for Computational Social Science
Varun Kotte
LLM annotators are increasingly used in computational social science (CSS), but it is unclear whether their
alignment-shaped errors preserve the empirical conclusions a researcher would report. We audit three open-source 7B instruction-tuned models (Zephyr, Mistral-Instruct, Qwen2.5-Instruct) across six TweetEval tasks under four
prompt conditions (72
cells) and find that social-desirability failures do not run in a single direction. Zephyr exhibits leniency bias, systematically under-applying harmful labels (offensive
language: false benign rate 0.729, false alarm rate 0.031). Mistral and Qwen exhibit overcorrection, over-applying the same labels (Mistral hate-
speech FAR = 0.604). All three models exhibit neutrality bias on abortion stance, underestimating opposition prevalence by 24 to 40 percentage points and inflating the neutral label. None of the four prompting interventions we test (neutral, safety framing, depersonalized, chain-of-thought) corrects these failures across models; safety framing can worsen stance distortion. Strikingly, Zephyr's hate-
speech prevalence estimate matches the gold rate exactly while its class-conditional errors are large in both directions, an accidental cancellation that misleads aggregate validation. We translate these patterns into a three-part taxonomy with diagnostic FBR/FAR signatures and a lightweight gold-sample validation protocol. The headline for trustworthy CSS: a model that looks calibrated on aggregate metrics can still flip the substantive empirical conclusion a researcher would report.
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Machine Learning · Cross-listed from cs.CY
An Explainable AI Assistant for Introductory Programming Education: Improving Feedback Reliability with Instructor-AI Collaboration
Muntasir Hoq, Griffin Pitts, Bradford Mott et al. (+6 more)
Active learning is widely recognized as an effective approach for improving learning outcomes in introductory programming courses. However, insufficient instructional support often limits students' access to timely, personalized feedback, which is crucial for mastering foundational programming concepts. Although recent advances in AI, particularly large
language models, offer scalable opportunities for feedback, concerns about explainability and reliability remain. In this paper, we present an AI-driven classroom assistant that leverages an explainable AI model to analyze student code, map logical errors to instructor-identified misconceptions, and deliver instructor-authored feedback, thereby grounding reliability in instructor-defined pedagogical knowledge. To evaluate the effectiveness of our framework, we conducted an expert evaluation to examine its
alignment with instructor-verified feedback and deployed the system in a classroom setting to assess students' perceptions of its usability. Results indicate that the assistant can provide accurate, instructor-verified feedback to students while fostering a positive experience.
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All authors Muntasir Hoq, Griffin Pitts, Bradford Mott, Seung Lee, Jessica Vandenberg, Shuyin Jiao, Narges Norouzi, James Lester, Bita Akram
Machine Learning
SynIB: Informational Bottleneck for Maximizing Synergy in Multimodal Learning
Konstantinos Kontras, Teodora Gagaleska, Thomas Strypsteen et al. (+4 more)
A central objective in
multimodal learning is to capture synergy: task-relevant information that arises only from the joint use of multiple modalities, and is not available from any single modality alone. While most approaches operate at the architectural level through larger or more complex fusion models, we propose a complementary axis: shaping the training objective itself. Standard training often emphasizes unimodal or redundant information, falling short on examples that require cross-modal
reasoning. We formalize
multimodal synergy through information theory and introduce the Synergistic Information Bottleneck (SynIB), a scalable objective that targets synergy directly. To prioritize learning synergy, SynIB motivates the model to predict accurately from all modalities while penalizing confidence when information from any modality is withheld. Alongside the standard task loss, the model runs forward passes with one modality masked at a time and is penalized for remaining confident, which would indicate reliance on unimodal cues rather than cross-modal interactions. We validate SynIB in two regimes. On synthetic XOR tasks where the ground-truth synergy is known by construction, standard training fails to recover it while SynIB does. On five real-world
benchmarks, including three MultiBench affective tasks, Hateful Memes with CLIP-ViT and DeBERTa backbones, and a controllable irony extension of CREMA-D we introduce, SynIB improves accuracy on synergy-dependent examples by up to 7.8% and overall accuracy by up to 3.8%.
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All authors Konstantinos Kontras, Teodora Gagaleska, Thomas Strypsteen, Christos Chatzichristos, Matthew Blaschko, Maarten De Vos, Paul Pu Liang
Machine Learning · Cross-listed from quant-ph
Zero-shot Quantum Neural Architecture Search
Tung Dao, Son N. Tran and Huynh Thi Thanh Binh
Variational
Quantum Algorithms (VQAs) are a leading approach to exploiting near-term
quantum hardware, leveraging parameterized
quantum circuits and classical optimization to achieve advantage. Despite their promise, the practical deployment of VQAs is challenged by the difficulty of designing
quantum circuit architectures that balance expressivity, trainability, and hardware constraints. Existing evolutionary-based
quantum neural architecture search methods address these challenges but suffer from high computational costs due to repeated training of candidate
circuits. In this work, we identify a setting in which the Gram
matrix of the
Quantum Neural Tangent Kernel converges. Building on this observation, we design a zero-shot surrogate model to estimate candidate performance without full training, significantly accelerating the architecture search process. Using this surrogate, we propose MZeQAS, a Monte Carlo Tree Search (MCTS)-based Zero-Shot
Quantum Neural Architecture Search framework for VQAs. By integrating proxy-based performance estimation with MCTS exploration, MZeQAS efficiently discovers high-performing architectures. Experimental results demonstrate that MZeQAS outperforms existing approaches in terms of both search efficiency and solution quality, providing a scalable and effective framework for advancing VQA deployment on noisy intermediate-scale
quantum devices.
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Machine Learning · Cross-listed from cs.NE
STARS: Spike Tail-Aware Relational Synthesis for ANN-to-SNN Data-Free Knowledge Distillation
Shuhan Ye, Yi Yu, Qixin Zhang et al. (+5 more)
SNNs promise energy-efficient and low-latency inference, but their performance still trails that of ANNs. ANN-to-SNN knowledge
distillation helps narrow this gap, yet the original training data are often unavailable in practical deployment settings. Existing data-free knowledge
distillation (DFKD) methods synthesize surrogate data by matching teacher-side priors, especially BN statistics, but these ANN-oriented constraints mainly regularize mean and variance and therefore remain under-constrained for SNN students whose responses depend on threshold-crossing dynamics. In this paper, we propose Spike Tail-Aware Relational Synthesis (
STARS), a plug-and-play method for ANN-to-SNN DFKD that augments standard BN-guided synthesis with two complementary objectives: Relational Consistency
Alignment, which preserves cross-sample relational consistency between teacher and student, and Tail-Aware Regularization, which regularizes threshold-relevant tail probabilities through soft exceedance over teacher-derived thresholds. Together, these objectives generate synthetic batches that remain teacher-valid while becoming more informative for SNN students. Experiments on CIFAR-10, CIFAR-100, and Tiny-ImageNet across multiple ANN-SNN pairs show that our method consistently improves conventional DFKD baselines and even surpasses several KD methods, with gains of up to 4.6% on CIFAR-10 and 6.7% on CIFAR-100, highlighting the importance of complementing BN matching with relational and tail-aware constraints in SNN-oriented DFKD.
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All authors Shuhan Ye, Yi Yu, Qixin Zhang, Hui Lu, Jiaming He, Qinggang Zhang, Li Shen, Xudong Jiang
Machine Learning · Cross-listed from quant-ph
Neural Quantum Spectral Operator Learning for Solving Partial Differential Equations
Chanyoung Kim, Myeonghwan Seong, Yujin Kim et al. (+2 more)
Partial differential equations (PDEs) are central to modeling physical and engineering systems, but repeatedly solving parametric PDEs remains computationally expensive. Operator learning enables fast surrogate inference, yet typically requires large input-output paired
datasets generated by costly high-fidelity PDE solvers. Unsupervised operator learning frameworks alleviate data dependency but remain hindered by computational bottlenecks. To address this, we propose Neural Variational
Quantum Linear Solver (NVQLS), the first hybrid
quantum-classical operator learning framework leveraging the Legendre–Galerkin weak formulation. We critically resolve the sign ambiguity in VQLS energy minimization, preventing erroneous solution representations. Additionally, we introduce a neural
embedding, a novel encoding scheme to map varying forcings and PDE coefficients into parameterized
quantum circuit representations. These structural innovations provide theoretical computational complexity advantages under efficient state preparation schemes, while achieving superior accuracy compared to a representative classical baseline. Validations on 1D and 2D parametric PDEs under diverse boundary conditions demonstrate NVQLS's capability to simultaneously process varying inputs, offering a scalable unsupervised approach to
quantum-enhanced operator learning.
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All authors Chanyoung Kim, Myeonghwan Seong, Yujin Kim, Daniel K. Park, Youngjoon Hong
Machine Learning · Cross-listed from cs.AI
LGMT: Logic-Grounded Metamorphic Testing for Evaluating the Reasoning Reliability of LLMs
Zenghui Zhou, Man Li, Xiaoke Fang et al. (+3 more)
Large
Language Models (LLMs) achieve strong performance on logical
reasoning benchmarks, yet their reliability remains uncertain. Existing evaluations rely on static
benchmarks, which fail to assess robustness under logically equivalent transformations and often overestimate
reasoning capability. We propose LGMT (Logic-Grounded Metamorphic Testing), an oracle-free framework that leverages first-order logic (FOL) to evaluate LLM
reasoning. By deriving metamorphic relations from formal logical equivalences, LGMT constructs semantically invariant test cases and detects
reasoning defects through cross-case consistency checking. Experiments on six state-of-the-art LLMs show that LGMT exposes substantial hidden defects missed by traditional reference-based evaluations. We further find that models are particularly sensitive to symbol-level and conclusion-level variations, and that advanced prompting such as Few-shot CoT only partially mitigates these issues. These results suggest that LLM evaluation should move beyond isolated correctness toward robustness under logical invariance. LGMT provides a principled and scalable approach for diagnosing
reasoning failures.
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All authors Zenghui Zhou, Man Li, Xiaoke Fang, Xinyi Zhou, Weibin Lin, Zheng Zheng
Machine Learning · Cross-listed from q-fin.ST
From Index to Equity: Pre-Training Transformers for Stock Return Prediction
Marie Soehl Coolsaet, Roberto Gallardo and Zhen Gao
This research aims to leverage machine learning to improve stock price prediction and support informed investment decisions related to buying, selling, and holding assets. Specifically, this work investigates
transformer-based models for stock prediction and examines the impact of pre-training strategies on
forecasting performance. A
transformer model was first pre-trained on the Toronto Stock Exchange Index (TSX) to predict intra-day return direction and subsequently fine-tuned on individual TSX stocks. The model was further adapted for return-value regression tasks. Performance was benchmarked against Long Short-Term
Memory (LSTM) and XGBoost models. Pre-training on the
market index improved the binary cross-
entropy loss for individual stock prediction from 0.69 to 0.64. The fine-tuned
transformer regression model achieved lower mean squared error than the
benchmark models, although the ensemble and XGBoost models achieved higher average daily returns. In addition, a practical application was developed to deliver real-time stock predictions for trading support. Future work will focus on increasing
transformer model capacity, incorporating broader global technical indicators, and filtering out stocks with low predictability.
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Machine Learning · Cross-listed from q-bio.BM
Multimodal Alignment and Preference Optimization for Zero-Shot Conditional RNA Generation
Roman Klypa, Alberto Bietti and Sergei Grudinin
The design of RNA molecules that interact with specific
proteins is a critical challenge in experimental and computational biology. Despite recent progress in natural
language modeling and deep learning-based
protein design, there remains significant room to improve the frequency of successful interactions and the authenticity of generated sequences for functional applications. In this work, we frame conditional RNA sequence generation as a multi-stage
alignment problem, introducing Moirain: a suite of models optimized via
multimodal supervised fine-tuning (SFT) and Direct Preference Optimization (DPO). Our approach begins with large-scale pretraining on diverse RNA corpora to capture the fundamental grammars of sequence plausibility. To achieve target-specific generation, we employ a
multimodal SFT architecture that conditions RNA synthesis on
protein structural and sequential features. Finally, we leverage DPO to refine the model using synthetic interaction data: taking advantage of DPO's unique ability to navigate non-aligned preference spaces, we improve functional fitness without collapsing the learned natural distribution. Extensive evaluation of the Moirain series (Moirain-Base, -Multi, and -DPO) demonstrates that our framework consistently produces novel, diverse, and biologically plausible RNA sequences with superior binding affinities compared to existing baselines.
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Machine Learning · Cross-listed from q-bio.BM
Learning Protein Structure-Function Relationships through Knowledge-guided Representation Decomposition
Mingqing Wang, Zhiwei Nie, Athanasios V. Vasilakos et al. (+2 more)
Proteins encode diverse functions within complex three-dimensional structures, yet most deep learning representations remain highly entangled, obscuring the biophysical signals that underlie function. Here we introduce ProtDiS, a knowledge-guided framework that decomposes pretrained
protein micro-environment
embeddings into biologically grounded and task-relevant dimensions. Inspired by the information bottleneck principle, ProtDiS learns representations that balance informativeness and compression, yielding structural features that are more specific, independent, and information-efficient, and achieving consistent improvements across twelve downstream tasks, with the largest gains under structure-based splits.
Protein- and residue-level analyses further show that ProtDiS differentiates
proteins with similar folds but divergent functions and captures fine-grained biophysical signals critical. These findings suggest that knowledge-guided decomposition provides a general and interpretable approach for structuring latent spaces in
protein structural modeling. The source code and implementation details are publicly available at https://github.com/AI-HPC-Research-Team/ProtDiS.
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All authors Mingqing Wang, Zhiwei Nie, Athanasios V. Vasilakos, Yonghong He, Zhixiang Ren
Machine Learning · Cross-listed from cs.CR
The Misattribution Gap: When Memory Poisoning Looks Like Model Failure in Agentic AI Systems
Tanzim Ahad, Ismail Hossain, Md Jahangir Alam et al. (+3 more)
Multi-
agent AI pipelines typically assume that
agent misconduct originates from model misalignment. We identify a structural failure in this assumption, the Misattribution Gap, where
memory-layer attacks produce behaviors indistinguishable from model failure, causing defenders to apply the wrong remediation. We formalize Semantic Norm Drift (SND) as a third path to
agent misconduct, distinct from emergent misalignment and collusion. In SND, a policy-formatted document enters a shared vector store through normal uploads and later reappears as trusted system context after provenance is lost through a Trust Laundering Chain. Across 64 documented failures, attribution systems consistently blamed the model. Four safety classifiers, including one trained on
memory poisoning, produced zero detections across 510 checkpoints. In 59 of 65 valid cases,
agents explicitly cited the injected document as normative authority before complying. The attack requires no trigger, model access, or repeated interaction, achieves full effect within five sessions, and persists indefinitely. We introduce Counterfactual Composition Testing, which identifies the causal entry with 87.5% accuracy and zero false positives, while a forensics baseline fails across all 25 scenarios. We further prove the
Retrieval-Coverage Dilemma, showing that stronger evasion inherently weakens the attack, limiting adaptive bypass strategies. Finally, we propose
Memory-Persistent Information-Flow Control, which blocks 97% of attacks at the cross-session boundary where prior defenses fail. We release the SND Corpus, the first
adversarial memory benchmark with temporal persistence and multi-
agent composition across financial and Health Care domains.
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All authors Tanzim Ahad, Ismail Hossain, Md Jahangir Alam, Sai Puppala, Syed Bahauddin Alam, Sajedul Talukder
Machine Learning · Cross-listed from cs.SD
Advanced Scientific Methodology Plays Rossini
Silvia Licciardi, Daniela Macchione, Emmanuel Caronna et al. (+1 more)
A musical score provides the essential instructions for its performance while containing indications - at times implicit - regarding the composer's intentions. The presence of authorial variants, and even more so complex series of revisions associated with a single text, presents a challenging path for analytical study. This research, situated within the application of Scientific Methodologies to Music Philology, proposes a methodological approach oriented toward the structural analysis of one of the many settings composed by Gioachino Rossini on the same Metastasio arietta “Mi lagnerò tacendo”. Through Computational Analysis - incorporating parsing, data mining, and graph theory - the melodic, harmonic, and textual compositional choices have been rigorously explored. The results constitute a significant unicum in the field, laying the foundation for a systematic study that supports philological research and paves the way for the use of generative models to investigate the creative process.
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All authors Silvia Licciardi, Daniela Macchione, Emmanuel Caronna, Elisa Francomano
Machine Learning
Automated Big Data Quality Assessment using Knowledge Graph Embeddings
Hadi Fadlallah, Rima Kilany, Mitri Haber et al. (+1 more)
Automated data quality assessment is crucial for managing big data, but existing solutions face challenges in achieving accurate context-aware assessment. This paper presents a novel knowledge-based approach to enhance automated data quality assessment. Our approach utilizes knowledge graph
embeddings to predict missing edges between the input
dataset's context representation and the relevant quality rules and dimensions within a knowledge graph representing contextual data characteristics and the required quality assessment operations. We surpass conventional practices by integrating diverse representations within the knowledge graph, drawing insights from contextual information from a thorough literature investigation. This integration allows us to develop a comprehensive and context-specific data quality assessment plan tailored to each context. Leveraging the knowledge graph improves our understanding of the input
dataset's context, overcoming the limitations of traditional methods that rely solely on strict matching and overlook contextual characteristics. By injecting numerical edge attributes, we assign corresponding weights to each predicted quality measurement, providing a comprehensive data quality assessment plan for the input
dataset. To evaluate our approach, we leverage AmpliGraph, a framework developed and benchmarked by AccentureLabs. The evaluation involves employing a real-world radiation sensors
dataset provided by the Lebanese Atomic Energy Commission (LAEC-CNRS). The results obtained from this evaluation demonstrate the capability of our solution to generate a comprehensive data quality assessment plan for the given input
dataset.
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All authors Hadi Fadlallah, Rima Kilany, Mitri Haber, Ali Jaber
Machine Learning
Precision Tracked Transformer via Kalman Filtering, Kriging and Process Noise
Bo Long, Deepak Agarwal, Jelena Markovic-Voronov et al. (+2 more)
The
Transformer is the foundational building block of modern AI, yet offers no principled handling of uncertainty, which is prevalent in real applications: cold-start
tokens with sparse histories in sequential recommendation, heterogeneous signal quality in
language models, and
attention sinks induced by unconstrained softmax. Every
token is treated with uniform confidence. We show this uniformity is a degenerate case of our
Bayesian Filtering
Transformer (BFT):
attention becomes precision-weighted kriging, the residual connection becomes a Kalman update with adaptive gain, and the FFN becomes a dynamics model propagating precision via a Jacobian–plus–process-noise rule. Observation precision comes from a parameter-free Restricted Maximum Likelihood (REML)
estimator with a conjugate
Bayesian prior. BFT replaces any
Transformer layer with negligible overhead. On sequential recommendation, BFT applied to three major architectures yields significant gains on six
benchmarks, with the largest improvements on cold-start users and rare items where uncertainty is highest. On supervised fine-tuning of large
language models with noisy data, BFT improves robustness in two regimes: noisy supervision (
token-label corruption in question answering) and noisy context (
retrieval-augmented QA with real RAG distractors). A single principled modification – restoring precision – unlocks substantial headroom across both classical sequence-modeling and modern LLM regimes.
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All authors Bo Long, Deepak Agarwal, Jelena Markovic-Voronov, Yi Wang, Liuqing Li
Machine Learning · Cross-listed from q-bio.QM
Towards Discovery of Polymers for Insulin Delivery via Physics-Grounded Agentic Workflows
Martins Otun
Cold-chain storage limits access to insulin for hundreds of millions of people; a thermally protective patch
polymer could help, but the design space is too large for exhaustive experiment. Starting from that problem, we narrow to an agentic workflow: a large
language model (LLM) calls physics-based tools through the Model Context Protocol (MCP), searching the discrete PSMILES space under a budget of OpenMM Packmol-
matrix evaluations. The LLM acts as an implicit acquisition function conditioned on a persistent "discovery world": hypotheses, literature claims, and simulation outcomes updated each iteration. Under matched oracle budgets, the best autonomous campaign reaches an insulin-
polymer interaction energy of -2263 kJ/mol, outperforming reinforcement-learning baselines by 68% and
Bayesian optimization by 19%. Three independent campaigns converge on one structural motif (dense hydrogen-bond donors and acceptors per repeat unit) while physics checks reject infeasible packings and name-structure mismatches before they steer the next step. The science stage is CPU-bound and runs on commodity hardware. More broadly, the same architecture and workflow designed here applies to other
protein-stabilization tasks whenever a tractable screening oracle is available.
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Machine Learning
In-Context Learning Operates as Concept Subspace Learning
Wei Tang, Xinyan Jiang, Fakhri Karray et al. (+1 more)
Regression and
Bayesian accounts of in-context learning (ICL) explain how demonstrations can induce predictors, while mechanistic analyses often identify compact activation directions that steer prompted behavior. However, it remains unclear whether structured demonstrations induce low-dimensional concept inference. We study this question through a concept-subspace view of ICL, in which tasks vary only along intrinsic concept coordinates, although inputs are observed in a high-dimensional ambient space. For ridge and least-squares ICL proxies, prediction decomposes exactly into concept-coordinate regression and off-subspace leakage. Under block-diagonal or near-block-diagonal covariance assumptions, the leading estimation and nuisance-sensitivity terms scale with the dimension of the concept subspace, while residual effects are controlled by cross-subspace coupling. This separation gives a mechanistic prediction: recoverable task information should concentrate in a low-dimensional, task-aligned activation subspace. On CounterFact-derived multi-relation
prompts with Llama-3-8B, a 68–73-dimensional subspace of the 4096-dimensional residual stream restores 78.8% of the clean–corrupted accuracy gap, whereas patching the complementary subspace restores 0%. Concept swaps redirect predictions toward injected relations, while random and cross-task matched-rank controls are largely ineffective. Additional experiments on Qwen2.5-7B and a controlled cross-lingual rule task show the same qualitative pattern. These results support concept subspaces as compact, task-aligned mediators of recoverable ICL behavior in structured task families, without implying full-
circuit recovery.
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All authors Wei Tang, Xinyan Jiang, Fakhri Karray, Lijie Hu
Machine Learning
Zibo Diao, Jingchu Gai, Xinyue Ai et al. (+3 more)
Frontier commercial generative models face a growing threat from
distillation, whereby a distiller harvests generated responses and trains a competing model at drastically lower cost. Existing defenses either modify the generation to degrade
distillation performance, sacrificing response quality, or rely on behavioral detection mechanisms that can be readily bypassed through multi-account querying. In this work, we propose Lossless Anti-
Distillation Sampling (LADS), which leaves the generation itself unchanged while substantially reducing the effectiveness of
distillation. Concretely, LADS controls the latent randomness underlying inference through a coupling mechanism that preserves within-account generation independence while inducing cross-account dependence. By construction, each benign user, who typically holds only a single account, receives the same experience under LADS as they would without any defense, thereby enjoying a lossless experience. However, for a multi-account task-specific distiller, semantically similar queries submitted across different accounts are assigned coupled randomness, inducing dependence in the harvested data and thereby degrading the generalization performance of the distilled model. Using uniform convergence theory, we show that LADS provably degrades the distiller's generalization gap relative to standard i.i.d. sampling. Experiments on image generation, mathematical
reasoning, and code generation confirm that LADS substantially degrades the performance of distilled students while preserving exact statistical fidelity for individual users.
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All authors Zibo Diao, Jingchu Gai, Xinyue Ai, Zhang Zhang, Zhenyu He, Di He
Machine Learning · Cross-listed from cs.IR
Code-Guided Reasoning for Small Language Models: Evaluating Executable MCQA Scaffolds
Prateek Biswas, Dhaval Patel, Vedant Khandelwal et al. (+2 more)
Multiple-choice QA
benchmarks usually evaluate small
language models (SLMs) as direct answerers, but deployed
language-model systems increasingly rely on external scaffolds such as tools, code, and repeated model calls. We introduce Code-Guided
Reasoning (CGR), an evaluation protocol and generated-program resource for measuring when executable
reasoning scaffolds improve SLM performance on MCQA tasks. CGR standardizes six components: a normalized item interface, a direct solver
prompt, a generator
prompt, a Python scaffold, solver-call and extraction helpers, and a three-channel result record. On 20,498 retained result rows from a locally prepared MCQA bundle and six metadata-registered solver models, the observed non-zero-baseline partition shows 66.21% macro assisted accuracy versus 38.11% direct accuracy, a +28.10 percentage-point difference with a pair-bootstrap interval of [20.32, 36.43]. Under a stricter Ab > 30% direct-signal gate, the macro difference is +14.11 points. These estimates are descriptive. Assisted inference uses a larger solver-call budget, answer extraction is brittle, Time-MQA contains the observed regressions, and some generated programs violate the no-hard-coding instruction. CGR provides the trace package needed to interpret these results, including direct, assisted, and generator-side answers, partition definitions, generated programs, response metadata, and audits.
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All authors Prateek Biswas, Dhaval Patel, Vedant Khandelwal, Shuxin Lin, Amit Sheth
Machine Learning
The Routing and Filtering Structure of Attention
Shafayeth Jamil and Rehan Kapadia
The
attention interaction
matrix $QK^{\top}$ contains two entangled computations: a skew-symmetric component that redistributes information between positions (routing) and a symmetric component that scales mutual relevance (filtering). We decompose 1776 heads across five pretrained
transformers and find routing operating at low rank, well below the routing capacity allocated by the weight kernel. We introduce $S$-$D$
attention as a diagnostic parameterization that disentangles routing from filtering by construction with guaranteed stability ($\mathrm{Re}(\lambda) \le 0$) and trains stably without layer normalization. When disentangled and unnormalized, routing self-organizes into a spectral cascade, effective rank $2$ at the first layer, expanding with depth across six scales from 7M to 355M parameters. The cascade predicts where
attention can be simplified: linearizing the first seven layers of 125M $S$-$D$
attention costs ${<}5\%$ perplexity, whereas standard
attention collapses under the same intervention. The linearizable region widens with depth. Replacing the first four layers with ELU+1 linear
attention reaches within $1.4\%$ of baseline at full head dimension. Cascade-allocated architectures trade
attention parameters for perplexity ($47\%-65\%$ fewer
attention parameters at $+3.9\%$ to $+8.4\%$ PPL). The routing-filtering decomposition makes the spectral budget legible; the cascade makes it actionable.
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Machine Learning
Not All Tokens Are Worth Caching: Learning Semantic-Aware Eviction for LLM Prefix Caches
Shaoke Fang, Ziang Li, Wenfei Wu et al. (+3 more)
Prefix caching is a key optimization in Large
Language Model (LLM) serving, reusing
attention Key-Value (KV) states across requests with shared
prompt prefixes to reduce expensive prefill computation. However, its benefit depends critically on the eviction policy as GPU
memory is scarce, and existing policies such as LRU largely treat cached blocks uniformly. This view ignores a fundamental property of LLM
prompts: not all
tokens are equally worth caching. We show that different
token types within a
prompt, including system
prompts, user queries, tool outputs, model responses, and chain-of-thought
reasoning, exhibit up to 756x variation in reuse rates, yet no existing eviction policy exploits this signal. In this paper, we present SAECache (Semantic-Adaptive Eviction for prefix caches), a semantic-adaptive prefix cache eviction policy that addresses this gap through three innovations: (1) a multi-queue architecture that routes KV blocks to task-specific queues with tailored priority metrics, capturing both session reuse in multi-turn requests and structural reuse in templated single-turn requests; (2) a semantic-aware
token weighting mechanism that learns the reuse value of different
token types online through eviction feedback; and (3) a fully adaptive online learning schema for all parameter updates, including log-normal timing parameters, position decay power, queue weights, and meta-parameters, which eliminates manual tuning and enables automatic adaptation to deployment-specific workload characteristics. Through extensive evaluation across heterogeneous workloads, we demonstrate that SAECache achieves 1.4x-2.7x TTFT improvement over production-style baselines, while fixed-parameter alternatives can degrade by up to 2.7x under workload mismatch – a failure mode our adaptive approach avoids entirely.
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All authors Shaoke Fang, Ziang Li, Wenfei Wu, Jiatong Ji, Qingsong Liu, Ruizhi Pu
Machine Learning
Fine-Grained Benchmark Generation for Comprehensive Evaluation of Foundation Models
Mohammed Saidul Islam, Negin Baghbanzadeh, Farnaz Kohankhaki et al. (+5 more)
Evaluation of foundation models often rely on aggregate scores from
benchmarks that lack comprehensive coverage and metadata for a fine-grained evaluation. We introduce a framework for automated
benchmark generation. Our framework generates evaluation problems grounded in reference material, such as textbooks, producing
benchmarks with broad coverage, rich metadata, and robustness to contamination. The pipeline employs a multi-
agent architecture for problem generation and a solution-graph-driven strategy that significantly improves the reliability of ground truth solutions. Using the framework, we generate three
benchmarks in Machine Learning, Corporate Finance, and Personal Finance. Expert review finds a significantly lower ground-truth error rate than previous
benchmarks such as MMLU and GSM8K. Evaluation of 12 commercial and open-source models shows that our
benchmarks achieve near-uniform competency coverage and surface performance differences across models that existing
benchmarks fail to capture. We will open-source the framework and our curated
benchmarks soon.
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All authors Mohammed Saidul Islam, Negin Baghbanzadeh, Farnaz Kohankhaki, Afshin Cheraghi, Ali Kore, Shayaan Mehdi, Elham Dolatabadi, Arash Afkanpour
Machine Learning
Multi-Pedestrian Safety Warning at Urban Intersections Use Case of Digital Twin
Yongjie Fu, Qi Gao, Mahshid Ghasemi Dehkordi et al. (+2 more)
Digital twins (DTs) for urban transportation systems have gained increasing
attention; however, their systematic evaluation in safety-critical scenarios remains limited. This paper presents a multi-pedestrian safety warning system at urban intersections enabled by a tightly coupled physical-digital twin framework. Built upon the COSMOS city-scale wireless testbed in New York City, the proposed system integrates camera and ultra-wideband (UWB), edge-cloud computing, predictive trajectory modeling, and MQTT-based communication to deliver real-time safety alerts to vulnerable road users (VRUs). The system is evaluated through both field deployment and virtual reality (VR) experiments. Results demonstrate high warning generation accuracy, localization accuracy, efficient end-to-end latency under different model configurations, and significant reductions in user response time when warnings are issued. The proposed DT framework provides a scalable, modular, and generalizable solution for real-time multi-pedestrian safety enhancement at complex urban intersections.
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All authors Yongjie Fu, Qi Gao, Mahshid Ghasemi Dehkordi, Gil Zussman, Xuan Di
Machine Learning
Hybrid-LoRA: Bridging Full Fine-Tuning and Low-Rank Adaptation for Post-Training
Chengqian Zhang, Wei Zhu and Kyumin Lee
Post-training has become essential for adapting large
language models (LLMs) to complex downstream behaviors, including instruction following, preference
alignment, and multi-step
reasoning. Reinforcement learning with verifiable rewards (RLVR) has recently emerged as a particularly effective post-training paradigm for improving
reasoning capabilities, with critic-free
algorithms such as GRPO and GSPO enabling scalable optimization. However, RLVR post-training with full fine-tuning (FFT) requires substantial GPU
memory and incurs high training costs. Although parameter-efficient fine-tuning (PEFT) methods, such as Low-Rank Adaptation (LoRA), effectively reduce computational costs, they often suffer from a noticeable performance gap compared to full fine-tuning in post-training for complex
reasoning tasks. In this paper, we propose Hybrid-LoRA, an efficient hybrid post-training framework that selectively applies full fine-tuning to a small subset of modules less suited to low-rank adaptation, while adapting the remaining components with LoRA. We introduce a novel Hybrid-LoRA Score to rank candidate modules according to their sensitivity to low-rank adaptation under a fixed parameter budget. Experiments show that Hybrid-LoRA closely matches full fine-tuning performance under a 10% full fine-tuning module budget, with the remaining candidate modules adapted by LoRA, consistently outperforming four state-of-the-art PEFT post-training baselines, achieving improvements of up to 5.65% and on average 4.36% over the best baseline.
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Machine Learning
Quantum Adversarial Machine Learning: From Classical Adaptations to Quantum-Native Methods
Roozbeh Razavi-Far, Mohammad Meymani, Erfan Mahmoudinia et al. (+6 more)
Machine learning has revolutionized numerous industrial domains. Despite recent advances, machine learning models remain vulnerable to
adversarial threats.
Adversarial machine learning is a field that studies these vulnerabilities to build robust machine learning models.
Quantum machine learning is an interdisciplinary field that bridges
quantum computing and classical machine learning. While
quantum machine learning shows potentials to outperform classical machine learning in complex tasks such as regression, classification, and generative modeling, it remains vulnerable to
adversarial attacks. Given the recent advancements in
quantum computing and machine learning, the
quantum adversarial machine learning field has emerged to study the vulnerabilities of
quantum machine learning, possible attacks, and novel
quantum-enhanced defense strategies. In this survey, we provide a detailed overview on
quantum adversarial machine learning and explore the existing attacks and countermeasures. We also review the theoretical underpinnings of this area, emerging trends, and critical challenges.
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All authors Roozbeh Razavi-Far, Mohammad Meymani, Erfan Mahmoudinia, Dorsa Vazirzade, Peyman Paknezhad, Fateme Ghasemi, Saeed Saravani, Somayeh Nikkhoo, Kimia Haghjooei
Machine Learning
Emergence of Frontier Superposition: Möbius attractor and Cascade Supervision
Hongyu Gu and Jingwen Fu
Superposition allows
Transformers to reason in depth, carrying an entire
reasoning frontier in parallel through a bounded-depth forward pass instead of unrolling serial chain-of-thought
tokens. While Zhu et al. (2025) hand-crafted an equal-weight breadth-first frontier in a single residual stream for graph reachability, it remained open whether gradient descent could ever find this target amidst permutation-symmetric saddles. We close this gap on Reachability-by-Superposition over Erdős-Rényi graphs by isolating architectural and supervisional contributions. Architecturally, we identify a Möbius attractor: under $S_n$-
symmetry in the tree regime, layerwise dynamics reduce to a 1D Möbius map whose zero set is a codimension-one
manifold of global optima containing the equal-weight superposition state. On the supervision side, we identify Cascade Supervision: a loss class whose backward pass simultaneously delivers (A) selectivity bootstrap, (B) gradient persistence across depth, and (C) per-step discrimination (e.g., \mathcal{L}_{sup} and \mathcal{L}_{node}). End-to-end supervision fails condition (B) and is provably insufficient: internal gradients at layer c decay as (np)^{-(D-c-2)/2} in the graph fan-out and stall before the
manifold is reached. Our thesis: Möbius attractor + Cascade Supervision = emergence of superposition
reasoning. The parameter-free decay law predicts a final-step cosine of 0.35 vs. 0.71 (end-to-end vs. cascade) at depth D=3; experiments confirm 0.37 vs. 0.69, matching within 0.02 at every step.
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Machine Learning
Efficient Conditioning Why Pseudo Observation Batch Bayesian Optimization Works When It Does not
Kumbha Nagaswetha and Rabi Pathak
Constant Liar (CL), Kriging Believer (KB), and fantasy models are widely used for batch selection in parallel
Bayesian Optimization, yet a unified theory explaining their effectiveness and conditions under which they fail has been lacking. We identify efficient conditioning as the key surrogate property the ability to update predictions in closed form when data is augmented. We prove that Gaussian Processes satisfy this requirement, producing provably distinct batch points with separation of order l, and that this holds for any acquisition function monotonically non decreasing in posterior uncertainty (EI, UCB, PI), with qualitatively similar behavior for Thompson Sampling. We unify CL, KB, and fantasy models as instances of a single conditioning mechanism differing only in the lie value distribution, and draw quantitative connections to Local Penalization (LP) and qualitative connections to Determinantal Point Processes (DPPs). To disentangle model structure from optimizer randomness, we introduce the Structural Diversity Diagnostic (SDD), a reusable methodology for testing surrogate compatibility. Experiments on Hartmann6D, Ackley 8D, Levy10D, and SVM hyperparameter tuning validate all theoretical predictions: CL or KBs implicit penalty matches or outperforms explicit LP greedy conditioning achieves convergence on par with joint qEI efficient conditioning extends to Multiquadric RBF networks; and parametric surrogates produce degenerate batches even when fully retrained (random forests), while neural networks regain diversity only at 15x the wall clock cost of GP conditioning. Robustness is confirmed across multiple initial
datasets and under observation noise.
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Machine Learning · Cross-listed from cs.AI
Operationalizing Document AI: A Microservice Architecture for OCR and LLM Pipelines in Production
Yao Fehlis, Benjamin Bengfort, Zhangzhang Si et al. (+9 more)
Academic research tends to focus on new models for document understanding creating a wide gap in the literature between model definition and running models at production scale. To close that gap, we present a microservice architecture that encapsulates pipelines of multiple models for classification, optical character recognition (OCR), and large
language model structured field extraction as well as our experience running this pipeline on thousands of multi-page documents per hour. We describe our primary design decisions, including a hybrid classification, separation of GPU-bound inference from CPU-bound orchestration, use of asynchronous processing for the many IO-bound operations in the pipeline, and an independent, horizontal scaling strategy. Using batch profiling, we identified two surprising qualitative findings that shape production deployments: OCR, not
language-model parsing, dominates end-to-end latency, and the system saturates at a concurrency determined by shared GPU-inference capacity rather than worker count. Our goal is to provide practitioners with concrete architectural patterns for building document understanding systems that work beyond the
benchmark; effectively operationalizing models in production.
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All authors Yao Fehlis, Benjamin Bengfort, Zhangzhang Si, Vahid Eyorokon, Prema Roman, Patrick Deziel, Devon Slonaker, Steve Veldman, Ben Johnson, Joyce Rigelo, Michael Wharton, Steve Kramer
Machine Learning
Multi-Token Residual Prediction
Yufeng Xu, Zishuo Bao, Qian Wang et al. (+6 more)
Diffusion Language Models (DLMs) generate text by iteratively denoising masked
token sequences, offering a tradeoff between parallelism and quality compared to autoregressive models. In current practice, the number of
tokens decoded per step is controlled by a confidence threshold, and quality degrades monotonically as more
tokens are denoised per step. We introduce Multi-
token Residual Prediction (MRP), a lightweight module that enables dependency-aware multi-
token denoising within a single backbone forward pass. MRP exploits a key property of the denoising process: the logit distributions at adjacent denoising steps are remarkably similar. Rather than running the backbone a second time to obtain the next-step logits, MRP predicts the residual between steps from the backbone's hidden states, effectively denoising more
tokens per backbone forward at a fraction of the cost. We apply MRP across the two operating regimes of DLM decoding. In the high-quality-low-throughput static denoising regime, MRP serves as a drafter for speculative decoding: its proposals are verified against the backbone, yielding lossless acceleration of up to 1.4x in SGLang. In the low-quality-high-throughput dynamic denoising regime, MRP instead drives a remasking scheme that revokes over-eager reveals, recovering most of the accuracy lost to aggressive low-threshold decoding and improving accuracy by up to 22.6 points on code generation task HumanEval and 17.7 points on
reasoning task GSM8K.
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All authors Yufeng Xu, Zishuo Bao, Qian Wang, Zeshen Zhang, Haoqi Zhang, Bowen Peng, Ang Li, Rahul Chalamala, Yucheng Lu
Machine Learning
When Does Equivariance Help? Canonical Alignment in Neural Fluid Surrogates
Patryk Rygiel, Julian Suk, Kak Khee Yeung et al. (+2 more)
Neural surrogates can accelerate computational fluid dynamics (CFD) simulations by orders of magnitude, but practical deployment in engineering and healthcare applications requires architectures that scale to high-resolution meshes and learn effectively from limited data. Explicit equivariance offers a principled inductive bias, yet its accuracy benefits may depend on the prediction task and the distribution of anatomical orientations. We investigate this dependence across three hemodynamic
benchmarks with different degrees of natural canonical
alignment. To support this study, we introduce the Anchored-Branched
Geometric Algebra Transformer (AB-GATr), an $E(3)$-equivariant surrogate that efficiently predicts coupled surface and volume quantities. Across these
benchmarks, AB-GATr consistently outperforms the evaluated non-equivariant models, including variants trained with rotational augmentation, while achieving accuracy competitive with $E(3)$-equivariant LaB-GATr at substantially lower training cost. In comparison, rotational augmentation provides inconsistent benefits across architectures and can reduce accuracy. A controlled experiment on ShapeNet-Car shows that strong canonical
alignment can favor non-equivariant models, but their accuracy generally deteriorates as training orientations broaden and can decline sharply under broader test rotations. We further investigate these patterns using extended
symmetry-breaking diagnostics and probes of the predictive information associated with canonical
alignment across all
benchmarks. Together, these results support explicit equivariance for the evaluated hemodynamic tasks with natural orientation variation, while showing that its accuracy benefits depend on the task and orientation distribution.
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All authors Patryk Rygiel, Julian Suk, Kak Khee Yeung, Christoph Brune, Jelmer M. Wolterink
Machine Learning
DynaTrain: Fast Online Parallelism Switching for Elastic LLM Training
Yuanqing Wang, Yuchen Zhang, Hao Lin et al. (+9 more)
Modern large
language model (LLM) training is inherently dynamic: resource fluctuations, RLHF phase shifts, and cluster elasticity continually reshape the optimal parallelism layout, posing a significant challenge to existing training frameworks built around a static execution model. We present DynaTrain, a distributed training system for sub-second, online reconfiguration across arbitrary multi-dimensional parallelism. At its core, we propose a Virtual Parameter Space (VPS) abstraction that unifies all distributed training states under one logical coordinate space, turning any parallelism configuration into a deterministic mapping and collapsing complex transition into manageable
geometric intersections. On top of VPS, a state routing-and-transition layer executes rank-local transfers under a
memory-aware, deadlock-free schedule, and an Elastic Device Manager overlaps new-world construction with ongoing training to mask
topology-change cost. On dense and MoE models up to 235B parameters, DynaTrain reconfigures a 70B dense model in under 2s and a 235B MoE model in 4.36s, outperforming state-of-the-art checkpoint-based and elastic systems by up to three orders of magnitude while preserving correctness.
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All authors Yuanqing Wang, Yuchen Zhang, Hao Lin, Junhao Hu, Chunyang Zhu, Quanlu Zhang, Boxun Li, Guohao Dai, Zhi Yang, Daning Cheng, Yunquan Zhang, Yu Wang