TRACE: Traceroute-based Internet Route change Analysis with Ensemble Learning
arXiv:2604.02361v1 Announce Type: cross Abstract: Detecting Internet routing instability is a critical yet challenging task, particularly when relying solely on endpoint active measurements. This study introduces TRACE, a MachineLearning (ML)pipeline designed to identify route changes using only traceroute latency data,...
Prism: Policy Reuse via Interpretable Strategy Mapping in Reinforcement Learning
arXiv:2604.02353v1 Announce Type: cross Abstract: We present PRISM (Policy Reuse via Interpretable Strategy Mapping), a framework that grounds reinforcement learning agents' decisions in discrete, causally validated concepts and uses those concepts as a zero-shot transfer interface between agents trained with...
Analytic Drift Resister for Non-Exemplar Continual Graph Learning
arXiv:2604.02633v1 Announce Type: new Abstract: Non-Exemplar Continual Graph Learning (NECGL) seeks to eliminate the privacy risks intrinsic to rehearsal-based paradigms by retaining solely class-level prototype representations rather than raw graph examples for mitigating catastrophic forgetting. However, this design choice inevitably...
Agentic-MME: What Agentic Capability Really Brings to Multimodal Intelligence?
arXiv:2604.03016v1 Announce Type: new Abstract: Multimodal Large Language Models (MLLMs) are evolving from passive observers into active agents, solving problems through Visual Expansion (invoking visual tools) and Knowledge Expansion (open-web search). However, existing evaluations fall short: they lack flexible tool...
Analysis of Optimality of Large Language Models on Planning Problems
arXiv:2604.02910v1 Announce Type: new Abstract: Classic AI planning problems have been revisited in the Large Language Model (LLM) era, with a focus of recent benchmarks on success rates rather than plan efficiency. We examine the degree to which frontier models...
DrugPlayGround: Benchmarking Large Language Models and Embeddings for Drug Discovery
arXiv:2604.02346v1 Announce Type: cross Abstract: Large language models (LLMs) are in the ascendancy for research in drug discovery, offering unprecedented opportunities to reshape drug research by accelerating hypothesis generation, optimizing candidate prioritization, and enabling more scalable and cost-effective drug discovery...
SWAY: A Counterfactual Computational Linguistic Approach to Measuring and Mitigating Sycophancy
arXiv:2604.02423v1 Announce Type: new Abstract: Large language models exhibit sycophancy: the tendency to shift outputs toward user-expressed stances, regardless of correctness or consistency. While prior work has studied this issue and its impacts, rigorous computational linguistic metrics are needed to...
Compositional Neuro-Symbolic Reasoning
arXiv:2604.02434v1 Announce Type: new Abstract: We study structured abstraction-based reasoning for the Abstraction and Reasoning Corpus (ARC) and compare its generalization to test-time approaches. Purely neural architectures lack reliable combinatorial generalization, while strictly symbolic systems struggle with perceptual grounding. We...
Supreme Court issues statement that Justice Alito was hospitalized approximately two weeks ago
Justice Samuel Alito was hospitalized on March 20 “[o]ut of an abundance of caution” and at the recommendation of his security detail, the Supreme Court’s Public Information Officer, Patricia McCabe, […]The postSupreme Court issues statement that Justice Alito was hospitalized...
What oral argument told us in the birthright citizenship case
Empirical SCOTUS is a recurring series by Adam Feldman that looks at Supreme Court data, primarily in the form of opinions and oral arguments, to provide insights into the justices’ decision making and […]The postWhat oral argument told us in...
The inscrutable Chief Justice John Roberts
As much of the legal media (including SCOTUSblog) reported last month, Chief Justice John Roberts offered some rare public remarks in an appearance at Rice University, rebuking personal attacks on […]The postThe inscrutable Chief Justice John Robertsappeared first onSCOTUSblog.
SCOTUStoday for Friday, April 3
Comedian John Mulaney appeared on “The Late Show with Stephen Colbert” earlier this week and gave a shoutout to SCOTUSblog as he described being a “Supreme Court argument nerd.” Mama, […]The postSCOTUStoday for Friday, April 3appeared first onSCOTUSblog.
Trump ignores biggest reasons his AI data center buildout is failing
Nearly 50% of data center projects delayed as China holds key to power infrastructure.
Netflix must refund customers for years of price hikes, Italian court rules
Consumer group says it will sue if Netflix doesn't reduce current prices.
The Privileges or Immunities Clause, Abridged: A Critique of Kurt Lash on the Fourteenth Amendment
ARTICLE The Privileges or Immunities Clause, Abridged: A Critique of Kurt Lash on the Fourteenth Amendment Randy E. Barnett* & Evan D. Bernick** The Privileges or Immunities Clause of the Fourteenth Amendment reads: “No State shall make or enforce any...
The Enumerated-Rights Reading of the Privileges or Immunities Clause: A Response to Barnett and Bernick
ARTICLE The Enumerated-Rights Reading of the Privileges or Immunities Clause: A Response to Barnett and Bernick Kurt T. Lash* In 1871, John Bingham explained the meaning of the Fourteenth Amendment’s Privileges or Immunities Clause—a clause Bingham himself drafted and had...
Towards Reliable Truth-Aligned Uncertainty Estimation in Large Language Models
arXiv:2604.00445v1 Announce Type: new Abstract: Uncertainty estimation (UE) aims to detect hallucinated outputs of large language models (LLMs) to improve their reliability. However, UE metrics often exhibit unstable performance across configurations, which significantly limits their applicability. In this work, we...
Learning from the Right Rollouts: Data Attribution for PPO-based LLM Post-Training
arXiv:2604.01597v1 Announce Type: new Abstract: Traditional RL algorithms like Proximal Policy Optimization (PPO) typically train on the entire rollout buffer, operating under the assumption that all generated episodes provide a beneficial optimization signal. However, these episodes frequently contain noisy or...
Expert-Choice Routing Enables Adaptive Computation in Diffusion Language Models
arXiv:2604.01622v1 Announce Type: new Abstract: Diffusion language models (DLMs) enable parallel, non-autoregressive text generation, yet existing DLM mixture-of-experts (MoE) models inherit token-choice (TC) routing from autoregressive systems, leading to load imbalance and rigid computation allocation. We show that expert-choice (EC)...
SCOTUStoday for Wednesday, April 1
This morning, the court will hear argument in the birthright citizenship case, Trump v. Barbara. We will be live blogging beginning at 9:30 a.m. EDT. For a great introduction to […]The postSCOTUStoday for Wednesday, April 1appeared first onSCOTUSblog.
Optimizing EEG Graph Structure for Seizure Detection: An Information Bottleneck and Self-Supervised Learning Approach
arXiv:2604.01595v1 Announce Type: new Abstract: Seizure detection from EEG signals is highly challenging due to complex spatiotemporal dynamics and extreme inter-patient variability. To model them, recent methods construct dynamic graphs via statistical correlations, predefined similarity measures, or implicit learning, yet...
Adaptive Parallel Monte Carlo Tree Search for Efficient Test-time Compute Scaling
arXiv:2604.00510v1 Announce Type: new Abstract: Monte Carlo Tree Search (MCTS) is an effective test-time compute scaling (TTCS) method for improving the reasoning performance of large language models, but its highly variable execution time leads to severe long-tail latency in practice....
Birthright citizenship live blog for Wednesday, April 1
On Wednesday, April 1, we will be live blogging as the court hears argument in Trump v. Barbara, on the constitutionality of President Donald Trump’s executive order on birthright citizenship. […]The postBirthright citizenship live blog for Wednesday, April 1appeared first...
BloClaw: An Omniscient, Multi-Modal Agentic Workspace for Next-Generation Scientific Discovery
arXiv:2604.00550v1 Announce Type: new Abstract: The integration of Large Language Models (LLMs) into life sciences has catalyzed the development of "AI Scientists." However, translating these theoretical capabilities into deployment-ready research environments exposes profound infrastructural vulnerabilities. Current frameworks are bottlenecked by...
CircuitProbe: Predicting Reasoning Circuits in Transformers via Stability Zone Detection
arXiv:2604.00716v1 Announce Type: new Abstract: Transformer language models contain localized reasoning circuits, contiguous layer blocks that improve reasoning when duplicated at inference time. Finding these circuits currently requires brute-force sweeps costing 25 GPU hours per model. We propose CircuitProbe, which...
MiCA Learns More Knowledge Than LoRA and Full Fine-Tuning
arXiv:2604.01694v1 Announce Type: new Abstract: Minor Component Adaptation (MiCA) is a novel parameter-efficient fine-tuning method for large language models that focuses on adapting underutilized subspaces of model representations. Unlike conventional methods such as Low-Rank Adaptation (LoRA), which target dominant subspaces,...
Advisory Opinions broadcast: President Donald Trump and birthright citizenship
Oral arguments in Trump v. Barbara, on the constitutionality of President Donald Trump’s executive order on birthright citizenship, have concluded, but the conversation isn’t over. Listen now to a special […]The postAdvisory Opinions broadcast: President Donald Trump and birthright citizenshipappeared...
Label Shift Estimation With Incremental Prior Update
arXiv:2604.01651v1 Announce Type: new Abstract: An assumption often made in supervised learning is that the training and testing sets have the same label distribution. However, in real-life scenarios, this assumption rarely holds. For example, medical diagnosis result distributions change over...
DISCO-TAB: A Hierarchical Reinforcement Learning Framework for Privacy-Preserving Synthesis of Complex Clinical Data
arXiv:2604.01481v1 Announce Type: new Abstract: The development of robust clinical decision support systems is frequently impeded by the scarcity of high-fidelity, privacy-preserving biomedical data. While Generative Large Language Models (LLMs) offer a promising avenue for synthetic data generation, they often...
Can Large Language Models Self-Correct in Medical Question Answering? An Exploratory Study
arXiv:2604.00261v2 Announce Type: new Abstract: Large language models (LLMs) have achieved strong performance on medical question answering (medical QA), and chain-of-thought (CoT) prompting has further improved results by eliciting explicit intermediate reasoning; meanwhile, self-reflective (self-corrective) prompting has been widely claimed...