Pareto
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Related Articles from SNS
Pareto Optimality in Approval-Based Multiwinner Voting
arXiv:2605.30490v1 Announce Type: new Abstract: In approval-based multiwinner voting, Pareto optimality is used as an axiom capturing efficiency of committees. We study the structure of the space of Pareto optimal committees in restricted domains and in general by investigating the monotonicity and reconfigurability of such committees. For the Candidate Interval and Voter Interval domains, we propose the Single Dominance Only property, which provides a simple characterization of Pareto...
CoAction: Cross-task Correlation-aware Pareto Set Learning
Announce Type: replace Abstract: Pareto set learning (PSL) is an emerging paradigm in multi-objective optimization that trains neural networks to map preference vectors to Pareto optimal solutions. However, existing PSL methods primarily focus on solving a single multi-objective optimization problem at a time. This limitation not only increases computational costs in multi-objective multitask optimization scenarios by requiring a separate model for each task, but also fails to exploit the...
Population-Free Pareto Tracking for Sample-Efficient Multi-Policy MORL
Announce Type: replace Abstract: Multi-objective reinforcement learning (MORL) is a fundamental framework for real-world decision-making problems involving multiple conflicting criteria. Existing multi-policy (MP) methods typically rely on online evolutionary frameworks that maintain large policy populations, leading to high sample complexity and excessive agent-environment interactions. To mitigate these limitations, we present Multi-policy Pareto Front Tracking (MPFT), a framework without...
BigMac: Breaking the Pareto Frontier of Compute and Memory in Multimodal LLM Training
Announce Type: replace Abstract: Training multimodal large language models (MLLMs) is challenged by both model and data heterogeneity. Existing systems redesign the training pipeline to address these challenges, but remain bound by a Pareto frontier between compute and memory efficiency, improving one only at the expense of the other. We present BigMac, a new training pipeline for multimodal LLMs.
EFX for Additive Chores: Nonexistence, Pareto Incompatibility, and Bi-Valued Existence
arXiv:2606.08872v1 Announce Type: new Abstract: We consider the fair division problem of indivisible chores and resolve the long-standing open problem for the existence of EFX allocations with additive cost functions. We show that, even for tri-valued additive cost functions, for every $n\geq 4$, there exists an instance with $n$ agents where no EFX allocation exists. Our counterexample only uses three types of chores, which is also tight on the number of types, as an EFX allocation is known...
Tuning Dispatch Thresholds for Fixed Last-Mile Routes: A Simulation-Based Pareto Analysis of a Production Policy
arXiv:2606.09455v1 Announce Type: new Abstract: Many parcel networks dispatch vehicles on \emph{fixed routes} using a simple load-accumulation rule: a truck leaves the depot for a fixed route as soon as the volume (or item count) waiting for that route crosses a threshold. The threshold is usually parameterised as an affine function of route length, $\tau_r=\beta+\gamma\,d_r$, and the pair $(\beta,\gamma)$ is chosen once and frozen into production. This paper studies how good that frozen...
MailoHLS: Multi-Adapter Structure-Aware Learning for Pareto-Driven HLS Pragma Optimization
arXiv:2606.07246v1 Announce Type: new Abstract: High-Level Synthesis (HLS) enables rapid development of FPGA accelerators, yet achieving high-quality results (QoR) remains challenging due to the large and irregular design space induced by compiler directives (a.k.a pragmas). Selecting effective configurations requires reasoning over complex interactions between program structure, memory behavior, and often conflicting objectives such as latency and resource utilization. Prior model-driven...
Activation-Informed Pareto-Guided Low-Rank Compression for Efficient LLM/VLM
Announce Type: replace Abstract: Large language models (LLM) and vision-language models (VLM) have achieved state-of-the-art performance, but they impose significant memory and computing challenges in deployment. We present a novel low-rank compression framework to address this challenge. First, we upper bound the change of network loss via layer-wise activation-based compression errors, filling a theoretical gap in the literature.
PMF-CL: Pareto-Minimal-Forgetting Continual Learner for Conflicting Tasks
arXiv:2605.19145v2 Announce Type: replace Abstract: In the literature, many continual learning (CL) algorithms have been proposed to address the issue of catastrophic forgetting in ML models (i.e., learning new tasks leads to the loss of performance on previously learned tasks). Although all CL approaches use some form of memory to retain information about past tasks, a grounded understanding of what information needs to be stored to minimize catastrophic forgetting remains elusive....
EvoDrive: Pareto Evolution for Safety-Critical Autonomous Driving via Self-Improving LLM Agents
new Abstract: Generating safety-critical scenarios is essential for validating and improving autonomous driving systems, yet it inherently requires maximizing adversariality to expose failures while preserving realism. Existing methods usually manage this trade-off with handcrafted heuristics, confining generation to known priors and overlooking underexplored patterns. While recent open-ended agentic evolution can push this limit, unconstrained general agents lack strict simulator grounding...