Artificial Intelligence · Cross-listed from cs.CV
ReCalMatch:Reliability-Calibrated Semantic Guidance for Semi-Supervised Fine-Grained Recognition
Yundi Hong, Hongyang He, Zheng Fang et al. (+2 more)
Semi-supervised fine-grained visual recognition is highly vulnerable to overconfident pseudo-label errors: visually similar categories frequently produce high-confidence yet incorrect predictions, and consistency regularization then reinforces these errors throughout training. Existing semi-supervised learning (SSL) methods estimate pseudo-label reliability almost entirely from the visual classifier itself—maximum probability, adaptive thresholds, or
entropy—signals that remain blind to whether a predicted class is semantically compatible with the visual representation. We propose ReCalMatch, a reliability-calibrated semantic framework for semi-supervised fine-grained recognition. Rather than treating textual semantics as auxiliary supervision, ReCalMatch uses multi-aspect semantic prototypes as calibration evidence for pseudo-label learning. We construct class-conditioned semantic prototypes from class names and domain-specific semantic aspects, and measure a visual–semantic agreement score between each unlabeled
embedding and its pseudo-label prototype. This agreement is combined with prediction confidence and
entropy into a single reliability weight that down-weights pseudo-labels that are visually confident but semantically inconsistent. A semantic consistency term and a semantic margin regularizer further sharpen prototype separability under limited labels. Extensive experiments on CUB-200-2011, Stanford Dogs, NABirds, and iNaturalist18 show that ReCalMatch consistently improves strong SSL baselines, with the largest gains in low-label regimes where pseudo-label noise is most severe.
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All authors Yundi Hong, Hongyang He, Zheng Fang, Xuanyu Liu, Victor Sanchez
Artificial Intelligence · Cross-listed from cs.LG
The Sequential Price of Continual Learning
Zonghuan Xu and Xingjun Ma
Sequential task updates are fundamental to continual learning, but their recency bias can impose a lasting performance cost. We study this cost in an overparameterized linear-regression model with i.i.d. task sampling. We prove that distribution-level forgetting and population loss converge to the same stationary limit. We quantify the additional loss incurred by sequential exact fitting, or the sequential price. In more homogeneous task geometries, it equals the intrinsic loss asymptotically attained by joint training, making the total loss twice as large. We further analyze fixed-strength elastic weight consolidation (EWC) under general task
curvatures and characterize its stationary sequential price at every regularization strength. Under strong regularization, the price decays inversely with EWC strength while the mean-square coupling horizon grows proportionally. Experiments on Jester and Rotated MNIST support the predicted sequential price and its reduction by EWC, with quantitative agreement on real-world tasks satisfying the theory's assumptions and qualitative agreement under nonlinear finite-step training.
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Artificial Intelligence · Cross-listed from cs.RO
Do World Models Make Better Robots? A Survey of Evaluation Benchmarks for Predictive Embodied Intelligence
Gaytri Jena, Kapil Wanaskar, Vinija Jain et al. (+3 more)
Robot learning now advances along two tracks that rarely meet. On one side, direct
Vision-
Language-Action (VLA) policies map observations to actions and are scored by closed-loop task success. On the other, predictive and generative world models forecast future observations and are scored by open-loop prediction or generation quality. A natural question sits between them: does world modelling earn a measurable, closed-loop advantage over a direct policy, and for which robotic capabilities? We argue that the field cannot yet answer this question, and that the reason is a gap in how it is measured, not in the models themselves. World-model
benchmarks score prediction without ever executing it, while task-success suites host a single policy and never build a world-model versus VLA contrast. This survey maps the evaluation landscape around that gap. We catalogue 160 web-verified
benchmarks spanning 2017 to 2026 and organise them by evaluation mode, robotic capability, and model family into four lanes: policy suites,
embodied agents, world model evaluation, and prediction-to-action bridges. Across the corpus, 138 of 160
benchmarks are model-agnostic and only 11 (7%) build an explicit VLA-versus-world-model contrast; counterfactual capability is almost entirely unmeasured, and only four
benchmarks turn prediction into executed action. We contribute an operational taxonomy, a coverage comparison against the eight closest surveys (ours is the only one to cross capability with model family), an evaluation loop that isolates the advantage of prediction, and an actionable protocol of four advantage-aware metrics anchored on named testbeds. The organising claim is not that world models help or do not help, but that answering the question requires
benchmarks built to ask it.
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All authors Gaytri Jena, Kapil Wanaskar, Vinija Jain, Aman Chadha, Vasu Sharma, Amitava Das
Artificial Intelligence · Cross-listed from cs.LG
Graph, Loop, and Harness Engineering for Zero-Trust Agentic Data Engineering and Analytical Processing
Sagar Srinivas Sakhinana and Venkataramana Runkana
Large
language model
agents increasingly automate data workflows, but end-to-end cloud data engineering and analytical execution require reliable coordination across code, data, infrastructure, and runtime environments. We present two zero-trust frameworks. Zero-Trust Agentic Data Engineering generates, deploys, and verifies complete cloud data-engineering solutions from natural-
language tasks, with completion conditioned on repository, deployment, runtime, and policy evidence. Zero-Trust Agentic OLAP combines governed Data Preparation with verified Online Analytical Processing (OLAP), permitting production promotion only after validation and evidence-bound approval, and releasing analytical answers only after Same-Snapshot Execution, Exact Result Equivalence, deterministic grounding, and reflection. Both frameworks share three abstractions: graph engineering for evidence-gated workflow structure, loop engineering for bounded recovery, and
agent-harness engineering for zero-trust execution. We evaluate both frameworks under nominal execution, controlled failures, bounded recovery, and policy-constrained conditions, measuring verified completion, recovery, authorization enforcement, production promotion, and verified OLAP execution.
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Artificial Intelligence
To Think or Not to Think: Allocating Reasoning Where It Helps
Zhengdong He, Yunfan Zhou, Jianguo Yao et al. (+2 more)
Reinforcement learning (RL) has proven effective in enhancing the
reasoning performance of large
language models (LLMs), particularly in complex mathematical and programming tasks. However, this capability comes with systematic length misallocation, in which models devote excessive
reasoning to simple questions while terminating prematurely on harder ones, degrading inference efficiency with negligible accuracy improvement. Many length-adaptive methods mitigate this issue by allocating
token budgets according to question difficulty, under the implicit assumption that harder questions benefit monotonically from extended
reasoning. In contrast, we find that the effect of
reasoning length on accuracy is concentrated on partially solvable questions. Our further analysis reveals that explicit length rewards can produce unintended training dynamics. Motivated by these findings, we propose CARE—Contrastive Accuracy Reward Estimation—which compares the beneficial length adjustment per question from online sampled responses and applies adaptive length rewards within Group Relative Policy Optimization, with no extra hyperparameters or additional inference cost. Experiments across multiple
reasoning benchmarks demonstrate that our method improves Pass@1 by up to \(4\%\) while simultaneously reducing
reasoning length by \(37\%\), achieving higher
token efficiency. Code will be available upon the acceptance of this paper.
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All authors Zhengdong He, Yunfan Zhou, Jianguo Yao, Haibing Guan, Xijun Li
Artificial Intelligence · Cross-listed from cs.CV
Investigating White Blood Cells as a Source of False-Positive Malaria Parasite Detection in African Blood-Smear Images
Samuel A. Adeniji, Goodness C. Obasi, Chris-Victor Ntwali et al. (+4 more)
White blood
cells (WBCs) present on every Giemsa-stained thick blood smear share visual properties with early-stage Plasmodium falciparum ring-form trophozoites: small size, round morphology, and intense purple staining. They are a plausible but untested source of false positives in parasite-only detectors. We trained two YOLOv12s models on the Lacuna Malaria Detection
dataset (8,000 images from Uganda and Ghana): Model A with parasite labels only, and Model B with both parasite and WBC labels. Seven independent spatial and statistical analyses tested whether false positive (FP) predictions cluster near WBC locations. All seven refute the hypothesis. In both models, 95% of FPs are pure background detections (IoU below 0.10 against any ground-truth box); zero are WBC class confusions. Ripley's Cross-K analysis shows spatial repulsion between FP centroids and WBC positions at every radius tested. Model B outperforms Model A overall (mAP50 0.859 vs. 0.755), and the advantage is uniform across all WBC-proximity bands, pointing to multi-task representation learning rather than WBC suppression as the cause. False positives arise from Giemsa stain debris and preparation artifacts. Effective mitigation requires staining artifact augmentation and annotation of unannotated early-stage ring forms rather than WBC labeling alone.
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All authors Samuel A. Adeniji, Goodness C. Obasi, Chris-Victor Ntwali, Aondana M. Iorumbur, Confidence Raymond, Lowami Uwimana, Ahmed Tahiru Issah
Artificial Intelligence · Cross-listed from quant-ph
Hybrid Variational Quantum-Classical Framework with Adaptive Weighting and Efficiency Assessment
Dilli Hang Rai
Hybrid
quantum-classical neural networks have emerged as a promising approach for leveraging
quantum computing in machine learning while mitigating current hardware limitations. This paper presents Sim-HVQC, a hybrid Deep
Quantum Neural Network that couples an adaptive, parameter-free SimAM weighting module with classical feature extraction to preserve class-discriminative information prior to encoding into a Variational
Quantum Circuit (VQC). Previous studies are restricted to binary classification [1] [2] [3] [4] [5]. In contrast, the proposed framework is trained and evaluated on various multi-class
datasets(MNIST, KMNIST, Fashion-MNIST, and EMNIST). The framework further demonstrates reproducibility, parameter efficiency, and interpretability through multi-seed evaluation, parameter analysis, and latent/
quantum feature inspection. The source code is publicly available at https://github.com/Dilli822/ SimAM-HVQC
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Artificial Intelligence · Cross-listed from cs.CL
The Role of AI in Online Reviews
Valeria Lerman, Oren Rigbi and Yaniv Dover
The rapid adoption of large
language models (LLMs) creates new opportunities for strategic content generation on online platforms, including potentially harmful forms of manipulation that may undermine platform effectiveness and reshape platform dynamics. However, measuring such activity is difficult because AI-generated content is rarely directly observable. We introduce an empirical approach that leverages discrete LLM supply shocks - abrupt changes in model prices and capabilities, and contrasts verified with non-verified reviews to identify changes in platform activity associated with generative AI supply improvements. We apply this approach to more than 13 million reviews from Trustpilot, one of the leading online platforms for business reviews. A robust finding is that following LLM supply shocks, unverified reviews shift toward greater negativity: more 1-
stars, fewer 5-
stars, and lower ratings, with effects driven primarily by new model releases and concentrated among firms with the lowest and highest review volumes, suggesting that strategic AI use may reshape platform competition dynamics. We further find that LLM supply shocks trigger short, concentrated bursts of review activity. Together, these findings suggest that generative AI is already reshaping how reputation and competition operate on online platforms.
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Artificial Intelligence · Cross-listed from cs.LG
Dissecting Hierarchical Reasoning Models: A Mechanistic Study
Leo Raphael Rodrigues and Jian Kang
We study Hierarchical
Reasoning Model (HRM), a representative hierarchical
Transformer-based latent
reasoning model with many variants, on Sudoku, Maze, and ARC-AGI-2. We mechanistically understand how HRM reasons and what information it encodes. Our analyses compare HRM against
Transformer baselines with and without recurrent modules, apply causal interventions on recurrent states, and utilize linear probes against random-direction ablations, as well as sparse autoencoders with feature ablations. Our results reveal several key findings: recurrent models outperform one-pass baselines, while single-state recurrent
Transformers are comparable to HRM. State interventions further show that the causal contributions of the high- and low-level states vary across task-specific checkpoints and inference stages. Selected task variables are linearly decodable from the recurrent states in HRM, yet ablating probe directions produce effects comparable to random controls. SAE ablations yield larger behavioral changes than probe-direction ablations. However, top-ranked SAE features show no stable advantage over size-matched random subsets at larger ablation sizes or across tasks; the same pattern persists in a Sudoku control with within-step BPTT. Together, we characterize that HRM is essentially implementing constraint-aware iterative refinement on a puzzle-specific solution state, in which the functional contributions of components at different levels vary without relying on a compact, causally important feature set. These results highlight the necessity of studying the different working mechanisms and the importance of developing mechanistic interpretability techniques better suited for latent-space, recursive
reasoning models.
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Artificial Intelligence · Cross-listed from cs.LG
EvoRank: LLM-Guided Evolution of Multi-Objective Learning-to-Rank Pipelines
Rayhan Patel and Shabaz Patel
We present EvoRank, an open autonomous ranking engineer: an LLM-guided evolutionary loop that discovers complete Learning-to-Rank pipelines (features, models, losses, ensembles) for multi-objective e-commerce search. On the Expedia ICDM 2013
dataset, with relevance, conversion, and revenue as competing objectives, three independent runs each converge within 50 iterations (about ten dollars) on interpretable pipelines that beat an Optuna-tuned LambdaMART on 60k held-out queries, an advantage that persists at full data scale and places in the top 6 percent of the original competition. A first campaign, evolving only training objectives, builds the central design rule: it appeared to work on its selection fold (the small
dataset it uses to pick winners) while a transfer audit, re-scoring winners on held-out data, showed the gains were almost entirely fitness noise (the randomness of its own scoring), and neither seeded domain knowledge nor richer diagnostic feedback changed what transferred. The deciding quantity is measurable in advance: search-space headroom relative to fitness noise. We package this as a headroom gate that predicts, before any LLM spend, whether the loop will pay off, and we release the system, the
auditing tools, and a catalog of failure modes with their guardrails, so teams can apply the procedure to their own ranking stacks.
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Artificial Intelligence · Cross-listed from cs.CL
The Situated Identity Test: Distinguishing Persistent Cognitive Identity from Persona Imitation
Jun He and Deying Yu
Large
language models can convincingly adopt personas, recall past dialogues, and weave rich autobiographies. Yet this conversational eloquence conceals a fundamental attribution problem: looking the part does not mean having lived the life. Two individuals can share identical public profiles–the same age, hometown, occupation, and personality traits–while possessing entirely distinct private histories, relationships, and acquired skills. When conditioned solely on that shared profile, an
agent lacks the information required to determine which lineage is correct. We introduce the Situated Identity Test (SIT), an architecture-independent framework that evaluates whether an
agent's behavior is functionally attributable to a specific developmental lineage. Grounded identity requires both appropriate knowledge of recorded experiences and appropriate ignorance of ungrounded ones, bounded by what the identity has actually acquired rather than what its underlying foundation model knows. We prove that any policy conditioned solely on a compressed profile is bounded by an average situated validity of at most 1/m across m colliding life histories on lineage-discriminative queries (at most 50% for paired lineages). We instantiate this framework in SITBench, an evaluation suite designed for 25 profile-collision pairs (50 distinct lineages) across 10,000 planned probes and nine architectural configurations. Supported by an open-source reference implementation, deterministic test fixtures, and empirical pilot evaluations on frontier foundation models (GPT-5.6 Sol and Claude Opus 5), we formalize the failure modes of persona prompting under profile collision and provide an assurance harness for evaluating episodic continuity, structured state, and epistemic boundaries.
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Artificial Intelligence · Cross-listed from q-bio.NC
Planning as Dynamics Relaxation: Hippocampal Recurrent Network Realizes Optimal Goal-Directed Navigation
Yuhang He, Junfeng Zuo, Tianhao Chu et al. (+1 more)
Neural correlates of spatial cognitive map are well documented, yet exactly how neural
circuits perform spatial
navigation in complex environments - e.g., reaching a goal while avoiding obstacles - remains largely unclear. Here, we show that a hippocampal network with appropriate recurrent connections can naturally achieve optimal goal-directed
navigation via its relaxation dynamics. Specifically, we consider that the recurrent weights between the
neurons represent the transition probabilities between spatial locations encoded by
neurons; obstacles such as walls and blocked corridors are therefore reflected by the vanishing of connection weights. This connection pattern can be learned in the hippocampus via behavioral-timescale synaptic plasticity (BTSP) while the animal is exploring the environment. When a goal signal is presented, the network dynamics will relax into an activity field representing the goal location. We prove that this field is mathematically equivalent to the desirability field of a Linearly-solvable Markov Decision Process (LMDP), and the local log-gradient of the field indicates the
navigation direction. Both theoretical analyses and simulations demonstrate that this recurrent network dynamics-mediated
navigation is efficient and robust in environments with complex obstacle layouts. Moreover, only low-rank updates of the network's connection pattern are needed when the environment has local changes. We hope this study offers insight into a general
circuit principle for planning in abstract rational maps in the
brain beyond spatial
navigation.
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All authors Yuhang He, Junfeng Zuo, Tianhao Chu, Si Wu
Artificial Intelligence · Cross-listed from cs.LG
When Do Options Help? Policy Necrosis and Redundant Coverage in Option-Critic
Bingyun Liu and Yuheng Jing
Option-critic learns options: sub-policies together with a learned rule for when each one hands control back. Its headline result is that performance improves as options are added. We explain that result, with theory and experiment. First, the termination rule option-critic learns by maximising return contributes nothing. When the termination test and the policy that picks options read the same values, the test fires at every step, so the learned rule is identical to always terminating. When that policy explores and the test does not, as in option-critic itself, the rule can block the exploration; there are instances where it suffers $\Omega(T)$ regret while always terminating holds to $O(\log T)$. Forcing termination at every step leaves the option-count curve intact. Second, the policy inside an option barely explores at all, so a state locks onto the first action that looked good and never updates again. We name this policy necrosis, give a state-level test for it, and find three fifths of states necrotic in a typical option. Restoring exploration repairs those states, and one option then solves the task. Third, extra options improve no option; what falls is the chance that all of them fail in the same state, from $59\\%$ to $4\\%$, and performance follows that joint quantity.
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Artificial Intelligence · Cross-listed from cs.SI
A Data-Driven Multimodal Method for Early Detection of Coordinated Abnormal Behaviors in Live-Streaming Platforms
Jingwen Luo, Pinrui Zhu, Yiyan Wang et al. (+4 more)
With the rapid growth of live-streaming e-commerce and digital marketing, abnormal marketing behaviors have become increasingly concealed and coordinated across heterogeneous modalities, challenging platform governance and early risk identification. We propose MM-FGDNet, a data-driven
multimodal framework for detecting abnormal behavior in large-scale live-streaming environments from complementary temporal-
evolution and group-structure perspectives. A cross-modal temporal
alignment module maps
video, text, audio, and user behavior into a unified temporal semantic space. A temporal fraud-pattern module captures the progression from weak early signals to abrupt outbreaks, while a cooperative manipulation module identifies coordinated interactions among organized user groups and automated accounts. Experiments on real-world multi-platform live-streaming e-commerce
datasets show that MM-FGDNet outperforms representative baselines, achieving an AUC of 0.927, F1 of 0.847, precision of 0.861, recall of 0.834, and an Early Detection Score of 0.689, while reducing false alarms. Ablation studies validate the contribution of each module, and cross-domain experiments demonstrate stable generalization to new streamers, product categories, and platforms. These results indicate that MM-FGDNet provides an effective and scalable solution for proactive detection of coordinated abnormal behavior in live-streaming systems.
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All authors Jingwen Luo, Pinrui Zhu, Yiyan Wang, Zilin Xiao, Jingqi Li, Xuebei Kong, Yan Zhan
Artificial Intelligence
When Prediction Error Is Not Enough: Evaluating Nuisance-Function Prediction for Causal Estimation
Cong Cao
Prediction error is widely used to evaluate nuisance-function
estimators in causal inference, but its relationship with the performance of the resulting causal
estimator is not well understood. We studied this question in a partially linear model using Monte Carlo simulations, comparing ordinary least squares (OLS), generalized additive models (GAMs), XGBoost, and Double Machine Learning with XGBoost (DML-XGBoost) with independent and with clustered observations. We evaluated nuisance-function prediction error, bias, RMSE, and 95% confidence interval coverage, and derived the second-order remainder through which errors in the exposure and outcome nuisance functions jointly determine the bias of the residualized
estimator. When all methods estimated the causal effect from the same training sample, DML-XGBoost had the lowest RMSE and the highest coverage among the non-oracle methods, although XGBoost fitted to the full training sample had lower out-of-sample prediction error for both nuisance functions. GAMs had the lowest prediction error of all methods but were biased, with coverage of 0.55 with independent observations. The remainder reproduced the average bias of OLS, GAMs, and DML-XGBoost, but only half to three-quarters of the bias of XGBoost, whose residuals were formed on the data used to fit it. A joint-error measure based on the absolute cross-product of the two nuisance errors was only weakly associated with causal bias. Prediction error is useful for assessing nuisance-function estimation, but it should not be treated as a direct measure of the quality of the resulting causal
estimator.
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Artificial Intelligence · Cross-listed from cond-mat.mtrl-sci
AutoXRD: Autonomous LLM Agents and Comprehensive Evaluation for Powder Diffraction Analysis
Yuetong Wu and Maojun Sun
Powder X-ray diffraction (XRD) is central to materials characterization, yet reliable end-to-end automation remains challenging. An XRD
agent must interpret diffraction evidence, operate refinement software, manage coupled parameters in a defensible order, and distinguish numerical improvement from physical validity. In this paper, we propose AutoXRD, an autonomous large
language model (LLM)
agent framework that organizes powder-XRD analysis as stepwise refinement, grounds actions in observed evidence, and applies deterministic crystallographic and physical checks before accepting results. We further introduce XRDBench with two complementary tracks. XRDBench-QA contains 100 bounded diagnostic tasks that isolate scientific
reasoning and decision-making, whereas XRDBench-E2E contains 34 executable workflows that test whether
agents can compose these capabilities into complete analyses requiring file inspection, crystallographic-software execution, iterative refinement, evidence preservation, and reporting. We evaluate ten recent LLMs across 1,340 model–task runs. Models average only 57.8 out of 100, falling from 61.9 on XRDBench-QA to 53.7 on XRDBench-E2E. They perform best on refinement-history assessment and result acceptance, but remain substantially weaker on refinement-action selection, phase quantification, indexing, and Rietveld refinement. GPT-5.6 Sol achieves the highest overall score of 81.1, GPT-5.6 Terra the highest XRDBench-E2E point estimate of 81.0, and GPT-5.6 Luna the best score–cost trade-off. Ablations show that all six AutoXRD components consistently improve performance, supporting the framework design. Finally, execution-trace analysis reveals recurring failures in coupled-parameter control, quantitative
reasoning, evidence preservation, and workflow termination, motivating stronger scientific constraints, uncertainty-aware decisions, and more efficient planning.
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Artificial Intelligence · Cross-listed from cs.CL
Auditing Harness Tampering in Self-Improving Agents
Xing Wang, Xiaoyi Zhang and Jie Shao
Self-improving
agents iteratively modify their own harness to push the frontier of their performance. However, such modifications can produce illusory performance gains or compromise integrity constraints such as authorization, provenance, and completeness without genuinely improving capability. We term this phenomenon as harness tampering, which extends the concept from reward and measurement tampering to the full self-improvement lifecycle. To systematically study this problem, we propose a two-axis taxonomy that categorizes each misaligned edit by the harness functional role in which it occurs and the obligation it violates. Then we build an annotated corpus by seeding tampered-benign edit pairs into the real trajectories of self-improving
agents. We adapt and
benchmark diverse audit methods on tampering classification and localization tasks. Finally we systematically audit real trajectories of self-improving
agents. The results demonstrate that harness tampering consistently occurs in real runs from different
agents, often persists in the lineage of the best
agent, and forms distinct system-specific profiles across the taxonomy.
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Artificial Intelligence · Cross-listed from cs.CL
Life Operators: a self-evolving framework for multiscale life modelling
Shuo Wang and Yike Guo
Medical AI is moving beyond recognition towards clinical dialogue and longitudinal prediction. Yet a central question remains: how would a patient's state change under intervention? Statistical models learn future observations, whereas mechanistic models describe selected processes. Neither provides a common framework for representing patient state, coupling scales or revising failed assumptions. We propose Life Operators: task-bounded mappings that define three scientific roles. Perception operators infer task-relevant biological states from
multimodal observations,
Evolution operators propagate these states under natural or intervention-conditioned dynamics, and Generation operators map them to measurable signals. Each role may be realised by equations, statistical models, neural networks or hybrids. Bridge operators connect components with different variables, scales and time steps. Selected operators and bridges form task-specific Operator Graphs containing the smallest set of states and mechanisms sufficient for a declared claim. This modular structure also makes scientific revision localisable. An AI co-scientist may propose changes to states, operators, bridges or graph structure, while independent evidence determines which variants are retained, restricted or retired. Over time, validated components could accumulate into broader multiscale models of the human body and provide a computational foundation for medical artificial superintelligence.
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Artificial Intelligence · Cross-listed from cs.CL
Do Multimodal LLMs See Before They Read? Diagnosing Contextual Sycophancy
Yi-Cheng Lai and Hen-Hsen Huang
External text can override conflicting image evidence in
multimodal large
language models, a failure we call
multimodal contextual sycophancy. We introduce a 998-case diagnostic that independently varies visual evidence, commonsense priors, and external text, and probe when this failure arises by moving the information boundary around a context-blind visual witness. On abnormal images paired with Gemini-generated false text, GPT-5.1 scores 7.9% under joint conditioning, 49.7% when the context-blind witness report is scored directly, 63.7% under a matched two-call witness-arbiter pipeline that exposes the witness to the text, and 84.2% under System-2 Visual Arbitration (S2VA), which withholds the text from the witness. Across six models, S2VA improves over the direct witness report by 19.7 to 44.1 points, with all paired 95% confidence intervals excluding zero. The best information boundary is not uniform: textual context scaffolds some models, and a GPT-4o-regenerated subset changes the relative ordering of joint conditioning, Witness-Only, and S2VA. Contextual sycophancy is therefore sensitive to when text is introduced, as well as to the model and context source.
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Artificial Intelligence · Cross-listed from cs.CL
OCGQuant: Outlier-Companion Grouping for NVFP4 Quantization
Yishan Yao, Binjun Li, Hanling Yi et al. (+5 more)
NVFP4 is an efficient microscaling format for low-bit inference, but activation outliers can still degrade quantization accuracy within NVFP4 blocks. Within each quantization block, large activations can dominate the block scale, increasing the quantization error of the remaining values sharing the same scale. Existing post-training quantization (PTQ) methods mitigate outlier errors through strategies such as mixed precision, rotation, or residual compensation, but these approaches are either not specifically tailored to NVFP4 or introduce additional computation. In this work, we revisit NVFP4 from a channel-grouping perspective and define the reducible error incurred by remaining block values under the scale set by the block maximum as Collateral Quantization Error. Based on this insight, we propose OCGQuant, a post-training quantization method centered on Outlier-Companion Grouping (OCG), which adaptively pairs outlier channels with low-magnitude companion channels to improve NVFP4 activation block composition. Experiments on Llama3 and Qwen3 show that OCGQuant achieves the lowest WikiText-2 perplexity and highest average downstream accuracy among evaluated PTQ methods, while maintaining prefill speedup close to RTN and matching its peak decoding
memory. Code is available at https://github.com/Eshamont/OCGQuant.
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All authors Yishan Yao, Binjun Li, Hanling Yi, Pengyu Li, Xiaoqing Liu, Zihan Yang, Xiaotian Yu, Zhiwen Yu
Artificial Intelligence · Cross-listed from cs.CL
Scientific Agent Skills: A Library of Procedural Knowledge for Research Agents
Timothy Kassis, Vinayak Agarwal, Yuhuan He et al. (+2 more)
A
language-model
agent asked to analyse an experiment will usually return working code. Whether the analysis is defensible is a different question. A defensible analysis depends on procedural choices: which test the field accepts, which identifier namespace is authoritative, and which caveats must accompany a result. We present Scientific
Agent Skills, an open library of 163 such procedures in 16 areas of practice, including genomics, cheminformatics, medical imaging, study design and scientific communication. Each skill is a directory built around a versioned, human-readable instruction file. An
agent loads the file only when a task calls for it; the directory often also contains reference material and runnable scripts. We report no task-level evaluation and no host selection rate. We measure two properties of the documentation corpus: the always-resident descriptions of all 163 skills cost 7.1% of a 200,000-
token window, and the median documented workflow fits within 23.9% of it, although 29 of 46 would overflow if every reference file were loaded. Openly licensed and available at https://github.com/K-Dense-AI/scientific-
agent-skills.
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All authors Timothy Kassis, Vinayak Agarwal, Yuhuan He, Darshil Patel, Aubrey M. Brueckner
Artificial Intelligence · Cross-listed from cs.LG
Attention Sensitivity Is Not Enough: Dissociating Attention-Level and Behavioural In-Context Learning under Fine-Tuning
Jinyuan Zhang, Peng He, He Hu et al. (+2 more)
In-context learning (ICL) lets large
language models adapt to new tasks from demonstrations, and fine-tuning can erode this behaviour. Many preservation diagnostics inspect
attention: if
attention changes when demonstrations change, the model is treated as context-sensitive. This paper asks how far that proxy can be trusted once it is optimised. We formalise In-Context Sensitivity (ICS), the average row distance between last-
token attention on matched and mismatched demonstration prefixes, and pair it with ICL-GAP, the behavioural accuracy gap between the same prefixes. In a controlled four-arm ablation on Llama-2-7B, an ICS-maximising regulariser ($\armKL$) drives ICS to $1.413$, within $0.5\%$ of its
geometric ceiling. The behavioural readout tells a different story: ICL-GAP stays near zero and MMLU accuracy moves from $0.371$ to $0.279$, a Goodhart dissociation of the bounded
attention proxy. Endpoint statistics locate the mechanism:
attention grows sharp and near-disjoint across prefixes yet routes to formatting and demonstration-body
tokens rather than labels. A random-label protocol confirms that the behavioural probe family retains dynamic range at the same checkpoints. In a constructive sweep, behaviour gating partially mitigates the effect, while objectives anchored to pretrained computation hold the high-MMLU, moderate-ICS region that divergence maximisers leave. The main lesson is diagnostic:
attention-level ICL proxies earn their place as training targets only after validation against behavioural gaps.
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All authors Jinyuan Zhang, Peng He, He Hu, Yin Yuan, ShengShuo Jiao
Artificial Intelligence · Cross-listed from cs.CL
Medical Causal Hypothesis Verification with Large Language Models
Safiyyah Ahmed, Abrar Ansari, Md Aminul Islam et al. (+1 more)
The growing use of large
language models (LLMs) for search and information
retrieval underscores the need to evaluate their reliability in high-stakes domains such as healthcare. Although LLMs can effectively answer questions about diseases, symptoms, and treatments, their ability to accurately assess causal relationships and ground their conclusions in verified scientific evidence remains unclear. Here, we present a preliminary, small-scale study that investigates the accuracy of LLMs in evaluating causal medical claims and supporting them with peer-reviewed research. We propose an evaluation framework for causal hypothesis verification that can be used to systematically track the performance of existing and future LLMs. We assess the performance of eight LLMs on 17 medical causal hypotheses to evaluate whether they can reliably verify these hypotheses using scientific evidence from the literature. We systematically annotate the scientific evidence they provide according to six criteria (a total of 1,067 annotation points) and assess them with nine evaluation metrics. Our analysis shows that while LLMs exhibit strong recall, they often perform poorly at providing valid scientific articles and evidence for support and at rejecting unsupported hypotheses. These findings highlight a critical limitation of current LLMs, as they cannot yet be trusted fully to verify causal relationships from the biomedical literature. This work underscores the need for rigorous evaluation before using LLMs for search and
retrieval in healthcare settings.
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All authors Safiyyah Ahmed, Abrar Ansari, Md Aminul Islam, Elena Zheleva
Artificial Intelligence · Cross-listed from cs.CL
RePro: Proof-Verified Benchmark Rewriting for Reliable Evaluation of LLM Mathematical Problem Solving
Xiyuan Zhou, Zhuoqi Li, Xinlei Wang et al. (+6 more)
Data contamination undermines the reliable evaluation of large
language models (LLMs) on mathematical problem solving. While rewriting-based evaluation mitigates memorization, existing methods lack guarantees of problem validity and answer correctness. We propose Proof-Verified
Benchmark Rewriting (RePro), the first framework to integrate Lean-oriented neural automated
theorem provers (ATPs) into
benchmark rewriting, which rewrites problems and regenerates answers with correctness ensured by Lean-verified proofs. Experiments on GSM8K and MATH show that RePro's retained rewritten instances achieve 100% well-definedness, feasibility, and answer correctness, while existing methods still produce invalid or incorrect instances. Moreover, several models exhibit accuracy drops on proof-verified rewritten
benchmarks, suggesting that their performance is sensitive to surface-level and structural variations and may partly reflect memorization effects. Our source code and data are available at https://github.com/AI4Engi/RePro.
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All authors Xiyuan Zhou, Zhuoqi Li, Xinlei Wang, Yirui He, Yuhao Wu, Yuheng Cheng, Yan Xu, Junhua Zhao, Jinjin Gu
Artificial Intelligence · Cross-listed from cs.LG
ReNFT: Repairing Mode Collapse in Reward Post-Training via Internal Probability-Mass Recalibration
Yuchen Bao, Chao Wen, Haowei Wang et al. (+10 more)
Reward post-training of
diffusion generators inevitably concentrates probability mass on a few reward-favored modes, a mode collapse that erases within-
prompt diversity. Existing methods for mitigating collapse rely on external signals or interfaces, augmenting the reward with perceptual objectives, adjusting reference regularization, or modifying the text encoder, but none repairs an adapter that has already collapsed while preserving the acquired reward. We observe that online post-training primarily reallocates probability mass over capabilities inherited from pretraining rather than learning new visual content. Collapse is therefore suppression, not deletion, and can be reversed from within the generator. We propose ReNFT, which repairs a high-reward, low-diversity adapter through internal probability-mass recalibration. Unconditional probes first prioritize "anti-hub"
prompts where the
prompt-independent bias is easiest to expose. Two policy-dominated mixed routes then generate matched counterfactual proposals from the same
prompt and initial noise, one probing the frozen base direction for suppressed alternatives and the other exposing the post-trained unconditional tendency. Reward ranking with an adaptive flipping guard assigns pull and push roles, and a joint-and-paired NFT update realizes the repair. On PickScore and GenEval, ReNFT retains 98.9% and 99.0% of NFT's reward while improving DreamSim-Div by 58.8% and 55.0%, respectively, offering a complementary alternative to external interventions.
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All authors Yuchen Bao, Chao Wen, Haowei Wang, Ruoxin Chen, Donghao Luo, Jiahui Zhan, Wenjian Huang, Shen Chen, Yiting Wang, Taiping Yao, Chengjie Wang, Shouhong Ding, Jianguo Zhang
Artificial Intelligence · Cross-listed from cs.CR
A Formal Analysis of Agent Payment Protocols
Ke Jiang, Mohan Yu, Yuan Chang et al. (+4 more)
Agent payment protocols are emerging as a key transaction layer for autonomous commerce, enabling AI
agents to purchase goods and services and execute payments on users' behalf. Unlike conventional payment flows, they distribute user intent, delegated authority, credential use, settlement, and fulfillment across multiple actors and stages, creating security dependencies that no single message or participant can enforce. Yet these guarantees remain largely implicit across evolving specifications, schemas, and reference implementations, with little systematic formal analysis. We formalize four representative
agent payment protocols: x402, MPP, ACP, and AP2 in Tamarin. Using a common abstraction of the
agent payment lifecycle, we construct source-grounded models that capture each protocol's roles, state, trust assumptions, and lifecycle transitions. Rather than assuming a complete property taxonomy, we use source-backed verification questions and counterexample traces to expose missing bindings, state constraints, and cross-stage correspondences, consolidating them into 18 shared security principles. Across 86 verification cases, our analysis reproduces 46 known or calibration cases and identifies 40 previously undocumented formal-consistency findings. For each retained violation, we isolate the missing protocol relation, construct a minimally strengthened reference model, and reverify the intended property. We further evaluate the new x402 findings across three implementations and validate ten representative findings through implementation PoCs, SDK/schema-level witnesses, and source-aligned executable traces spanning five security principles. Our results show that delegated authorization must remain consistent with its resulting economic and service effects across actors, states, and protocol stages.
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All authors Ke Jiang, Mohan Yu, Yuan Chang, Mohit Kumar Jangid, Jianyu Niu, Cong Wang, Yinqian Zhang
Artificial Intelligence · Cross-listed from cs.LG
DISTAL: Distillation and Self-Supervised Pretraining for Structure-Agnostic Materials Property Prediction
Weiran Wang, Xintong Huo, Yueying Wang et al. (+7 more)
Materials property prediction remains difficult in low-data settings, where many target properties are supported by only a limited number of labeled samples. Models with the strongest predictive accuracy often depend on
crystal structures, which restricts their use in early-stage screening when structural information is limited or unavailable. To address this challenge, we propose DISTAL, a dual-prior framework for structure-agnostic materials property prediction that combines self-supervised compositional pretraining with structure-aware knowledge
distillation. DISTAL first learns transferable compositional representations from a large virtual composition space using 145 composition-derived descriptors. It then distills structural knowledge from a pretrained ALIGNN teacher into a composition-conditioned student. This setting allows structural priors to be used during training without requiring structural inputs at inference. By integrating explicit compositional descriptors, pretrained latent features, and distilled structural features within a unified prediction pipeline, DISTAL captures complementary signals that are difficult to recover from any single representation alone. Across 39
benchmark tasks, the best-performing
multimodal configuration combines all three signals, and improves over the reference
benchmark on 37 tasks. DISTAL achieves the strongest overall performance among all evaluated feature combinations. These results indicate that compositional pretraining and structural
distillation provide complementary priors and offer a practical route to robust composition-only prediction in small-data materials informatics. The source code and the pre-trained models are anonymously available at: https://osf.io/eq96d/overview?view_only=451617f42f7849e08750bd1852b48980 and will be released at the official link after acceptance.
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All authors Weiran Wang, Xintong Huo, Yueying Wang, Yusi Fan, Wenyan Wang, Xin Feng, Ruihao Xin, Lan Huang, Kewei Li, Fengfeng Zhou