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Spatially heterogeneous relaxational dynamics and the evolution of recoverable strain following flow cessation of a ductile nanocolloidal glass
Chloe W. Lindeman, James J. Griebler, Penelope Grace Kovakas, Miaoqi Chu, Qingteng Zhang, Suresh Narayanan, James L. Harden, Simon A. Rogers, Robert L. Leheny
https://arxiv.org/abs/2608.11470 https://arxiv.org/pdf/2608.11470 https://arxiv.org/html/2608.11470
arXiv:2608.11470v1 Announce Type: new
Abstract: We report a combined rheology and x-ray photon correlation spectroscopy (XPCS) study of the structural and mechanical relaxation of a ductile, nanocolloidal glass following the cessation of shear flow. After the glass is sheared to 300% strain at various shear rates and then held at fixed strain, the stress undergoes a protracted, quasi-logarithmic decay with hold time that depends weakly on the initial strain rate. Recovery rheology measurements reveal that this stress relaxation is accompanied by a logarithmic decrease in the elastic component of the recoverable strain; hence, the rates of decrease of the stress and recoverable strain are proportional. XPCS measurements during the stress relaxation reveal dynamics dominated by a convection-like backflow that is divided into two dynamically distinct regions indicative of banded motion. In one region, the flow can be modeled by an affine strain, while in the other region the glass moves as a plug while undergoing slow, glassy relaxation. The rates of these dynamics approximately track the rate of loss of recoverable strain, indicating this motion is the predominant microscopic mechanism driving the conversion of recoverable to unrecoverable strain during stress relaxation. In contrast, XPCS measurements during strain recovery reveal purely affine flow with no evidence of heterogeneity and with strain rates that agree quantitatively with the rheometry measurements. Together, these results provide a unified microscopic picture connecting the evolving internal dynamics of a ductile glass to its macroscopic mechanical relaxation following flow cessation.
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Text2Villa: Hierarchical Generation of 3D Indoor Environments with Physics-Aware Analysis-by-Synthesis
Xiang Tang, Ruotong Li, Xiaopeng Fan
https://arxiv.org/abs/2607.17145 https://arxiv.org/pdf/2607.17145 https://arxiv.org/html/2607.17145
arXiv:2607.17145v1 Announce Type: new
Abstract: Generating 3D indoor scenes from natural language holds tremendous potential, yet existing methods predominantly fail to generate multi-room structures with vertical connectivity and arbitrary polygonal boundaries. Furthermore, they lack a deep grounding in continuous 3D physical laws, leading to severe geometric penetrations and floating artifacts. In this work, we propose Text2Villa, a novel hierarchical generative framework. At the macro level, we construct a multi-story dataset to fine-tune an autoregressive layout generator, ensuring the direct parsing of text into 3D building foundations featuring polygonal boundaries and multi-story connectivity. To enforce physical laws during micro-level asset arrangement, we introduce the Affordance-driven Physical-Semantic Scene Graph (A-PSSG) to explicitly abstract physical affordances (such as support surfaces and containment cavities) into node attributes, establishing strict geometric and semantic edge constraints. Guided by the A-PSSG, we formulate scene instantiation as a constrained closed-loop optimization problem following the analysis-by-synthesis paradigm. By integrating an underlying geometric collision detection engine with the high-level semantic reasoning of multimodal large language models (MLLMs), our heuristic solver dynamically executes physics-aware actions under the observation-evaluation-modification mechanism to effectively resolve mesh collisions, floating artifacts, and fine-grained cavity containment failures. Extensive experiments demonstrate that Text2Villa outperforms previous methods across various metrics, robustly generating high-fidelity and physically plausible villa-level 3D environments from text, thereby providing a reliable and interactive 3D content foundation for downstream applications.
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I just watched the #BlackHat #OpenAI #HuggingFace talk (#OpenAI understands it themselves. However, emergence is still no indication of actual intelligence, and the talk is again ripe with anthropomorphism.
3. Yes, it is surprising and somewhat terrifying how good these LLM agent loops have become in finding and exploiting vulnerabilities. We are seeing a much stronger version of the earlier next-generation fuzzing leap in tool capabilities.
4. The comparison with fuzzing and other tools is important for another reason: #OpenAI conducted their experiments on the live Internet. No, forcing internal test runs to go through a proxy server is *not the same as running in an isolated environment*. If there is a connection to a live production system, it is by definition not an effective sandbox. What happened to people letting their cool new fuzzers run live on non-consenting production targets on the Internet back then? They were criminally liable. Why should #OpenAI not be held liable for any damages caused by their systems?
Just because you don't understand the complexity that you built doesn't mean you aren't responsible for what it does!
5. The agent loops first successfully started exploited their proxy server on May 26th, discovered July 4th-6th as active exploitation and takeover. Credentials were revoked and the proxy server patched. "At this point, AI training and evaluations resume."
WHAT THE ACTUAL F...? You discovered emergent behavior in your overly complex system that you didn't understand, saw active exploitation of your internal systems to an extent that they were owned with admin privileges, and your response was to patch the uppermost surface holes, and then continue letting the out-of-control complex system run and continue on the same course? You didn't think to, let's see, maybe stop or at least pause the highly irresponsible experiment while figuring out - in detail - what went wrong, why, and how to avoid the same thing happening again on a systematic level?
6. On July 8th, completely unsurprisingly, the LLM agent loops continue to do the same thing and find another surface hole that hasn't been patched yet to take over again. Why should this have stopped? You haven't done any root cause analysis on the system level. Why do you expect that the problem should have stopped?
7. It takes another 11 days to discover that this is happening again. So you turned the system that had broken something back on again without a detailed root cause analysis and then didn't even watch carefully? I can't even...
8. And no, the response is not to fight fire with fire. Complexity on the attacker side (#OpenAI is the attacker, not the defender - they are the guilty perpetrator, not the innocent victim of circumstance) should be fought with *reduced* attack surface on the defender systems. Adding LLM agent loops that the "frontier" companies themselves quite obviously have no control over to already brittle systems with the hope of auto-patching your way out of vulnerabilities does not seem like a wise course of action. You don't mitigate complexity with even more complexity. The next 2 years will be ... exciting - and your best bet is going to be to disable all dependencies and complex interactions that your production systems don't absolutely require.