Industrial tabular data often suffers from scarcity, class imbalance, and covariate shift between training and deployment. This paper defines the Augmented and Weighted Learning under Covariate Shift (AWL-CS) problem and proposes IGDPR, a framework that steers latent diffusion with invariant potentials and aggregates sample reliability through prototype-based reweighting. The core idea is to synthesize data that aligns with stable decision boundaries rather than outdated source correlations, while filtering out validation noise via structural clusters.

MemFit tackles the high cost of LLM-driven memory consolidation by keeping the LLM out of the write path entirely. It stores each conversational turn verbatim in an append-only store, indexes via BM25 and dense embeddings, and retrieves through a multi-stage pipeline combining lexical matching, dense similarity, pseudo-relevance feedback, and cross-encoder reranking. The work claims state-of-the-art accuracy on three long-term conversational benchmarks while cutting memory construction cost and latency several-fold.

Conceptual database design is difficult to tutor at scale because students iterate on entity–relationship diagrams and need feedback timed to each revision. This paper proposes an educator-guided LLM agent that separates hidden diagnosis of ERD errors from a pedagogical workflow controlling what is disclosed and when. A four-stage scaffold—concept check, guided application, low-detail feedback, and Q&A—was deployed across 383 feedback episodes. The work matters because it trades unrestricted LLM dialogue for educator-governed, artifact-grounded scaffolding that preserves student agency.

Offline goal-conditioned RL (GCRL) struggles with long-horizon tasks because discounting shrinks value differences between distant states until they fall below function approximation error. This work introduces GITA, which learns a single value network conditioned on a temporal abstraction stride $k$ and trains one high-level policy by aggregating advantage-weighted regression updates across multiple scales. By implicitly letting the best-suited scale dominate per state-goal pair, GITA raises average success rate on OGBench by 25 percentage points over HIQL without requiring a problem-specific fixed $k$.

RelationVGGT tackles 3D spatial relation segmentation: given multi-view images, a reference subject mask, and a text relation (e.g., "resting on"), it segments the target object across unposed views without per-scene optimization. The core idea is to replace expensive volumetric relation search with per-pixel relation prediction by fusing semantic DINOv2 features with geometry-aware Pi3 tokens through a subject-conditioned relation transformer. This matters because it moves holistic scene understanding beyond isolated object recognition toward reasoning about how objects are spatially arranged, a capability critical for embodied agents and spatial QA.

This paper studies masked diffusion multimodal language models and shows that answer candidates often stabilize retrospectively long before their positions are permanently committed or the rationale-side canvas is filled. The authors introduce two clocks—token stabilization $T_{\mathrm{stab}}$ and commitment $T_{\mathrm{commit}}$—and measure how much of the rationale region $\mathcal{R}$ remains unwritten at stabilization. They pair this with matched-canvas visual-sensitivity ablations and a coverage-accuracy decomposition to explain why prompting changes affect overall accuracy. The analysis matters because it reveals that observing the final rationale alone is insufficient to understand when or how a diffusion model decides its answer.

This paper attacks a growing bottleneck in text-to-image training: how to audit billion-scale recaptioned corpora before spending millions of GPU-hours on downstream training. It proposes a matched-budget audit framework that fixes a text budget $B=64$ and profiles any supervision distribution $D_{\pi,V_c,C}$ across five axes—prompt-side coverage, image-conditioned faithfulness, and caption-surface health—using controllable basic units (CBUs) as a common claim currency. The work matters because it disentangles corpus quality from model-training confounders and releases both a ~490M-caption corpus and a reusable artifact bundle.

Robotic foundation models currently rely on multi-step flow matching to generate actions, which creates a severe inference bottleneck for real-time control. This paper proposes Kinematic MeanFlow (K-MF), a one-step action policy that reformulates MeanFlow by decoupling the time derivative across two sub-intervals to accommodate the unique late-stage surge and widening spread of local acceleration in robotic velocity fields. By introducing an intermediate anchor point and a learnable partition strategy, K-MF cuts action-head latency by up to 74% while matching or exceeding the success rates of multi-step baselines across manipulation and navigation tasks.

This paper studies adversarial attacks under a shared global mean-$\ell_1$ budget with per-coordinate magnitude caps. The core idea is to add a centered, clean-gradient importance prior to the attack objective to softly guide perturbation mass toward model-sensitive regions without changing the feasible set. This matters because, under local capacity constraints, simply finding a useful gradient direction is insufficient—attackers must also decide where to spend a budget that cannot be arbitrarily concentrated. Empirically, the mechanism raises attack success by 2.52–17.70 percentage points over matched baselines across ten robust model–dataset configurations.

Vision-language models are increasingly used as backbones for all-in-one low-level image restoration, yet how they internally organize pixel-level perceptual representations remains unclear. This paper introduces GeoSim, a unified four-level framework that probes global similarity, local geometry, sparse feature decomposition, and topological structure across 24 tasks in autoregressive and diffusion transformer VLMs. By revealing that task representations are mechanism-centric and that cross-model agreement is primarily global rather than local, the study offers an actionable interpretability lens for designing transferable unified restoration systems.

Predicting how complex scenes evolve is essential for intelligent systems, yet dense video generation expends enormous compute on appearance rather than dynamics. This paper introduces Myriad, an autoregressive diffusion model that predicts future motion via sparse point trajectories, explicitly avoiding the 'visual tax' of pixel-level generation. By modeling step-wise uncertainty accumulation through flow matching and utilizing fused transformer blocks, the method achieves throughput of 2200 samples/min compared to less than 1 for video models, while matching or exceeding their predictive accuracy on motion-focused benchmarks.

Neural Computers (NCs) propose a new machine form where computation, memory, and I/O are unified inside a learned latent runtime state rather than separated as in conventional computers or external as in agents. This work instantiates early NC prototypes as video models that roll out terminal and desktop interfaces from text, pixels, and actions—showing that basic I/O alignment and short-horizon control are learnable without privileged program state. The results demonstrate early runtime primitives but also highlight that symbolic stability, routine reuse, and runtime governance remain unsolved on the long path toward the envisioned Completely Neural Computer (CNC).

This paper introduces Vision Transformer (ViT), which applies a standard Transformer encoder directly to sequences of image patches for image classification. The core insight is that convolutional inductive biases (locality and translation equivariance) are unnecessary when models are pre-trained at sufficient scale—specifically on datasets containing 14M to 300M images. When transferred to downstream benchmarks, ViT matches or exceeds state-of-the-art CNNs while requiring substantially less computational resources to pre-train.

This paper addresses the static nature of Large Language Models that prevents dynamic adaptation to streaming contexts. The authors introduce In-Place Test-Time Training, which repurposes existing MLP down-projection matrices as “fast weights” that update during inference via a Next-Token Prediction (NTP)-aligned objective. Unlike prior TTT methods that require architectural changes, this approach enables “drop-in” enhancement of pretrained models without retraining from scratch.

GISTBench evaluates whether LLMs can accurately extract user interests from behavioral interaction histories in recommendation systems. Unlike traditional benchmarks that optimize for item prediction accuracy, it verifies if predicted interests are actually grounded in engagement signals using two novel metrics: Interest Groundedness ($IG$) and Interest Specificity ($IS$). The authors find that current LLMs struggle primarily with recall—discovering all verifiable interests—rather than hallucination, revealing critical bottlenecks in evidence counting across heterogeneous signal types.

Recursive Language Models (RLMs) tackle the long-context problem by treating prompts as external environment variables that an LLM can programmatically manipulate through a REPL. Instead of feeding long prompts directly into the neural network, RLMs use symbolic code execution to decompose, filter, and recursively invoke sub-models over prompt snippets. This allows processing inputs up to 10M+ tokens—two orders of magnitude beyond typical context windows—while maintaining strong performance on complex aggregation tasks.

UniMotion addresses the fragmentation in human motion modeling by unifying motion, text, and RGB understanding/generation within a single 1.5B parameter architecture. Unlike prior work relying on discrete tokenization or handling only partial modality subsets, it treats motion as a continuous first-class modality via a Cross-Modal Aligned Motion VAE (CMA-VAE). The framework introduces Dual-Posterior KL Alignment to distill visual semantics into motion representations without requiring images at inference, and Latent Reconstruction Alignment to bootstrap the motion pathway through dense self-supervision before sparse text calibration.

ThinkJEPA addresses the limitation of JEPA-style latent world models that rely on short, densely sampled windows, which bias predictions toward local dynamics while missing long-horizon semantics. The paper proposes a dual-temporal architecture combining a dense-frame V-JEPA branch for fine-grained motion with a sparsely sampled VLM "thinker" branch that provides semantic guidance via multi-layer feature pyramids. This matters because it attempts to marry the physical consistency of latent world models with the general knowledge of vision-language models for robust trajectory forecasting.

3D-Layout-R1 tackles language-guided 3D spatial editing by training LLMs/VLMs to perform structured reasoning over explicit scene graphs. Instead of free-form chains-of-thought, the model outputs JSON graph edits that iteratively transform object poses and relations, combined with GRPO-based RL using dense 3D IoU and collision-aware rewards. This approach yields measurable gains in layout accuracy while maintaining interpretability across sorting, spatial alignment, and room-editing tasks.

TiCo tackles a critical gap in spoken dialogue models: the inability to control response duration, which is essential for time-constrained scenarios like driving assistants or emergency healthcare. Unlike text length control, speech duration depends on complex factors including phonetics, prosody, and speaking rate. The paper proposes Spoken Time Markers (STMs)—special tokens like <15.0 seconds> inserted during generation—to enable real-time temporal awareness. Using a two-stage post-training framework (self-generated supervised fine-tuning followed by reinforcement learning with verifiable rewards), TiCo equips models to estimate elapsed time and adjust content dynamically to meet target durations.