arXiv:2509.23728v3 Announce Type: replace-cross Abstract: In text-driven 3D scene generation, object layout serves as a crucial intermediate representation that bridges high-level language instructions with detailed geometric output. It not only provides a structural blueprint for ensuring physical plausibility but also supports semantic controllability and interactive editing. However, the learning capabilities of current 3D indoor layout […]
Cascade-Aware Multi-Agent Routing: Spatio-Temporal Sidecars and Geometry-Switching
arXiv:2603.17112v1 Announce Type: new Abstract: A common architectural pattern in advanced AI reasoning systems is the symbolic graph network: specialized agents or modules connected by delegation edges, routing tasks through a dynamic execution graph. Current schedulers optimize load and fitness but are geometry-blind: they do not model how failures propagate differently in tree-like versus cyclic […]
Unsupervised Decomposition and Recombination with Discriminator-Driven Diffusion Models
arXiv:2601.22057v2 Announce Type: replace-cross Abstract: Decomposing complex data into factorized representations can reveal reusable components and enable synthesizing new samples via component recombination. We investigate this in the context of diffusion-based models that learn factorized latent spaces without factor-level supervision. In images, factors can capture background, illumination, and object attributes; in robotic videos, they can […]
How Clued up are LLMs? Evaluating Multi-Step Deductive Reasoning in a Text-Based Game Environment
arXiv:2603.17169v1 Announce Type: new Abstract: Deducing whodunit proves challenging for LLM agents. In this paper, we implement a text-based multi-agent version of the classic board game Clue as a rule-based testbed for evaluating multi-step deductive reasoning, with six agents drawn from GPT-4o-mini and Gemini-2.5-Flash. We further investigate whether fine-tuning on structured logic puzzles transfers to […]
Less Is More in Chemotherapy of Breast Cancer
arXiv:2603.16894v1 Announce Type: new Abstract: This study presents a mathematical model that captures the interactions among tumor cells, healthy cells, and immune cells in a tumor-bearing host, with a specific focus on breast cancer. Incorporating the concept of delay, the model consists of four differential equations to analyze these cellular dynamics. The findings demonstrate the […]
AI Scientist via Synthetic Task Scaling
arXiv:2603.17216v1 Announce Type: new Abstract: With the advent of AI agents, automatic scientific discovery has become a tenable goal. Many recent works scaffold agentic systems that can perform machine learning research, but don’t offer a principled way to train such agents — and current LLMs often generate plausible-looking but ineffective ideas. To make progress on […]
Dropout Robustness and Cognitive Profiling of Transformer Models via Stochastic Inference
arXiv:2603.17811v1 Announce Type: cross Abstract: Transformer-based language models are widely deployed for reasoning, yet their behavior under inference-time stochasticity remains underexplored. While dropout is common during training, its inference-time effects via Monte Carlo sampling lack systematic evaluation across architectures, limiting understanding of model reliability in uncertainty-aware applications. This work analyzes dropout-induced variability across 19 transformer […]
Draft-and-Prune: Improving the Reliability of Auto-formalization for Logical Reasoning
arXiv:2603.17233v1 Announce Type: new Abstract: Auto-formalization (AF) translates natural-language reasoning problems into solver-executable programs, enabling symbolic solvers to perform sound logical deduction. In practice, however, AF pipelines are currently brittle: programs may fail to execute, or execute but encode incorrect semantics. While prior work largely mitigates syntactic failures via repairs based on solver feedback, reducing […]
Unified Policy Value Decomposition for Rapid Adaptation
arXiv:2603.17947v1 Announce Type: cross Abstract: Rapid adaptation in complex control systems remains a central challenge in reinforcement learning. We introduce a framework in which policy and value functions share a low-dimensional coefficient vector – a goal embedding – that captures task identity and enables immediate adaptation to novel tasks without retraining representations. During pretraining, we […]
Graph-Native Cognitive Memory for AI Agents: Formal Belief Revision Semantics for Versioned Memory Architectures
arXiv:2603.17244v1 Announce Type: new Abstract: While individual components for AI agent memory exist in prior systems, their architectural synthesis and formal grounding remain underexplored. We present Kumiho, a graph-native cognitive memory architecture grounded in formal belief revision semantics. The structural primitives required for cognitive memory — immutable revisions, mutable tag pointers, typed dependency edges, URI-based […]
Efficient LLM Safety Evaluation through Multi-Agent Debate
arXiv:2511.06396v3 Announce Type: replace Abstract: Safety evaluation of large language models (LLMs) increasingly relies on LLM-as-a-judge pipelines, but strong judges can still be expensive to use at scale. We study whether structured multi-agent debate can improve judge reliability while keeping backbone size and cost modest. To do so, we introduce HAJailBench, a human-annotated jailbreak benchmark […]
Integrative modelling of protein-glycan interactions with HADDOCK3
arXiv:2603.17251v1 Announce Type: new Abstract: Glycans are structurally diverse and flexible biomolecules that play key roles in many biological processes. Their conformational variability makes the modeling of their interactions with proteins particularly challenging. This chapter presents a step-by-step protocol for modeling protein-glycan interactions using HADDOCK3, an integrative modeling platform that supports the inclusion of experimental […]