Continual learners that keep a task-specific adapter in every ViT block accumulate parameters linearly in the number of tasks. This paper asks which blocks should specialize, showing that exhaustive placement search traces an inverted-U curve peaking at intermediate depth, while cheap internal criteria systematically favor the deep but suboptimal layers. To sidestep expensive search, the authors propose LS-B, which observes the first few tasks through a frozen fMRI encoding model of human visual cortex and commits task-specific capacity to the blocks whose brain readouts vary most across tasks relative to their stable structure.

Reinforcement learning for reachability specifications has been limited to model-based methods that explicitly estimate transition probabilities. This paper introduces Quasar, a model-free Q-learning variant that provably converges to optimal reachability policies on MDPs without non-terminal maximal end components, using only $O(|S||A|)$ memory. By replacing the missing discount factor with an intrinsic expected hitting time, the authors obtain a weighted contraction and demonstrate orders-of-magnitude sample improvements over the state of the art.

Iterative tractography must reconcile noisy local diffusion signals with globally plausible fiber pathways, yet standard deep learning models optimize only a local next-step loss. This paper tackles that gap by systematically evaluating RNN and Transformer architectures and introducing a generation-validation (GV) phase that supervises training at the streamline level via connectivity scores. It reports top scores on the ISMRM 2015 phantom and provides open-source models, while also arguing that current phantoms and in vivo evaluation tools are too limited for definitive benchmarking.

Continual reinforcement learning demands a delicate balance between plasticity (learning new tasks) and stability (not forgetting old ones). This paper argues that neuroevolution—specifically evolution strategies (ES) and genetic algorithms (GAs)—offers a compelling alternative to gradient-based policy-gradient methods, which tend to lose plasticity as tasks change. Across a broad sweep of environments and task-shift types, the authors show that ES consistently achieves the best stability-plasticity trade-off, and they propose a clear mechanism: parameter-space exploration produces wider "neighborhoods" of robust solutions that overlap across sequential tasks. The work matters because it challenges the prevailing focus on fixing gradient-based RL and instead revives a classic optimization paradigm with strong empirical and conceptual justification.

This paper addresses multi-class ransomware family classification by repurposing the Structured State Space Sequence (S4) model—designed for natural sequences—on static PE metadata. The authors reshape 71 tabular features into pseudo-sequences of length 32 and jointly train for binary detection and family classification, claiming S4 captures long-range dependencies in malware execution flow. They benchmark against CNN, LSTM, GRU, Transformer, and MLP baselines on the Ransomware Dataset 2024, reporting strong aggregate F1-scores. However, the study is limited to a single public dataset and single-run metrics, so its broader validity for IoT security remains unproven.

Spatial climate downscaling aims to recover fine-grained meteorological fields from coarse-resolution inputs, but standard pixel-level metrics such as MAE can hide critical failures in variability or spatial structure. This paper benchmarks five downscaling methods—from a residual U-Net to optimal-transport and contrastive-retrieval approaches—using five complementary criteria that disentangle pointwise accuracy, spectral fidelity, spatial alignment, distribution match, and gradient sharpness. The central finding is that no single method dominates across all measures, and method rankings systematically reverse depending on whether the evaluator rewards smooth alignment or realistic fine-scale variability. This motivates the core message: robust climate downscaling assessment must be explicitly multi-metric and application-aware.

LLMLeak demonstrates a covert channel where malware on a network-restricted device embeds secrets into URLs within fake error messages. When users paste these traces into LLM coding assistants, the model's legitimate web-fetching tool exfiltrates the secret via DNS or HTTP requests to attacker-controlled servers. The work challenges the assumption that local LLM deployment or prompt-injection defenses alone prevent data leakage to third parties.

Multimodal search agents traditionally interleave observations, freeform reasoning, and actions, but small models often waste inference budget generating lengthy reasoning traces that provide little actionable guidance. This paper introduces Selection-based Structured Reasoning (SSR), which replaces open-ended reasoning generation with a discrete selection among a small set of reusable natural-language reasoning candidates. By scoring all candidates in parallel via teacher-forced prefill with a shared KV cache, SSR cuts per-turn reasoning latency by over 90% while maintaining competitive task performance across seven benchmarks with 2B and 4B models.

Open-boundary hydrologic graphs such as river networks and coastal meshes exchange fluxes with unobserved exteriors, making long-horizon forecasting unstable because boundary forcing is unavailable at prediction time. This work proposes PRSOBG, which learns ghost-node proxies to approximate missing external forcing and applies local and global physics refiners to suppress error amplification during autoregressive rollout. Evaluated on the Danube river system and Chesapeake Bay, the framework cuts RMSE by up to 51% over a vanilla STGNN backbone with only about 8% training-time overhead.

Modern adaptive model development loops propose challengers against a moving incumbent: once a model is adopted, it becomes the reference for future candidates, meaning an unsupported replacement propagates into later hypotheses.

This paper introduces Evidence-Gated Research (EGR), a statistical adoption layer that freezes each challenger before its decision evidence is observed, constructs anytime-valid e-processes across predeclared environments under predictable routing, composes a persistent candidate e-value via an intersection–union minimum, and passes it to an online FDR controller.

The framework matters because it formally separates flexible proposal generation from the validity of state-changing adoptions and empirically demonstrates that naive development-only or single-environment validation can incur massive persistent false-discovery rates in closed-loop search.

This paper confronts the design problem of building dashboards for novel computational techniques whose real-world meaning is still contested. It introduces TopoLA, a system applying Zigzag Persistent Homology to learning-management data, and derives three principles for managing interpretive uncertainty: separating measurement from interpretation, graduated disclosure, and explicit acknowledgment of methodological limits. The argument is timely because fields from explainable AI to bioinformatics increasingly face the same gap between analytical power and interpretive consensus.

Autonomous driving system designers face a persistent dilemma: should they build a decomposed modular pipeline or train an end-to-end neural policy? This survey-style paper addresses that question by comparing modular, imitation-learning, reinforcement-learning, and hybrid architectures across interpretability, scalability, and robustness. Its central contribution is a practical twelve-criterion, four-dimensional framework intended to guide practitioners in selecting the right paradigm for a given deployment context.

World Action Models (WAMs) jointly predict visual futures and robot actions, yet they often hallucinate locally plausible trajectories that never execute the critical state transition needed to finish the task. This paper identifies "task-incomplete imagination" as a failure mode arising from the mismatch between long-horizon video pretraining and short-horizon control generation, and proposes Completion Aware Guidance (CAG), a training-free sampling method that up-weights instruction tokens already attended to by the model. Across two WAM architectures, CAG boosts success rates and cuts incomplete-imagination failures, offering a lightweight inference-time fix for a structural objective mismatch.

Sampling from discrete, unnormalized distributions without curated data is a fundamental bottleneck in physics, chemistry, and combinatorics, because the state space is exponential and Markov chains mix too slowly. This paper introduces DIG-IN (Discrete Gibbs Iterative Neural Sampler), which trains masked diffusion models via fixed-point iteration by using Gibbs soft labels—local single-site conditionals derived from energy differences—as rich supervision instead of one-hot reconstruction targets. The method tempers importance weights by effective sample size to stabilize the bootstrap, and it scales to high-dimensional systems such as $D=2500$ lattice spin models and alloy phase diagrams.

Asynchronous online learning structurally fragments learner communities, and cohort-level topological indicators like $\beta_0$ can misleadingly suggest convergence when learners simply drop out. This paper tests whether $\beta_0$ from Zigzag Persistent Homology is participation-sensitive and whether deadline-driven fragmentation–convergence cycles replicate across all 22 OULAD courses. Clarifying this matters because AI-augmented dashboards risk spreading systematically misleading cohesion narratives if they misread attrition-driven $\beta_0$ declines.

Latent world models plan by scoring action sequences with latent-space distances, yet physical success criteria demand precision finer than these models typically encode. This work proposes a training-only auxiliary loss that supervises encoder and predictor outputs against the exact success-criterion quantities via a shared linear head, then discards the head at test time so planning remains unchanged. On manipulation tasks the approach yields statistically significant absolute gains of 3-5%, with the key insight that a task's success criterion itself should dictate what the latent state must retain.

This paper studies nonlinear in-context learning via single-index models, comparing a kernel learner (fixed spherical-harmonic features followed by linear attention) against a feature learner (linear attention on raw inputs followed by a learned nonlinear readout). Using the replica method, the authors derive predictions for memorization and generalization errors across varying pretraining data, task diversity, and context lengths, and validate them numerically over a broad parameter range. The analysis reveals phase diagrams for architectural advantage and qualitatively different context-length scalings: the kernel learner needs $L = \Theta(d^k)$ to recover degree-$k$ harmonics, while the feature learner exploits all teacher components at $L = \Theta(d)$.

This work tackles algorithmic mathematical reasoning, where final-answer rewards fail to expose intermediate errors and successful code execution does not guarantee correctness. The proposed Function-Structured Graph Reinforcement Learning (FSG-RL) framework connects typed subproblem graphs to executable Python nodes, then trains a policy with supervised fine-tuning followed by Group Relative Policy Optimization (GRPO) using multi-verifier rewards and span-level credit assignment. The core claim is that verifying interface compliance, local functional behavior, and final consistency together pushes a 9B parameter model past a 27B API baseline, while also providing statistically significant gains over a strong SFT stage.

Instead of hand-designing or training a neural network for time-series anomaly detection, this paper outsources the job to an LLM-driven autonomous research loop. The agent iteratively edits a short NumPy program, scoring candidates against synthetic anomalies in a held-out training slice, until it discovers compact spectral-Gaussian novelty detectors. The resulting univariate and multivariate programs reportedly top the TSB-AD benchmark while running on a single CPU, offering an unusual blend of accuracy, speed, and interpretability.

QK-Wanda attacks a blind spot in activation-aware LLM pruning: Wanda scores query and key weights independently even though their dot products jointly determine attention. The paper derives closed-form single-deletion costs under a coupled QK reconstruction objective, letting queries and keys share a pruning budget without gradients or weight updates. Across 15 models up to 72B parameters, the method cuts local QK reconstruction error by roughly half, yet downstream gains are uneven—Llama 2 and Qwen2.5 improve while Llama 3 degrades—highlighting that local reconstruction fidelity is an imperfect proxy for end-to-end model quality.