Large language models often lack coverage in specialized, data-scarce domains where web text is limited. This paper proposes SPA (Scaling Prompt-engineered Augmentation), a baseline that generates large-scale synthetic corpora using just seven carefully designed prompt templates grounded in cognitive learning strategies (Concept Learning, Critical Thinking, and Generative Learning). The core finding is that this simple approach consistently outperforms complex RL-based methods like SEAL and multi-stage pipelines like EntiGraph across Wikipedia QA, long-document comprehension, and multi-hop reasoning benchmarks, suggesting that careful prompt design combined with straightforward scaling is surprisingly effective for knowledge injection.

This paper tackles the limitation that XAI systems assume static user models, ignoring diverse epistemic stances among domain experts. The authors propose agentic personas—structured representations of expert reasoning strategies derived from clustered feedback and instantiated via LLMs—to condition reinforcement learning-based explanation generation on knowledge graphs. This enables adaptive explanations that align with specific interpretive preferences (mechanistic rigor vs. focused clarity) without requiring extensive individual-level human feedback, demonstrated in drug discovery with 22 expert participants.

Personalized image generation with diffusion models relies on Low-Rank Adaptation (LoRA) to fine-tune models efficiently, but current practice uses a fixed rank across all layers regardless of subject complexity. This paper proposes LoRA2, which learns adaptive ranks per LoRA component via a variational framework that imposes an importance ordering over rank indices using a discretized exponential distribution. The method achieves better subject fidelity and prompt alignment while using significantly less memory than high-rank baselines, addressing the combinatorial explosion of searching $S K^L$ architectural configurations.

Multi-Objective Reinforcement Learning (MORL) agents must balance competing objectives like speed versus energy consumption, yet existing Explainable RL methods fail to clarify how specific behavioral choices drive Pareto trade-offs. This paper proposes TREX, a post-hoc trajectory attribution framework that clusters agent behaviors into semantically meaningful segments and quantifies each cluster's influence on objective trade-offs by training complementary policies that exclude specific trajectory groups. The work addresses a genuine gap in explainability by moving beyond policy selection to reveal which behavioral patterns (such as "long leaps" versus "short strides") justify the agent's learned trade-off logic.

The paper addresses sample-efficient selection among multiple pretrained generative models, formulated as a diversity-aware multi-armed bandit problem where the optimal solution may be a mixture rather than a single model. The authors challenge the necessity of explicit UCB exploration bonuses, proposing that Mixture-Greedy—which directly optimizes empirical diversity objectives without optimism bonuses—can achieve sublinear regret through implicit exploration induced by the objective geometry. This matters because sampling from suboptimal generative models is computationally expensive, and their results suggest that structural properties of diversity metrics (FID, Vendi, RKE) naturally enforce sufficient exploration without costly confidence bound computations.

CICTM addresses deformable brain MRI registration by combining transformer-based global context modeling with cycle inverse-consistency constraints. The core idea uses a Swin-UNet to jointly estimate forward and backward deformation fields, penalizing inconsistencies at both image and flow levels while enforcing topology preservation via Jacobian regularization. The work matters for large-scale neuroimaging studies where deformation stability and physical plausibility are as important as alignment accuracy.

This paper identifies a critical failure mode in multimodal AI evaluation called the 'mirage effect,' where vision-language models generate confident descriptions and reasoning about images that were never provided. The authors demonstrate that frontier models (GPT-5, Gemini-3-Pro, Claude Opus 4.5) retain 70–80% of their benchmark accuracy when evaluated without any visual input, with medical benchmarks showing 60–99% susceptibility to such non-visual inference. A text-only 3B-parameter model fine-tuned on chest X-ray questions outperforms both frontier multimodal systems and human radiologists, exposing how current benchmarks fail to distinguish genuine visual understanding from sophisticated textual pattern matching. The findings challenge the validity of accuracy metrics for multimodal systems and propose B-Clean, a method to filter benchmark questions that can be answered without images.

Constraint-based causal discovery algorithms like PC require exponentially many conditional independence (CI) tests in the worst case---specifically $p^{\mathcal{O}(d)}$ where $d$ is the maximum degree. This paper establishes that the fundamental complexity parameter is actually $s$, the maximum undirected clique size in the essential graph, which can be much smaller than $d$ (e.g., $s=2$ vs $d=p-2$ in Figure 1). The authors propose Greedy Ancestral Search (GAS), which achieves $p^{\mathcal{O}(s)}$ CI tests, and prove a matching lower bound of $2^{\Omega(s)}$, establishing exponent-optimality up to a logarithmic factor.

Long-tail class incremental learning (LT-CIL) suffers from scarce tail-class data and catastrophic forgetting. This paper tackles both issues by using large language models to generate a stratified language tree (SL-Tree) that hierarchically organizes semantic information from coarse to fine granularity. Two parallel guidance mechanisms—adaptive language guidance with learnable per-class weights and alignment language guidance using semantic space stability—dynamically supervise tail classes and constrain optimization. The approach achieves reported state-of-the-art results on ImageNet-R, CIFAR100, and CUB200 benchmarks.

EnterpriseLab tackles the challenge of deploying AI agents in enterprise settings where data sovereignty and cost constraints make frontier models impractical. The paper introduces a full-stack platform that unifies tool integration via Model Context Protocol (MCP), automated trajectory synthesis from environment schemas, and integrated training pipelines including a novel Agentic GRPO method. The core value proposition is that small 8B models can match GPT-4o on enterprise tasks while cutting inference costs by 8–10×, enabling on-premise deployment without sacrificing operational capability.

This paper addresses a critical gap in embodied AI: while Multimodal Large Language Models (MLLMs) excel at reactive, short-horizon planning, they fail at biological-like "mental navigation" — the ability to construct global cognitive maps from long egocentric videos and simulate paths before acting. To tackle this, the authors introduce Video2Mental, a benchmark requiring models to generate structured hierarchical cognitive maps from videos exceeding five minutes, then produce landmark-grounded navigation plans validated in the Habitat simulator. They also propose NavMind, a Qwen3-VL-based model trained via difficulty-stratified progressive supervised fine-tuning to internalize these structured representations.

This paper tackles the inefficiency of Interleaved-Modal Chain-of-Thought (ICoT) reasoning, where current methods statically insert visual tokens after every reasoning step, wasting compute on redundant image embeddings and using semantically broken patches. DaP-ICoT introduces a confidence-aware gating mechanism that only pulls visual context when model certainty drops below a threshold, combined with SAM2-based object segmentation to provide coherent visual thoughts instead of fragmented patches.

Standard Transformers apply fixed-depth computation regardless of problem difficulty, limiting their ability to solve tasks requiring variable-depth reasoning like multi-hop traversal or nested logic. This paper proposes a depth-recurrent Transformer that iteratively applies a shared-weight block in latent space—enabling 'vertical Chain-of-Thought' where models trade recurrence steps for deeper reasoning without consuming context window. The work demonstrates strong compositional generalization on three synthetic tasks and offers a mechanistic alternative to horizontal token-generation paradigms.

This work addresses zero-shot detection of AI-generated images by measuring how Vision Foundation Model (VFM) representations respond to structured high-frequency perturbations. The core idea is that synthetic images contain characteristic frequency biases, causing their embeddings to shift differently than real images when high-frequency noise is applied to local patches. The method achieves strong detection accuracy while requiring only a single Fourier transform and one forward pass, making it one to two orders of magnitude faster than comparable training-free approaches.

Long-context LLM inference hits a memory wall: each decode step requires scanning the entire KV cache, incurring $O(n)$ memory bandwidth that cannot be solved by faster arithmetic. PRISM proposes a thin-film lithium niobate photonic accelerator that performs the block-selection similarity search in $O(1)$ optical latency using a broadcast-and-weight architecture, eliminating the $O(n)$ scan entirely. The work claims $16\times$–$32\times$ traffic reduction at 64K–128K tokens and a four-order-of-magnitude energy advantage over GPU baselines by matching photonic hardware capabilities—passive query broadcast, quasi-static microring weights, and low-precision rank output—to the selection task.

MetaCompress addresses token reduction for multi-turn VQA in Large Vision-Language Models, where future questions are unpredictable and may target any image region. The paper proposes a learning-based prompt-agnostic compression module trained via KL divergence minimization between original and compressed outputs, demonstrating that heuristic attention-based pruning is suboptimal for this scenario. The method achieves strong efficiency-accuracy trade-offs across five LVLM architectures while training on only ~20k samples.

This paper tackles the domain generalization problem in image deraining, where models trained on synthetic data fail catastrophically on out-of-distribution (OOD) real-world scenarios. The authors propose a three-stage pipeline—Superpixel Generation, Resolution-adaptive Fusion, and Pseudo-label Re-Synthesis—that adapts source-domain models to target domains using only unpaired rain-free images, eliminating the need for costly paired rainy data collection.

This paper examines representation genesis—the transition from non-representational physical systems to those with content-manipulable states. It argues that major frameworks in philosophy of mind (Language of Thought, teleosemantics, predictive processing, enactivism, and genetic phenomenology) share a 'Representation Presupposition' structure that prevents them from explaining this first acquisition without circularity. With large language models now achieving high cognitive performance without clear genesis events, the absence of a satisfactory theory becomes urgent.

Retrieval-Augmented Generation (RAG) systems mitigate large language model hallucinations by integrating external knowledge bases, yet this multi-module architecture introduces complex security vulnerabilities spanning data poisoning, membership inference, and adversarial manipulation. This survey systematically maps threats across the RAG pipeline—vector database construction, retrieval, and generation—and categorizes corresponding defenses from input-side access control to output-side privacy preservation. As a comprehensive review of 152 papers, it aims to unify the analysis of threat models, defense mechanisms, and evaluation benchmarks to foster trustworthy RAG deployments in sensitive domains.

AgenticRec attacks a key gap in LLM-based recommenders: existing agents rely on frozen reasoning chains and cannot learn from ranking feedback to refine tool use. The paper proposes a two-stage training framework that combines ReAct-style tool invocation with list-wise Group Relative Policy Optimization (GRPO) and Progressive Preference Refinement (PPR) for hard-negative mining. The work matters because it demonstrates that end-to-end reinforcement learning can align multi-step tool use with ranking objectives, moving beyond prompt-engineered agent workflows.