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This paper identifies progress stagnation—unproductive reasoning-action loops—as a critical failure mode when distilling multi-turn agent capabilities from large teachers into small language models via supervised fine-tuning. To mitigate it, the authors propose Progress-Aware Distillation (ProD), which measures the stagnation gap between student-generated and teacher trajectories and encodes that discrepancy into an adaptive margin within a SimPO-style preference objective. The contribution matters because prior agent distillation improves tool-use accuracy yet collapses on longer horizons, directly undermining practical deployment of efficient small-model agents.
Steady PDE surrogates on unstructured meshes must resolve multiscale features from a single coarse discretization. This paper proposes routing a mixture of vanilla transformer experts using a learned discrete Hodge-Laplacian controller that measures high-frequency content per vertex. Each expert is biased toward a different spectral scale through auxiliary band targets, and the combined output predicts pressure, velocity, and turbulence fields in one forward pass. On a remeshed AirFRANS benchmark, the architecture cuts mean relative $L_2$ error by roughly a third compared with a Transolver baseline.
This paper tackles unsupervised pre-training for reinforcement learning: how to learn from reward-free trajectories so that a good policy can be extracted once a task reward is revealed. The core idea is to replace costly, iterative flow-based occupancy models with one-step drifting models, which makes it possible to extract policies by differentiating directly through the generator instead of distilling a separate critic. This matters because it cuts inference cost, removes an entire distillation stage, and improves performance on standard continuous-control benchmarks.
The paper studies how to learn interpretable binary classifiers as sparse Boolean rules by optimizing the weights of a fixed-structure DNF network. It proposes mixed integer linear programming (MILP) formulations that trade off training misclassification against rule sparsity, with an advanced formulation that dramatically reduces integer variables and redundant constraints while preserving exact Boolean semantics. A combined warm-start and dropout strategy is introduced to break symmetries and push scalability to larger datasets.
CoPE attacks the sample-efficiency bottleneck in training long-horizon language agents with reinforcement learning. Standard agentic RL such as GRPO optimizes only a sparse terminal reward $Y(\tau) \in \{0,1\}$, leaving the rich temporal structure of intermediate steps unused. The paper introduces CoPE, which augments GRPO with success-conditioned multi-step action forecasting: on successful on-policy trajectories, the shared policy backbone learns to predict future action chunks $a_{t:t+K}$ from each history $h_t$, so every action is supervised from both the moment it is taken and from the $K$ preceding histories. This implicit temporal ensemble yields stronger aggregate performance and up to $2\times$ sample-efficiency gains across five benchmarks, with no added inference cost.
Data-quality gates conflate within-region example selection with across-region mass redistribution, so a gate-versus-random comparison cannot isolate what improved downstream performance. This paper introduces Counterfactual Gate–Support Decomposition (CGSD), a four-arm, budget-matched design that decomposes the gate effect into within-support selection $E$, between-support redistribution $S$, and their interaction $I$ by crossing eligibility and support-share factors. The core contribution is not the factorial algebra itself, but operationalizing it for finite pools while characterizing when the required arms are unrealizable.
INTACT attacks the inference cost of chain-of-thought reasoning by replacing early textual reasoning steps with compact continuous latent vectors while keeping the backbone LLM frozen. A small 107M-parameter encoder iteratively builds a latent prefix from the frozen model's own hidden states, first distilling the effect of written reasoning steps and then refining via group-relative policy gradients. Across Llama and Qwen backbones from 1B to 8B, the method improves accuracy and shortens generation length, and a single encoder trained on math, science, and code transfers to twelve held-out benchmarks including commonsense without adaptation.
The paper studies the personalization–collaboration trade-off in federated learning via Generalized Total Variation Minimization (GTVMin) with least-squares models on an arbitrary communication graph. Using random matrix theory in the proportional high-dimensional limit, it derives exact asymptotic test-error formulas under Gaussian-mixture data and proves that the GTVMin solution concentrates at rate $1/\sqrt{p}$, independently of the graph structure or regularization strength $\alpha$. If the modeling assumptions hold, the results provide a principled way to tune $\alpha$ from training data alone.
Long-context LLM inference is bottlenecked by expanding key-value caches. This paper proposes treating the KV cache as a learnable layer and optimizing it per document via test-time training while keeping model weights frozen. The two-stage procedure first matches teacher attention statistics and then distills teacher predictions, yielding large accuracy gains on synthetic and natural-document benchmarks under aggressive compression.
This paper proposes SKG, a geometric reasoning engine that inverts the usual data-driven paradigm: physical laws, symmetries and conservation constraints are treated as hard, a priori structures, while data are used only to calibrate a small task-specific energy term. The core object is an octuple $\mathcal{S}=(M,g,\omega,H,S,G,\Delta,\Pi)$ where inference becomes geometric evolution on a manifold, and the metric $g$ is pulled back from invariants via a Fisher-determined kernel bandwidth. The authors claim this yields provable out-of-distribution bounds, decouples learning from the metric, and reduces induction to estimating only the task energy $S_{\text{task}}$.
InstructPro2 tackles de novo protein design by translating open-ended natural language instructions into amino acid sequences while incorporating biological context such as target ligands or antigens. The framework augments a large protein language model with text and modality encoders, using lightweight gated cross-attention adapters to condition generation on both semantic and molecular signals. If the approach holds up experimentally, it could replace narrow task-specific models with a single generalist system that designers steer through plain text.
Mobile manipulation demands precise coordination between a mobile base and a robotic arm, making real-world policy fine-tuning difficult because of high-dimensional, tightly coupled action spaces. This paper proposes HiRe-MoMa, a framework that freezes a pre-trained base policy and learns a small, hierarchical residual: one head corrects the base motion while the other corrects the arm conditioned on the base correction. It also guides learning with sparse human teleoperation interventions via an auxiliary critic-ranking loss, reporting large success-rate gains on a TIAGo++ robot after roughly 30 minutes of online interaction.
Wearable activity recognition suffers from sensor heterogeneity and subtle, scale-specific motion patterns that models often confuse with participant-specific artifacts. W-JEPA tackles this by replacing raw temporal patches with fixed wavelet tokens—indexed by physical scale, sensor axis, and time—as the units of masked latent prediction in a Joint-Embedding Predictive Architecture. The key payoff is improved generalization to unseen participants across six benchmarks.
DyG-WM tackles dynamic graph world modelling by predicting future latent node and relation states from temporal context, rather than fitting specific supervised tasks or reconstructing raw edges. The core idea is a dual-level joint-embedding predictive architecture that combines historical node-relation context encoders with path-signature encoders to capture ordered structural changes. This matters because it aims to generalize across both link prediction and node classification without expensive task-specific labels.
SPARS addresses catastrophic forgetting in replay-free continual learning by partitioning network parameters into three importance-aware regions—winning, neighboring, and non-important—and treating each with freezing, soft regularization, or unrestricted updates based on either the Fisher Information Matrix or a novel label-free Confidence Score. To further mitigate classifier bias in class-incremental learning without stored exemplars, the authors replace the standard fully connected classifier with Quadratic Discriminant Analysis at inference time.
While large language models concentrate attention on initial tokens to prevent over-mixing, state-of-the-art graph transformers rarely exhibit this 'attention sink' behavior. This paper identifies the attention scoring function—not graph symmetry or locality—as the decisive factor, showing that replacing GRIT's gated logit with a standard dot product raises sink rates dramatically. The finding reframes attention sinks as an artifact of dot-product scoring rather than an inherent property of deep transformers.
TopicMerge tackles the scalability bottleneck of LLM-based topic modeling by combining efficient clustering with targeted LLM reasoning. Its core idea is to first over-segment a corpus into fine-grained semantic clusters, then use a lightweight LLM to hierarchically merge them with contextual neighborhood guidance. The authors claim this slashes LLM prompts by over 92% while matching or exceeding state-of-the-art clustering alignment on standard benchmarks.
HiMeco attacks a critical bottleneck in embodied spatial intelligence: when a self-evolving agent fails, it is difficult to attribute the error to perception, exploration, or planning without an oracle. The paper proposes a parameter-frozen agent that evolves a strategy bank $\mathcal{E}$ and a parameterized skill library $\mathcal{K}$ between interaction rounds, guided by a brain-inspired Perception-Exploration-Planning (PEP) diagnostic module that scores trajectories with $\mathbf{u}(\tau)=(u_{\mathrm{per}}(\tau),u_{\mathrm{exp}}(\tau),u_{\mathrm{pla}}(\tau))$. On the active ESI-Bench benchmark, the method reports state-of-the-art accuracy of $69.19\%$ and improves its own active baseline by $12.30$ percentage points, while also yielding modest gains on the passive EmbSpatial-Bench without any evolution.
SGQN introduces selective growth and lazy pruning into decoupled Q-learning for continuous control, aiming to concentrate action-grid resolution where the value function peaks. On eight DMControl tasks it reports competitive mean returns, but its central—and most valuable—finding is that the adaptive grid is not responsible for the performance: growth behaves like uniform expansion, pruning changes returns by at most 1.1%, and what actually matters is starting from a coarse grid and using $\varepsilon$-greedy exploration. The paper is therefore best read as a rigorous self-critical analysis of which ingredients in growing action-grid methods matter and which do not.
When multiple LLM retrieval actions are generated jointly, downstream systems typically return a single reward for the merged result set, making it impossible to tell which action helped or hurt. CASI closes this information gap through snapshot-grounded set intervention: it caches per-action retrievals, evaluates all alternative subsets under a frozen training snapshot, and aggregates the resulting marginals into Shapley-based credit. This matters because broadcasting the same scalar reward ignores redundancy and competition among actions, which misleads policy optimization in capacity-limited regimes such as sponsored search.