Think Twice Before You Write -- an Entropy-based Decoding Strategy to Enhance LLM Reasoning
arXiv:2604.00018v1 Announce Type: cross Abstract: Decoding strategies play a central role in shaping the reasoning ability of large language models (LLMs). Traditional methods such as greedy decoding and beam search often suffer from error propagation, while sampling-based approaches introduce randomness...
Agent psychometrics: Task-level performance prediction in agentic coding benchmarks
arXiv:2604.00594v1 Announce Type: new Abstract: As the focus in LLM-based coding shifts from static single-step code generation to multi-step agentic interaction with tools and environments, understanding which tasks will challenge agents and why becomes increasingly difficult. This is compounded by...
No Third Term: Rejecting the Nonconsecutive Loophole – Wisconsin Law Review – UW–Madison
The text of the Twenty-Second Amendment seems clear that a president cannot be elected to a third term: “No person shall be elected to the office of the President more than twice.” This Essay looks further to the history surrounding...
BIAS, FAIRNESS, AND INCLUSIVITY IN GENERATIVE AI SYSTEMS: A CRITICAL EXAMINATION OF ALGORITHMIC BIAS, REPRESENTATION GAPS, AND THE CHALLENGES OF ENSURING EQUITY IN AI-GENERATED OUTPUTS
Generative AI systems such as large language models (LLMs), image synthesizers, and multimodal frameworks have transformed content creation while also exposing and amplifying systemic biases that undermine fairness and inclusivity. This study critically examines algorithmic bias in model outputs, representation...
Volume 110 Headnotes: Spring Issue - Minnesota Law Review
No More IEEPA Tariffs? The Legal Bases of an Alternative Regime By Lawrence J. Liu Full essay here. lawreview - Minnesota Law Review
About the Association for the Advancement of Artificial Intelligence (AAAI)
AAAI is an artificial intelligence organization dedicated to advancing the scientific understanding of AI.
The key arguments in the birthright citizenship case
On April 1, the Supreme Court will hear oral arguments in one of the highest-profile cases of the 2025-26 term – and indeed, one of the biggest cases in several […]The postThe key arguments in the birthright citizenship caseappeared first...
When the Supreme Court let a president get away with redefining birthright citizenship
The president finds the long-settled meaning of the citizenship clause to be an intolerable obstacle to his agenda. The reason? Each year it would make U.S. citizens of tens of […]The postWhen the Supreme Court let a president get away...
Prominent Scientists, Faith Leaders, Policymakers and Artists Call for a Prohibition on Superintelligence, as Poll Shows Americans Don’t Want It
Initial signatories include AI pioneers Yoshua Bengio and Geoffrey Hinton, leading media voices Steve Bannon and Glenn Beck, Obama's National Security Advisor Susan Rice, business trailblazers Steve Wozniak and Richard Branson, five Nobel Laureates, former Irish President Mary Robinson, actors...
Statement: Head of US Policy on the White House AI legislative recommendations
The White House published it’s long-awaited AI legislative recommendations on Friday, and it still includes a call for Congress to […]
AI Company Safety Practices Fall Short of Public Commitments and Show Structural Weaknesses, as Top Performers Widen the Gap
But in a win for transparency, five leading companies participated in the scorecard's survey for the first time, providing critical new information to the public.
David Sacks is done as AI czar — here’s what he’s doing instead
Sacks will be much further from the power center in Washington than since the outset of this second Trump administration.
Data centers get ready — the Senate wants to see your power bills
Senators Josh Hawley and Elizabeth Warren want the Energy Information Administration to gather more details about how data centers use power — and how that affects the grid.
MedMT-Bench: Can LLMs Memorize and Understand Long Multi-Turn Conversations in Medical Scenarios?
arXiv:2603.23519v1 Announce Type: new Abstract: Large Language Models (LLMs) have demonstrated impressive capabilities across various specialist domains and have been integrated into high-stakes areas such as medicine. However, as existing medical-related benchmarks rarely stress-test the long-context memory, interference robustness, and...
Chitrakshara: A Large Multilingual Multimodal Dataset for Indian languages
arXiv:2603.23521v1 Announce Type: new Abstract: Multimodal research has predominantly focused on single-image reasoning, with limited exploration of multi-image scenarios. Recent models have sought to enhance multi-image understanding through large-scale pretraining on interleaved image-text datasets. However, most Vision-Language Models (VLMs) are...
Do 3D Large Language Models Really Understand 3D Spatial Relationships?
arXiv:2603.23523v1 Announce Type: new Abstract: Recent 3D Large-Language Models (3D-LLMs) claim to understand 3D worlds, especially spatial relationships among objects. Yet, we find that simply fine-tuning a language model on text-only question-answer pairs can perform comparably or even surpass these...
From Physician Expertise to Clinical Agents: Preserving, Standardizing, and Scaling Physicians' Medical Expertise with Lightweight LLM
arXiv:2603.23520v1 Announce Type: new Abstract: Medicine is an empirical discipline refined through long-term observation and the messy, high-variance reality of clinical practice. Physicians build diagnostic and therapeutic competence through repeated cycles of application, reflection, and improvement, forming individualized methodologies. Yet...
Konkani LLM: Multi-Script Instruction Tuning and Evaluation for a Low-Resource Indian Language
arXiv:2603.23529v1 Announce Type: new Abstract: Large Language Models (LLMs) consistently under perform in low-resource linguistic contexts such as Konkani. This performance deficit stems from acute training data scarcity compounded by high script diversity across Devanagari, Romi and Kannada orthographies. To...
MSA: Memory Sparse Attention for Efficient End-to-End Memory Model Scaling to 100M Tokens
arXiv:2603.23516v1 Announce Type: new Abstract: Long-term memory is a cornerstone of human intelligence. Enabling AI to process lifetime-scale information remains a long-standing pursuit in the field. Due to the constraints of full-attention architectures, the effective context length of large language...
Internal Safety Collapse in Frontier Large Language Models
arXiv:2603.23509v1 Announce Type: new Abstract: This work identifies a critical failure mode in frontier large language models (LLMs), which we term Internal Safety Collapse (ISC): under certain task conditions, models enter a state in which they continuously generate harmful content...
Beyond Masks: Efficient, Flexible Diffusion Language Models via Deletion-Insertion Processes
arXiv:2603.23507v1 Announce Type: new Abstract: While Masked Diffusion Language Models (MDLMs) relying on token masking and unmasking have shown promise in language modeling, their computational efficiency and generation flexibility remain constrained by the masking paradigm. In this paper, we propose...
Compression Method Matters: Benchmark-Dependent Output Dynamics in LLM Prompt Compression
arXiv:2603.23527v1 Announce Type: new Abstract: Prompt compression is often evaluated by input-token reduction, but its real deployment impact depends on how compression changes output length and total inference cost. We present a controlled replication and extension study of benchmark-dependent output...
Fast and Faithful: Real-Time Verification for Long-Document Retrieval-Augmented Generation Systems
arXiv:2603.23508v1 Announce Type: new Abstract: Retrieval-augmented generation (RAG) is increasingly deployed in enterprise search and document-centric assistants, where responses must be grounded in long and complex source materials. In practice, verifying that generated answers faithfully reflect retrieved documents is difficult:...
MDKeyChunker: Single-Call LLM Enrichment with Rolling Keys and Key-Based Restructuring for High-Accuracy RAG
arXiv:2603.23533v1 Announce Type: new Abstract: RAG pipelines typically rely on fixed-size chunking, which ignores document structure, fragments semantic units across boundaries, and requires multiple LLM calls per chunk for metadata extraction. We present MDKeyChunker, a three-stage pipeline for Markdown documents...
Revisiting Real-Time Digging-In Effects: No Evidence from NP/Z Garden-Paths
arXiv:2603.23624v1 Announce Type: new Abstract: Digging-in effects, where disambiguation difficulty increases with longer ambiguous regions, have been cited as evidence for self-organized sentence processing, in which structural commitments strengthen over time. In contrast, surprisal theory predicts no such effect unless...
Swiss-Bench SBP-002: A Frontier Model Comparison on Swiss Legal and Regulatory Tasks
arXiv:2603.23646v1 Announce Type: new Abstract: While recent work has benchmarked large language models on Swiss legal translation (Niklaus et al., 2025) and academic legal reasoning from university exams (Fan et al., 2025), no existing benchmark evaluates frontier model performance on...
IslamicMMLU: A Benchmark for Evaluating LLMs on Islamic Knowledge
arXiv:2603.23750v1 Announce Type: new Abstract: Large language models are increasingly consulted for Islamic knowledge, yet no comprehensive benchmark evaluates their performance across core Islamic disciplines. We introduce IslamicMMLU, a benchmark of 10,013 multiple-choice questions spanning three tracks: Quran (2,013 questions),...
Infrequent Child-Directed Speech Is Bursty and May Draw Infant Vocalizations
arXiv:2603.23797v1 Announce Type: new Abstract: Children in many parts of the world hear relatively little speech directed to them, yet still reach major language development milestones. What differs about the speech input that infants learn from when directed input is...
Self-Distillation for Multi-Token Prediction
arXiv:2603.23911v1 Announce Type: new Abstract: As Large Language Models (LLMs) scale up, inference efficiency becomes a critical bottleneck. Multi-Token Prediction (MTP) could accelerate LLM inference by predicting multiple future tokens in parallel. However, existing MTP approaches still face two challenges:...
OmniACBench: A Benchmark for Evaluating Context-Grounded Acoustic Control in Omni-Modal Models
arXiv:2603.23938v1 Announce Type: new Abstract: Most testbeds for omni-modal models assess multimodal understanding via textual outputs, leaving it unclear whether these models can properly speak their answers. To study this, we introduce OmniACBench, a benchmark for evaluating context-grounded acoustic control...