Artificial Intelligence and Space Technologies: Legal, Ethical and Technological Issues
The article is devoted to the study of the specifics of the legal regulation of the use and development of artificial intelligence for the space area and the related issues of observation of fundamental human rights. Some approaches to the...
The Scored Society: Due Process for Automated Predictions
Big Data is increasingly mined to rank and rate individuals. Predictive algorithms assess whether we are good credit risks, desirable employees, reliable tenants, valuable customers—or deadbeats, shirkers, menaces, and “wastes of time.” Crucial opportunities are on the line, including the...
Curbing Private Enforcement of the Voting Rights Act: Thoughts on Recent Developments
For decades, private plaintiffs have brought claims to enforce key provisions of the Voting Rights Act (VRA). Recent decisions have tossed out these claims on the ground that enforcement authority lies solely with the Attorney…The postCurbing Private Enforcement of the...
Fly in the Face of Bias: Algorithmic Bias in Law Enforcement’s Facial Recognition Technology and the Need for an Adaptive Legal Framework
Text and Data Mining, Generative AI, and the Copyright Three-Step Test
Abstract In the debate on copyright exceptions permitting text and data mining (“TDM”) for the development of generative AI systems, the so-called “three-step test” has become a centre of gravity. The test serves as a universal yardstick for assessing the...
Ethical Considerations in AI: Bias Mitigation and Fairness in Algorithmic Decision Making
The rapid integration of artificial intelligence (AI) into critical decision-making domains—such as healthcare, finance, law enforcement, and hiring—has raised significant ethical concerns regarding bias and fairness. Algorithmic decision-making systems, if not carefully designed and monitored, risk perpetuating and amplifying societal...
Policies on Large Language Model Usage at ICLR 2026
Information-theoretic analysis of world models in optimal reward maximizers
arXiv:2602.12963v1 Announce Type: new Abstract: An important question in the field of AI is the extent to which successful behaviour requires an internal representation of the world. In this work, we quantify the amount of information an optimal policy provides...
Consistency of Large Reasoning Models Under Multi-Turn Attacks
arXiv:2602.13093v2 Announce Type: new Abstract: Large reasoning models with reasoning capabilities achieve state-of-the-art performance on complex tasks, but their robustness under multi-turn adversarial pressure remains underexplored. We evaluate nine frontier reasoning models under adversarial attacks. Our findings reveal that reasoning...
Quantum walk inspired JPEG compression of images
arXiv:2602.12306v1 Announce Type: cross Abstract: This work proposes a quantum inspired adaptive quantization framework that enhances the classical JPEG compression by introducing a learned, optimized Qtable derived using a Quantum Walk Inspired Optimization (QWIO) search strategy. The optimizer searches a...
Perceptual Self-Reflection in Agentic Physics Simulation Code Generation
arXiv:2602.12311v1 Announce Type: cross Abstract: We present a multi-agent framework for generating physics simulation code from natural language descriptions, featuring a novel perceptual self-reflection mechanism for validation. The system employs four specialized agents: a natural language interpreter that converts user...
RankLLM: Weighted Ranking of LLMs by Quantifying Question Difficulty
arXiv:2602.12424v1 Announce Type: cross Abstract: Benchmarks establish a standardized evaluation framework to systematically assess the performance of large language models (LLMs), facilitating objective comparisons and driving advancements in the field. However, existing benchmarks fail to differentiate question difficulty, limiting their...
Unleashing Low-Bit Inference on Ascend NPUs: A Comprehensive Evaluation of HiFloat Formats
arXiv:2602.12635v1 Announce Type: new Abstract: As LLMs scale, low-bit floating-point formats like MXFP and NVFP4 offer new opportunities for precision and efficiency. In this work, we evaluate HiFloat (HiF8 and HiF4), a family of formats tailored for Ascend NPUs. Through...
BaziQA-Benchmark: Evaluating Symbolic and Temporally Compositional Reasoning in Large Language Models
arXiv:2602.12889v1 Announce Type: new Abstract: We present BaziQA-Benchmark, a standardized benchmark for evaluating symbolic and temporally compositional reasoning in large language models. The benchmark is derived from 200 professionally curated, multiple-choice problems from the Global Fortune-teller Competition (2021--2025), where each...
Semantic Chunking and the Entropy of Natural Language
arXiv:2602.13194v1 Announce Type: new Abstract: The entropy rate of printed English is famously estimated to be about one bit per character, a benchmark that modern large language models (LLMs) have only recently approached. This entropy rate implies that English contains...
AMPS: Adaptive Modality Preference Steering via Functional Entropy
arXiv:2602.12533v1 Announce Type: new Abstract: Multimodal Large Language Models (MLLMs) often exhibit significant modality preference, which is a tendency to favor one modality over another. Depending on the input, they may over-rely on linguistic priors relative to visual evidence, or...
Block-Sample MAC-Bayes Generalization Bounds
arXiv:2602.12605v1 Announce Type: new Abstract: We present a family of novel block-sample MAC-Bayes bounds (mean approximately correct). While PAC-Bayes bounds (probably approximately correct) typically give bounds for the generalization error that hold with high probability, MAC-Bayes bounds have a similar...
The Balancing Act: Looking Backward, Looking Ahead
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Intelligence as Trajectory-Dominant Pareto Optimization
arXiv:2602.13230v1 Announce Type: new Abstract: Despite recent advances in artificial intelligence, many systems exhibit stagnation in long-horizon adaptability despite continued performance optimization. This work argues that such limitations do not primarily arise from insufficient learning, data, or model capacity, but...
NL2LOGIC: AST-Guided Translation of Natural Language into First-Order Logic with Large Language Models
arXiv:2602.13237v1 Announce Type: new Abstract: Automated reasoning is critical in domains such as law and governance, where verifying claims against facts in documents requires both accuracy and interpretability. Recent work adopts structured reasoning pipelines that translate natural language into first-order...
TemporalBench: A Benchmark for Evaluating LLM-Based Agents on Contextual and Event-Informed Time Series Tasks
arXiv:2602.13272v1 Announce Type: new Abstract: It is unclear whether strong forecasting performance reflects genuine temporal understanding or the ability to reason under contextual and event-driven conditions. We introduce TemporalBench, a multi-domain benchmark designed to evaluate temporal reasoning behavior under progressively...
Information Fidelity in Tool-Using LLM Agents: A Martingale Analysis of the Model Context Protocol
arXiv:2602.13320v1 Announce Type: new Abstract: As AI agents powered by large language models (LLMs) increasingly use external tools for high-stakes decisions, a critical reliability question arises: how do errors propagate across sequential tool calls? We introduce the first theoretical framework...
AllMem: A Memory-centric Recipe for Efficient Long-context Modeling
arXiv:2602.13680v1 Announce Type: new Abstract: Large Language Models (LLMs) encounter significant performance bottlenecks in long-sequence tasks due to the computational complexity and memory overhead inherent in the self-attention mechanism. To address these challenges, we introduce \textsc{AllMem}, a novel and efficient...
Language Model Memory and Memory Models for Language
arXiv:2602.13466v1 Announce Type: new Abstract: The ability of machine learning models to store input information in hidden layer vector embeddings, analogous to the concept of `memory', is widely employed but not well characterized. We find that language model embeddings typically...
Think Deep, Not Just Long: Measuring LLM Reasoning Effort via Deep-Thinking Tokens
arXiv:2602.13517v1 Announce Type: new Abstract: Large language models (LLMs) have demonstrated impressive reasoning capabilities by scaling test-time compute via long Chain-of-Thought (CoT). However, recent findings suggest that raw token counts are unreliable proxies for reasoning quality: increased generation length does...