This work examines whether FVs extracted from a source language can steer task behavior in another language under both standard clean and perturbed zero-shot settings without providing demonstrations during inference, and observes that each LLM exhibits a relatively stable optimal range of attention heads for constructing effective FVs, and the pattern remains consistent across languages.
Jieying Xue, Phuong Minh Nguyen, Minh Le Nguyen et al.· 0 citations
A paradigm for tactile-enabled embodied manipulation, which integrates tactile sensing hardware, large-scale multimodal data, tactile representation learning, and standardized evaluation is presented, aiming at supporting future work on tactile-enabled embodied manipulation.
A unified systems foundation and reference architecture for the agentic skills ecosystem is established, formalize skills as externalized procedural knowledge bridging high-level cognitive planning with deterministic execution environments, and systematically delineate the architecture across a nine-stage lifecycle.
Sanket Badhe, D. Shah, Priyanka Tiwari et al.· 0 citations
TACS is proposed, a trajectory-aware candidate selection framework for jailbreak suffix optimization that augments per-step evaluation with a trajectory-aware proxy and stabilizes selection with reference-policy regularization and a discriminator-estimated chi-squared correction, encouraging choices that remain effective beyond the current step.
Shi-Liang Xiao· 0 citations
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LoGo, a token-level dynamic local-global attention mechanism that uses attention span as a direct proxy for attention budget allocation, is proposed and results suggest that learned token-level span allocation is an effective and scalable way to improve the long-context performance-compute trade-off.
Yuqi Pan, Zheng Li, Bohao Tang et al.· 0 citations
Adversarial Robustness with Manifold-Oriented Training (ARMOR), a novel defense that realizes the core insights of on-manifold adversarial training (OMAT) in low-data regimes and translates insights from manifold-based training to defend object detectors amidst training data scarcity.
Haoran Wang, Matthew Lau, Alec Helbling et al.· 0 citations
This work proposes \texttt{RADAR} (Regret-based Assessment of Decision Adequacy and Risk), a decision-focused framework that uses inverse optimization to infer latent preferences and tests the deployed decision's optimality gap under the current distribution.
Minxing Zheng, H. Wiberg, Shixiang Zhu· 0 citations
A benchmark score is a joint property of the model, the evaluation harness, the elicitation budget, the sampled population, and contamination status. Leaderboards publish the model and the score, so capability and leakage stay observationally equivalent. Existing taxonomies classify contamination for automated detection, not the question a reporter faces at publication: given the mitigations already applied, which validity threats remain open? We introduce a taxonomy organized by the mitigation each type defeats -- direct, derivative, temporal, distributional, and acquired -- spanning training-time and evaluation-time leakage. Holding out a private test set closes the first alone. The fifth is acquired during the evaluation itself; because it is a property of one run, it must be recorded with the reported score rather than with the benchmark release. We operationalize it as a four-field disclosure protocol in which"unknown"is a valid entry, released under CC BY 4.0 with a JSON Schema, a validator, and worked examples. Two coders external to the design team applied a pre-registered instrument to 41 documents. Per-variable linear-weighted $\kappa$ runs from 0.00 to 0.35 (median 0.21) over 29 main-pass documents against a single-coder test-retest ceiling of 0.84, collapsing under the class skew the registration anticipated; pooling raises it to 0.46 through chance correction rather than better agreement. Two variables fall below the prevalence-robust threshold registered in advance: strata reporting and the acquired type introduced here. Disagreement concentrates on when a variable applies rather than on what a document states. Elicitation budgets are reported in 13% of documents, and no document addresses all five types. The contribution is the taxonomy, the score-side artifact that follows from it, and a pre-registered measurement of instrument reliability and current disclosure.
Johanna Angulo, Víctor Yeste, H. Espinós-Morató· 0 citations
LLMs are increasingly used as human surrogates, often on the premise that richer persona data could make them substitutes or exploratory tools for specific individuals. We test this premise across four datasets covering more than 400,000 participants and more than 6,000 survey items and experimental outcomes. LLMs perform well at the aggregate level: their average responses closely align with average human responses to the same items. But this success largely reflects predicting each item's average human response. Once each item's human mean is removed, LLM predictions explain only 3.05% of the remaining respondent-specific variation, far below the 53.6% human test-retest benchmark. Richer personas, model variants, and fine-tuning do not close this gap. In variance analyses, once item means are removed, the reliable remaining signal is person-by-item. It captures how a respondent departs from the mean on a particular item and is about 8.9x larger than the stable person effect. Persona data encode the respondent, but not this item-specific deviation. LLM responses also compress human response distributions, using less spread, fewer response categories, and distorted distributional shapes. We call this pattern item-mean surrogacy. Current LLM surrogates can approximate item averages, but not the distributions or respondent-specific deviations needed to replace individual humans. We propose four empirical tests for LLM-based human-surrogate claims.
This work analyzes four experiments on a major short-video platform, establishing a positive creator response, measuring the gross corpus flow visible within three weeks, and showing the design and duration needed to identify total value.
Yuanyuan Shen, Yiren Yan, Wenjie Li et al.· 0 citations
The results show that distributionally-regularized joint embedding architectures can be successful on challenging city-scale 3D scenes, and that transfer improves when self-supervision is designed for the capture geometry and spatial context of this domain while also revealing the limits of this specialization.
A. Rusnak, S. Kovalenko, Jingru Wang et al.· 0 citations
An AI-based approach called ChatNDE Figure to Caption is introduced, which aims to automate the interpretation of NDE images using deep learning and natural language processing (NLP). A Vision-and-Language Pretraining (VLP) strategy is developed to help the model learn how to connect visual features with meaningful language. Basically, we built a large NDE image dataset, trained the model using annotated examples, and then evaluated how well it performed using BLEU scores to compare its output to expert written descriptions. So, the system combines a ResNet50 model to extract important features from the images and a GPT2 language model to turn those features into natural sounding text. Even though the accuracy of the model has been low the generated caption results have been solid so far, the captions were shorter but mentioned some important features of images what human experts would say, which shows the model is learning to pick up on key details. Also, a Visual Question Answering (VQA) model is used as part of the system. VQA models are designed to take an image and a question about that image (like Is there a crack? or Where is the defect located?) and generate a useful answer. By adding this layer, the platform will not just describe what it sees, it can also respond to specific questions, making it even more interactive and helpful for inspectors in the field. This whole approach is a big step toward speeding up NDE workflows, reducing human error, and making the technology more accessible.
A weeklong summer workshop brought higher education faculty to campus to explore how AI and machine learning materials can be adapted for their classrooms.
Adaptive AI agents can help make BIM data more machine-readable by navigating IFC models, interpreting inconsistent information, and mapping it to defined standards. In this blog, Alok Rawat shares findings from a real-world pilot in construction workflows. The post Adaptive AI Agents in Construction Workflows appeared first on GPT-Lab.
