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.

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.

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.

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.

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.

Understanding when neural-network loss landscapes contain spurious local minima is a foundational problem in deep learning. This paper studies shallow bias-free ReLU networks in a Gaussian teacher-student setting and proves that a learned linear skip connection eliminates all spurious local minima for planar teacher features with positive output weights once the student is at least as wide as the teacher. In contrast, without the skip, spurious minima can persist at every width—a striking separation between architectural bias and overparameterization.

Existing methods for structured imitation learning rely on extensive human input or static LLM knowledge that can conflict with expert demonstrations. This paper proposes a closed-loop framework that treats policy development like software debugging: an LLM writes diagnostic analysis code to test semantic latents from tabular rollouts, identifies structure-demonstration misalignment, and iteratively edits the symbolic policy structure before re-fitting continuous parameters through imitation learning. The work matters because it automates refinement of interpretable policies and shows on two control tasks that it can outperform zero-shot LLM structures while using far less subsequent RL compute.

Primal-dual hybrid gradient methods can solve large linear programs on GPUs, but their speed depends on delicately coordinated step sizes, restarts, and acceleration heuristics. GALLOP replaces these hand-tuned rules with a reinforcement learning policy that jointly picks continuous parameters and discrete restart decisions inside a generalized PDHG update. The approach is notable because the learned policy is dimension-agnostic and reportedly generalizes from small training instances to LPs hundreds of times larger without retraining.

Generative recommenders represent items as short discrete Semantic IDs (SIDs) that are decoded autoregressively, yet most prior work derives these codes by quantising isolated content embeddings. This paper argues that SID construction is fundamentally a recursive clustering problem over a collaborative item graph, and proposes GrIS, a framework that separates graph construction from hierarchical partition to balance semantic and behavioural signals. By exposing these two axes, the authors show that content-only tokenisers are merely empty-graph instances of a broader design space, and they demonstrate new instantiations that improve SID quality for downstream recommendation.

This paper asks whether a fixed offline corpus generated by a behaviour policy can ever distinguish two candidate Regular Decision Processes (RDPs). It proves that when two candidates are $\pi$-observationally equivalent, their posterior odds remain frozen at the prior for any sample size (Theorem 6), gives an exact measure-zero characterization via the product-automaton blame language (Theorem 12), and derives PEC, a linear-time algorithm that certifies equivalence and names the missing experiment. The work matters because existing PAC offline guarantees for RDPs assume a distinguishability condition that was previously impossible to verify.

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.

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.

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.

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.

This survey systematises the sprawling literature at the intersection of optimal transport and reinforcement learning through a four-question taxonomy: what role OT plays, which distributions are compared, which formulation is used, and how temporal structure is handled. By foregrounding the geometry-aware nature of transport-based discrepancies, the authors explain why OT can outperform $f$-divergences when learner and expert distributions overlap weakly—a common scenario in imitation learning, offline RL and robust control. The result is a broad methodological map spanning occupancy matching, trust regions, world-model learning and multi-agent settings.

The paper argues that attributing model behavior to synthetic training data requires first recording how each item was generated, because a waveform-label pair alone erases upstream history. The authors introduce a structured provenance substrate that binds scenario seeds, generated content, audio waveforms, fact checklists, quality signals, and review lineage into an immutable manifest. They instantiate this in a five-pass audit of 113 synthetic Japanese care-handoff speech assets totaling 1.552 hours, finding that while versioned, seed-disjoint manifests support some downstream attribution research, exact upstream tracing is blocked by floating generator aliases and missing per-item TTS stamps. The work reframes data attribution as a two-stage problem: establish a typed, versioned generation path $z \longrightarrow x \longrightarrow m \longrightarrow r \longrightarrow b$ before attempting causal or contributive claims.