Parallel algorithm for matroid basis computation with O(n^{1/3} log^{1/3} n) round complexity, nearly matching the KUW lower bound.
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Rodrigo Nogueira, Zhiying Jiang, Ronak Pradeep, and Jimmy Lin
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2026 18representative citing papers
Extending curvature to all submodular functions yields the first multiplicative greedy approximation guarantees that apply even when the function takes negative values.
Develops polynomial-time algorithms achieving competitive ratios of ~1/14.85 (general) and 1/6.86 (unit costs) for submodular welfare maximization with budgets under random-order item arrival.
A new algorithm finds a matroid basis in tilde O(n to the 3/7) adaptive rounds via independence oracle.
ContextualJailbreak uses evolutionary search over simulated primed dialogues with novel mutations to reach 90-100% attack success on open LLMs and transfers to some closed frontier models at 15-90% rates.
A covariance-adapting algorithm for semi-bandits achieves asymptotically tight regret bounds under a new sub-exponential distribution family, with direct application to sparse rewards.
The observability and controllability Gramians parameterized by sensor and actuator node subsets are determinantal point processes.
Presents the Cascade Log, a reference-stable tiered append structure using a coalescing interval map for handles, with Θ(A) space, O(log A) point resolution, and sublinear cost on append-dominated histories where A is the fragmentation measure.
Multi-response training retains multiple responses per prompt to reduce uncertainty about the conditional output distribution, yielding improved distributional generalization especially in high response-diversity and low prompt-redundancy regimes.
SeedER uses initial dense seeding followed by RL-driven selective expansion to improve recall on compositional KG queries while limiting candidate set size.
SkillLens organizes skills into policies-strategies-procedures-primitives layers, retrieves via degree-corrected random walk, and uses a verifier for local adaptation, yielding up to 6.31 pp gains on MuLocbench and raising ALFWorld success from 45% to 51.31%.
A reformulation of Bayesian OED as dense matrix subset selection plus a pipelined Schur-complement greedy algorithm on hundreds of GPUs enables optimization of 175-sensor networks for billion-degree-of-freedom tsunami models with near-perfect scaling.
A question-adaptive greedy frame selector combines SigLIP relevance and DINOv2 coverage under a submodular objective with a text classifier routing to preset trade-offs, yielding accuracy gains on MLVU especially at low frame budgets.
Revenue maximization for pricing datasets to budget-constrained buyers is APX-hard, with a 2-approximation for online arrivals and a (1-1/e)^{-1}-approximation for offline.
Selective prediction abstains unless all Lipschitz-consistent heads in the version space agree on a certified label for each pool point.
RCD balances relevance, coverage, and diversity in a knapsack-constrained selection framework, with experiments showing that selector choice and budget level determine optimal unitization strategies on clinical datasets.
Empirical tests on three real networks show Shapley-value node selection for coverage under reachability rules reaches ~0.9 approximation ratio and beats degree baseline, with one case covering half of Cora using 26 nodes.
citing papers explorer
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Curvature Beyond Positivity: Greedy Guarantees for Arbitrary Submodular Functions
Extending curvature to all submodular functions yields the first multiplicative greedy approximation guarantees that apply even when the function takes negative values.
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SkillLens: Adaptive Multi-Granularity Skill Reuse for Cost-Efficient LLM Agents
SkillLens organizes skills into policies-strategies-procedures-primitives layers, retrieves via degree-corrected random walk, and uses a verifier for local adaptation, yielding up to 6.31 pp gains on MuLocbench and raising ALFWorld success from 45% to 51.31%.
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Sensor Placement for Tsunami Early Warning via Large-Scale Bayesian Optimal Experimental Design
A reformulation of Bayesian OED as dense matrix subset selection plus a pipelined Schur-complement greedy algorithm on hundreds of GPUs enables optimization of 175-sensor networks for billion-degree-of-freedom tsunami models with near-perfect scaling.
- Neuron-Anchored Rule Extraction for Large Language Models via Contrastive Hierarchical Ablation