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Unmanned aerial vehicle swarms operating with independent edge-deployed language models face a dual challenge: reasoning context degrades as interaction histories grow, and indiscriminate information sharing wastes limited bandwidth. This paper proposes a distributed architecture in which each UAV maintains structured atomic notes in bounded core, local, and peer-specific memory queues, coupled with a deterministic Interest-Aware Gossip Engine that forwards messages based on semantic novelty and recency. The work matters because it targets the practical gap between centralized LLM coordination, which introduces bottlenecks and single points of failure, and fully decentralized control, which risks context degradation and communication collapse.
Federated learning on edge devices must simultaneously handle varying resource budgets and domain-shifted local data. FedSAP frames this coupled challenge as a structured pruning problem where channels are assigned to Global, Private, or Dropped states. By inferring pseudo-domains from shallow gradient similarity and allocating layer-wise budgets adaptively, the method improves global mean accuracy under aggressive heterogeneous compression.
Explaining why neural networks generalize and how training unfolds remains a central challenge in deep learning. This paper introduces Magnitude Potential (MP), a point-set coverage measure from metric space theory, and defines the MP ratio $\mathcal{R}^t_{X,X_c}(z)$ as the within-class potential divided by the full-dataset potential at the logit layer. The authors argue this single geometric score predicts per-sample memorization, detects decision-boundary phase transitions, and foreshadows grokking without requiring model retraining or architectural assumptions.
LITL addresses the problem of recovering target Langevin dynamics when only biased or static source samples are available, complemented by black-box evaluative feedback. The core idea is to learn the dominant spectral subspace of the target infinitesimal generator and its projected drift via importance-weighted Dirichlet representation learning, explicitly avoiding target trajectories, gradients, or retraining. This matters for molecular dynamics, auditing generative models, and post-hoc steering of learned representations under distribution shift.
Deep learning audio research hides a massive energy footprint behind the final training run. This paper develops a methodology to estimate the full development-phase cost of audio DL projects by mining job logs from the Grid5000 shared computing platform. By tracing thousands of individual jobs across four representative projects, the authors show that prototyping, debugging, and hyper-parameter search can consume $3 imes$ to $256 imes$ more energy than training the reported best models, calling for systematic lifecycle reporting.
Hob-VL introduces a diagnostic benchmark for visually grounded Boolean reasoning, asking vision-language models to evaluate ten-literal Boolean combinations over images and to identify objects satisfying Boolean descriptions. The core idea is to pair each expression with a mechanically verified De Morgan equivalent and to test both symbolic and natural-language presentations, isolating compositional reasoning from surface memorization. It matters because real-world visual queries—warehouse checks, agricultural monitoring, medical imaging—depend on reliably combining multiple visual facts under logical constraints.
Decision models that score a variable-sized set of candidate actions from a serialized sequence are vulnerable to permutation sensitivity because standard causal attention lets later candidates attend to earlier ones and uses absolute positions. The paper proposes candidate-independent block-causal attention (CI), which blocks cross-candidate information flow and resets per-candidate positions so that each candidate is scored based only on the shared prefix and its own tokens. This matters for System 1 components inside generative systems, where runtime-dependent candidate order should not become a latent feature; the authors show that CI cuts flip rates by an order of magnitude across three backbones while preserving competitive accuracy.
Iterative policy improvement is central to practical RL, but approximate value estimates make greedification unreliable. This paper defines optimal policy improvement restricted to a state subset as solving an induced MDP, and derives $\mathcal{I}_{\text{opt}}$, a novel greedification operator that formulates approximate evaluation as probabilistic decision-making under uncertainty. It weights advantages by action-dependent uncertainty, generalizes popular regularized operators, and improves aggregate performance across model-based and model-free, discrete and continuous, and online and offline settings.
This paper extends one-step generative modeling to finite discrete spaces by replacing Euclidean drift with a discrete Wasserstein gradient flow. Using Maas geometry on a reversible Markov kernel, it defines a relative-KL flow that is realized at the particle level by Markov jumps and amortized into a neural generator. The result is a theoretically grounded analogue of continuous drifting that preserves one-step inference. The work is validated only on a small controlled torus, leaving real-world scalability as open future work.
AURAL tackles the tension between reasoning quality and low latency in speech language models by replacing explicit chain-of-thought with adaptive latent reasoning. Instead of generating slow text intermediates, the model predicts chunks of continuous latent states drawn from a Gaussian mixture, cutting time to the first token by more than 11x while largely matching explicit CoT accuracy across two backbones. The work also constructs AuralReason-683K, a bilingual corpus of 683K utterances with concise reasoning traces used to initialize latent training.
Tabular foundation models (TFMs) achieve strong zero-shot performance through in-context learning, but repeatedly encoding the labeled context at inference time is expensive. This paper studies how to distill TFMs into lightweight, dataset-specific students. It systematically examines two design choices—how to construct teacher supervision (full-context versus out-of-fold, mixed with labels or not) and whether cheap synthetic queries improve distillation—and derives a simple recipe that yields median inference speedups of $3.0\times$–$21.6\times$ while recovering roughly two-thirds of the teacher's accuracy advantage over a standard supervised student.
This paper tackles deterministic multi-impulse interplanetary rendezvous by embedding classical astrodynamics inside a reinforcement-learning policy. The proposed Reachability Analysis-Informed RL (RARL) framework maps bounded velocity perturbations into a local ellipsoidal waypoint set via the first-order position-from-velocity sensitivity matrix; the policy selects a waypoint, and a Lambert solver reconstructs the dynamically consistent impulsive arc. The core contribution is a physically structured action interface that supports both near-optimal nominal transfers and policy reuse across a family of dispersed departure states.
Bridging quantum-mechanical accuracy with mesoscopic length and time scales remains a grand challenge in materials modeling. This paper develops an ab initio phase-field framework that derives the mesoscopic evolution equation from molecular dynamics via Mori–Zwanzig projection, leaving only the free energy and mobility to be learned by neural networks from short machine-learning-interatomic-potential trajectories. The result is a data-efficient approach that propagates ab initio accuracy to scales far beyond direct atomistic simulation, demonstrated here on iron–boron melts and hydrogen–helium mixtures under planetary conditions.
Neuro-symbolic predictors require hand-crafted logical constraints, which limits adoption by practitioners without formal-methods expertise. This paper asks whether LLMs can translate natural language into propositional constraints that plug into NeSy predictors, and introduces auto-nesy-bench—an 18-task benchmark that measures both formalization fidelity and downstream accuracy. The core finding is that frontier LLMs can generate formulas approaching expert quality, and when the formulas are syntactically valid they often preserve downstream predictor performance, suggesting a plausible path toward making reliable NeSy AI more accessible.
Most LLM benchmarks treat generalization as aggregate accuracy on fixed prompts, which can hide per-example fragility when the same user intent is rephrased. This paper reframes generalization as behavioral stability across semantically equivalent input variations and proposes SAGO, a per-prompt, multi-axis framework that tracks changes in generation consistency, internal activations, confidence/uncertainty, and response mirroring. By evaluating eleven open- and closed-source models across six datasets and three variation families, the authors show that instability is heterogeneous, axis-specific, and not resolved by scale, challenging the assumption that high benchmark scores imply reliable generalization.
Standard gradient flow never reaches zero risk in finite time, while naive finite-time modifications drive the weights to the optimum with infinite speed. This paper formulates and solves a regularized least-time problem for the dissipation schedule $u(e)$ that controls the risk clock via $\dot{e}=-u(e)$. By minimizing $\int_0^{e_0}(\frac{\lambda}{2}|u'|^2+1/u)de$ subject to $u(0)=0$, the authors prove the optimal schedule is a cycloid and that it lands with the universal cusp $u^*(e)\sim c e^{2/3}$, placing the dynamics in the finite-time Hölder regime with vanishing weight speed.
Train–validation separation is the widening empirical gap between training and held-out accuracy during fine-tuning of pretrained models. This paper proposes a dynamic structural explanation: optimization progressively shifts from broadly reusable shared features that transfer well toward narrower, example-specific residual patterns that fit training data but generalize poorly. The authors formalize this intuition with a local mixture model and introduce fixed training-probe measurements of normalized absolute gradient-allocation dispersion, $D_{\mathrm{den},t}$, to observe this internal evolution without using validation data. Empirical tests span a controlled ResMLP hierarchy simulation, a 90-run NLP cohort across three backbones, and a 40-epoch ResNet-18 vision study.
Muon trains hidden matrices with a spectral-norm steepest-descent update but falls back to AdamW for embeddings, output heads, and auxiliary parameters. This paper focuses on the tied vocabulary table—one tensor aliased between sparse token lookup and dense softmax classification—and proposes AF-Muon, an AdamW-free Muon variant that assigns a finite-cap clipped-row linear-minimization oracle to the tied table, an RMS-normalized update to one-dimensional parameters, and the standard spectral update to hidden matrices. By eliminating all second-moment state, AF-Muon cuts optimizer-state memory by roughly 20% and improves mean validation loss across nine tied-token settings spanning text, image, and protein modeling.
CLEAR proposes a lightweight, task-agnostic post-hoc recalibrator for evidential deep learning that improves OOD and adversarial robustness without retraining the base predictor. Using held-out calibration data, it models group-conditioned latent geometry and measures conflict among perturbed latent views to detect unsupported evidence, selectively down-weighting overconfident predictions while preserving the original prediction. The method achieves strong gains across classification, regression, and object detection, though it trades off some in-distribution calibration and coverage for improved shift detection.
SupraTITO tackles the costly problem of predicting how peptide sequences drive supramolecular self-assembly. It learns transferable implicit transfer operators conditioned on sequence, topology, and periodic boundary geometry to generatively propagate many-molecule configurations over long physical lags. Evaluated on 400 coarse-grained dipeptide systems, it generalizes to held-out sequences and unseen concentrations while preserving molecular integrity through recursive rollouts. The work extends generative molecular dynamics from single-molecule conformations to collective periodic assembly.