VisiFold: Long-Term Traffic Forecasting via Temporal Folding Graph and Node Visibility
arXiv:2603.11816v1 Announce Type: new Abstract: Traffic forecasting is a cornerstone of intelligent transportation systems. While existing research has made significant progress in short-term prediction, long-term forecasting remains a largely uncharted and challenging frontier. Extending the prediction horizon intensifies two critical...
LLM-Assisted Causal Structure Disambiguation and Factor Extraction for Legal Judgment Prediction
arXiv:2603.11446v1 Announce Type: new Abstract: Mainstream methods for Legal Judgment Prediction (LJP) based on Pre-trained Language Models (PLMs) heavily rely on the statistical correlation between case facts and judgment results. This paradigm lacks explicit modeling of legal constituent elements and...
Measuring AI Agents' Progress on Multi-Step Cyber Attack Scenarios
arXiv:2603.11214v1 Announce Type: new Abstract: We evaluate the autonomous cyber-attack capabilities of frontier AI models on two purpose-built cyber ranges-a 32-step corporate network attack and a 7-step industrial control system attack-that require chaining heterogeneous capabilities across extended action sequences. By...
Can Small Language Models Use What They Retrieve? An Empirical Study of Retrieval Utilization Across Model Scale
arXiv:2603.11513v1 Announce Type: new Abstract: Retrieval augmented generation RAG is widely deployed to improve factual accuracy in language models yet it remains unclear whether smaller models of size 7B parameters or less can effectively utilize retrieved information. To investigate this...
Legal-DC: Benchmarking Retrieval-Augmented Generation for Legal Documents
arXiv:2603.11772v1 Announce Type: new Abstract: Retrieval-Augmented Generation (RAG) has emerged as a promising technology for legal document consultation, yet its application in Chinese legal scenarios faces two key limitations: existing benchmarks lack specialized support for joint retriever-generator evaluation, and mainstream...
IndexCache: Accelerating Sparse Attention via Cross-Layer Index Reuse
arXiv:2603.12201v1 Announce Type: new Abstract: Long-context agentic workflows have emerged as a defining use case for large language models, making attention efficiency critical for both inference speed and serving cost. Sparse attention addresses this challenge effectively, and DeepSeek Sparse Attention...
CLASP: Defending Hybrid Large Language Models Against Hidden State Poisoning Attacks
arXiv:2603.12206v1 Announce Type: new Abstract: State space models (SSMs) like Mamba have gained significant traction as efficient alternatives to Transformers, achieving linear complexity while maintaining competitive performance. However, Hidden State Poisoning Attacks (HiSPAs), a recently discovered vulnerability that corrupts SSM...
Comparison of Outlier Detection Algorithms on String Data
arXiv:2603.11049v1 Announce Type: new Abstract: Outlier detection is a well-researched and crucial problem in machine learning. However, there is little research on string data outlier detection, as most literature focuses on outlier detection of numerical data. A robust string data...
Learning Tree-Based Models with Gradient Descent
arXiv:2603.11117v1 Announce Type: new Abstract: Tree-based models are widely recognized for their interpretability and have proven effective in various application domains, particularly in high-stakes domains. However, learning decision trees (DTs) poses a significant challenge due to their combinatorial complexity and...
A Learning-Based Superposition Operator for Non-Renewal Arrival Processes in Queueing Networks
arXiv:2603.11118v1 Announce Type: new Abstract: The superposition of arrival processes is a fundamental yet analytically intractable operation in queueing networks when inputs are general non-renewal streams. Classical methods either reduce merged flows to renewal surrogates, rely on computationally prohibitive Markovian...
Deep Learning Network-Temporal Models For Traffic Prediction
arXiv:2603.11475v1 Announce Type: new Abstract: Time series analysis is critical for emerging net- work intelligent control and management functions. However, existing statistical-based and shallow machine learning models have shown limited prediction capabilities on multivariate time series. The intricate topological interdependency...
KEPo: Knowledge Evolution Poison on Graph-based Retrieval-Augmented Generation
arXiv:2603.11501v1 Announce Type: new Abstract: Graph-based Retrieval-Augmented Generation (GraphRAG) constructs the Knowledge Graph (KG) from external databases to enhance the timeliness and accuracy of Large Language Model (LLM) generations.However,this reliance on external data introduces new attack surfaces.Attackers can inject poisoned...
Birthright citizenship: Originalism 101
These days, everyone wants to be an originalist. But in Trump v. Barbara, the birthright-citizenship case at the Supreme Court, not everyone is doing originalism well. Alas, the Trump administration […]The postBirthright citizenship: Originalism 101appeared first onSCOTUSblog.
An interview with Jerry Goldman, founder of the Oyez Project
Welcome to our SCOTUS Innovators series, a new recurring column on people who have shaped our understanding of the Supreme Court. A few weeks ago, I had the opportunity to […]The postAn interview with Jerry Goldman, founder of the Oyez...
When presidents attack the Supreme Court
During a roundtable at the White House on Friday, March 6, President Donald Trump returned to what has become a familiar refrain in the weeks since the Supreme Court struck […]The postWhen presidents attack the Supreme Courtappeared first onSCOTUSblog.
SCOTUStoday for Thursday, March 12
On this day in 1804, the House of Representatives voted to impeach Justice Samuel Chase, who had been accused of abusing his power by refusing to dismiss biased jurors and […]The postSCOTUStoday for Thursday, March 12appeared first onSCOTUSblog.
A Retrieval-Augmented Language Assistant for Unmanned Aircraft Safety Assessment and Regulatory Compliance
arXiv:2603.09999v1 Announce Type: cross Abstract: This paper presents the design and validation of a retrieval-based assistant that supports safety assessment, certification activities, and regulatory compliance for unmanned aircraft systems. The work is motivated by the growing complexity of drone operations...
Verbalizing LLM's Higher-order Uncertainty via Imprecise Probabilities
arXiv:2603.10396v1 Announce Type: new Abstract: Despite the growing demand for eliciting uncertainty from large language models (LLMs), empirical evidence suggests that LLM behavior is not always adequately captured by the elicitation techniques developed under the classical probabilistic uncertainty framework. This...
HEAL: Hindsight Entropy-Assisted Learning for Reasoning Distillation
arXiv:2603.10359v1 Announce Type: new Abstract: Distilling reasoning capabilities from Large Reasoning Models (LRMs) into smaller models is typically constrained by the limitation of rejection sampling. Standard methods treat the teacher as a static filter, discarding complex "corner-case" problems where the...
Nurture-First Agent Development: Building Domain-Expert AI Agents Through Conversational Knowledge Crystallization
arXiv:2603.10808v1 Announce Type: new Abstract: The emergence of large language model (LLM)-based agent frameworks has shifted the primary challenge in building domain-expert AI agents from raw capability to effective encoding of domain expertise. Two dominant paradigms -- code-first development, which...
OpenClaw-RL: Train Any Agent Simply by Talking
arXiv:2603.10165v1 Announce Type: new Abstract: Every agent interaction generates a next-state signal, namely the user reply, tool output, terminal or GUI state change that follows each action, yet no existing agentic RL system recovers it as a live, online learning...
Dynamic Knowledge Fusion for Multi-Domain Dialogue State Tracking
arXiv:2603.10367v1 Announce Type: new Abstract: The performance of task-oriented dialogue models is strongly tied to how well they track dialogue states, which records and updates user information across multi-turn interactions. However, current multi-domain DST encounters two key challenges: the difficulty...
Aligning Large Language Models with Searcher Preferences
arXiv:2603.10473v1 Announce Type: new Abstract: The paradigm shift from item-centric ranking to answer-centric synthesis is redefining the role of search engines. While recent industrial progress has applied generative techniques to closed-set item ranking in e-commerce, research and deployment of open-ended...
Revisiting Sharpness-Aware Minimization: A More Faithful and Effective Implementation
arXiv:2603.10048v1 Announce Type: new Abstract: Sharpness-Aware Minimization (SAM) enhances generalization by minimizing the maximum training loss within a predefined neighborhood around the parameters. However, its practical implementation approximates this as gradient ascent(s) followed by applying the gradient at the ascent...
Dissecting Chronos: Sparse Autoencoders Reveal Causal Feature Hierarchies in Time Series Foundation Models
arXiv:2603.10071v1 Announce Type: new Abstract: Time series foundation models (TSFMs) are increasingly deployed in high-stakes domains, yet their internal representations remain opaque. We present the first application of sparse autoencoders (SAEs) to a TSFM, training TopK SAEs on activations of...
Marginals Before Conditionals
arXiv:2603.10074v1 Announce Type: new Abstract: We construct a minimal task that isolates conditional learning in neural networks: a surjective map with K-fold ambiguity, resolved by a selector token z, so H(A | B) = log K while H(A | B,...
Large Spikes in Stochastic Gradient Descent: A Large-Deviations View
arXiv:2603.10079v1 Announce Type: new Abstract: We analyse SGD training of a shallow, fully connected network in the NTK scaling and provide a quantitative theory of the catapult phase. We identify an explicit criterion separating two behaviours: When an explicit function...
ES-dLLM: Efficient Inference for Diffusion Large Language Models by Early-Skipping
arXiv:2603.10088v1 Announce Type: new Abstract: Diffusion large language models (dLLMs) are emerging as a promising alternative to autoregressive models (ARMs) due to their ability to capture bidirectional context and the potential for parallel generation. Despite the advantages, dLLM inference remains...
Rethinking Adam for Time Series Forecasting: A Simple Heuristic to Improve Optimization under Distribution Shifts
arXiv:2603.10095v1 Announce Type: new Abstract: Time-series forecasting often faces challenges from non-stationarity, particularly distributional drift, where the data distribution evolves over time. This dynamic behavior can undermine the effectiveness of adaptive optimizers, such as Adam, which are typically designed for...
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...