Partial-volume (PV) effects blur tissue boundaries in Magnetic Resonance Fingerprinting (MRF), and conventional pipelines rely on multi-stage dictionary matching or statistical segmentation that propagate errors. DeepPV-MRF proposes an end-to-end deep network that directly maps raw, aliased MRF time-series signals to voxel-wise CSF, GM, and WM fraction maps, bypassing intermediate $T_1$/$T_2$ reconstructions. On simulated data the method reports large accuracy gains over dictionary-matching and pseudoinverse baselines, but its real-world evidence rests on synthetic training labels and a small in vivo cohort without ground truth.

Model selection for time-series anomaly detection remains a critical open problem because no single detector dominates across heterogeneous datasets. This paper proposes RAMSAD, a retrieval-based framework that replaces trainable meta-learners with non-parametric similarity search over a knowledge base of past series and their detector-performance profiles. By matching new queries to previously seen instances via time-series foundation model embeddings—optionally augmented with multimodal semantic alignment—the authors argue they can achieve stronger in-distribution accuracy and out-of-distribution robustness at a fraction of the computational cost.

Agent skills let frozen models adapt through editable external instructions, but optimizing them faces a tension: batching many changes obscures which edit mattered, while judging each edit only by its immediate score risks discarding useful stepping stones. AtomSearch proposes contrastive atomic editing—one primitive rule edit tied to execution evidence—and Skill-State Tree Search (STS), which tests short edit compositions and keeps a composed state only if it beats its primitive child. The hope is to find compact, inspectable skills without greedy myopia.

Standard ReAct agents carry every failed attempt and off-path observation in one linear context, which eventually buries the evidence they need. TreeFlex solves this by saving intermediate states as nodes in a search tree and letting the same language model choose where to resume and what to do next, operating in two roles—Reflection for global navigation and Execution for local work—trained end-to-end with a tree-aware adaptation of GRPO. The paper shows that this design improves accuracy on deep research and software engineering benchmarks, especially on harder tasks, though it comes with higher inference costs and small-scale training.

Training large language models is memory-intensive, and subspace projection methods like GaLore can diverge when stochastic noise dominates the selected low-rank gradient space. This paper proposes SMRC and PMRC, which keep optimizer states in a low-dimensional subspace while adding the orthogonal residual gradient back into the parameter update. The authors prove $O(T^{-1/4})$ convergence rates for momentum SGD and for Muon under a mixed stationarity measure, and demonstrate empirical gains on LLM pretraining and fine-tuning up to $1.3$B parameters.

Length-Conditioned Majority Voting (LCMV) improves LLM reasoning by exploiting the empirical bias that longer chain-of-thought traces are less likely to be correct. Instead of filtering out long responses, it learns a logistic prior over correctness conditioned on CoT length and adapts it at test time using only unlabeled data and pseudo-labels from standard majority voting. The method is model-agnostic, adds negligible computational overhead, and is evaluated across 11 models and 8 benchmarks, making it a pragmatic drop-in replacement for standard MV.

The paper studies Protection Mode: a tendency of frontier language models to prioritize protecting a low-risk living subject over an object facing greater immediate physical damage. The authors introduce ProtectionBench, a controlled benchmark of 285 prompts spanning 34 physical scenarios, and find that models frequently choose the subject first even when correctly assessing the object as higher-risk. This matters for understanding how safety alignment and reasoning interact in high-stakes agent decisions.

Existing robot policies use a single fixed parameter configuration across all interaction contexts, which forces shared weights to reconcile competing adaptation signals from diverse manipulation operations. MorphAct addresses this by introducing two coupled generators that synthesize context-dependent low-rank updates for the vision-language and action modules, where the action-side generator is conditioned on the adapted visual-language representations. This allows the effective policy weights to morph at each replanning step without test-time optimization or operation labels. Evaluations across three VLA backbones ($\pi_0$, $\pi_{0.5}$, and X-VLA), two simulation benchmarks, and four real-world tasks report closed-loop success gains of up to 16.30%, accompanied by ablations isolating the value of contextual generation and cross-module coupling.

Long-horizon tasks like real-world software engineering expose the brittleness of monolithic agent loops, where a single model must simultaneously decompose, execute, and verify within one trajectory. LAGO addresses this by treating specialized language agents as temporally extended options—encapsulating role specifications, intra-option policies, termination conditions, and verifiers—that a meta-agent can retrieve from a persistent library or spawn on demand. The core proposition is that this hierarchical scaffold turns task decomposition into a reusable, growing component that requires no weight updates, and the authors report 58.6% on the full 500-task SWE-bench Verified with a frozen Qwen3.5-35B-A3B.

Population dynamics are often observed only as unpaired snapshots, leaving the evolution between measurements fundamentally underdetermined. This paper introduces Neural Wasserstein Splines (NWSP), a method that learns a continuous curve of distributions by penalizing the intrinsic Wasserstein acceleration rather than the acceleration of individual particles, which is representation-dependent. It couples a neural spline generator with a finite-dictionary projection of velocity and its time derivative, achieving competitive results on synthetic and single-cell benchmarks.

Reliable uncertainty quantification for reasoning LLMs is expensive when it requires sampling multiple long chain-of-thought traces, and token-level signals collapse or mis-calibrate on deep reasoning. This paper introduces Argument Graph Uncertainty (AGU), a post-hoc framework for multiple-choice QA that segments a single reasoning trace into propositional nodes, scores pairwise and node-to-option relations with a small NLI model, and aggregates the resulting evidence into a Dirichlet posterior over answer options. The concentration of that posterior, expressed as normalized entropy $U_{\mathrm{AGU}}(R) = H_{\mathrm{norm}}$, yields an uncertainty estimate that is cheap to compute and structurally grounded.

Hindsight-Privileged RLVR tackles the inefficiency of group-based RLVR methods like GRPO, which require multiple rollouts per prompt and lose their learning signal when all sampled outcomes are identical. The core idea is to replace group comparisons with a single rollout per prompt, using the trajectory's own realized future as training-only privilege: a frozen snapshot of the generating policy scores each token under both causal and hindsight views, and normalized Jensen--Shannon divergence between these full-vocabulary distributions weights a historically centered reward. This yields bounded token-level credit without a learned critic or reference solution, training $1.2$--$2.3\times$ faster than GRPO on Qwen3-8B but with task-dependent accuracy tradeoffs.

PAWA tackles multi-agent world modeling in shared environments like Minecraft, where one player's actions change what every other player sees—not just in their local field of view, but in directions they are not currently looking. The core idea is to jointly forecast each player's point-of-view (POV) video alongside a world-stabilized panoramic video, conditioned on all players' actions. The model uses a dual-tower architecture with ray-aligned cross-attention that links tokens viewing the same world direction regardless of whether they appear in rectilinear POV or equirectangular panorama coordinates, plus a quadratic-phase player encoding that scales identities as $m^2$ instead of $m+1$. The work also introduces a scalable dataset pipeline that records gameplay without graphics clients and renders replays offline, supporting more than 64 simultaneous players.

Momentum is a pillar of modern optimization, yet its benefits in reinforcement learning with verifiable rewards (RLVR) remain unclear. This paper studies a KL-regularized bandit formulation of RLVR and proves that, with exact policy gradients, larger EMA coefficients strictly accelerate convergence within a safe overshoot-free range. Its central insight is that stochastic sampling obscures this gain unless the coefficient scales with the learning rate so that $\eta/(1-\beta)$ stays constant, a principle validated in bandits and in LLM post-training on Qwen models.

Post-legalization placement refinement seeks to reduce wirelength after global placement without breaking site constraints. This paper audits whether a GNN-PPO agent can handle that task by systematically exploring 18 training configurations on three ISPD2015 benchmarks, varying rewards, action spaces, GNN architectures, and graph-partitioning strategies. The study finds that RL lags behind classical tools, and it dissects why—pinning blame on credit-assignment bottlenecks, GNN over-smoothing, and netlist regularity—while proposing a lightweight netlist classifier to guide training decisions.

This paper asks whether releasing learned embeddings from pretrained brain MRI encoders—instead of raw scans—actually protects patient privacy. The authors audit five diverse encoders on two longitudinal datasets, measuring patient linkage, re-identification, and sex-attribute inference under an explicit attacker model. They find that several embeddings retain enough subject-specific structure to enable rank-1 re-identification above 90% on both datasets, and that sex can be inferred with balanced accuracy up to 95.5%. The core takeaway is that embedding release alone is insufficient to establish anonymity.

This paper reframes backdoor stealthiness in deep neural networks as a multi-space measurement problem rather than a single latent property. The authors benchmark ten attacks across four datasets and three architectures, measuring observable footprints in input, feature, and parameter space to show that stealthiness is fragmented: an attack can be quiet in one space and conspicuous in another. They also introduce two diagnostic metrics—CDBI and TUP—to address blind spots in global feature clustering and channel-level concentration. The central message is that future stealthiness claims should be backed by evidence from all three spaces, not by a single metric or defense.

Semi-gradient TD with nonlinear function approximation is notoriously unstable because the bootstrap target moves as the parameters evolve. This paper studies the problem in the mean-field regime for two-layer neural networks and identifies a stability regime where entropy-driven relaxation dominates the sensitivity of the moving target. Under the resulting condition, the authors prove exponential last-iterate convergence and quantify the regularization bias, then extend the same mechanism to soft Q-learning.

MeanFlow normally couples the prediction horizon $h_k$ to the update step $\delta_k$. This paper decouples them, showing on CIFAR-10 that longer horizons hurt FID near the source $(t=0)$ but help at the midpoint $(t=0.5)$, and that this midpoint gain depends on earlier guidance choices. A frozen four-query rule improves mean FID by 2.0 without retraining or extra calls, yet ImageNet calibration finds no benefit beyond shifting query starts. The work establishes prediction horizon as a distinct but configuration-sensitive inference control.

Multi-party floor control is the next bottleneck for full-duplex voice agents: systems must decide not just when speech ends, but whether overlapping listeners are backchanneling or genuinely taking the floor. MultiPartyTurnBench introduces 240 synthetically generated, channel-separated conversations spanning two to five speakers and six interaction types, evaluating End-of-Turn (EOT) and Interruption (INT) detection with exact causal annotations. The results reveal a stark capability asymmetry: turn completion is largely tractable, while selective interruption handling remains near chance even for state-of-the-art commercial agents.