ρ-NPTS_SG is asymptotically optimal for continuous risk functionals on bounded-density sub-Gaussian distributions, with the first instance-optimal guarantees for non-Lipschitz cases like the Sharpe ratio.
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LLM evolutionary program synthesis learns domain-dependent admissible heuristics for optimal planning by generating pattern collections combined via saturated cost partitioning.
New cumulative natural direct and indirect effects are defined to decompose local causal effects into direct and indirect components for continuous and ordinal treatments while preserving skew-symmetry and additivity.
HDSL is a tree-structured DSL for 3D indoor scenes that lets LLM agents generate subtrees recursively and perform localized edits via hierarchical retrieval and deterministic merge.
A graph neural network with axial attention learns admissible cost partitions for planning heuristics by predicting weights that satisfy partition constraints by construction via Lagrangian dual equivalence.
P²-DPO generates on-policy preference pairs targeting focus-and-enhance perception and visual robustness, combined with a calibration loss, to reduce hallucinations in LVLMs more effectively than human-feedback baselines.
Fair fine-tuning under Equalized Odds yields a tight bound Adv(A, M_f) ≤ Δ_EO · W on adversarial advantage in distribution inference attacks, with empirical reductions below detection threshold across six datasets.
Survey of 112 agentic AI for social good papers reveals moral-geographic asymmetry with 73% lacking geographic context (lowest for SDG 16) and only 25% reporting deployments.
SymTrack is the first systematic detection-free framework for scene text tracking that constructs benchmarks from video text spotting datasets and reports up to 11.97% AUC gains over prior trackers.
Introduces the Indic-CodecFake dataset for Indic codec deepfakes and SATYAM, a novel hyperbolic ALM that outperforms baselines through dual-stage semantic-prosodic fusion using Bhattacharya distance.
CoEvoer is a new cross-dependency transformer framework for upper-body expressive human pose and shape estimation that achieves state-of-the-art performance by enabling mutual enhancement between body parts.
Neuro-symbolic pipeline using multi-agent translation and SAT solving detects conflicts in multimorbidity guidelines with 0.861 F1, finding 90.6% are local conflicts on 12 SGLT2 guidelines.
EvoPrompt uses LLMs to run evolutionary operators on populations of prompts, outperforming human-engineered prompts by up to 25% on BIG-Bench Hard tasks across 31 datasets.
Introduces Pareto C-optimality and PrefUCB algorithm for PDMOB with instance-dependent logarithmic regret bounds, validated on selective ensemble and asset allocation tasks.
BCL introduces a particle-filtering Bayesian update framework to systematically refine label representations in in-context learning for information extraction, claiming consistent gains over prior methods.
SCAIL-2 achieves end-to-end character animation via direct video concatenation, in-context mask conditioning, mode-specific RoPE, the synthetic MotionPair-60K dataset, and Bias-Aware DPO, outperforming prior methods on multiple tasks.
Trio proposes Temporal-Spatial-Sample attention and a TS-SCM synthetic data generator to improve multivariate time-series forecasting by reusing historical patterns and structural priors.
Graph Cascades uses contagion diffusion to rewire graphs by promoting reinforced multi-hop node pairs to direct neighbors, improving GNN performance on heterophilic and moderate-degree homophilic graphs under specified conditions.
Many LLMs exhibit stronger environmental cognition, affect, and behavioral recommendations than human survey averages and shift with persona prompts.
DisImpact introduces a two-stage MLLM framework to classify disaster-related social media posts into ten impact categories and compute a unified physi-social impact index validated against FEMA and NASA ground-truth data.
MixRea benchmark reveals LLMs achieve at most 42.8% consistency on explicit-implicit reasoning tasks, with PRCP prompting proposed to recover overlooked relations.
ST-TGExplainer disentangles stability and transition patterns in temporal graphs via a self-explainable TGNN guided by a disentangled information bottleneck objective to produce more faithful explanations.
MAFIG is a multi-agent framework that uses LLM agents and evaluators to generate reading comprehension items with significantly higher adherence to specified feature constraints than single-agent baselines.
Introduces BanglaMedVQA dataset of clinically validated image-question-answer pairs and benchmarks foundation models, finding substantially lower performance than on English MedVQA especially on diagnostic questions.
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EvoPrompt: Connecting LLMs with Evolutionary Algorithms Yields Powerful Prompt Optimizers
EvoPrompt uses LLMs to run evolutionary operators on populations of prompts, outperforming human-engineered prompts by up to 25% on BIG-Bench Hard tasks across 31 datasets.