A Survey of Weight Space Learning: Understanding, Representation, and Generation
arXiv:2603.10090v1 Announce Type: new Abstract: Neural network weights are typically viewed as the end product of training, while most deep learning research focuses on data, features, and architectures. However, recent advances show that the set of all possible weight values...
Denoising the US Census: Succinct Block Hierarchical Regression
arXiv:2603.10099v1 Announce Type: new Abstract: The US Census Bureau Disclosure Avoidance System (DAS) balances confidentiality and utility requirements for the decennial US Census (Abowd et al., 2022). The DAS was used in the 2020 Census to produce demographic datasets critically...
Hardware Efficient Approximate Convolution with Tunable Error Tolerance for CNNs
arXiv:2603.10100v1 Announce Type: new Abstract: Modern CNNs' high computational demands hinder edge deployment, as traditional ``hard'' sparsity (skipping mathematical zeros) loses effectiveness in deep layers or with smooth activations like Tanh. We propose a ``soft sparsity'' paradigm using a hardware...
DT-BEHRT: Disease Trajectory-aware Transformer for Interpretable Patient Representation Learning
arXiv:2603.10180v1 Announce Type: new Abstract: The growing adoption of electronic health record (EHR) systems has provided unprecedented opportunities for predictive modeling to guide clinical decision making. Structured EHRs contain longitudinal observations of patients across hospital visits, where each visit is...
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...
GSVD for Geometry-Grounded Dataset Comparison: An Alignment Angle Is All You Need
arXiv:2603.10283v1 Announce Type: new Abstract: Geometry-grounded learning asks models to respect structure in the problem domain rather than treating observations as arbitrary vectors. Motivated by this view, we revisit a classical but underused primitive for comparing datasets: linear relations between...
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...
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...
Abandoning the separation of powers in times of war
Courtly Observations is a recurring series by Erwin Chemerinsky that focuses on what the Supreme Court’s decisions will mean for the law, for lawyers and lower courts, and for people’s lives. […]The postAbandoning the separation of powers in times of...
WordPress debuts a private workspace that runs in your browser via a new service, my.WordPress.net
WordPress’s new browser-based service lets users create private sites without hosting or signing up, turning the platform into a personal workspace for writing, research, and AI tools.
What is a Tort?
What is a tort, and what is tort law for? On one leading scholarly account, torts are legal liability rules that seek to promote the welfare of society at large by disincentivizing socially suboptimal behavior and distributing the costs of...
Reward Prediction with Factorized World States
arXiv:2603.09400v1 Announce Type: new Abstract: Agents must infer action outcomes and select actions that maximize a reward signal indicating how close the goal is to being reached. Supervised learning of reward models could introduce biases inherent to training data, limiting...
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...
Social-R1: Towards Human-like Social Reasoning in LLMs
arXiv:2603.09249v1 Announce Type: new Abstract: While large language models demonstrate remarkable capabilities across numerous domains, social intelligence - the capacity to perceive social cues, infer mental states, and generate appropriate responses - remains a critical challenge, particularly for enabling effective...
Curveball Steering: The Right Direction To Steer Isn't Always Linear
arXiv:2603.09313v1 Announce Type: new Abstract: Activation steering is a widely used approach for controlling large language model (LLM) behavior by intervening on internal representations. Existing methods largely rely on the Linear Representation Hypothesis, assuming behavioral attributes can be manipulated using...
Evaluate-as-Action: Self-Evaluated Process Rewards for Retrieval-Augmented Agents
arXiv:2603.09203v1 Announce Type: new Abstract: Retrieval-augmented agents can query external evidence, yet their reliability in multi-step reasoning remains limited: noisy retrieval may derail multi-hop question answering, while outcome-only reinforcement learning provides credit signals that are too coarse to optimize intermediate...
The Confidence Gate Theorem: When Should Ranked Decision Systems Abstain?
arXiv:2603.09947v1 Announce Type: new Abstract: Ranked decision systems -- recommenders, ad auctions, clinical triage queues -- must decide when to intervene in ranked outputs and when to abstain. We study when confidence-based abstention monotonically improves decision quality, and when it...
Logics-Parsing-Omni Technical Report
arXiv:2603.09677v1 Announce Type: new Abstract: Addressing the challenges of fragmented task definitions and the heterogeneity of unstructured data in multimodal parsing, this paper proposes the Omni Parsing framework. This framework establishes a Unified Taxonomy covering documents, images, and audio-visual streams,...
Meissa: Multi-modal Medical Agentic Intelligence
arXiv:2603.09018v1 Announce Type: new Abstract: Multi-modal large language models (MM-LLMs) have shown strong performance in medical image understanding and clinical reasoning. Recent medical agent systems extend them with tool use and multi-agent collaboration, enabling complex decision-making. However, these systems rely...
Think Before You Lie: How Reasoning Improves Honesty
arXiv:2603.09957v1 Announce Type: new Abstract: While existing evaluations of large language models (LLMs) measure deception rates, the underlying conditions that give rise to deceptive behavior are poorly understood. We investigate this question using a novel dataset of realistic moral trade-offs...
TaSR-RAG: Taxonomy-guided Structured Reasoning for Retrieval-Augmented Generation
arXiv:2603.09341v1 Announce Type: new Abstract: Retrieval-Augmented Generation (RAG) helps large language models (LLMs) answer knowledge-intensive and time-sensitive questions by conditioning generation on external evidence. However, most RAG systems still retrieve unstructured chunks and rely on one-shot generation, which often yields...
Abundant Intelligence and Deficient Demand: A Macro-Financial Stress Test of Rapid AI Adoption
arXiv:2603.09209v1 Announce Type: new Abstract: We formalize a macro-financial stress test for rapid AI adoption. Rather than a productivity bust or existential risk, we identify a distribution-and-contract mismatch: AI-generated abundance coexists with demand deficiency because economic institutions are anchored to...
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...
One Language, Two Scripts: Probing Script-Invariance in LLM Concept Representations
arXiv:2603.08869v1 Announce Type: new Abstract: Do the features learned by Sparse Autoencoders (SAEs) represent abstract meaning, or are they tied to how text is written? We investigate this question using Serbian digraphia as a controlled testbed: Serbian is written interchangeably...
Telogenesis: Goal Is All U Need
arXiv:2603.09476v1 Announce Type: new Abstract: Goal-conditioned systems assume goals are provided externally. We ask whether attentional priorities can emerge endogenously from an agent's internal cognitive state. We propose a priority function that generates observation targets from three epistemic gaps: ignorance...
The Reasoning Trap -- Logical Reasoning as a Mechanistic Pathway to Situational Awareness
arXiv:2603.09200v1 Announce Type: new Abstract: Situational awareness, the capacity of an AI system to recognize its own nature, understand its training and deployment context, and reason strategically about its circumstances, is widely considered among the most dangerous emergent capabilities in...
Reading, Not Thinking: Understanding and Bridging the Modality Gap When Text Becomes Pixels in Multimodal LLMs
arXiv:2603.09095v1 Announce Type: new Abstract: Multimodal large language models (MLLMs) can process text presented as images, yet they often perform worse than when the same content is provided as textual tokens. We systematically diagnose this "modality gap" by evaluating seven...
Time, Identity and Consciousness in Language Model Agents
arXiv:2603.09043v1 Announce Type: new Abstract: Machine consciousness evaluations mostly see behavior. For language model agents that behavior is language and tool use. That lets an agent say the right things about itself even when the constraints that should make those...
ALARM: Audio-Language Alignment for Reasoning Models
arXiv:2603.09556v1 Announce Type: new Abstract: Large audio language models (ALMs) extend LLMs with auditory understanding. A common approach freezes the LLM and trains only an adapter on self-generated targets. However, this fails for reasoning LLMs (RLMs) whose built-in chain-of-thought traces...
RbtAct: Rebuttal as Supervision for Actionable Review Feedback Generation
arXiv:2603.09723v1 Announce Type: new Abstract: Large language models (LLMs) are increasingly used across the scientific workflow, including to draft peer-review reports. However, many AI-generated reviews are superficial and insufficiently actionable, leaving authors without concrete, implementable guidance and motivating the gap...