PolyGLU: State-Conditional Activation Routing in Transformer Feed-Forward Networks
arXiv:2603.13347v1 Announce Type: new Abstract: Biological neural systems employ diverse neurotransmitters -- glutamate, GABA, dopamine, acetylcholine -- to implement distinct signal-processing modalities within shared neural circuits. In contrast, modern transformers apply a single fixed activation function across all feed-forward neurons....
Thermal Robustness of Retrieval in Dense Associative Memories: LSE vs LSR Kernels
arXiv:2603.13350v1 Announce Type: new Abstract: Understanding whether retrieval in dense associative memories survives thermal noise is essential for bridging zero-temperature capacity proofs with the finite-temperature conditions of practical inference and biological computation. We use Monte Carlo simulations to map the...
A Hierarchical End-of-Turn Model with Primary Speaker Segmentation for Real-Time Conversational AI
arXiv:2603.13379v1 Announce Type: new Abstract: We present a real-time front-end for voice-based conversational AI to enable natural turn-taking in two-speaker scenarios by combining primary speaker segmentation with hierarchical End-of-Turn (EOT) detection. To operate robustly in multi-speaker environments, the system continuously...
Justices will hear argument on Trump administration’s removal of protected status for Syrian and Haitian nationals
The Supreme Court announced on Monday afternoon that it will hear oral argument on whether the Trump administration can end a program that allows several thousand Syrians and approximately 350,000 […]The postJustices will hear argument on Trump administration’s removal of...
Haitian nationals ask court to deny Trump administration’s request to remove their protected status
A group of Haitian nationals urged the Supreme Court on Monday to leave in place a ruling by a federal judge in Washington, D.C., that allows them to stay in […]The postHaitian nationals ask court to deny Trump administration’s request...
Birthright citizenship: a response to Pete Patterson
Brothers in Law is a recurring series by brothers Akhil and Vikram Amar, with special emphasis on measuring what the Supreme Court says against what the Constitution itself says. For more content from […]The postBirthright citizenship: a response to Pete...
A 95th birthday tribute to legendary SCOTUSblog reporter Lyle Denniston
The inimitable Lyle Denniston, who served as the primary reporter for SCOTUSblog from 2004 until 2016, celebrates his 95th birthday today. Lyle began his reporting career in 1948 at the […]The postA 95th birthday tribute to legendary SCOTUSblog reporter Lyle...
Trump and his FCC chair demand more positive news coverage of Iran war
Carr makes evidence-free claim of "hoaxes and news distortions." Trump is thrilled.
Jensen Huang just put Nvidia’s Blackwell and Vera Rubin sales projections into the $1 trillion stratosphere
Nvidia CEO Jensen Huang said he expects $1 trillion worth of orders for the chips.
How to watch Jensen Huang’s Nvidia GTC 2026 keynote — and what to expect
GTC is Nvidia's flagship annual event, where the chipmaker typically announces new products, partnerships, and its vision for the future of computing. Huang's keynote will focus on Nvidia's role in the future of computing and AI.
Another deep tech chip startup becomes a unicorn: Frore hits $1.64B
At Nvidia CEO Jensen Huang's urging, Frore developed liquid-cooling tech for chips. That shift helped it raise $143 million.
The dictionary sues OpenAI
Encyclopedia Britannica and Merriam-Webster say that OpenAI violated the copyright of almost 100,000 articles by using them for LLM training.
Semantic Invariance in Agentic AI
arXiv:2603.13173v1 Announce Type: new Abstract: Large Language Models (LLMs) increasingly serve as autonomous reasoning agents in decision support, scientific problem-solving, and multi-agent coordination systems. However, deploying LLM agents in consequential applications requires assurance that their reasoning remains stable under semantically...
The DIME Architecture: A Unified Operational Algorithm for Neural Representation, Dynamics, Control and Integration
arXiv:2603.12286v1 Announce Type: cross Abstract: Modern neuroscience has accumulated extensive evidence on perception, memory, prediction, valuation, and consciousness, yet still lacks an explicit operational architecture capable of integrating these phenomena within a unified computational framework. Existing theories address specific aspects...
ODRL Policy Comparison Through Normalisation
arXiv:2603.12926v1 Announce Type: new Abstract: The ODRL language has become the standard for representing policies and regulations for digital rights. However its complexity is a barrier to its usage, which has caused many related theoretical and practical works to focus...
Structured Distillation for Personalized Agent Memory: 11x Token Reduction with Retrieval Preservation
arXiv:2603.13017v1 Announce Type: new Abstract: Long conversations with an AI agent create a simple problem for one user: the history is useful, but carrying it verbatim is expensive. We study personalized agent memory: one user's conversation history with an agent,...
On Using Machine Learning to Early Detect Catastrophic Failures in Marine Diesel Engines
arXiv:2603.12733v1 Announce Type: new Abstract: Catastrophic failures of marine engines imply severe loss of functionality and destroy or damage the systems irreversibly. Being sudden and often unpredictable events, they pose a severe threat to navigation, crew, and passengers. The abrupt...
Diagnosing Retrieval Bias Under Multiple In-Context Knowledge Updates in Large Language Models
arXiv:2603.12271v1 Announce Type: cross Abstract: LLMs are widely used in knowledge-intensive tasks where the same fact may be revised multiple times within context. Unlike prior work focusing on one-shot updates or single conflicts, multi-update scenarios contain multiple historically valid versions...
Thermodynamics of Reinforcement Learning Curricula
arXiv:2603.12324v1 Announce Type: cross Abstract: Connections between statistical mechanics and machine learning have repeatedly proven fruitful, providing insight into optimization, generalization, and representation learning. In this work, we follow this tradition by leveraging results from non-equilibrium thermodynamics to formalize curriculum...
Efficient Reasoning with Balanced Thinking
arXiv:2603.12372v1 Announce Type: new Abstract: Large Reasoning Models (LRMs) have shown remarkable reasoning capabilities, yet they often suffer from overthinking, expending redundant computational steps on simple problems, or underthinking, failing to explore sufficient reasoning paths despite inherent capabilities. These issues...
Context-Enriched Natural Language Descriptions of Vessel Trajectories
arXiv:2603.12287v1 Announce Type: new Abstract: We address the problem of transforming raw vessel trajectory data collected from AIS into structured and semantically enriched representations interpretable by humans and directly usable by machine reasoning systems. We propose a context-aware trajectory abstraction...
Synthetic Data Generation for Brain-Computer Interfaces: Overview, Benchmarking, and Future Directions
arXiv:2603.12296v1 Announce Type: cross Abstract: Deep learning has achieved transformative performance across diverse domains, largely driven by the large-scale, high-quality training data. In contrast, the development of brain-computer interfaces (BCIs) is fundamentally constrained by the limited, heterogeneous, and privacy-sensitive neural...
Beyond Final Answers: CRYSTAL Benchmark for Transparent Multimodal Reasoning Evaluation
arXiv:2603.13099v1 Announce Type: new Abstract: We introduce **CRYSTAL** (*__C__lear __R__easoning via __Y__ielded __S__teps, __T__raceability and __L__ogic*), a diagnostic benchmark with 6,372 instances that evaluates multimodal reasoning through verifiable intermediate steps. We propose two complementary metrics: *Match F1*, which scores step-level...
AI Planning Framework for LLM-Based Web Agents
arXiv:2603.12710v1 Announce Type: new Abstract: Developing autonomous agents for web-based tasks is a core challenge in AI. While Large Language Model (LLM) agents can interpret complex user requests, they often operate as black boxes, making it difficult to diagnose why...
Developing and evaluating a chatbot to support maternal health care
arXiv:2603.13168v1 Announce Type: new Abstract: The ability to provide trustworthy maternal health information using phone-based chatbots can have a significant impact, particularly in low-resource settings where users have low health literacy and limited access to care. However, deploying such systems...
Detecting Miscitation on the Scholarly Web through LLM-Augmented Text-Rich Graph Learning
arXiv:2603.12290v1 Announce Type: cross Abstract: Scholarly web is a vast network of knowledge connected by citations. However, this system is increasingly compromised by miscitation, where references do not support or even contradict the claims they are cited for. Current miscitation...
Optimizing Task Completion Time Updates Using POMDPs
arXiv:2603.12340v1 Announce Type: cross Abstract: Managing announced task completion times is a fundamental control problem in project management. While extensive research exists on estimating task durations and task scheduling, the problem of when and how to update completion times communicated...
Budget-Sensitive Discovery Scoring: A Formally Verified Framework for Evaluating AI-Guided Scientific Selection
arXiv:2603.12349v1 Announce Type: cross Abstract: Scientific discovery increasingly relies on AI systems to select candidates for expensive experimental validation, yet no principled, budget-aware evaluation framework exists for comparing selection strategies -- a gap intensified by large language models (LLMs), which...
SPARROW: Learning Spatial Precision and Temporal Referential Consistency in Pixel-Grounded Video MLLMs
arXiv:2603.12382v1 Announce Type: cross Abstract: Multimodal large language models (MLLMs) have advanced from image-level reasoning to pixel-level grounding, but extending these capabilities to videos remains challenging as models must achieve spatial precision and temporally consistent reference tracking. Existing video MLLMs...
Test-Time Strategies for More Efficient and Accurate Agentic RAG
arXiv:2603.12396v1 Announce Type: cross Abstract: Retrieval-Augmented Generation (RAG) systems face challenges with complex, multihop questions, and agentic frameworks such as Search-R1 (Jin et al., 2025), which operates iteratively, have been proposed to address these complexities. However, such approaches can introduce...