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Reformulating Energy Storage Capacity Accreditation Problem with Marginal Reliability Impact
arXiv:2601.22096v2 Announce Type: replace Abstract: To enhance the efficiency of capacity markets, many electricity markets in the U.S. are adopting or planning to implement marginal capacity accreditation reforms. This paper provides new insights into energy storage capacity accreditation using Marginal Reliability Impact (MRI). We reformulate the commonly used reliability-based storage dispatch model as an optimization problem, enabling direct calculation of the MRI from the Lagrange...
Reformulating Neural Operators in $d+1$ Dimensions for Embedding Evolution
arXiv:2505.11766v4 Announce Type: replace Abstract: Neural Operators (NOs) are powerful architectures for learning mappings between function spaces. While most advances focus on refining kernel parameterizations over the $d$-dimensional physical domain, the evolution of lifted embeddings remains underexplored, which often drives models toward computationally expensive embedding-scaling designs to improve approximation. In this paper, we introduce an auxiliary function dimension that models...
Reformulate LLM Reinforcement Learning for Efficient Training under Black-box Discrepancy
Announce Type: new Abstract: Reinforcement Learning (RL) has emerged as a pivotal post-training paradigm, yet it frequently suffers from unpredictable sub-optimum performance or even training collapses. Recent findings attribute these failures to a hidden train-inference discrepancy (or mismatch), stemming from the disparate underlying engines and architecture. We find that the training policy can actively self-correct such a discrepancy when provided with an appropriate learning signal.
Approximating Hartree-Fock theory via an efficiently local reformulation
Announce Type: new Abstract: We explore a reorganized framework for the Hartree Fock equations that allows varying patterns of locality to be imposed on the molecular orbitals while maintaining a highly efficient self-consistent field optimization algorithm. Rather than limiting orbitals' spread and then variationally minimizing the energy within those limits, our reorganization neatly pairs each local degree of freedom with a specific solution condition that itself has a naturally local...
Distributed Algorithm for Robust Wardrop Equilibrium in Uncertain Aggregative Congestion Games
Announce Type: new Abstract: This paper considers a class of aggregative congestion games with uncertain coupling constraints, and devises a distributed algorithm to seek the robust generalized Wardrop equilibrium (RGWE) under worst-case uncertainty. Utilizing robust optimization theory, we reformulate the original aggregative congestion game with uncertainty into a tractable and deterministic augmented problem. Building upon this reformulation, we design a fully distributed algorithm to...
Revisiting Reinforcement Learning with Verifiable Rewards from a Contrastive Perspective
arXiv:2605.12969v3 Announce Type: replace Abstract: Group Relative Policy Optimization (GRPO) is one of the most widely adopted RLVR algorithms for post-training large language models on reasoning tasks. We first show that GRPO admits an equivalent discriminative reformulation, in which policy optimization maximizes the expected score gap between verified positive and negative rollouts. This reformulation reveals two objective-level limitations: likelihood-misaligned surrogate scores, in...
Graph Edit Distance Formulation for the Vehicle Routing Problem: Theory and Analysis
arXiv:2606.01987v1 Announce Type: new Abstract: We show that the Vehicle Routing Problem (VRP) can be reformulated as a Graph Edit Distance (GED) maximization problem. Under a simple edge-deletion cost model, minimizing total route cost is equivalent to maximizing the total weight of edges deleted from the complete instance graph. This formulation models VRP at the edge level, where solutions are defined by selected edges rather than route sequences, enabling structural analyses that are...
ePC: Fast and Deep Predictive Coding in Digital Simulation
arXiv:2505.20137v5 Announce Type: replace Abstract: Predictive Coding (PC) offers a brain-inspired alternative to backpropagation for neural network training, described as a physical system minimizing its internal energy. However, in practice, PC is predominantly digitally simulated, requiring excessive amounts of compute while struggling to scale to deeper architectures. This paper reformulates PC to overcome this hardware-algorithm mismatch.
TALKPLAY: Multimodal Music Recommendation with Large Language Models
Announce Type: replace Abstract: We present TALKPLAY, a novel multimodal music recommendation system that reformulates recommendation as a token generation problem using large language models (LLMs). By leveraging the instruction-following and natural language generation capabilities of LLMs, our system effectively recommends music from diverse user queries while generating contextually relevant responses. While pretrained LLMs are primarily designed for text modality, TALKPLAY extends their...
Learnable Token Sparsification for Efficient Gigapixel Whole Slide Image Reasoning
new Abstract: The processing of gigapixel whole slide images within vision language models faces a major difficulty due to an excessive number of visual tokens. Existing solutions typically rely on spatial downsampling or heuristic pruning strategies that operate without training, and these methods often discard subtle but clinically meaningful patterns because pathological evidence is scattered irregularly across the tissue. To overcome this limitation, we reformulate token reduction in...