Ctrl-A addresses automated data augmentation by framing it as a control problem, dynamically adjusting per-operation augmentation strengths via a feedback loop that balances training and validation loss ratios. The method introduces Relative Operation Response (ROR) curves to individually tune transformation distributions without manual initialization or expensive search phases. While it achieves competitive results on CIFAR and SVHN benchmarks with minimal computational overhead (~10% vs. TrivialAugment), the evaluation relies on a modified training setup with extended epochs, raising questions about separability of algorithmic gains from training protocol changes.

Video diffusion models suffer from prohibitive inference costs, but standard image distillation techniques like DMD cause severe oversaturation and temporal collapse when naively extended to video. This work introduces a video-specific distillation framework featuring an adaptive regression loss that dynamically reweights real-data supervision to prevent color artifacts, a temporal variance regularizer to combat static output, and an inference-time frame interpolation module that halves sequence length during high-noise steps to accelerate generation. Applied to Wan2.1, the method enables stable 4-step synthesis with state-of-the-art VBench scores.

ROM tackles overthinking in Large Reasoning Models, where models generate redundant reasoning after reaching correct answers. The core idea is a lightweight streaming detector—an 8.13M parameter head attached to late-layer hidden states of a frozen LLM—that predicts overthinking probability token-by-token and triggers early stopping. It matters because it promises 47% token reduction without full model retraining. We find the method empirically effective but note concerns regarding data scaling limits and labeling costs.

This paper presents Oph-Guid-RAG, a multimodal retrieval-augmented generation system for ophthalmic clinical decision support. Unlike conventional text-based RAG systems, it retrieves full guideline page images using ColQwen2.5, preserving tables, flowcharts, and layout information without OCR errors. The system introduces a controllable retrieval framework with routing and filtering to selectively introduce external evidence, evaluated on a specialized ophthalmology subset extracted from HealthBench.

As users increasingly consult multiple large language models for decision support, a critical question arises: does increasing the number of AI advisors improve accuracy or amplify harmful conformity pressures? This paper investigates how panel size, within-panel consensus, and human-likeness of presentation shape human reliance and decision accuracy across three prediction tasks (income, recidivism, and dating). Through two crowdsourced experiments with 348 participants, the authors reveal a surprising non-monotonic relationship: three AI advisors improve accuracy over a single advisor, but five provide no additional benefit, while unanimous consensus fosters overreliance and wide disagreement creates confusion.

The paper proposes CEBaG, a deterministic hallucination detection method for medical Visual Question Answering that eliminates the need for costly stochastic sampling. By combining token-level predictive variance with visual evidence magnitude derived from log-probabilities, the method detects when models generate responses that contradict input images. This approach achieves superior detection accuracy while reducing computational cost from 20+ generations to just three forward passes, addressing a critical safety bottleneck in clinical AI deployment.

Dyadic is a web-based platform for studying human-human and human-AI conversations through text or voice-based interaction. It attempts to solve the methodological gap in conversation research by providing turnkey tools for experimental manipulation, live monitoring, and in-situ survey delivery during ongoing chats. The core value proposition is lowering barriers to entry for researchers studying dyadic interaction processes without requiring programming expertise.

SegMaFormer proposes a hybrid encoder for 3D medical image segmentation that places Mamba state-space layers in early high-resolution stages (for linear-complexity sequence mixing) and self-attention only in deeper low-resolution stages (where quadratic cost is manageable). The goal is to reduce the prohibitive compute of full 3D attention while preserving global context. With just 2M parameters and 15 GFLOPs, the authors claim competitive results on BraTS, Synapse, and ACDC benchmarks against models up to 75\times larger.

This paper addresses video moment retrieval (VMR) for complex multi-verb queries by proposing a two-stage framework that generates auxiliary short videos via text-to-video diffusion (CogVideoX) as temporal motion priors, then processes them through a linear-time Mamba network. The approach tackles the limitation of static image augmentations—which miss motion dynamics—while avoiding the quadratic complexity of Transformer-based methods on long untrimmed videos. The framework achieves state-of-the-art results on TVR with particular strength on multi-verb queries, though its effectiveness depends heavily on external video generation quality.

This paper identifies a subtle but important distinction between two interpretations of the TD error in reinforcement learning: the explicit form (bootstrapped target minus prediction) commonly used in deep RL, and the implicit form (difference between temporally successive predictions) from the original Sutton (1988) formulation. While equivalent in tabular settings, the authors demonstrate that increasingly nonlinear architectures cause these to diverge significantly, with profound implications for average-reward and differential RL algorithms.

This paper investigates whether LLMs exhibit genuine moral reasoning or merely produce convincing moral rhetoric through a large-scale empirical study of 13 models across 6 classical moral dilemmas. Using Kohlberg's stages of moral development as a diagnostic framework, the authors evaluate whether model outputs track human developmental patterns or reflect alignment training artifacts. The core finding is "moral ventriloquism" — the hypothesis that models acquire post-conventional moral language through RLHF without the underlying cognitive architecture, evidenced by distributional inversions (86% Stages 5-6 vs. human Stage 4 dominance), near-robotic cross-dilemma consistency (ICC > 0.90), and "moral decoupling" where stated justifications misalign with action choices.

Evaluating LLM outputs at scale remains a bottleneck for deploying safe AI systems. This paper conducts a comprehensive empirical study of 37 conversational LLMs serving as automated judges across eight security and quality assessment tasks. The work identifies viable open-source alternatives to GPT-4o for judgment tasks while demonstrating that popular techniques like second-level judging and specialized evaluator models underperform compared to well-prompted general models.

As AI agents move from human-supervised copilots to fully autonomous infrastructure, organizations face a critical observability gap: existing systems capture computational state and execution traces but lack structured records of the agent's reasoning. This paper introduces the Agent Execution Record (AER), a schema-level primitive that captures intent, observation, and inference as first-class queryable fields at execution time. The core claim is that reasoning provenance cannot be faithfully reconstructed from state checkpoints due to fundamental non-identifiability (intent multiplicity, observation ambiguity, inference volatility). If validated, AERs would enable population-level behavioral analytics—systematic comparison of reasoning patterns across thousands of investigations, confidence calibration against expert judgments, and counterfactual regression testing via mock replay—that existing tooling achieves only through fragile post-hoc extraction.

WorldCache addresses the prohibitive latency of Diffusion Transformers (DiTs) for video world models by replacing static feature caching with a content-aware dynamical approximation framework. The method introduces motion-adaptive thresholds, saliency-weighted drift estimation, and optimal feature blending to eliminate ghosting artifacts during fast motion. Achieving 2.3× speedup on Cosmos-Predict2.5 with 99.4% quality retention, it offers a training-free path toward interactive world simulation.

This paper proposes that AI inference tokens are evolving into a standardized commodity like electricity, and designs a complete futures market framework including the "Standard Inference Token" (SIT) contract, settlement mechanisms, and margin systems. The core motivation is hedging compute cost risk for application-layer enterprises as inference displaces training as the dominant AI cost.

This work attacks the friction between smooth GELU training (ubiquitous in Transformers) and piecewise-linear deployment pipelines (quantization, formal verification). The authors parametrize GELU as $f(x;\lambda) = x\Phi(\lambda x)$ with learnable sharpness $\lambda \geq 1$, deriving a principled annealing target from an $\ell_1$ approximation bound to the Heaviside step. While the hardening protocol reduces validation-drop upon ReLU substitution in vision and tabular tasks, the 25% annealing switch is heuristic and actual downstream benefits in integer-only inference or SMT verification remain unevaluated.

SHAPE addresses unsupervised domain adaptation for medical image segmentation, where models trained on one imaging modality (e.g., MRI) degrade sharply when applied to another (e.g., CT). The core innovation shifts the paradigm from pixel-level correctness to global anatomical plausibility through a DINOv3 foundation model, a Hierarchical Feature Modulation (HFM) module for class-aware alignment, and a Hypergraph Plausibility Estimation (HPE) pipeline that validates pseudo-labels using higher-order anatomical relationships. This matters for deploying robust clinical segmentation models across diverse imaging environments without costly manual re-annotation.

PW-FouCast addresses the degradation of radar-only precipitation nowcasting at long lead times by proposing a frequency-domain fusion framework that integrates Pangu-Weather foundation model priors with radar observations. The core insight is that meteorological forecasts and radar reflectivity share similar phase structure despite differing amplitudes, enabling spectral alignment through phase-aware modulation and memory-based correction. The approach achieves quantitative improvements on standard benchmarks and offers a novel alternative to spatial fusion methods.

Traditional latent diffusion models require staging—first train a VAE tokenizer, freeze it, then train a diffusion model on top. UNITE proposes a single-stage approach where a shared "Generative Encoder" serves as both tokenizer and denoiser via weight sharing, achieving FID 1.73 on ImageNet 256×256 without adversarial losses or pretrained encoders like DINOv2.

This paper investigates how Reinforcement Learning with Verifiable Rewards (RLVR) improves LLM reasoning by focusing on the *direction* of policy updates rather than their magnitude. The authors introduce $\Delta \log p$, the signed log-probability difference between base and RLVR models, and argue it better captures reasoning-critical tokens than magnitude-based metrics like entropy or KL divergence. They validate this through token-replacement interventions and propose two practical applications: a test-time extrapolation method that amplifies the learned direction without additional training, and a training-time reweighting scheme that focuses learning on low-probability tokens.