arXiv:2604.21068v1 Announce Type: cross Abstract: The most technologically consequential materials are often the rarest: they occupy narrow regions of chemical space, obey competing physical constraints, and appear only sparsely in existing databases. High-kappa dielectrics, high-Tc superconductors, and ferromagnetic insulators are to name a few. This scarcity fundamentally limits today’s data-driven materials discovery, where machine-learning models […]
Materialistic RIR: Material Conditioned Realistic RIR Generation
arXiv:2604.21119v1 Announce Type: cross Abstract: Rings like gold, thuds like wood! The sound we hear in a scene is shaped not only by the spatial layout of the environment but also by the materials of the objects and surfaces within it. For instance, a room with wooden walls will produce a different acoustic experience from […]
TAPO-Description Logic for Information Behavior: Refined OBoxes, Inference, and Categorical Semantics
arXiv:2604.21172v1 Announce Type: cross Abstract: This paper develops a refined version of TAPO-description logic for the analysis of information behavior. The framework is treated not as a single homogeneous object logic, but as a layered formalism consisting of a static descriptive layer (TBox/ABox), a procedural layer (PBox), and an oracle-sensitive layer (OBox). To make this […]
EngramaBench: Evaluating Long-Term Conversational Memory with Structured Graph Retrieval
arXiv:2604.21229v1 Announce Type: cross Abstract: Large language model assistants are increasingly expected to retain and reason over information accumulated across many sessions. We introduce EngramaBench, a benchmark for long-term conversational memory built around five personas, one hundred multi-session conversations, and one hundred fifty queries spanning factual recall, cross-space integration, temporal reasoning, adversarial abstention, and emergent […]
Cross-Entropy Is Load-Bearing: A Pre-Registered Scope Test of the K-Way Energy Probe on Bidirectional Predictive Coding
arXiv:2604.21286v1 Announce Type: cross Abstract: Cacioli (2026) showed that the K-way energy probe on standard discriminative predictive coding networks reduces approximately to a monotone function of the log-softmax margin. The reduction rests on five assumptions, including cross-entropy (CE) at the output and effectively feedforward inference dynamics. This pre-registered study tests the reduction’s sensitivity to CE […]
From Physical Difference to Meaning: A Constructor-Theoretic Framework for Prebiotic Information in Casimir-Lifshitz-Coupled Protocell Clusters
arXiv:2604.20885v1 Announce Type: cross Abstract: This paper develops a physical framework for the prebiotic emergence of information and meaning. Building on Constructor Theory, we define information as a reproducible physical difference and meaning as a difference with stable functional consequences. Casimir-Lifshitz-coupled protocell clusters serve as a minimal model that exhibits reproducible attractors, ordered transitions, and […]
Causal Disentanglement for Full-Reference Image Quality Assessment
arXiv:2604.21654v1 Announce Type: cross Abstract: Existing deep network-based full-reference image quality assessment (FR-IQA) models typically work by performing pairwise comparisons of deep features from the reference and distorted images. In this paper, we approach this problem from a different perspective and propose a novel FR-IQA paradigm based on causal inference and decoupled representation learning. Unlike […]
Frequency-Forcing: From Scaling-as-Time to Soft Frequency Guidance
arXiv:2604.20902v1 Announce Type: cross Abstract: While standard flow-matching models transport noise to data uniformly, incorporating an explicit generation order – specifically, establishing coarse, low-frequency structure before fine detail – has proven highly effective for synthesizing natural images. Two recent works offer distinct paradigms for this. K-Flow imposes a hard frequency constraint by reinterpreting a frequency […]
Mind the Prompt: Self-adaptive Generation of Task Plan Explanations via LLMs
arXiv:2604.21092v1 Announce Type: new Abstract: Integrating Large Language Models (LLMs) into complex software systems enables the generation of human-understandable explanations of opaque AI processes, such as automated task planning. However, the quality and reliability of these explanations heavily depend on effective prompt engineering. The lack of a systematic understanding of how diverse stakeholder groups formulate […]
Omission Constraints Decay While Commission Constraints Persist in Long-Context LLM Agents
arXiv:2604.20911v1 Announce Type: cross Abstract: LLM agents deployed in production operate under operator-defined behavioral policies (system-prompt instructions such as prohibitions on credential disclosure, data exfiltration, and unauthorized output) that safety evaluations assume hold throughout a conversation. Prohibition-type constraints decay under context pressure while requirement-type constraints persist; we term this asymmetry Security-Recall Divergence (SRD). In a […]
TraceScope: Interactive URL Triage via Decoupled Checklist Adjudication
arXiv:2604.21840v1 Announce Type: cross Abstract: Modern phishing campaigns increasingly evade snapshot-based URL classifiers using interaction gates (e.g., checkbox/slider challenges), delayed content rendering, and logo-less credential harvesters. This shifts URL triage from static classification toward an interactive forensics task: an analyst must actively navigate the page while isolating themselves from potential runtime exploits. We present TraceScope, […]
SafeRedirect: Defeating Internal Safety Collapse via Task-Completion Redirection in Frontier LLMs
arXiv:2604.20930v1 Announce Type: cross Abstract: Internal Safety Collapse (ISC) is a failure mode in which frontier LLMs, when executing legitimate professional tasks whose correct completion structurally requires harmful content, spontaneously generate that content with safety failure rates exceeding 95%. Existing input-level defenses achieve a 100% failure rate against ISC, and standard system prompt defenses provide […]