Discriminator-Guided RL (DRL) uses a pretrained representation space to train a discriminator that separates real data from model-generated samples. Its logit is used as a reward in KL-regularized RL, aligning model outputs with visual and semantic realism without human preferences. DRL improves FID and semantic FD across models like SiT and JiT, and enhances the Pareto frontier between preference and fidelity.
Essential Subspace Merging (ESM) reduces inter-task interference by focusing on principal directions of activation shifts. ESM++ extends this with dynamic expert selection via prototype-based routing, enabling efficient, training-free multi-task model merging.
Safety Reflection Pretraining inserts short safety reflections into pretraining data to enable self-monitoring in language models. Experiments with 1.7B models on FineWeb-Edu show improved safety accuracy and reduced attack success rates, with MedSafetyWorld demonstrating that the method better prevents unsafe behaviors from being generalized from safe data than data filtering or rewriting.
Stochastic momentum methods like HB and ASGD show distinct batch-size tradeoffs in compute efficiency and serial runtime. HB maintains SGD-level compute efficiency over a batch-size window up to a factor \sqrt{\kappa} larger than SGD's critical batch size, while ASGD improves small-batch efficiency for rapidly decaying spectra but sacrifices it for larger batches in exchange for reduced serial runtime.
AGDN introduces a graph neural network framework that addresses topological priors and connectivity loss in TSP. It uses a MixScore transition matrix and anisotropic diffusion to enable efficient information exchange, outperforming existing methods across diverse problem sizes and distributions while maintaining competitive computation time. The implementation is available on GitHub.
A new decision-focused RL framework jointly trains a forecaster and charging controller to handle unknown EV departure times. By aligning forecast accuracy with downstream decision quality, the method achieves up to 14% higher total reward and a 55% reduction in unsupplied energy compared to standard RL approaches.
MAST, a mechanism-guided unlearning method, achieves targeted forgetting of RLVR-induced reasoning with minimal collateral damage. On Qwen2.5-Math-1.5B and Qwen3-1.7B-Base, it significantly reduces MATH performance (45/150 to 37/15-0) while preserving GSM8K accuracy by +0.8 points and maintaining MATH retention at -0.5 points. Results hold across different seeds, objectives, and models, showing superior stability over full-parameter unlearning.
STARE addresses policy entropy collapse in GRPO-based reinforcement learning by identifying entropy-critical token subsets via surprisal quantiles and reweighting their advantages. It maintains stable policy entropy across model scales and tasks, outperforming DAPO and other baselines by 4%-8% on AIME24 and AIME25, with consistent exploration-exploitation balance.
A graph neural network enhances algebraic multigrid solvers by predicting optimal polynomial coefficients for sparse pseudo-inverse operators. The method reduces V-cycle iterations and achieves wall-clock speedups of 4% to 37% across benchmarks, with robust performance on meshes up to 128 times larger than training data and on unseen industry problems like AirfRANS.
Large Language Gibbs uses LLM conditional distributions as transition operators for iterative variable resampling. This method enables coherent, order-independent probabilistic inference by achieving a stationary distribution that balances local conditionals, offering a practical alternative to single-pass generation for structured reasoning tasks.
NeSyCat Torch provides a differentiable tensor implementation of categorical semantics for neurosymbolic learning, unifying classical, fuzzy, probabilistic, and neural systems under a single inductive truth definition. It outperforms LTN and DeepProbLog in speed and accuracy on MNIST addition, matching DeepStochLog's accuracy while operating within a uniform framework extendable to continuous probability via monad instantiation.
P-K-GCN enables high-fidelity spatiotemporal super-resolution on irregular geometries by combining graph convolutional networks with Koopman operator theory. It incorporates a physics-based loss to enforce adherence to physical laws, reducing super-resolution error through improved generalization and accuracy, as validated in cardiac electrodynamics reconstruction.
Diffusion-Proof is the first framework to train and apply diffusion language models for formal theorem proving. It introduces dLLM-Prover-7B for whole-proof writing with long-range coherence and dLLM-Corrector-7- for local proof correction using bidirectional information. The framework outperforms auto-regressive LLM baselines by 1.61% on ProofNet-Test and 6.14% on MiniF2F-Test, and solves an IMO problem beyond the capability of DeepSeek-Prover-V2-7B.
UBP2 introduces a model-based method that actively explores environments by jointly reasoning over uncertainties in reward, dynamics, and value functions. It achieves superior sample efficiency in preference-based reinforcement learning, outperforming both model-free and non-optimistic model-based baselines on the Meta-World benchmark.
Essential Subspace Merging (ESM) reduces inter-task interference by focusing on principal directions of activation shifts. ESM++ extends this with dynamic expert selection via prototype-based routing, enabling training-free multi-task model merging with preserved task knowledge.
Safety Reflection Pretraining inserts short safety reflections into pretraining data to enable self-monitoring in language models. Experiments with 1.7B models on FineWeb-Edu show improved safety accuracy and reduced attack success rates, with MedSafetyWorld demonstrating that the method better prevents unsafe behaviors from being generalized from safe data than data filtering or rewriting.
Stochastic momentum methods like HB and ASGD show distinct batch-size tradeoffs in compute efficiency and serial runtime. HB maintains SGD-level compute efficiency over a batch-size window up to a factor \sqrt{\kappa} larger than SGD's critical batch size, while ASGD improves small-batch efficiency for rapidly decaying spectra but sacrifices it for larger batches in exchange for reduced serial runtime.
MAST, a mechanism-guided unlearning method, achieves targeted forgetting of RLVR-induced reasoning with minimal collateral damage. On Qwen2.5-Math-1.5B and Qwen3-1.7B-Base, it significantly reduces MATH performance (45/150 to 37/15-0) while preserving GSM8K accuracy by +0.8 points and maintaining MATH retention at -0.5 points. Results hold across seeds, objectives, and models, showing superior stability over full-parameter unlearning.
STARE addresses policy entropy collapse in GRPO-based reinforcement learning by identifying entropy-critical token subsets via surprisal quantiles and reweighting their advantages. It maintains stable policy entropy across model scales and tasks, outperforming DAPO and other baselines by 4%-8% on AIME24 and AIME25, with consistent exploration-exploitation balance.
NeSyCat Torch provides a differentiable tensor implementation of categorical semantics for neurosymbolic learning, unifying classical, fuzzy, probabilistic, and neural systems under a single inductive truth definition. It outperforms LTN and DeepProbLog in speed and accuracy on MNIST addition, matching DeepStochLog's accuracy while operating within a uniform framework extensible to continuous probability via monad instantiation.