Agents are increasingly expected to negotiate and collaborate across organizational boundaries where each party maintains private tools, documents, and incentives. This paper studies how agent-to-agent (A2A) systems fail under these conditions, introducing A2A²-Failures, a taxonomy of 20 failure modes derived from over 2,500 sandboxed runs spanning cooperative to competitive scenarios. Its central thesis is that evaluating only final outcomes masks critical behavioral breakdowns—such as unverified peer claims, private data leakage, and scope distortion—making per-agent principal-alignment auditing essential for safe cross-boundary deployment.

LLM-jp-4 is a family of Japanese sovereign language models—an 8B dense Transformer and a 32B-A3B mixture-of-experts—trained from scratch on roughly 12 trillion tokens with an explicit emphasis on transparency, legal reproducibility, and practical deployment. The authors impose strict open-source selection criteria on data, external models, and software, and they document every stage from tokenizer design through pre-training, mid-training, and post-training. The work aims to provide a publicly verifiable reference for sovereign AI development in Japan.

Minimax optimization underlies adversarial training, GANs, and AUC maximization, yet biased stochastic gradient descent-ascent methods—zeroth-order and clipped variants—have had no generalization theory. This paper develops the first stability framework for biased SGDA in convex-concave smooth problems, replacing the standard bounded-gradient assumption with a bounded second-moment condition. It derives generalized Lipschitz-type stability bounds and proves that zeroth-order and clipped SGDA can achieve the minimax-optimal $O(1/\sqrt{n})$ excess risk.

This paper claims the first global joint lower bound for zeroth-order nonconvex optimization under unrestricted spatial queries and sample reuse. The core idea is to force any algorithm to advance through $T\asymp\min\{\Delta H,L_0^2\}/\epsilon^2$ hidden geometric stages and to prove that each fresh stage requires $\Omega(dL_0^2/\epsilon^2)$ adaptive scalar evaluations, yielding an overall $\Omega(dL_0^2\min\{\Delta H,L_0^2\}/\epsilon^4)$ bound. If correct, this would settle the open problem posed by Kornowski and Shamir (2024), though the manuscript is dated 14 October 2026, cites several future works, and explicitly states that its mathematics was primarily AI-generated.

In medical and crowdsourced annotation, the absence of clean ground truth biases standard expected calibration error (ECE) evaluation because annotator errors confound predictor miscalibration. This paper proposes Noise Corrected ECE (NC-ECE), which inverts annotator-specific class-conditional noise channels to recover clean-label ECE, and provides methods to estimate those channels from repeated annotations alone. It then derives post-hoc recalibration algorithms with either clean-risk or calibration guarantees. The work is directly motivated by settings such as medical imaging, where expert disagreement is common and reliable uncertainty assessment is essential.

Density functional theory (DFT) is accurate but expensive, while pretrained machine-learned interatomic potentials (MLIPs) are fast yet erratically wrong across configurations. This paper frames the decision of when to trust an MLIP and when to fall back to DFT as a risk-controlled selection problem solved with conformal risk control (CRC). A lightweight, chemistry-informed descriptor feeds a small neural network that estimates MLIP-DFT disagreement in energy and forces; CRC then calibrates a threshold so the expected retained error stays below a user-chosen fraction $\alpha$ of the fitting-set mean. The method cuts DFT costs—by 7–14% at $\alpha=0.3$ versus committee baselines—without requiring access to the target MLIP's internals, which matters for practitioners who want certified savings on a fixed, pretrained model.

Feel-Good Thompson Sampling (FGTS) addresses slow exploration in vanilla TS by adding an optimism term to the posterior. This paper proves the first high-probability regret bounds for both cumulative and current-step FGTS under general realizable function approximation, showing that in linear contextual bandits the regret scales as $O(d\sqrt{T}\log(2T)+\sqrt{T}\log(1/\delta))$ with probability at least $1-\delta$. The core technique is applying Markov's inequality to exponential quantities derived from the posterior normalizer, avoiding explicit confidence-set constructions.

This paper tackles measurement-constrained linear regression, where responses are expensive to collect and must be selected from a large candidate pool before being observed. The authors propose Expected Loss Optimal Selection (ELOS), which derives an A-optimality-type criterion that depends only on covariates and remains valid under potential model misspecification. The core insight is that averaging the expected prediction loss over uniformly random misspecification directions recovers the same trace-inverse criterion as under a correctly specified model, yielding a principled response-free selection rule.

Sequential conformal prediction struggles when the recent past is irrelevant and similar predictive states recur at nonadjacent times. This paper proposes State-Matched Sequence Conformal Prediction (SMSCP), which constructs an interpretable local-linear pre-target state and uses Mahalanobis similarity to select past calibration scores for ordinary conformal cutoff estimation. The work matters because it replaces fragile recency assumptions with an explicit state-matching mechanism, offering both modular implementation and a quantile-targeted theory that isolates when state matching helps and when it fails.

This paper addresses non-parametric estimation of the background intensity in Hawkes and ETAS models by treating the background as an unknown finite positive measure with a regular density. The core idea is to derive a distribution-valued Wasserstein-Fisher-Rao (WFR) gradient from the Hawkes likelihood and then project it onto finite-dimensional statistical manifolds, yielding an explicit finite-dimensional ODE system. This avoids spatial discretization and is motivated by seismic applications, though the practical algorithmic and statistical guarantees remain partially conditional.

Random reshuffling (RR) is the de facto standard for training machine learning models, yet theory has long lagged practice for last-iterate convergence. Existing guarantees required stepsizes as small as $1/(nL)$, while averaged-iterate analyses allowed $1/L$. Chen and Zhang close this gap for smooth convex objectives, proving that the last iterate of RR-SGD converges with stepsizes up to $1/(L(1+\log(nK)))$---removing the explicit $1/n$ barrier at the cost of only logarithmic factors. The result matters because practitioners return the final parameter, not an average, and large constant stepsizes are common in deep learning.

RGB vision-language-action models struggle to distinguish waste objects that appear identical in color but differ spectrally. SPECTRA-VLA learns a compact, base-orthogonal spectral residual that enriches frozen visual tokens at their native width without retraining the visual encoder or action decoder. On the SpectralWaste dataset, the method improves category balanced accuracy across 86 held-out acquisition sessions while retaining within-category spectral shape better than category-level codes.

Arctic coastal polynyas drive critical ocean-atmosphere heat exchange and sea-ice production, yet mapping them from passive microwave sea ice concentration (SIC) is fraught with ambiguity because narrow coastal openings share SIC ranges with the marginal ice zone and open ocean. The authors develop a daily mapping framework that couples hierarchical SIC partitioning, connected-component analysis, and differentiated region growing to separate enclosed low-concentration polynyas from ocean-connected water, then apply it to September–May seasons from 2002/03 to 2024/25. Their central finding is that absolute polynya extent is highly sensitive to both the upper SIC threshold and the input product—varying by up to roughly 65% across thresholds and showing 68% MAPE across products—while broad temporal patterns remain comparatively stable. This makes a clear case that reproducible long-term monitoring requires a fixed, explicitly documented configuration rather than ad hoc threshold choices.

Bayesian posterior sampling offers a potentially 'free' source of differential privacy, but existing guarantees are fragmented and often require strong boundedness assumptions. This paper develops a unified Rényi-divergence framework that characterizes the inherent privacy loss of One-Posterior-Sampling (OPS) through exponential moments of the single-record log-likelihood ratio. The results yield exact, tighter bounds for a broader class of models and clarify how prior strength, sample size, and neighboring relations jointly shape privacy.

HyrCap addresses inaccurate temporal localization and low-quality proposals in long untrimmed videos by treating dense query-based predictions as an over-complete temporal event hypothesis space rather than final detections. It hybridizes complementary video representations—InternVideo2 for strong proposal quality and V-JEPA2 for localization reliability—and recalibrates proposal confidence via cross-representation consensus, localization quality estimation, and redundancy suppression. This matters because reliable temporal proposals are framed as a critical interface between detection and LLM-driven semantic understanding, motivating a new two-stage evaluation protocol that decouples localization from segment-level semantics.

The paper tackles whether iterative image generation with flow matching can actually perform deep sequential reasoning, or whether the many sampling steps are merely an illusion of compute. It proves that vanilla flow matching collapses to constant-depth circuits because each ODE step changes state by only $O(1/K)$, but shows that self-conditioning—feeding back an endpoint prediction as an internal scratchpad—breaks this barrier and can reach $\mathsf{P/poly}$ and even $\mathsf{PSPACE/poly}$. The result is a sharp theoretical and empirical argument that how information flows between sampling steps matters more than how many steps you take.

Cameras and foundation models recognize what a 3D scene contains, but not what it is made of: surfaces of the same colour can be different materials. Spectral World Renderer tackles this by unifying world generation and hyperspectral unmixing inside 3D Gaussian splatting, so that each Gaussian carries material abundances decoded before ray compositing. The resulting SWR-Bench provides eight synthetic worlds with exact per-pixel material labels, enabling verifiable decomposition benchmarks and real-time material queries.

Holi-Spatial 360 targets a key data bottleneck for spatial intelligence: creating large-scale, photorealistic, semantically grounded 3D indoor environments without costly LiDAR or dense video capture. The pipeline converts sparse real-estate panoramas into coherent 3D Gaussian Splatting scenes by anchoring optimization with cross-view correspondences from a large reconstruction model, then densifies trajectories with adapted image and video generative priors. The output is Holi360, a dataset of over 1,000 multi-room scenes with dense RGB-D, 3D instance annotations, and 2 million spatial question-answer pairs, plus a benchmark for VLM spatial reasoning.

Active visual recognition fixes both field of view and observation count, yet images vary in the scale and amount of evidence they need. This paper extends the public AdaptiveNN-DeiT-S pipeline with a controllable observation interface that jointly adapts fixation geometry via a learned scale action, local evidence quality via scale-gated foveation and a residual detail-token branch, and observation count via label-grounded confidence-conditioned stop heads. The resulting single checkpoint exposes threshold-controlled accuracy-compute trade-offs without retraining, directly addressing the mismatch between uniform observation budgets and heterogeneous image complexity.

Single-image reconstruction of standalone garments remains under-supported by existing benchmarks, which mostly target clothed humans or rigid objects. MagicGarment introduces a dataset of 1,518 synthetic assets across 17 composite types, paired with aligned multimodal annotations and a unified evaluation protocol. The authors also propose a garment-aware baseline that adapts a frozen Hunyuan3D backbone via input-level semantic refinement (GSR) and a dynamic timestep- and routing-conditioned LoRA (FTR-LoRA), demonstrating that domain-specific adaptation improves over generic 3D priors on this benchmark.