Guided diffusion forces a costly trade-off between enforcing requirements and preserving sample quality, usually resolved by small guidance scales and long sampling trajectories. This paper introduces LEEGS, which learns a time-dependent guidance schedule end-to-end by backpropagating the guidance loss through the sampling trajectory. A cheap gradient approximation—treating the guidance gradient as a stop-gradient—cuts training time by roughly 4$\times$, letting the method train on only 30 examples and match or beat strong baselines across image inverse problems, face-ID guidance, and PDEs with as few as 50–100 NFEs.

Quantum key distribution deployments provision security margins assuming stationary channels, yet physical devices drift between calibrations. This paper frames adaptive eavesdropping under trusted channel noise and device drift as a constrained Markov decision process, where an attacker selects compact learnt quantum circuits each round while the noise follows an Ornstein–Uhlenbeck process. By bounding the adaptive value between the best fixed circuit and a dynamic-programming upper bound, the authors let operators measure how conservative their margins are and tune recalibration intervals, addressing a key economic barrier to QKD adoption.

Molecular tumor boards must distinguish truly unsupported therapies from evidence-supported recommendations that still require oncologist review because of incomplete clinical information or poor performance status. This paper introduces OpenMTB-Audit, a 500-case synthetic NSCLC benchmark with four safety labels, and exposes pervasive over-refusal: every tested LLM configuration collapses the Partially Supported label, misclassifying valid-but-caveated therapies as either Unsupported or Supported. To address this, the authors propose MTB-AuditAgent, a deterministic seven-module pipeline that reduces over-refusal to 6.7% and achieves 91.2% accuracy. The work matters because label collapse in high-stakes oncology auditing can conceal unsafe endorsements or inappropriately block appropriate treatments.

iADD tackles the alignment-diversity trade-off in reinforcement-learning finetuning of diffusion models such as Stable Diffusion. Its core idea is to update early denoising timesteps rather than late ones, combined with a sparse incremental curriculum and Feynman–Kac particle branching during training. Better alignment without mode collapse matters because existing reward-optimization methods frequently degrade visual quality and diversity.

The paper isolates matrix-valued transport from the broader sheaf-diffusion framework by casting it through quiver representations. Its core insight is that standard multi-head attention implements diagonal transport over the head space, and that allowing edge-dependent off-diagonal routing before aggregation yields a strictly richer message-passing primitive. The authors introduce Topological Attention (Top-A), which adds cross-head communication while exactly recovering vanilla attention when the learned routing vanishes. Benchmarks on relational, heterogeneous, and algorithmic tasks show that the gains are task-dependent: largest when individual interactions require distinct transformations, but not systematically present under heterophily alone.

Multi-agent motion planning with diffusion models currently demands costly training on large feasible-trajectory datasets, making learned planners map-specific and data-hungry. This paper introduces TFDP, a training-free diffusion-style planner that replaces global neural score networks with analytical local scores derived from smoothness, obstacle-avoidance, inter-agent separation, and velocity-limit terms. By exploiting the spatial and temporal locality of trajectory refinement, the method scales to hundreds of agents and obstacles in seconds without any demonstrations or neural network training.

Comparing high-dimensional discrete distributions is notoriously difficult because the state space grows exponentially and differences can hide in complex joint dependencies. This paper introduces Zero Flux, a flow-based discrepancy that extends the continuous Zero Flow criterion to discrete state spaces. The core idea is to construct a discrete bridge between two distributions via coordinate-wise independent coupling, measure local probability fluxes at the midpoint, and show that all fluxes vanish if and only if the distributions are identical. This yields a decomposable, sample-based discrepancy with finite-sample guarantees, offering both a global comparison score and interpretable coordinate-wise contributions.

Personalization encoders compress lengthy user interaction histories into a fixed preference state for downstream ranking or generation, but this compression can suppress evidence that remains available in cached per-timestep representations. This paper proposes REPAIR, a lightweight module that identifies and aggregates corrective signals from a frozen encoder's own cache across long, short, and episodic temporal views, then adds the correction back to the compressed state before the task head. By keeping the host encoder and task head frozen, REPAIR offers a practical way to improve deployed personalization systems without retraining them.

TACO attacks the memory bottleneck of full-parameter LLM fine-tuning by replacing dense AdamW states with a geometry-induced sparse update. The core idea is to use a dimension-normalized $\ell_1 \to \ell_1$ operator norm, whose exact steepest-descent direction is a ternary, column-wise one-sparse matrix. By retaining only a tiny FP8 heavy-hitter history per column, TACO slashes persistent optimizer state by over $170\times$ and peak memory by nearly $3\times$, enabling 30–32B parameter models to fine-tune on a single 80 GB GPU.

IQS-BO tackles the per-iteration cost of Bayesian optimisation by replacing surrogate fitting and acquisition-function maximisation with a single transformer forward pass. The core idea is to cast query selection as supervised classification: given a finite candidate set, the model predicts which candidate maximises the objective, trained with cross-entropy on synthetic priors where the true best candidate is known for free. The method can run fully in-context or on top of a fixed surrogate, and it introduces a mixture prior of GP and non-stationary functions to improve optimisation performance.

Quadrupedal locomotion requires balancing command tracking, stability, and energy efficiency, yet conventional RL bakes these trade-offs into a static scalar reward that cannot change after training. PROMO proposes a preference-conditioned multi-objective framework that takes the relative priority vector $w_t \in \Delta^{K-1}$ with $K=3$ as a runtime input to a single policy, while keeping embodiment-specific locomotion priors fixed. On a Unitree Go2, the same policy transferred zero-shot modulates behavior through preference changes alone, cutting specific energy by up to $30.4\%$, position error by $38.7\%$, and peak body-attitude deviation by $59.0\%$.

LESS introduces a lightweight neural architecture search pipeline that marries a brief fair supernet warm-up with candidate-conditioned hard-path updates inside a CMA-ES loop. The core claim is that just six balanced SGD steps per proposal are enough to sharply reduce selection regret, enabling NAS-Bench-201 searches in roughly seven minutes on a single RTX 4090 without retuning across datasets. If this speed–accuracy trade-off holds more broadly, it could lower the barrier to reproducible NAS experiments.

This paper studies how to make Quantum Reinforcement Learning (QRL) for combinatorial optimization more robust on near-term quantum devices. Its core idea is APGEM, a LinUCB contextual-bandit controller that dynamically selects among error-mitigation techniques—Zero-Noise Extrapolation, Probabilistic Error Cancellation, Clifford Data Regression, and Readout Error Mitigation—based on a cost-aware utility that balances fidelity gain against shot cost and estimator variance. The work matters because static error mitigation cannot adapt to changing noise regimes or training phases, and adaptive orchestration could improve the reliability of hybrid quantum-classical loops.

Unified language models must handle heterogeneous capabilities such as mathematics, coding, instruction following, and controllable reasoning, yet standard single-trajectory training is notoriously sensitive to ordering and data ratios. Naive weight-space merging of single-objective experts offers a modular alternative but often collapses distinct behaviors or degrades cross-domain performance. This paper proposes Mixture-Trained Merging (MTM), which trains each branch on an objective-biased data mixture rather than a pure objective so that the branches stay in mutually compatible regions of parameter space and can be merged at deployment time.

Catastrophic forgetting remains a critical barrier in multi-phase training of large language models. This paper recasts forgetting as a geometric phenomenon at the level of each weight matrix: standard gradient updates act on all inputs, not just those from the new task, corrupting prior mappings. The authors propose Local Support Learning (LSL), which pairs task-specific adapters with per-matrix Gaussian Mixture Model gates that restrict updates to activations near the current training distribution. Empirically, LSL retains pretrained math, coding, and instruction-following capabilities in models up to 7B parameters while learning new domains efficiently.

Policy-gradient methods suffer from high variance, while pathwise actor updates require accurate action derivatives that standard value-function training does not guarantee. FERPO addresses this by deriving an entropy- and KL-regularized target action distribution from critic values alone, then fitting the policy with forward KL via self-normalized importance sampling---never differentiating the critic with respect to actions. The method targets massively parallel on-policy robot learning, where reliable policy improvement is critical for both sample and wall-clock efficiency.

Where-OPD proposes a new on-policy self-distillation paradigm for multimodal large language models. Instead of giving the teacher zoomed-in image crops, it provides textual spatial hints that identify question-relevant visual elements and their coordinates, using procedurally generated synthetic counting scenes where object locations are known by design. The student learns to match the teacher’s behavior from the raw image and question alone, and the authors claim that training on just 3,000 such synthetic scenes transfers to broad real-world perception gains across 15 benchmarks.

The paper solves the generalized Schrödinger bridge on finite graphs—transporting probability mass between distributions while charging costs for visited states—by showing that state costs fold into the reference process via a Feynman–Kac tilt. This reduces the problem to solving a plain bridge against the tilted generator $A = Q - \operatorname{diag}(f)$ with sparse matrix-exponential iterations, eliminating the need for learned controls or tuned temporal-difference penalties. The method scales to millions of nodes and extends to congestion costs through a provably convergent damped best-response outer loop.

Hierarchical Continuous Diffusion Language Models tackle a core bottleneck in non-autoregressive generation: discrete diffusion decodes parallel tokens as independent marginals, while continuous diffusion lacks valid token anchors until the final step. The authors propose HC-DLM, a hierarchy where a continuous latent trajectory is the sole persistent state and tokens are read out and fed back at every step to scaffold the next latent update. A single variational bound yields the training objective, and experiments span structured reasoning, mathematical planning, and language modeling.

Schrödinger bridge (SB) transport assumes a fixed initial distribution, which breaks at test time when inputs shift. This paper introduces Distributionally Robust Schrödinger Bridge (DRSB), which learns a single controller by minimizing the worst-case sum of control energy $\int_0^1 \frac{1}{2}\|u_t^\theta(X_t)\|^2 dt$ and a terminal KL penalty $\rho \mathrm{KL}(p_1^{u,\mu}\|\nu)$ over an ambiguity set $\mathfrak{M}_\epsilon(\hat{\mu})$ of initial distributions. The authors derive a variational reformulation, connect it to stochastic optimal control and distributionally robust optimization, and develop Wasserstein and Sinkhorn variants with alternating block updates. Experiments on 2D transport and FFHQ image-to-image translation show improved robustness to input perturbations, with a tradeoff in nominal performance.