Telogenesis: Goal Is All U Need
arXiv:2603.09476v1 Announce Type: new Abstract: Goal-conditioned systems assume goals are provided externally. We ask whether attentional priorities can emerge endogenously from an agent's internal cognitive state. We propose a priority function that generates observation targets from three epistemic gaps: ignorance...
Abundant Intelligence and Deficient Demand: A Macro-Financial Stress Test of Rapid AI Adoption
arXiv:2603.09209v1 Announce Type: new Abstract: We formalize a macro-financial stress test for rapid AI adoption. Rather than a productivity bust or existential risk, we identify a distribution-and-contract mismatch: AI-generated abundance coexists with demand deficiency because economic institutions are anchored to...
AI Act Evaluation Benchmark: An Open, Transparent, and Reproducible Evaluation Dataset for NLP and RAG Systems
arXiv:2603.09435v1 Announce Type: new Abstract: The rapid rollout of AI in heterogeneous public and societal sectors has subsequently escalated the need for compliance with regulatory standards and frameworks. The EU AI Act has emerged as a landmark in the regulatory...
LCA: Local Classifier Alignment for Continual Learning
arXiv:2603.09888v1 Announce Type: new Abstract: A fundamental requirement for intelligent systems is the ability to learn continuously under changing environments. However, models trained in this regime often suffer from catastrophic forgetting. Leveraging pre-trained models has recently emerged as a promising...
SciTaRC: Benchmarking QA on Scientific Tabular Data that Requires Language Reasoning and Complex Computation
arXiv:2603.08910v1 Announce Type: new Abstract: We introduce SciTaRC, an expert-authored benchmark of questions about tabular data in scientific papers requiring both deep language reasoning and complex computation. We show that current state-of-the-art AI models fail on at least 23% of...
Explainable Innovation Engine: Dual-Tree Agent-RAG with Methods-as-Nodes and Verifiable Write-Back
arXiv:2603.09192v1 Announce Type: new Abstract: Retrieval-augmented generation (RAG) improves factual grounding, yet most systems rely on flat chunk retrieval and provide limited control over multi-step synthesis. We propose an Explainable Innovation Engine that upgrades the knowledge unit from text chunks...
PrivPRISM: Automatically Detecting Discrepancies Between Google Play Data Safety Declarations and Developer Privacy Policies
arXiv:2603.09214v1 Announce Type: new Abstract: End-users seldom read verbose privacy policies, leading app stores like Google Play to mandate simplified data safety declarations as a user-friendly alternative. However, these self-declared disclosures often contradict the full privacy policies, deceiving users about...
Time, Identity and Consciousness in Language Model Agents
arXiv:2603.09043v1 Announce Type: new Abstract: Machine consciousness evaluations mostly see behavior. For language model agents that behavior is language and tool use. That lets an agent say the right things about itself even when the constraints that should make those...
Evaluate-as-Action: Self-Evaluated Process Rewards for Retrieval-Augmented Agents
arXiv:2603.09203v1 Announce Type: new Abstract: Retrieval-augmented agents can query external evidence, yet their reliability in multi-step reasoning remains limited: noisy retrieval may derail multi-hop question answering, while outcome-only reinforcement learning provides credit signals that are too coarse to optimize intermediate...
Logics-Parsing-Omni Technical Report
arXiv:2603.09677v1 Announce Type: new Abstract: Addressing the challenges of fragmented task definitions and the heterogeneity of unstructured data in multimodal parsing, this paper proposes the Omni Parsing framework. This framework establishes a Unified Taxonomy covering documents, images, and audio-visual streams,...
EsoLang-Bench: Evaluating Genuine Reasoning in Large Language Models via Esoteric Programming Languages
arXiv:2603.09678v1 Announce Type: new Abstract: Large language models achieve near-ceiling performance on code generation benchmarks, yet these results increasingly reflect memorization rather than genuine reasoning. We introduce EsoLang-Bench, a benchmark using five esoteric programming languages (Brainfuck, Befunge-98, Whitespace, Unlambda, and...
SPAR-K: Scheduled Periodic Alternating Early Exit for Spoken Language Models
arXiv:2603.09215v1 Announce Type: new Abstract: Interleaved spoken language models (SLMs) alternately generate text and speech tokens, but decoding at full transformer depth for every step becomes costly, especially due to long speech sequences. We propose SPAR-K, a modality-aware early exit...
Quantifying and extending the coverage of spatial categorization data sets
arXiv:2603.09373v1 Announce Type: new Abstract: Variation in spatial categorization across languages is often studied by eliciting human labels for the relations depicted in a set of scenes known as the Topological Relations Picture Series (TRPS). We demonstrate that labels generated...
Modelling the Diachronic Emergence of Phoneme Frequency Distributions
arXiv:2603.09503v1 Announce Type: new Abstract: Phoneme frequency distributions exhibit robust statistical regularities across languages, including exponential-tailed rank-frequency patterns and a negative relationship between phonemic inventory size and the relative entropy of the distribution. The origin of these patterns remains largely...
You Didn't Have to Say It like That: Subliminal Learning from Faithful Paraphrases
arXiv:2603.09517v1 Announce Type: new Abstract: When language models are trained on synthetic data, they (student model) can covertly acquire behavioral traits from the data-generating model (teacher model). Subliminal learning refers to the transmission of traits from a teacher to a...
Build, Borrow, or Just Fine-Tune? A Political Scientist's Guide to Choosing NLP Models
arXiv:2603.09595v1 Announce Type: new Abstract: Political scientists increasingly face a consequential choice when adopting natural language processing tools: build a domain-specific model from scratch, borrow and adapt an existing one, or simply fine-tune a general-purpose model on task data? Each...
Surgical Repair of Collapsed Attention Heads in ALiBi Transformers
arXiv:2603.09616v1 Announce Type: new Abstract: We identify a systematic attention collapse pathology in the BLOOM family of transformer language models, where ALiBi positional encoding causes 31-44% of attention heads to attend almost entirely to the beginning-of-sequence token. The collapse follows...
Fusing Semantic, Lexical, and Domain Perspectives for Recipe Similarity Estimation
arXiv:2603.09688v1 Announce Type: new Abstract: This research focuses on developing advanced methods for assessing similarity between recipes by combining different sources of information and analytical approaches. We explore the semantic, lexical, and domain similarity of food recipes, evaluated through the...
EPIC-EuroParl-UdS: Information-Theoretic Perspectives on Translation and Interpreting
arXiv:2603.09785v1 Announce Type: new Abstract: This paper introduces an updated and combined version of the bidirectional English-German EPIC-UdS (spoken) and EuroParl-UdS (written) corpora containing original European Parliament speeches as well as their translations and interpretations. The new version corrects metadata...
N-gram-like Language Models Predict Reading Time Best
arXiv:2603.09872v1 Announce Type: new Abstract: Recent work has found that contemporary language models such as transformers can become so good at next-word prediction that the probabilities they calculate become worse for predicting reading time. In this paper, we propose that...
CREATE: Testing LLMs for Associative Creativity
arXiv:2603.09970v1 Announce Type: new Abstract: A key component of creativity is associative reasoning: the ability to draw novel yet meaningful connections between concepts. We introduce CREATE, a benchmark designed to evaluate models' capacity for creative associative reasoning. CREATE requires models...
VeriInteresting: An Empirical Study of Model Prompt Interactions in Verilog Code Generation
arXiv:2603.08715v1 Announce Type: cross Abstract: Rapid advances in language models (LMs) have created new opportunities for automated code generation while complicating trade-offs between model characteristics and prompt design choices. In this work, we provide an empirical map of recent trends...
Fish Audio S2 Technical Report
arXiv:2603.08823v1 Announce Type: cross Abstract: We introduce Fish Audio S2, an open-sourced text-to-speech system featuring multi-speaker, multi-turn generation, and, most importantly, instruction-following control via natural-language descriptions. To scale training, we develop a multi-stage training recipe together with a staged data...
From Word2Vec to Transformers: Text-Derived Composition Embeddings for Filtering Combinatorial Electrocatalysts
arXiv:2603.08881v1 Announce Type: cross Abstract: Compositionally complex solid solution electrocatalysts span vast composition spaces, and even one materials system can contain more candidate compositions than can be measured exhaustively. Here we evaluate a label-free screening strategy that represents each composition...
BiCLIP: Domain Canonicalization via Structured Geometric Transformation
arXiv:2603.08942v1 Announce Type: cross Abstract: Recent advances in vision-language models (VLMs) have demonstrated remarkable zero-shot capabilities, yet adapting these models to specialized domains remains a significant challenge. Building on recent theoretical insights suggesting that independently trained VLMs are related by...
Generalized Reduction to the Isotropy for Flexible Equivariant Neural Fields
arXiv:2603.08758v1 Announce Type: new Abstract: Many geometric learning problems require invariants on heterogeneous product spaces, i.e., products of distinct spaces carrying different group actions, where standard techniques do not directly apply. We show that, when a group $G$ acts transitively...
SPREAD: Subspace Representation Distillation for Lifelong Imitation Learning
arXiv:2603.08763v1 Announce Type: new Abstract: A key challenge in lifelong imitation learning (LIL) is enabling agents to acquire new skills from expert demonstrations while retaining prior knowledge. This requires preserving the low-dimensional manifolds and geometric structures that underlie task representations...
SoftJAX & SoftTorch: Empowering Automatic Differentiation Libraries with Informative Gradients
arXiv:2603.08824v1 Announce Type: new Abstract: Automatic differentiation (AD) frameworks such as JAX and PyTorch have enabled gradient-based optimization for a wide range of scientific fields. Yet, many "hard" primitives in these libraries such as thresholding, Boolean logic, discrete indexing, and...
Are Expressive Encoders Necessary for Discrete Graph Generation?
arXiv:2603.08825v1 Announce Type: new Abstract: Discrete graph generation has emerged as a powerful paradigm for modeling graph data, often relying on highly expressive neural backbones such as transformers or higher-order architectures. We revisit this design choice by introducing GenGNN, a...
Expressivity-Efficiency Tradeoffs for Hybrid Sequence Models
arXiv:2603.08859v1 Announce Type: new Abstract: Hybrid sequence models--combining Transformer and state-space model layers--seek to gain the expressive versatility of attention as well as the computational efficiency of state-space model layers. Despite burgeoning interest in hybrid models, we lack a basic...