arXiv:2605.06384v1 Announce Type: cross Abstract: We show that the MinMax algebra provides a form of recurrence that is expressively powerful, efficiently implementable, and most importantly it is not affected by vanishing or exploding gradient. We call MinMax Recurrent Neural Cascades (RNCs) the models obtained by cascading several layers of neurons that employ such recurrence. We […]
Learning to Cut: Reinforcement Learning for Benders Decomposition
arXiv:2605.06516v1 Announce Type: cross Abstract: Benders decomposition (BD) is a widely used solution approach for solving two-stage stochastic programs arising in real-world decision-making under uncertainty. However, it often suffers from slow convergence as the master problem grows with an increasing number of cuts. In this paper, we propose Reinforcement Learning for BD (RLBD), a framework […]
Concept-Based Abductive and Contrastive Explanations for Behaviors of Vision Models
arXiv:2605.06640v1 Announce Type: cross Abstract: *Concept-based explanations* offer a promising approach for explaining the predictions of deep neural networks in terms of high-level, human-understandable concepts. However, existing methods either do not establish a causal connection between the concepts and model predictions or are limited in expressivity and only able to infer causal explanations involving single […]
SynBench: A Benchmark for Differentially Private Text Generation
arXiv:2509.14594v2 Announce Type: replace Abstract: Synthetic text generation with Differential Privacy (DP) guarantees emerges as a principled approach that can enable the sharing of sensitive datasets across institutional and regulatory boundaries, while bounding the risks of re-identification and membership inference. LLM-based methods deliver promising results; however, comparisons are exacerbated by differing evaluation setups and “private” […]
An Efficient Insect-inspired Approach for Visual Point-goal Navigation
arXiv:2601.16806v2 Announce Type: replace Abstract: In this work we develop a novel insect-inspired model for visual point-goal navigation. This combines abstracted models of two insect brain structures that have been implicated, respectively, in associative learning and path integration. We draw an analogy between the formal benchmark of the Habitat point-goal navigation task and the ability […]
MAT-Cell: A Multi-Agent Tree-Structured Reasoning Framework for Batch-Level Single-Cell Annotation
arXiv:2604.06269v2 Announce Type: replace Abstract: Automated single-cell annotation is difficult when the most abundant genes are not the most discriminative ones, or when a target state is poorly covered by a fixed reference atlas. GPTCelltype-style one-shot prompting allows large language models (LLMs) to produce plausible labels from generic expression signals, while reference-based annotators can force […]
Cohort-Based Active Modality Acquisition
arXiv:2505.16791v4 Announce Type: replace-cross Abstract: Real-world multimodal machine learning often faces missing, costly-to-acquire modalities, raising the problem of which samples to prioritize for additional acquisition under a budget. Prior work mainly studies per-sample or training-time acquisition while test-time, cohort-level acquisition is less explored. We propose Cohort-based Active Modality Acquisition (CAMA), a novel test-time cohort-level modality […]
Beyond Fixed Psychological Personas: State Beats Trait, but Language Models are State-Blind
arXiv:2601.15395v2 Announce Type: replace-cross Abstract: User interactions with language models vary due to static properties of the user (trait) and the specific context of the interaction (state). However, existing persona datasets (like PersonaChat, PANDORA etc.) capture only trait, and ignore the impact of state. We introduce Chameleon, a dataset of 5,001 contextual psychological profiles from […]
DOGMA: Weaving Structural Information into Data-centric Single-cell Transcriptomics Analysis
arXiv:2602.01839v2 Announce Type: replace-cross Abstract: Recently, data-centric AI methodology has been a dominant paradigm in single-cell transcriptomics analysis, which treats data representation rather than model complexity as the fundamental bottleneck. In the review of current studies, earlier sequence methods treat cells as independent entities and adapt prevalent ML models to analyze their directly inherited sequence […]
AI Agents Alone Are Not (Yet) Sufficient for Social Simulation
arXiv:2603.00113v2 Announce Type: replace-cross Abstract: Recent advances in large language models (LLMs) have spurred growing interest in using LLM-integrated agents for social simulation, often under the implicit assumption that realistic population dynamics will emerge once role-specified agents are placed in a networked multi-agent setting. This position paper argues that LLM-based agents alone are not (yet) […]
Spectral Alignment in Forward-Backward Representations via Temporal Abstraction
arXiv:2603.20103v3 Announce Type: replace-cross Abstract: Forward-backward (FB) representations provide a powerful framework for learning the successor representation (SR) in continuous spaces by enforcing a low-rank factorization. However, a fundamental spectral mismatch often exists between the high-rank transition dynamics of continuous environments and the low-rank bottleneck of the FB architecture, making accurate low-rank representation learning difficult. […]
Low-Rank Adaptation for Critic Learning in Off-Policy Reinforcement Learning
arXiv:2604.18978v2 Announce Type: replace-cross Abstract: Scaling critic capacity is a promising direction for improving off-policy reinforcement learning (RL). However, recent work shows that larger critics are prone to overfitting and instability in replay-based bootstrapped training. In this paper, we propose using Low-Rank Adaptation (LoRA) as a structural regularizer for critic learning. Our approach freezes randomly […]