RheOPT addresses the data bottleneck in non-Newtonian flow simulation by pretraining a transformer-based neural operator on inexpensive Newtonian simulations before fine-tuning on a handful of complex-fluid examples. The core hypothesis is that geometry-conditioned momentum transport—not the Newtonian constitutive law itself—transfers across the rheological boundary. In geometry-matched settings, the authors show that a model fine-tuned on as few as four non-Newtonian samples can match or exceed one trained from scratch on sixteen.

Whole-body vision-language-action (VLA) policies for humanoid loco-manipulation usually condition only on the current view, so they fail when brief object cues vanish before the robot must act. This paper proposes WholeBodyMem, which uses a frozen SAM3 to track task objects, detects object events (appear, disappear, move, change shape) to causally select up to $B_{\max}=7$ key frames, and feeds those native tokens together with mask-pooled object tokens into a flow-matching action expert via cross-attention and FiLM. On the new HuMeGo benchmark and standard SIMPLE tasks, the method raises the $\Psi_0$ base policy's simulated success from $29.1\%$ to $72.6\%$ and real-world Unitree G1 success from $36.7\%$ to $88.3\%$, while also improving reactive tasks to $86.9\%$.

High-quality presentation generation remains labor-intensive, and existing agentic frameworks rely mostly on foundation-model priors without task-specific training. This paper presents a unified training pipeline integrating cross-validated data synthesis, guided teacher enhancement for supervised fine-tuning, and verifiable aesthetic rewards via GRPO-based reinforcement learning. Experiments on PresentBench and GDPVal show that the pipeline improves both content quality and visual design, with compact models surpassing larger zero-shot baselines.

The paper studies whether facts deleted from knowledge-graph embeddings stay deleted under ordinary continued training. It shows that standard approximate unlearners fail because entity embeddings are shared between forgotten and new facts, and proposes Kernel-Anticipated Unlearning (KAU), which uses the tangent kernel to predict and counteract this relearning inside the forgetting objective. This matters for the right to be forgotten in search, recommendation, and question-answering systems built on evolving knowledge graphs.

Standard computer-model calibration focuses on parameter posteriors, not the predictive accuracy of the calibrated simulator. The authors propose a two-layer framework: a flow-matching model amortizes posterior inference, then conformal risk control tunes parameter sets so the simulator pushforward hits a target miscoverage on synthetic calibration data. This targets the quantity that actually matters downstream—predictive coverage—while exploiting amortization to avoid expensive per-observation MCMC. Experiments on synthetic and real epidemiological data show roughly 5× sharper bands than KOH–MCMC with near-nominal coverage.

The paper tackles occupational activity recognition for construction workers using wearable IMUs. It shows that headline ~99% accuracies on the VTT-ConIoT benchmark collapse to ~77% mean under strict leave-one-subject-out evaluation, with a 27pp per-subject spread. The work systematically maps how sensor placement, window length, classifier choice, augmentation, and personalised calibration affect true subject-independent performance.

Masked diffusion models freeze tokens once revealed, making decoding errors irreversible and bounding compute by sequence length. This paper reformulates MDM inference as a Markov chain over the clean sequence space via a remask-denoise kernel, derives a KL contraction bound that disentangles timestep schedule from model error, and proposes Random Remask-Denoise (R2D) inference and Trajectory Correction Training (TCT). The result is a reward-free, finetuning-free test-time scaling procedure that reports near-log-linear accuracy gains on hard Sudoku and 3-SAT and modest improvements on a coding benchmark.

Large language models can pick up hidden behavioral preferences—termed subliminal learning—when trained on data generated by other models. This paper investigates whether such transfer occurs in pretrained base models under hard-label and on-policy soft-label distillation, testing across different initializations, data orders, corpora, and architectures. By tracing how the abilities to transmit and receive a trait develop separately during pretraining, the authors challenge the notion that shared model origins are a prerequisite for unwanted preference transfer.

The paper attacks a structural restriction in the Bayesian Learning Rule (BLR): its reliance on KL geometry ties it to fixed-support exponential families, ruling out posteriors with adaptive tails or bounded support. The authors show that mixed-space Bregman discrepancies—specifically Fenchel-Young discrepancies—generalize the BLR while preserving its efficient additive canonical-coordinate updates. This matters because it yields a principled framework for scalable variational optimizers, such as $q$-VON, that use heavy-tailed or bounded-support $q$-Gaussian posteriors and can be implemented by modifying only a few constants in existing code.

GM-GReFEL attacks two coupled problems in dynamic facial expression recognition (DFER): the quadratic cost of self-attention in long clips and noisy crowdsourced labels. It replaces a spatiotemporal Vision Transformer with a Modulated GroupMamba backbone that scans six channel groups—four spatially and two temporally—at linear cost $\mathcal{O}(N)$, re-mixing them via a Channel Affinity Modulation gate. A geometry-aware reliability module blends the classifier output with trainable facial-landmark anchors, weighted by per-sample normalized entropy, to fall back on anatomy when the prediction is uncertain. The authors report best-fold results within 0.3 points of the top method on DFEW and ahead on MAFW, while openly noting that five-fold means are more mixed.

This paper tackles certified neural approximation of the Dirichlet Monge--Amp\`ere equation, a fully nonlinear PDE whose solutions must be convex. The authors study implicit-depth equilibrium networks built from softplus units with nonnegative weights, proving that a resolvent--curvature cancellation improves the Hessian growth bound from $(1-q)^{-3}$ to the sharp exponent $(1-q)^{-2}$. They then derive residual-based a posteriori estimates and validate them with interval-arithmetic certificates on trained models, yielding rigorous uniform evaluation guarantees for implicit neural PDE solvers.

Language models fail to learn shortest-path search on unseen symbolic graphs as graphs scale beyond about 50 nodes, a wall this paper diagnoses and breaks through. The authors show that next-token prediction provides too little and too skewed supervision, while standard tokenization introduces routing and permutation artifacts. By combining multi-token prediction—which densely supervises all sub-paths and implicitly mixes hop lengths—with structural annotation (STAN) and a node list (+NN), they stably train small transformers on graphs up to 125 nodes with far lower variance. The work makes a strong case that objective design and input formatting matter more than model size for latent multi-hop reasoning.

This survey argues that the central bottleneck for advancing remote sensing vision-language models has shifted from architectural innovation to data curation. It introduces GeoChef, an end-to-end framework that maps dataset taxonomy, construction pipelines, fine-tuning recipes, and evaluation protocols onto a culinary metaphor. By cataloging over 120 datasets and launching a community-driven warehouse, the authors aim to turn small academic teams into proactive data chefs rather than passive consumers of foundation models.

LLM agents with persistent external memory often leave derived artifacts—such as summaries—intact after a user deletes a source record, creating a gap between storage-level deletion and behavioral privacy. AfterDeleteBench introduces a paired behavioral audit that compares an exposed-then-deleted agent against a never-exposed twin to attribute post-deletion secret recovery to the original exposure. The paper shows that exact reconstruction can persist even when no verbatim copy of the secret survives, and that the response interface itself is part of the measurement system, making the benchmark a useful narrow probe into a broader agent-memory privacy problem.

Collective ordering in correlated electron systems produces emergent, non-Markovian dynamics that cannot be closed at the level of coarse-grained observables alone. This paper proposes a graph-recurrent neural network (GRNN) that fuses gated recurrent memory with graph message passing to learn finite-time evolution maps directly from trajectories, bypassing repeated microscopic force evaluations. Site-resolved hidden states encode unresolved history and communicate across neighboring sites via graph-coupled hidden messages, giving a scalable, translationally invariant update rule. The authors demonstrate the approach on charge-density-wave domain coarsening in the semiclassical Holstein model, showing quantitative agreement with kernel-polynomial-method reference dynamics.

The paper challenges the widespread assumption that reinforcement learning (RL) invents the reflection marker "wait" in reasoning models. Using historical-linguistics-inspired "grammaticalization" tracking, the authors trace the word through 120 open-model checkpoints and find it almost always arrives from training data—mid-training, fine-tuning on reasoning traces, or reasoning-rich pretraining—while zero-RL alone rarely coins it.

Time-series dataset similarity is task-dependent: no single metric reliably predicts downstream transfer error across models and protocols. This paper proposes DISC, a learnable aggregation framework that calibrates a base mixture of standardized similarity metrics with a rank-based prior gate. On a shared 46-dataset split, DISC raises the average source-wise Pearson correlation with forecasting error by about 10% over the best single metric, supporting the claim that learned integration is a stronger proxy for transfer behavior than manual metric selection.

On-policy self-distillation improves reasoning by training a model on its own trajectories with dense token-level guidance from a reference-conditioned teacher. The authors identify a critical flaw: because any single reference is just one valid realization of reasoning, conditioning the teacher on the full reference makes the target distribution overly sensitive to that specific solution's wording, ordering, and path. They propose Monte Carlo Privileged Distillation (MCPD), which averages the teacher's next-token distributions over multiple randomly masked partial views of the same reference to produce a more stable marginal target. Across math and science benchmarks, MCPD consistently outperforms standard full-reference distillation.

This paper asks whether robot learning must rely on embodied data by training in-context action predictors entirely on procedurally generated, non-robotic mathematical abstractions—tabular MDPs, contextual bandits, and linear dynamical systems. Using a Prior-Data Fitted Network framework, the authors show that these synthetic control priors achieve non-trivial zero-shot action prediction on held-out simulated and real-world robots, including manipulators, wheeled vehicles, quadrupeds, and aerial platforms. The work matters because it cheaply decouples structural prior acquisition from expensive physical data collection, offering a scalable complement to embodied foundation model pipelines.

This paper proposes replacing the physics-based BSIM-CMG compact model with tiny MLPs (8–16 neurons) to predict drain current and charges for a 3 nm nanosheet, then embedding them in Verilog-A to perform a full standard cell library characterization and subsequent static timing analysis on industrial-scale circuits. The authors claim sub-percent delay and power errors alongside up to 8× faster library characterization, positioning the approach as a viable alternative to equation-based transistor models in modern EDA flows.