Improving TabPFN's Synthetic Data Generation by Integrating Causal Structure
arXiv:2603.10254v1 Announce Type: new Abstract: Synthetic tabular data generation addresses data scarcity and privacy constraints in a variety of domains. Tabular Prior-Data Fitted Network (TabPFN), a recent foundation model for tabular data, has been shown capable of generating high-quality synthetic...
Discovery of a Hematopoietic Manifold in scGPT Yields a Method for Extracting Performant Algorithms from Biological Foundation Model Internals
arXiv:2603.10261v1 Announce Type: new Abstract: We report the discovery and extraction of a compact hematopoietic algorithm from the single-cell foundation model scGPT, to our knowledge the first biologically useful, competitive algorithm extracted from a foundation model via mechanistic interpretability. We...
Robust Post-Training for Generative Recommenders: Why Exponential Reward-Weighted SFT Outperforms RLHF
arXiv:2603.10279v1 Announce Type: new Abstract: Aligning generative recommender systems to user preferences via post-training is critical for closing the gap between next-item prediction and actual recommendation quality. Existing post-training methods are ill-suited for production-scale systems: RLHF methods reward hack due...
Taming Score-Based Denoisers in ADMM: A Convergent Plug-and-Play Framework
arXiv:2603.10281v1 Announce Type: new Abstract: While score-based generative models have emerged as powerful priors for solving inverse problems, directly integrating them into optimization algorithms such as ADMM remains nontrivial. Two central challenges arise: i) the mismatch between the noisy data...
Copula-ResLogit: A Deep-Copula Framework for Unobserved Confounding Effects
arXiv:2603.10284v1 Announce Type: new Abstract: A key challenge in travel demand analysis is the presence of unobserved factors that may generate non-causal dependencies, obscuring the true causal effects. To address the issue, the study introduces a novel deep learning based...
Regime-aware financial volatility forecasting via in-context learning
arXiv:2603.10299v1 Announce Type: new Abstract: This work introduces a regime-aware in-context learning framework that leverages large language models (LLMs) for financial volatility forecasting under nonstationary market conditions. The proposed approach deploys pretrained LLMs to reason over historical volatility patterns and...
How to make the most of your masked language model for protein engineering
arXiv:2603.10302v1 Announce Type: new Abstract: A plethora of protein language models have been released in recent years. Yet comparatively little work has addressed how to best sample from them to optimize desired biological properties. We fill this gap by proposing...
Data-Driven Integration Kernels for Interpretable Nonlocal Operator Learning
arXiv:2603.10305v1 Announce Type: new Abstract: Machine learning models can represent climate processes that are nonlocal in horizontal space, height, and time, often by combining information across these dimensions in highly nonlinear ways. While this can improve predictive skill, it makes...
Optimal Expert-Attention Allocation in Mixture-of-Experts: A Scalable Law for Dynamic Model Design
arXiv:2603.10379v1 Announce Type: new Abstract: This paper presents a novel extension of neural scaling laws to Mixture-of-Experts (MoE) models, focusing on the optimal allocation of compute between expert and attention sub-layers. As MoE architectures have emerged as an efficient method...
Variance-Aware Adaptive Weighting for Diffusion Model Training
arXiv:2603.10391v1 Announce Type: new Abstract: Diffusion models have recently achieved remarkable success in generative modeling, yet their training dynamics across different noise levels remain highly imbalanced, which can lead to inefficient optimization and unstable learning behavior. In this work, we...
Graph-GRPO: Training Graph Flow Models with Reinforcement Learning
arXiv:2603.10395v1 Announce Type: new Abstract: Graph generation is a fundamental task with broad applications, such as drug discovery. Recently, discrete flow matching-based graph generation, \aka, graph flow model (GFM), has emerged due to its superior performance and flexible sampling. However,...
On the Learning Dynamics of Two-layer Linear Networks with Label Noise SGD
arXiv:2603.10397v1 Announce Type: new Abstract: One crucial factor behind the success of deep learning lies in the implicit bias induced by noise inherent in gradient-based training algorithms. Motivated by empirical observations that training with noisy labels improves model generalization, we...
The 14th Amendment’s citizenship clause does not codify English principles of subjectship
Critics and supporters of President Donald Trump’s executive order on birthright citizenship often focus on the order’s barring of automatic citizenship to children born to individuals unlawfully present in the […]The postThe 14th Amendment’s citizenship clause does not codify English...
Amazon expands a program that lets customers shop from other retailers’ sites
The changes allow more merchants to participate in Amazon's Shop Direct program, which sends Amazon customers to other retailers' websites.
Sun Valley Orchards, LLCv. United States Department of Labor
In SEC v. Jarkesy, the Supreme Court failed to fully clarify the “unquestionably muddy” relationship between Article III and the Seventh Amendment. Yet it...The post<em>Sun Valley Orchards, LLC<br>v. United States Department of Labor</em>appeared first onHarvard Law Review.
Automated Thematic Analysis for Clinical Qualitative Data: Iterative Codebook Refinement with Full Provenance
arXiv:2603.08989v1 Announce Type: new Abstract: Thematic analysis (TA) is widely used in health research to extract patterns from patient interviews, yet manual TA faces challenges in scalability and reproducibility. LLM-based automation can help, but existing approaches produce codebooks with limited...
A Consensus-Driven Multi-LLM Pipeline for Missing-Person Investigations
arXiv:2603.08954v1 Announce Type: new Abstract: The first 72 hours of a missing-person investigation are critical for successful recovery. Guardian is an end-to-end system designed to support missing-child investigation and early search planning. This paper presents the Guardian LLM Pipeline, a...
DataFactory: Collaborative Multi-Agent Framework for Advanced Table Question Answering
arXiv:2603.09152v1 Announce Type: new Abstract: Table Question Answering (TableQA) enables natural language interaction with structured tabular data. However, existing large language model (LLM) approaches face critical limitations: context length constraints that restrict data handling capabilities, hallucination issues that compromise answer...
Cognitively Layered Data Synthesis for Domain Adaptation of LLMs to Space Situational Awareness
arXiv:2603.09231v1 Announce Type: new Abstract: Large language models (LLMs) demonstrate exceptional performance on general-purpose tasks. however, transferring them to complex engineering domains such as space situational awareness (SSA) remains challenging owing to insufficient structural alignment with mission chains, the absence...
Quantifying and extending the coverage of spatial categorization data sets
arXiv:2603.09373v1 Announce Type: new Abstract: Variation in spatial categorization across languages is often studied by eliciting human labels for the relations depicted in a set of scenes known as the Topological Relations Picture Series (TRPS). We demonstrate that labels generated...
SPAR-K: Scheduled Periodic Alternating Early Exit for Spoken Language Models
arXiv:2603.09215v1 Announce Type: new Abstract: Interleaved spoken language models (SLMs) alternately generate text and speech tokens, but decoding at full transformer depth for every step becomes costly, especially due to long speech sequences. We propose SPAR-K, a modality-aware early exit...
Emotion is Not Just a Label: Latent Emotional Factors in LLM Processing
arXiv:2603.09205v1 Announce Type: new Abstract: Large language models are routinely deployed on text that varies widely in emotional tone, yet their reasoning behavior is typically evaluated without accounting for emotion as a source of representational variation. Prior work has largely...
Logos: An evolvable reasoning engine for rational molecular design
arXiv:2603.09268v1 Announce Type: new Abstract: The discovery and design of functional molecules remain central challenges across chemistry,biology, and materials science. While recent advances in machine learning have accelerated molecular property prediction and candidate generation, existing models tend to excel either...
PathMem: Toward Cognition-Aligned Memory Transformation for Pathology MLLMs
arXiv:2603.09943v1 Announce Type: new Abstract: Computational pathology demands both visual pattern recognition and dynamic integration of structured domain knowledge, including taxonomy, grading criteria, and clinical evidence. In practice, diagnostic reasoning requires linking morphological evidence with formal diagnostic and grading criteria....
Influencing LLM Multi-Agent Dialogue via Policy-Parameterized Prompts
arXiv:2603.09890v1 Announce Type: new Abstract: Large Language Models (LLMs) have emerged as a new paradigm for multi-agent systems. However, existing research on the behaviour of LLM-based multi-agents relies on ad hoc prompts and lacks a principled policy perspective. Different from...
LCA: Local Classifier Alignment for Continual Learning
arXiv:2603.09888v1 Announce Type: new Abstract: A fundamental requirement for intelligent systems is the ability to learn continuously under changing environments. However, models trained in this regime often suffer from catastrophic forgetting. Leveraging pre-trained models has recently emerged as a promising...
Quantifying the Necessity of Chain of Thought through Opaque Serial Depth
arXiv:2603.09786v1 Announce Type: new Abstract: Large language models (LLMs) tend to externalize their reasoning in their chain of thought, making the chain of thought a good target for monitoring. This is partially an inherent feature of the Transformer architecture: sufficiently...
AutoAgent: Evolving Cognition and Elastic Memory Orchestration for Adaptive Agents
arXiv:2603.09716v1 Announce Type: new Abstract: Autonomous agent frameworks still struggle to reconcile long-term experiential learning with real-time, context-sensitive decision-making. In practice, this gap appears as static cognition, rigid workflow dependence, and inefficient context usage, which jointly limit adaptability in open-ended...
Enhancing Debunking Effectiveness through LLM-based Personality Adaptation
arXiv:2603.09533v1 Announce Type: new Abstract: This study proposes a novel methodology for generating personalized fake news debunking messages by prompting Large Language Models (LLMs) with persona-based inputs aligned to the Big Five personality traits: Extraversion, Agreeableness, Conscientiousness, Neuroticism, and Openness....
Vibe-Creation: The Epistemology of Human-AI Emergent Cognition
arXiv:2603.09486v1 Announce Type: new Abstract: The encounter between human reasoning and generative artificial intelligence (GenAI) cannot be adequately described by inherited metaphors of tool use, augmentation, or collaborative partnership. This article argues that such interactions produce a qualitatively distinct cognitive-epistemic...