This paper proposes a bold interdisciplinary bridge between holographic string dualities and artificial intelligence, hypothesizing that AI tasks such as language modeling can be viewed as particle trajectory prediction on graphs admitting a holographically dual "string" description. Drawing on the AdS/CFT correspondence, the authors conjecture that word metrics on $S_n$ Cayley graphs correspond to areas under lattice paths in dual planar polygons, verified computationally via their CayleyPy library.

This paper tackles the visual perception gap in automated text layout generation. While existing Multimodal Large Language Models (MLLMs) generate layout code (SVG/JSON) to render text on images, they operate blind to the actual rendered output, producing layouts with overlapping text, poor contrast, or misalignment. The authors propose Visual Feedback Layout Model (VFLM), which closes the loop by rendering generated SVGs and feeding the visual results back to the model for iterative reflection and refinement. The framework uses a two-stage pipeline—cold-start supervised fine-tuning followed by reinforcement learning with GRPO—and introduces a specialized layout reward model trained on fine-grained quality hierarchies. A surprising finding is that simple outcome-based rewards outperform complex process-oriented rewards that explicitly encode step-wise incentives.

Large language models have historically lagged behind specialized encoder-decoder MT systems, but their superior context modeling makes them natural candidates for document-level translation. This paper tackles two key obstacles: the scarcity of high-quality document-level parallel corpora and LLM tendencies toward hallucinations and omissions. The authors propose a two-stage fine-tuning framework that first generates synthetic document-level data from summarization corpora via LLM augmentation, filters this data using sacreBLEU, COMET, and LaBSE cosine similarity, and then trains models first on sentence-level data before adapting to the filtered document corpus.

Autonomous vehicles struggle with adverse weather perception. This paper proposes LRC-WeatherNet, a lightweight fusion network combining LiDAR, RADAR, and camera via early BEV fusion and mid-level gating to classify weather conditions in real-time. The approach achieves $86.66\%$ accuracy on the MSU-4S dataset with $7.13\,\mathrm{ms}$ inference, demonstrating that adaptive multi-modal fusion outperforms unimodal baselines, though dataset limitations restrict generalization to rare weather events.

This paper distinguishes different forms of reasoning by the structural properties they demand from underlying representational systems. The core insight is that deduction requires four specific properties (operability, consistency, structural preservation, and compositionality) that cannot be secured through mere statistical scaling. This has significant implications for AI systems and cognitive science, providing a principled boundary between reasoning that can rely on associative approximations versus reasoning requiring structural guarantees.

MARCUS tackles the bottleneck of human interpretation in cardiovascular diagnosis by creating an agentic, multimodal vision-language model that jointly reasons over raw ECG signals, echocardiogram videos, and cardiac MRI. The core innovation is a hierarchical architecture where modality-specific expert encoders feed into an orchestrating agent that synthesizes findings while resisting 'mirage reasoning'—the tendency of VLMs to confabulate explanations without actually processing the image. Trained on 13.5 million clinical images and 1.6 million expert-curated Q&A pairs, MARCUS aims to bridge the gap between single-task diagnostic AI and interactive clinical reasoning.

This paper attacks the expensive problem of annotating NLP test sets by importing Active Testing (AT) from computer vision into language tasks. Given a labeling budget $B$, the goal is to select a subset $X_A$ that minimizes the estimation error $|M(X_F) - M(X_A)|$ between full and sampled test-set metrics, potentially cutting annotation costs by up to 95% while keeping prediction error under 1%. The core mechanism couples importance-weighted unbiased estimators with acquisition strategies (including a novel Agreement strategy based on attention-head disagreement) and an adaptive stopping criterion that removes the need to pre-specify the budget.

SecureBreak introduces a response-level safety dataset designed to detect harmful LLM outputs that bypass alignment mechanisms. Unlike existing benchmarks that classify prompts, this work focuses on binary classification of generated responses (safe vs. unsafe) across 3,059 samples from multiple model families including Llama, Qwen, Gemma, and Mistral. The core value proposition is providing a 'last-line defense' layer for post-generation filtering and supervisory signals to guide security re-alignment, addressing the growing threat of jailbreak attacks.

This paper argues that frictionless AI interfaces pose a systemic risk of "cognitive agency surrender"—the habitual abdication of human reasoning to algorithmic systems. Drawing on cognitive psychology, the authors theorize "Scaffolded Cognitive Friction" as a defense: intentionally injecting epistemic tension via Multi-Agent Systems (MAS) that expose structured disagreements (computational Devil's Advocates) to force System 2 activation. The work positions itself as a bridge between HCI, cognitive science, and AI governance.

Foundation models for Earth observation risk learning spurious correlations when pretraining with random masking. This paper proposes SpecTM (Spectral Targeted Masking), which deterministically masks pigment-sensitive spectral bands (phycocyanin, chlorophyll-a, red-edge) to enforce physics-based cross-spectral learning. Validated on microcystin concentration prediction using NASA PACE hyperspectral imagery over Lake Erie, the method achieves $R^2=0.695$ (current week) and $R^2=0.620$ (8-day-ahead), showing strong label efficiency but limited geographic validation.

Symbolic regression search spaces suffer from structural redundancy: expression DAGs with $k$ internal nodes admit $\Theta(k!)$ distinct node-numberings that encode the same mathematical expression. This paper proposes IsalSR, a representation framework that computes a pruned canonical string—a complete labeled-DAG isomorphism invariant—to collapse all equivalent forms into a single canonical representation. The approach promises to reduce effective search space size by $O(k!)$ and can be integrated into any existing SR algorithm as a preprocessing step.

GSEM addresses the challenge of building structured experience memory for clinical LLM agents. Unlike flat memory banks storing isolated records, it organizes clinical decisions into a dual-layer graph capturing both internal decision structure (entity layer) and inter-experience relational dependencies (experience layer), supporting applicability-aware retrieval and online feedback-driven calibration of node quality and edge weights. Experiments on medical benchmarks report strong improvements over RAG and memory-augmented baselines, achieving 70.90% average accuracy with DeepSeek-V3.2.

EvoIdeator addresses the challenge of iteratively refining scientific research ideas using LLMs by bridging the gap between scalar RL rewards and coarse language feedback. The core innovation is a dual-signal approach combining lexicographic rewards with checklist-grounded, span-level language feedback integrated directly into the RL training loop using Dr. GRPO. This allows a 4B parameter model to outperform larger frontier models like Gemini 3 Flash and DeepSeek-V3.2 on scientific rigor criteria.

The paper studies calibeating—post-processing external forecasts online to minimize cumulative losses while matching an informativeness-based benchmark. Unlike prior work that used loss-specific arguments, the authors reduce calibeating to standard online learning primitives, showing it is minimax-equivalent to regret minimization. This yields optimal rates for general proper losses and improves bounds for simultaneous calibration and calibeating.

Enterprise AI agents face a fundamental dilemma: complex reasoning demands large-scale training data, yet enterprise domains offer limited, noisy trajectories and prohibit online self-play. This paper proposes Context Engineering via DT-MDP (DT-MDP-CE), a framework that abstracts LLM agent behavior into a finite Digital-Twin Markov Decision Process, learns per-step rewards via contrastive inverse RL (T-REX) from ranked offline trajectories, and deploys the resulting policy to guide context engineering—enabling performance gains without fine-tuning the base model or interacting with the environment during training.

Medical text summarization helps clinicians process millions of biomedical articles, but fine-tuning large language models demands prohibitive resources. This paper compares Low-Rank Adaptation (LoRA), Prompt Tuning, and full fine-tuning across Flan-T5-Small, Base, and Large on PubMed summarization. The counter-intuitive finding is that updating fewer than 1% of parameters via LoRA consistently outperforms full fine-tuning, suggesting that low-rank constraints provide effective regularization.

Most Time Series Foundation Models treat channels independently and ignore cross-channel correlations, which limits their performance on multivariate forecasting. This paper proposes CoRA (CoRrelation-aware Adapter), a lightweight plug-in that learns three correlation types—dynamic (time-varying), heterogeneous (positive/negative), and partial (sparse)—through a low-rank decomposition and dual contrastive learning. The key insight is that these correlations can be captured during fine-tuning without re-pretraining the foundation model, and with only linear complexity at inference time.

This paper tackles precipitation nowcasting by enhancing the lightweight SmaAT-UNet architecture with two modifications: a vector-quantization (VQ) bottleneck that discretizes latent representations into a learned codebook, and Mixed Convolution (MixConv) blocks that blend multiple kernel sizes to reduce parameters. The goal is to cut model size for edge deployment while preserving forecast skill at a 30-minute lead time.

Suiren-1.0 introduces a family of molecular foundation models designed to bridge the gap between microscopic 3D quantum-mechanical conformations and macroscopic 2D molecular property prediction. The framework comprises Suiren-Base (a 1.8B-parameter SE(3)-equivariant GNN pre-trained on 70M DFT samples), Suiren-Dimer (continued pre-training on intermolecular interactions), and Suiren-ConfAvg (a lightweight 2D model distilled via a novel Conformation Compression Distillation diffusion framework). This work matters because it attempts to unify quantum-accurate representations with practical cheminformatics workflows where only SMILES or graph inputs are available.

Directional abliteration removes refusal behavior from language models by projecting refusal-mediating directions out of weight matrices, where these directions are extracted by contrasting harmful against harmless prompt activations. This paper investigates whether topically matching the harmless baseline to harmful prompts — using, for example, defensive cybersecurity prompts to contrast against hacking prompts — yields cleaner refusal directions than the standard practice of using general-purpose harmless prompts. The central finding is that topic-matched contrast completely fails to produce functional refusal directions while unmatched baselines succeed, because matched subtraction cancels the dominant topic component shared between prompts of the same subject, leaving residue too small to perturb the residual stream.