Search: “architecture-design”

Enabling Creative Exploration for Vibe Design Agents

Separating design-direction exploration from code generation via structured specifications broadens UI alternatives without destabilizing output.

The paper proposes an inference architecture for vibe design agents that makes design direction an explicit intermediate decision: a Verbalized Sampling-inspired pre-pass proposes structured design specifications with typicality scores, an external selector samples one, and a downstream generator realizes it under fixed settings. Across 168 prompts with 1,255 paired comparisons per temperature, theme sampling broadens selection coverage and screenshot variation, with LLM-judge preferences varying across interventions and prompt complexity. An online experiment with over 300,000 tasks found the code-export increase statistically uncertain, though negative feedback events decreased alongside modest operational costs.

Hugging Face daily papers·Sep 13, 20:00 UTC · 3d agoAI research

Embodied-BenchForge: A Closed-Loop Agentic Workflow for Embodied Benchmark Construction

Embodied-BenchForge automates embodied benchmark construction via closed-loop synthesis with verification and repair, yielding seven benchmarks for MLLM evaluation.

Embodied-BenchForge is an agentic framework that transforms user-specified evaluation intents into complete embodied benchmark artifacts via Closed-Loop Benchmark Synthesis. Skill-Orchestrated Artifact Synthesis composes typed reusable skills while an artifact dependency graph records intermediate outputs; Requirement-Guided Verification and Repair triggers local re-execution or upstream rollback on failures. It constructs six Offline EQA benchmarks plus one interactive benchmark with 220 executable tasks, distinguishing MLLM and embodied agent capabilities in observation-based understanding and closed-loop execution.

arXiv cs.AI / cs.LG / cs.CL·Sep 11, 17:16 UTC · 5d agoAI research

To See a World in a Living Context: Unified Indoor-Outdoor Urban World Generation

Researchers introduce HoloWorld, a unified text-driven framework generating coherent indoor-outdoor 3D urban worlds, improving average AQS over SOTA by 7.68%.

HoloWorld is a text-driven 3D generation framework that unifies indoor and outdoor urban world generation using a continuously updated cross-scale world context. It autoregressively generates urban exteriors with consistent spatial organization, grounded in 3D building instances and footprints, then produces building-specific interiors with geometry-constrained layouts that inherit exterior appearance. The authors claim it is the first framework to unify indoor and outdoor generation within one coherent 3D urban world, reporting a 7.68% average AQS improvement over prior SOTA and the highest average RDR score.

Hugging Face daily papers·Aug 28, 20:00 UTC · 19d agoAI research

Agentic Visual Generation: From Generative Models to Agentic Control

Researchers propose an L0-L4 control taxonomy for agentic visual generation, classifying controllers from fixed conditioning to experience-adaptive decision-making.

This paper proposes a taxonomy for agentic visual generation organized by what the controller can directly control in the generation process, rather than by planning depth, tool count, or model size. Levels range from L1 Conditioning Control through L2 Execution Control, L3 Outcome-Adaptive Control, and L4 Experience-Adaptive Control, with L0 Fixed Support denoting systems without a deployed decision-making controller. The framework is applied across image, video, editing, 3D, world, slide, and user-interface generation to map how controller capabilities and mechanisms have evolved across the field.

Hugging Face daily papers·Sep 5, 20:00 UTC · 11d agoAI research

τ^τ-Bench: An Environment for End-To-End, Realistic Agent Construction

New τ^τ-bench tasks coding agents with building deployable customer-service agents; best config, Claude Opus 5, passes only 23.9% of simulations.

Researchers introduce τ^τ-bench, an end-to-end benchmark where a developer agent must build a complete customer-service agent from real business records, a client with requirements, a production API, an inherited codebase, and cost/model limits, then is scored by deploying it against held-out simulated users. Across 53 tasks in four domains, the strongest configuration, Claude Opus 5 under Claude Code, passes just 23.9% of evaluation simulations versus an 82.2% expert-authored reference ceiling. Failure modes mirror those of human developers: shallow queries instead of deep record comprehension, almost no client communication, and shipping the first architecture that runs rather than experimenting.

Hugging Face daily papers·Sep 3, 20:00 UTC · 13d agoAI research

Design Docs Are All You Need: An AI-native Machine-Learning Performance Tool

Researchers present SMART, an ML performance-modeling library regenerated by AI coding agents from natural-language design docs instead of code.

The paper describes SMART, a symbolic performance-modeling library whose main branch contains almost no code: the repository is a DAG of self-contained design documents, and coding sub-agents regenerate implementations from only the docs on version updates. Reliability rests on a worked-example doc style used as in-context demonstrations and a minimal operator IR with SymPy cost expressions, offering both fast analytical roll-up and fine-grained modulo-scheduling modes. Regenerated implementations reproduce hand-audited reference models, including DeepSeek-V3 serving on a TPU pod slice, to round-off precision.

arXiv cs.AI / cs.LG / cs.CL·Sep 4, 17:08 UTC · 12d agoAI research1

Ambient @ EgoLongQA 2026: Distilling Long-Video perception into a Sub-2B Model

Ambient team wins EgoLongQA 2026 sub-2B division by distilling an agentic long-video perception pipeline into a 2B vision-language model.

Ambient's entry to the EgoLongQA track of the Wearable-AI Challenge at ECCV 2026 placed first in the <=2B parameter division with 0.8279 on the held-out test set. The system distills the junior perception module of a tool-using agentic pipeline into a 2B student, reaching 89% of the pipeline's accuracy with 1.1% of its parameters and lifting a 27.1% base model to 81.4%. To meet the division limit, the multilingual embedding table is pruned from 248,320 to 143,469 rows, reaching 1.9985B parameters with provably identical logits on retained rows.

Hugging Face daily papers·Sep 9, 20:00 UTC · 7d agoAI research

Another Blueprint In The Wall: How to Ask Frontier AI Like a Kid?

Six frontier models from OpenAI, Anthropic, xAI, and Google DeepMind converge on one imagined successor architecture when asked under a school-audience framing.

Researchers ran ten independent sessions per model type across six frontier models using a three-stage prompt sequence progressing to a full ASCII backbone architecture. Under school-audience framing, responses repeatedly converged on a shared motif including persistent latent state, adaptive computation, memory, specialist routing, verification, and stopping control, while control runs without the framing produced heterogeneous responses. A GPT-5.6 Sol output closely overlapped an architecture independently sketched by GPT-6 Astra, raising questions about shared design priors or motif propagation between model families. The paper coins 'epistemic jailbreak' for the observed loss of provenance discipline as prompt specificity increases.

Hugging Face daily papers·Sep 12, 20:00 UTC · 4d agoAI research 10 sources

OmniHarness: Harnessing Generalizable Visual Generation via Symbolic Policy Learning

OmniHarness learns symbolic policies for visual generation agents, reaching a 95.0% resolve rate on ComfyBench Creative tasks, 27.5 points above the strongest baseline.

OmniHarness abstracts verified executions into symbolic policies for visual generation task families, which are instantiated, adapted, and composed for new tasks while model parameters remain fixed. Intermediate verification guides refinement and failure recovery during execution, and self-directed inquiry generates practice tasks near capability limits before downstream objectives are specified. Experiments across six benchmarks, three MLLM backbones, and three visual agent frameworks show strong performance; on ComfyBench Creative tasks it achieves a 95.0% resolve rate, exceeding the strongest baseline by 27.5 percentage points. Frozen policy snapshots improve existing visual agent systems through plug-and-play reuse.

Hugging Face daily papers·Sep 12, 20:00 UTC · 4d agoAI research

Cartesian – AI 3D Modeling for Design

Formas launches Cartesian, an AI 3D modeling tool for architecture and product design with natural-language editing and CAD exports.

Cartesian by Formas is an AI-powered 3D modeling tool aimed at architecture and product design that converts photos, rough plans, sketches, and scans into precise geometry. Users can edit models conversationally while explicitly preserved elements stay unchanged. It creates real solids and NURBS geometry and exports to AutoCAD DWG, Rhino 3DM, and SketchUp SKP, with BIM IFC support planned, removing the need for a separate desktop CAD license.

Hacker News · AI·Sep 15, 15:26 UTC · 1d agoAI industryHN 42↑ · 32 comments

Large Language Models for HVAC Operations in Building Energy Systems: A Critical Review of Methods, Applications, and Deployment Readiness

Systematic review of 66 studies finds LLMs for HVAC operations are mostly research-stage, with no ready-now deployment and only four pilot-level studies.

A critical review of 66 peer-reviewed studies from 2023 to March 2026 examines LLMs for HVAC operations in building energy systems. Only four studies reach pilot-level evidence, none reports sustained operational deployment, and 63 of 66 are research-only. Conventional ML, MPC, and RL remain dominant for high-frequency control and short-horizon forecasting, and the evidence supports LLMs primarily as semantic and workflow layers rather than autonomous controllers.

arXiv cs.AI / cs.LG / cs.CL·Sep 4, 16:08 UTC · 12d agoAI research

From Protocols to Evidence: Bounded Claims for AI in Service of the Common Good

Paper proposes a rupture test and RISE AI architecture for evidence-bounded responsible-AI claims, framed via EU AI Act and NIST AI RMF.

The paper argues AI deployment intervenes in pre-existing institutional failures of responsiveness, belonging, care, and accountability, and must therefore evaluate both the system and the institutional rupture it enters. It reviews how the EU AI Act, NIST AI RMF, and ISO/IEC 42001 translate principles into protocols, and draws on Pope Leo XIV's Magnifica Humanitas to develop a rupture test linking institutional baselines to system evaluation. It distinguishes evidence-bounded deployment from measurement-bounded governance and introduces RISE AI, an architecture for bounded claims about Responsibility, Inclusivity, Safety, and Empowerment.

arXiv cs.AI / cs.LG / cs.CL·Sep 10, 17:56 UTC · 6d agoAI policy

Show HN: Panel – A research workspace where the agent can build its own panes

Panel is an open-source research workspace where AI agents dynamically build their own panes, posted on Hacker News.

Panel, shared as a Show HN project on GitHub, is a research workspace in which the agent can construct its own panes rather than using a fixed interface. The post received 41 points and 8 comments on Hacker News. It targets agentic research workflows with a dynamically generated UI.

Hacker News · AI·Sep 15, 13:58 UTC · 1d agoAI tools & infraHN 41↑ · 8 comments

SceneMosaic: Efficient and Diverse Simulation-Ready Scene Generation via Hybrid Agentic Layout Evolution

SceneMosaic combines image-based 3D priors with VLM agent refinement to generate diverse, simulation-ready indoor scenes 24x faster than agentic baselines.

SceneMosaic is a hybrid framework that takes an initial candidate from a learned image-to-3D prior and evolves it with VLM agents for efficiency and physical validity. It decomposes scenes into independent local units, evolves each separately, and composes the global scene via Cartesian product. On SceneEval-100 it matches the strongest agentic baseline in semantic layout quality with a 24x speedup, substantially reduces physical violations, and receives the highest human ratings. Code is publicly available.

Hugging Face daily papers·Sep 3, 20:00 UTC · 13d agoAI research

Deconstructing the Architecture of AI-Orchestrated Web Attacks

Akamai analyzes the architecture of AI-orchestrated web attacks, examining how AI-driven automation is reshaping offensive web operations.

Akamai published an analysis deconstructing the architecture of web attacks orchestrated with AI, based on the available title. The piece examines how AI-driven automation changes the structure of offensive web operations. No article text was provided, so specific findings are limited.

Akamai Blog·Aug 24, 06:00 UTC · 23d agoResearch

Atria Dawn: The Dawn of Agentic Superintelligence

Atria Dawn Preview, an agentic foundation model trained on verifiable experiences, tops five of 16 research and engineering benchmarks.

Atria Dawn Preview is a foundation agentic language model for scientific research and engineering workflows, trained via a Verifiable Experience Pipeline connecting tool-mediated interactions to executable environments and externally verified outcomes. Across 16 benchmarks spanning research, engineering, and digital work it is competitive with frontier agents and achieves the highest reported score on five of them. The release includes a human-AI collaboration case study analyzing 769 task records from 56 participants, where about one-third of completed AI-assisted tasks were rated infeasible without AI and agents frequently proposed methods and implemented revisions while humans retained final decisions.

Hugging Face daily papers·Sep 13, 20:00 UTC · 3d agoModel release

The Agentic SOC – From AI Theater to Real Defense

Recorded Future and Accenture experts outline how security teams can move beyond 'AI theater' toward agentic SOC operations guided by measurable KPIs.

Recorded Future published a blog featuring perspectives from its own and Accenture experts on building an agentic security operations center. The piece argues organizations should prioritize measurable KPIs and proactively mitigate risks from autonomous agents. It also discusses evolving the analyst role from managing alerts to managing agents.

Recorded Future·Sep 1, 00:00 UTC · 15d agoIndustry

AI for Games in the Foundation Model Era

Survey organizes foundation-model AI for games into six roles and analyzes which capabilities transfer across playing, design, building, runtime adaptation, and testing.

A survey maps foundation-model and learned world-model research across the game lifecycle into six roles: playing/acting, modeling players and games, designing games, building/maintaining games, runtime generation/adaptation, and testing/evaluation. The authors identify cross-role connections such as trajectories training world models and design specifications driving executable implementations. Control schemes, rules, engine interfaces, state representations, and player contexts often remain setting-specific, so downstream claims require validation in the target setting. Evaluation is most standardized for bounded game playing, while persistent state, repeated revision, validated player modeling, and automated testing remain less established.

Hugging Face daily papers·Sep 14, 20:00 UTC · 2d agoAI research

Your enterprise AI footprint is about three times bigger than your model list

Help Net Security·Aug 5, 00:00 UTC · Aug 5, 2026AI safety & security

Architecting memory and storage in the AI era

Analysis argues AI inference shifts data-center bottlenecks to memory and storage, urging balanced compute, memory, storage, and network architecture over raw compute.

MIT Technology Review, citing Tirias Research principal analyst Jim McGregor, argues that AI inference and agentic workloads make data movement the key constraint, elevating memory and storage from background hardware to strategic assets. The piece says RAG and real-time inference require continuous data retrieval and caching that legacy infrastructure cannot support. It frames infrastructure planning as a business decision balancing performance, efficiency, cost, and scalability in healthcare, finance, and customer-facing AI.

MIT Technology Review · AI·Sep 4, 18:39 UTC · 12d agoAI industry

Powering AI is an architecture problem

Sponsored analysis argues AI data centers need medium-voltage, inline power architecture after Virginia grid faults knocked over 3GW of load offline.

A sponsored MIT Technology Review piece recounts a July 22, 2026 transmission fault in Ashburn, Virginia that shed more than 3 GW of data center load, and a 2024 incident where one failed surge arrester dropped about 60 facilities and 1,500 MW. It argues legacy UPS-based power stacks fail at AI scale because campuses can swing 70% of load in milliseconds and trip offline during grid disturbances. The proposed fix moves protection to medium voltage (13.8 kV and above) in inline enclosures near substations, improving density, permitting timelines, and backup power economics. A full-scale system tested at the DOE National Laboratory of the Rockies cleared ERCOT large-load ride-through requirements.

MIT Technology Review · AI·Sep 10, 11:00 UTC · 6d agoAI industry1

Reflection-aware Generative Novel View Synthesis

Ref-GeNVS is a training-free method for reflection-consistent generative novel view synthesis that treats mirror images as two complementary views.

An arXiv paper proposes Ref-GeNVS, a training-free approach to generative novel view synthesis in scenes containing mirrors. It estimates the mirror plane, reflects camera poses to create virtual views, and applies mirror-gated attention plus reflection injection within a multi-view diffusion model. On synthetic and real mirror scenes, Ref-GeNVS outperforms recent generative NVS methods while requiring no fine-tuning.

arXiv cs.AI / cs.LG / cs.CL·Sep 4, 17:33 UTC · 12d agoAI research

From Intent to Execution Grant: An Execution-Boundary Conformance Profile for High-Risk AI Actions

Researchers specify EBL-Core, an execution-boundary conformance profile binding AI agent intents, policies, and evidence into verifiable execution grants, validated with bounded tests.

The paper defines EBL-Core, a conformance profile deciding whether one fully materialized AI-generated candidate action may receive action-scoped execution authority. It binds a structured intent object, Root and Operational Policies, typed evidence, and a verifiable Decision Derivation through an Execution Release Contract, with lifecycle rules for Redemption and Revocation. Evaluation included 34 static vectors, 15 lifecycle checks, and 100 trials of 32 concurrent Redemption attempts yielding exactly one winner per trial. The authors state these bounded results demonstrate executability of the specified subset, not production readiness or complete mediation.

arXiv cs.CR·Sep 10, 14:21 UTC · 6d agoAI safety & security1

VoT: Vision-of-Thought for Unified Multimodal Representation Alignment

Researchers propose Vision-of-Thought (VoT), a discrete visual-planning token layer between VLMs and diffusion transformers improving text-to-image semantic alignment.

VoT introduces a discrete visual-thinking layer between vision-language models and diffusion transformers, letting the VLM act as a multimodal planner that emits tokens describing objects and layouts before pixel generation. A specialized VoT tokenizer is trained with VLM alignment, feature reconstruction, and vector-quantization losses. Experiments show improved semantic alignment and a structured, interpretable interface for controllable generation.

arXiv cs.AI / cs.LG / cs.CL·Sep 7, 17:53 UTC · 9d agoAI research

Mines, Minds, and Machines: The Journey of AI

Recorded Future essay traces AI's supply chain from minerals and chips to embodied AI, mapping geopolitical rivalry and cyber operations at every stage.

Recorded Future published an analysis following the supply chain of the fourth industrial revolution: minerals become chips, chips become data centers, and data centers become models that are acquiring physical capabilities. It argues geopolitical rivalry and cyber operations now run along every link, from mine to embodied AI machines.

Recorded Future·Aug 11, 00:00 UTC · Aug 11, 2026AI industry

Procedural Graphs: Self-Evolving Execution Structures for LLM Agents

Researchers introduce Procedural Graphs, self-evolving (procedure, relation, procedure) structures guiding LLM agent tool use and planning.

Procedural Graphs organize procedural knowledge into (procedure, relation, procedure) triplets to guide LLM agent actions, addressing drift such as lost objectives, out-of-order tool calls, and repeated unproductive steps. At each decision step the framework localizes the active node and a guidance model translates the surrounding subgraph into step-level situational guidance. An LLM refiner edits graph topology by contrasting failed with successful trajectories, and across datasets, task types and LLMs the approach outperforms memory-based baselines and matches or surpasses hand-designed graphs.

arXiv cs.AI / cs.LG / cs.CL·Sep 8, 17:59 UTC · 8d agoAI research1

Involving before Evolving: A Vision for Trustworthy Enterprise Digital Twin Engineering

Vision paper proposes 'involving before evolving' staged approach for trustworthy enterprise digital twins, validated via an ongoing Michelin prototype.

The paper presents a three-stage vision for enterprise digital twin (EDT) engineering that prioritizes early organizational buy-in before evolving toward federation and full interoperability. The approach combines foundation models for rapid prototyping, an ontological backbone for federated interoperability, and observability tooling to build stakeholder trust. The vision is grounded in an ongoing collaboration with Michelin, where an initial working prototype helped secure stakeholder buy-in.

arXiv cs.AI / cs.LG / cs.CL·Sep 11, 17:06 UTC · 5d agoAI research

One Symptom, Three Levers: A Critical Review of On-Policy Self-Distillation

A review paper frames on-policy self-distillation collapse as governed by three levers: token weighting, privileged information, and guidance decay.

The paper critically reviews On-Policy Self-Distillation (OPSD), where a language model trains on its own generations scored token-by-token by a teacher conditioned on privileged information such as reference solutions or environment feedback. It identifies collapse, the progressive narrowing of producible reasoning paths, as the dominant failure mode and analyzes it through three levers: signal weighting, the nature of privileged information, and teacher dynamics. The review is restricted to mathematical reasoning, reports no new experiments, and offers a shared vocabulary separating settled findings from disputed ones.

Hugging Face daily papers·Aug 25, 20:00 UTC · 22d agoAI research

Governing Bring Your Own AI: A Parameterized Maturity Model

Researchers propose a parameterized governance model and maturity ladder for Bring Your Own AI, finding data exposure and compliance dominate BYOAI risks.

The paper studies Bring Your Own AI (BYOAI), where employees use personal generative AI accounts such as ChatGPT, Gemini, and Claude outside enterprise identity and security controls. Drawing on a curated corpus of 30 records (24 studies and 6 framework documents), the authors build a risk taxonomy, a five-level governance maturity ladder, and a parameterized model linking control-layer coverage to residual risk. Findings highlight data exposure and compliance as the most prominent risks, inconsistent framework engagement, and evidence that layered technical controls reduce modeled exfiltration risk more than prohibition-based approaches.

arXiv cs.CR·Sep 4, 15:03 UTC · 12d agoResearch

SenseNova-U1.5: Towards Native Unified Visual Intelligence

SenseTime releases SenseNova-U1.5, an 8B-MoT encoder-free multimodal model unifying visual understanding, reasoning, and generation with native 4K resolution.

SenseNova-U1.5 is an 8B mixture-of-transformers multimodal model with an encoder-free, VAE-free architecture that understands, reasons about, and generates visual content at native resolutions up to 4K. Post-training optimizes specialized experts for visual aesthetics, bilingual text rendering, infographic generation, and image editing, consolidated through multi-expert on-policy distillation. Evaluations report gains in image fidelity, text rendering, multi-reference editing, and instruction following. The team plans to open-source training code including supervised fine-tuning, reinforcement learning, and on-policy distillation.

Hugging Face daily papers·Sep 9, 20:00 UTC · 7d agoModel release