arXiv:2607.27528v1 Announce Type: cross Abstract: Threat modeling is essential for secure software development, yet manual analysis of cloud-native architectures is slow and demands scarce security expertise.…
arXiv:2410.06458v2 Announce Type: replace Abstract: Instruction following is a key capability for LLMs. However, recent studies have shown that LLMs often struggle with instructions containing multiple…
arXiv:2607.27537v1 Announce Type: new Abstract: Predicting future structural MRI of a brain is challenging because longitudinal changes are often subtle and confined to specific anatomical regions,…
arXiv:2607.27274v1 Announce Type: new Abstract: EEG-based disease diagnosis requires one prediction per subject, yet common pipelines segment recordings into short instances, inherit the subject label for…
arXiv:2607.27303v1 Announce Type: new Abstract: Temporal heterogeneous graphs offer a natural abstraction for dynamic relational systems in which diverse node and relation types co-exist and evolve…
arXiv:2607.28248v1 Announce Type: new Abstract: The deployment of deep neural networks in safety-critical domains demands reliable estimates of predictive confidence, yet conventional architectures lack principled uncertainty…
arXiv:2607.26393v1 Announce Type: new Abstract: Social deduction games (SDGs) such as Werewolf have become challenging testbeds for AI agents. These games require complex social skills such…
arXiv:2607.27379v1 Announce Type: new Abstract: High-quality, diverse data are vital for large language models (LLMs) but remain scarce and costly. Data synthesis is a viable alternative…
arXiv:2607.27557v1 Announce Type: new Abstract: While Large Language Models (LLMs) demonstrate remarkable general instruction-following capabilities, they often fall short of human experts in highly specialized, open-ended…
arXiv:2503.15293v2 Announce Type: replace Abstract: Object detection models are vulnerable to backdoor attacks, where attackers poison a small subset of training samples by embedding a predefined…
arXiv:2607.27275v1 Announce Type: new Abstract: Post-training quantization to 4-bit weights is widely reported to be nearly lossless. We test this claim for multi-turn, tool-calling agents, where…
arXiv:2607.28219v1 Announce Type: cross Abstract: Hash Chemistry is a family of minimalistic evolutionary models in which a deterministic hash function assigns a scalar score to entities…
arXiv:2607.28567v1 Announce Type: new Abstract: Longitudinal causal studies often record histories as irregular functional fragments: laboratory values, physiologic signals, sensor streams, and image-derived summaries measured at…
arXiv:2607.24847v2 Announce Type: replace-cross Abstract: We introduce an extremal invariant associated with Chowla-type order conditions in finite groups. A nonempty subset $S$ of a finite group…
arXiv:2607.27384v1 Announce Type: new Abstract: Large language models used for clinical diagnostic reasoning are sensitive to sociolinguistic register, not just clinical content. We term this failure…
arXiv:2607.27549v1 Announce Type: cross Abstract: Recent progress in large-scale imitation learning for robot manipulation has been driven by leveraging datasets across a wide range of robot…
arXiv:2607.27558v1 Announce Type: new Abstract: Recovering Parametric CAD sequences from raster-format 2D Computer-Aided Design (CAD) drawings accumulated prior to digital transformation is important for part reproduction…
arXiv:2607.27271v1 Announce Type: new Abstract: Code models are increasingly trained with execution feedback, but most training signals still stop at correctness. This leaves an important gap…
arXiv:2307.07191v3 Announce Type: replace-cross Abstract: Energy forecasting is crucial for the power grid, but fundamentally different from general time series analysis: it highly relies on covariates…
arXiv:2607.23914v2 Announce Type: replace-cross Abstract: High dimensional statistical theory has established the importance of constant aspect ratio, when the number of dimensions ($d$) and samples ($n$)…
arXiv:2606.06133v4 Announce Type: replace-cross Abstract: TLA+ is a formal specification language for verifying distributed systems and safety-critical protocols. Large language models (LLMs) frequently produce TLA+ specifications…
arXiv:2607.27143v1 Announce Type: cross Abstract: High-stakes decision systems in credit scoring, fraud detection, healthcare, and industrial safety require reliable uncertainty quantification under severe class imbalance and…
arXiv:2607.27366v1 Announce Type: new Abstract: While data synthesis for large language models (LLMs) is prevalent, it primarily targets domains with verifiable answers, overlooking open-ended humanities and…
arXiv:2607.27924v1 Announce Type: cross Abstract: In the physical world we inhabit, space and time are fundamentally continuous. However, existing machine learning paradigms for world modeling are…