Tootfinder

May I have your Attention? Breaking Fine-Tuning based Prompt Injection Defenses using Architecture-Aware Attacks
Nishit V. Pandya, Andrey Labunets, Sicun Gao, Earlence Fernandes
https://arxiv.org/abs/2507.07417

May I have your Attention? Breaking Fine-Tuning based Prompt Injection Defenses using Architecture-Aware Attacks
A popular class of defenses against prompt injection attacks on large language models (LLMs) relies on fine-tuning the model to separate instructions and data, so that the LLM does not follow instructions that might be present with data. There are several academic systems and production-level implementations of this idea. We evaluate the robustness of this class of prompt injection defenses in the whitebox setting by constructing strong optimization-based attacks and showing that the defenses d…

Series B, Episode 05 - Pressure Point
TRAVIS: Come on, come on. Where were you to rendezvous with Blake?
KASABI: You can rot, Travis. I'll tell you nothing, nothing. [Kasabi spits on Travis. Travis crosses to Servalan.]
https://blake.torpidity.net/m/205/170 B7B5

Blakes7Bot | Search
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Design and optimization of neural networks for multifidelity cosmological emulation
Yanhui Yang, Simeon Bird, Ming-Feng Ho, Mahdi Qezlou
https://arxiv.org/abs/2507.07184

Design and optimization of neural networks for multifidelity cosmological emulation
Accurate and efficient simulation-based emulators are essential for interpreting cosmological survey data down to nonlinear scales. Multifidelity emulation techniques reduce simulation costs by combining high- and low-fidelity data, but traditional regression methods such as Gaussian processes struggle with scalability in sample size and dimensionality. In this work, we present T2N-MusE, a neural network framework characterized by (i) a novel 2-step multifidelity architecture, (ii) a 2-stage Ba…

Jigsaw: Training Multi-Billion-Parameter AI Weather Models with Optimized Model Parallelism
Deifilia Kieckhefen, Markus G\"otz, Lars H. Heyen, Achim Streit, Charlotte Debus
https://arxiv.org/abs/2507.05753

Jigsaw: Training Multi-Billion-Parameter AI Weather Models with Optimized Model Parallelism
AI-based methods have revolutionized atmospheric forecasting, with recent successes in medium-range forecasting spurring the development of climate foundation models. Accurate modeling of complex atmospheric dynamics at high spatial resolutions and longer lead times requires large neural networks and gigabyte-sized data samples, making accelerator memory and I/O-bandwidth the bottlenecks for model training. We introduce WeatherMixer, a multi-layer-perceptron-based architecture whose workload sc…

One-third of Sun-like stars are born with misaligned planet-forming disks
Lauren I. Biddle (The University of Texas at Austin), Brendan P. Bowler (University of California Santa Barbara), Marvin Morgan (The University of Texas at Austin, University of California Santa Barbara), Quang H. Tran (Yale University), Ya-Lin Wu (Taiwan Normal University)
https://

One-third of Sun-like stars are born with misaligned planet-forming disks
Exoplanets are organized in a broad array of orbital configurations that reflect their formation along with billions of years of dynamical processing through gravitational interactions. This history is encoded in the angular momentum architecture of planetary systems--the relation between the rotational properties of the central star and the orbital geometry of planets. A primary observable is the alignment (or misalignment) between the rotational axis of the star and the orbital plane of its p…

Residuality Theory: A Rebellious Take on Building Systems That Actually Survive
#resiliency

Consistent Updates for Scalable Microservices
Devora Chait-Roth, Kedar S. Namjoshi, Thomas Wies
https://arxiv.org/abs/2508.04829 https://arxiv.org/pdf/2508…

Consistent Updates for Scalable Microservices
Online services are commonly implemented with a scalable microservice architecture, where isomorphic worker processes service client requests, recording persistent state in a backend data store. To maintain service, any modifications to the service functionality must be made on the fly -- i.e., as the service continues to process client requests -- but doing so is challenging. The central difficulty is that of avoiding potential inconsistencies caused by ''mixed mode'' operation, where workers …

I actually really like neoclassical #architecture, but I enjoyed this interesting essay by Jeffery Tyler Syck in favor of diversity of style.
https://lawliberty.org/the-promise-of-

I didn't want to let the day go without posting a photo ;-) So I just grabbed one from last year when we visited #vienna in autumn.
A bit of context can be found on the blog: https://www.

MaLV-OS: Rethinking the Operating System Architecture for Machine Learning in Virtualized Clouds
Stella Bitchebe, Oana Balmau
https://arxiv.org/abs/2508.03676 https://

MaLV-OS: Rethinking the Operating System Architecture for Machine Learning in Virtualized Clouds
A large body of research has employed Machine Learning (ML) models to develop learned operating systems (OSes) and kernels. The latter dynamically adapts to the job load and dynamically adjusts resources (CPU, IO, memory, network bandwidth) allocation to respond to the actual user demand. What this work has in common is that it utilizes ML to improve kernel decisions. To this day, and to the best of our knowledge, no work has taken the opposite direction, i.e., using OS to improve ML. While som…

I had an architecture class where the professor spent an entire class discussing doorknobs. It was one of my favorite classes.
So the headline was all I needed to click this link:
“On the Architectural Hostility of Doorknobs”
https://sightlessscribbles.com/writing

This https://arxiv.org/abs/2503.09492 has been replaced.
initial toot: https://mastoxiv.page/@arXiv_csIR_…

Learning Cascade Ranking as One Network
Cascade Ranking is a prevalent architecture in large-scale top-k selection systems like recommendation and advertising platforms. Traditional training methods focus on single-stage optimization, neglecting interactions between stages. Recent advances have introduced interaction-aware training paradigms, but still struggle to 1) align training objectives with the goal of the entire cascade ranking (i.e., end-to-end recall of ground-truth items) and 2) learn effective collaboration patterns for d…

Series B, Episode 05 - Pressure Point
VERON: No!
GAN: It's all right, it's all right. We won't hurt you. [Hands her pill he has taken from a container.] Take this.
VERON: No.
GAN: Please.
VERON: [Swallows pill] Who are you?
https://blake.torpidity.net/m/205/255

FLARE: A Dataflow-Aware and Scalable Hardware Architecture for Neural-Hybrid Scientific Lossy Compression
Wenqi Jia, Ying Huang, Jian Xu, Zhewen Hu, Sian Jin, Jiannan Tian, Yuede Ji, Miao Yin
https://arxiv.org/abs/2507.01224

FLARE: A Dataflow-Aware and Scalable Hardware Architecture for Neural-Hybrid Scientific Lossy Compression
Scientific simulation leveraging high-performance computing (HPC) systems is crucial for modeling complex systems and phenomena in fields such as astrophysics, climate science, and fluid dynamics, generating massive datasets that often reach petabyte to exabyte scales. However, managing these vast data volumes introduces significant I/O and network bottlenecks, limiting practical performance and scalability. While cutting-edge lossy compression frameworks powered by deep neural networks (DNNs) …

A while ago, I've followed the example given by #Fedora and unbundled ensurepip wheels from #Python in #Gentoo (just checked — "a while ago" was 3 years ago). This had the important advantage that it enabled us to update these wheels along with the actual pip and setuptools packages, meaning new virtual environments would get fresh versions rather than whatever CPython happened to bundle at the time of release.
I had considered using our system packages to prepare these wheels, but since we were already unbundling dependencies back then, that couldn't work. So I just went with fetching upstream wheels from PyPI. Why not build them from source instead? Well, besides feeling unnecessary (it's not like the PyPI wheels are actually binary packages), we probably didn't have the right kind of eclass support for that at the time.
Inspired by @…, today I've tried preparing new revisions of ensurepip packages that actually do build everything from source. So what changed, and why should building from source matter now? Firstly, as part of the wheel reuse patches, we do have a reasonably clean architecture to grab the wheels created as part of the PEP517 build. Secondly, since we're unbundling dependencies from pip and setuptools, we're effectively testing different packages than these installed as ensurepip wheels — and so it would be meaningful to test both variants. Thirdly, building from source is going to make patching easier, and at the very least enable user patching.
While at it, I've refreshed the test suite runs in all three regular packages (pip, setuptools and wheel — we need an "ensurepip" wheel for the last because of test suites). And of course, I hit some test failures in testing the versions with bundled dependencies, and I've discovered a random bug in #PyPy.
https://github.com/gentoo/gentoo/pull/42882 (yes, we haven't moved yet)
https://github.com/pypy/pypy/issues/5306

APTx Neuron: A Unified Trainable Neuron Architecture Integrating Activation and Computation
Ravin Kumar
https://arxiv.org/abs/2507.14270 https://

APTx Neuron: A Unified Trainable Neuron Architecture Integrating Activation and Computation
We propose the APTx Neuron, a novel, unified neural computation unit that integrates non-linear activation and linear transformation into a single trainable expression. The APTx Neuron is derived from the APTx activation function, thereby eliminating the need for separate activation layers and making the architecture both computationally efficient and elegant. The proposed neuron follows the functional form $y = \sum_{i=1}^{n} ((α_i + \tanh(β_i x_i)) \cdot γ_i x_i) + δ$, where all parameter…

Ken Utilization Layer: Hebbian Replay Within a Student's Ken for Adaptive Knowledge Tracing
Grey Kuling, Marinka Zitnik
https://arxiv.org/abs/2507.00032

Ken Utilization Layer: Hebbian Replay Within a Student's Ken for Adaptive Knowledge Tracing
We introduce KUL-KT, a biologically inspired architecture for knowledge tracing (KT), combining Hebbian memory encoding with gradient-based consolidation in a scalable, input-agnostic framework. KUL-KT adapts the principle of memory consolidation in neural systems, to student modeling by introducing two key innovations: (i) a time-decaying Hebbian memory update that enables graceful forgetting, and (ii) a novel Loss-aligned Internal Target (LIT) method to compute an ideal internal state, allowi…

Domain Knowledge-Enhanced LLMs for Fraud and Concept Drift Detection
Ali \c{S}enol, Garima Agrawal, Huan Liu
https://arxiv.org/abs/2506.21443 https://arxiv.org/pdf/2506.21443 https://arxiv.org/html/2506.21443
arXiv:2506.21443v1 Announce Type: new
Abstract: Detecting deceptive conversations on dynamic platforms is increasingly difficult due to evolving language patterns and Concept Drift (CD)\-i.e., semantic or topical shifts that alter the context or intent of interactions over time. These shifts can obscure malicious intent or mimic normal dialogue, making accurate classification challenging. While Large Language Models (LLMs) show strong performance in natural language tasks, they often struggle with contextual ambiguity and hallucinations in risk\-sensitive scenarios. To address these challenges, we present a Domain Knowledge (DK)\-Enhanced LLM framework that integrates pretrained LLMs with structured, task\-specific insights to perform fraud and concept drift detection. The proposed architecture consists of three main components: (1) a DK\-LLM module to detect fake or deceptive conversations; (2) a drift detection unit (OCDD) to determine whether a semantic shift has occurred; and (3) a second DK\-LLM module to classify the drift as either benign or fraudulent. We first validate the value of domain knowledge using a fake review dataset and then apply our full framework to SEConvo, a multiturn dialogue dataset that includes various types of fraud and spam attacks. Results show that our system detects fake conversations with high accuracy and effectively classifies the nature of drift. Guided by structured prompts, the LLaMA\-based implementation achieves 98\% classification accuracy. Comparative studies against zero\-shot baselines demonstrate that incorporating domain knowledge and drift awareness significantly improves performance, interpretability, and robustness in high\-stakes NLP applications.
toXiv_bot_toot

Salesforce launches Agentforce 3 with an observability tool called Command Center and MCP support, and says 8,000 customers have signed up to deploy Agentforce (Larry Dignan/Constellation Research)
https://www.constellationr.com/blog-news/i

Salesforce launches Agentforce 3, Command Center for visibility
Salesforce launched Agentforce 3, which features Agentforce Command Center, support for Model Context Protocol (MCP), and updated Atlas architecture to speed up reasoning and performance. The rollout maintains Salesforce's Agentforce cadence, which includes updates every few months as the company learns from enterprise use cases. In addition, Salesforce has added more than 30 partners to AgentExchange including AWS, Box, Cisco, Google Cloud, IBM and payments players such as PayPal and Stripe.

I think we will soon see an AlphaGo moment somewhere in embodiment. Maybe in robot football?
pi_0 is the Atari moment: https://www.physicalintelligence.company/blog/pi0 We now know that training at scale works and generalizes remarkably well.
This is the trigge…

Our First Generalist Policy
Our first generalist policy, π0, a prototype model that combines large-scale multi-task and multi-robot data collection with a new network architecture to enable the most capable and dexterous generalist robot policy to date.

A Denotational Semantics for Quantum Loops
Nicola Assolini, Alessandra Di Pierro
https://arxiv.org/abs/2506.23320 https://arxiv.org/p…

A Denotational Semantics for Quantum Loops
Programming a quantum computer, i.e., implementing quantum algorithms on a quantum processor-based copmputer architecture, is a task that can be addressed (just as for classical computers) at different levels of abstraction. This paper proposes a denotational semantics for high-level quantum programming constructs, focusing on the conceptual meaning of quantum-controlled branching and iteration. We introduce a denotational domain where a mathematical meaning of a quantum control flow with loops…

Decision Models for Selecting Architecture Patterns and Strategies in Quantum Software Systems
Mst Shamima Aktar, Peng Liang, Muhammad Waseem, Amjed Tahir, Mojtaba Shahin, Muhammad Azeem Akbar, Arif Ali Khan, Aakash Ahmad, Musengamana Jean de Dieu, Ruiyin Li
https://arxiv.org/abs/2507.11671

Decision Models for Selecting Architecture Patterns and Strategies in Quantum Software Systems
Quantum software represents disruptive technologies in terms of quantum-specific software systems, services, and applications - leverage the principles of quantum mechanics via programmable quantum bits (Qubits) that manipulate quantum gates (QuGates) - to achieve quantum supremacy in computing. Quantum software architecture enables quantum software developers to abstract away implementation-specific details (i.e., mapping of Qubits and QuGates to high-level architectural components and connect…

KBest: Efficient Vector Search on Kunpeng CPU
Kaihao MA, Meiling Wang, Senkevich Oleg, Zijian LI, Daihao Xue, Dmitriy Malyshev, Yangming Lv, Shihai Xiao, Xiao Yan, Radionov Alexander, Weidi Zeng, Yuanzhan Gao, Zhiyu Zou, Yao xin, Liu Lin, Junhao Wu, Yiding Liu, Yaoyao Fu, Gongyi Wang, Gong Zhang, Fei Yi, Yingfan Liu
https://arxiv.org/abs/2…

KBest: Efficient Vector Search on Kunpeng CPU
Vector search, which returns the vectors most similar to a given query vector from a large vector dataset, underlies many important applications such as search, recommendation, and LLMs. To be economic, vector search needs to be efficient to reduce the resources required by a given query workload. However, existing vector search libraries (e.g., Faiss and DiskANN) are optimized for x86 CPU architectures (i.e., Intel and AMD CPUs) while Huawei Kunpeng CPUs are based on the ARM architecture and c…

Transient Stability-Driven Planning for the Optimal Sizing of Resilient AC/DC Hybrid Microgrids
Yi Wang, Goran Strbac
https://arxiv.org/abs/2507.17110 https://

Transient Stability-Driven Planning for the Optimal Sizing of Resilient AC/DC Hybrid Microgrids
This paper proposes a transient stability-driven planning framework for the optimal sizing problem of resilient AC/DC hybrid microgrids (HMGs) under different types of contingencies, capturing frequency and voltage stability requirements as well as the frequency-voltage coupling dynamics of AC/DC interlinking converters (ICs). The planning model is formulated into a defender-attacker-defender (DAD) architecture, which can be further merged into two levels, i.e., upper-level and low-level proble…

Virtualizing RAN: Science, Strategy, and Architecture of Software-Defined Mobile Networks
Ryan Barker
https://arxiv.org/abs/2506.09878 https://

Virtualizing RAN: Science, Strategy, and Architecture of Software-Defined Mobile Networks
Virtualising the Radio Access Network (RAN) is widely touted as the corner-stone of affordable 5G and a prerequisite for AI-native 6G. Yet current discourse often isolates spectrum policy, cloud engineering and organisational readiness into silos. This paper delivers an integrated analysis that spans science, technology, business strategy and culture. I first review spectrum-auction economics and show-via a comparative study of T-Mobile US and Verizon-that mid-band contiguity leveraged through …

Series C, Episode 05 - The Harvest of Kairos
VILA: We got it, we got it!
TARRANT: Yes, I know, only I was saving that trick for when they both attacked at once.
VILA: There's only two left now; no trouble.
https://blake.torpidity.net/m/305/300 B7B3

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Mix-of-Language-Experts Architecture for Multilingual Programming
Yifan Zong, Yuntian Deng, Pengyu Nie
https://arxiv.org/abs/2506.18923 https://

Mix-of-Language-Experts Architecture for Multilingual Programming
Large language models (LLMs) have demonstrated impressive capabilities in aiding developers with tasks like code comprehension, generation, and translation. Supporting multilingual programming -- i.e., coding tasks across multiple programming languages -- typically requires either (1) finetuning a single LLM across all programming languages, which is cost-efficient but sacrifices language-specific specialization and performance, or (2) finetuning separate LLMs for each programming language, whi…

Incentivised Orchestrated Training Architecture (IOTA): A Technical Primer for Release
Felix Quinque, Alan Aboudib, Szymon Fonau, Rodrigo Lopez Portillo Alcocer, Brian McCrindle, Steffen Cruz
https://arxiv.org/abs/2507.17766

Incentivised Orchestrated Training Architecture (IOTA): A Technical Primer for Release
In August 2024, Bittensor's Subnet 9 (SN9) demonstrated that a distributed network of incentivized, permissionless actors could each pretrain large language models (LLMs) ranging from 700 million to 14 billion parameters, while surpassing established baselines. While that work validated blockchain-based decentralized pretraining as viable, it contained core issues: (i) every miner had to fit an entire model locally, and (ii) "winner-takes-all" rewards encouraged model hoarding. Here we introd…

Causal Graph Fuzzy LLMs: A First Introduction and Applications in Time Series Forecasting
Omid Orang, Patricia O. Lucas, Gabriel I. F. Paiva, Petronio C. L. Silva, Felipe Augusto Rocha da Silva, Adriano Alonso Veloso, Frederico Gadelha Guimaraes
https://arxiv.org/abs/2507.17016

Causal Graph Fuzzy LLMs: A First Introduction and Applications in Time Series Forecasting
In recent years, the application of Large Language Models (LLMs) to time series forecasting (TSF) has garnered significant attention among researchers. This study presents a new frame of LLMs named CGF-LLM using GPT-2 combined with fuzzy time series (FTS) and causal graph to predict multivariate time series, marking the first such architecture in the literature. The key objective is to convert numerical time series into interpretable forms through the parallel application of fuzzification and c…

DCN^2: Interplay of Implicit Collision Weights and Explicit Cross Layers for Large-Scale Recommendation
Bla\v{z} \v{S}krlj, Yonatan Karni, Grega Ga\v{s}per\v{s}i\v{c}, Bla\v{z} Mramor, Yulia Stolin, Martin Jakomin, Jasna Urban\v{c}i\v{c}, Yuval Dishi, Natalia Silberstein, Ophir Friedler, Assaf Klein
https://arxiv.org/abs/2506.21…

DCN^2: Interplay of Implicit Collision Weights and Explicit Cross Layers for Large-Scale Recommendation
The Deep and Cross architecture (DCNv2) is a robust production baseline and is integral to numerous real-life recommender systems. Its inherent efficiency and ability to model interactions often result in models that are both simpler and highly competitive compared to more computationally demanding alternatives, such as Deep FFMs. In this work, we introduce three significant algorithmic improvements to the DCNv2 architecture, detailing their formulation and behavior at scale. The enhanced archi…

Secular Resonances in Planet-Hosting Binary Stars. I. General Theory
Nader Haghighipour, Michael Andrew
https://arxiv.org/abs/2507.17092 https://

Secular Resonances in Planet-Hosting Binary Stars. I. General Theory
Motivated by the diversity of circumstellar planets in binary stars and the strong effects of the secular resonances of Jupiter and Saturn on the formation and architecture of the inner solar system, we have launched an expansive project on studying the effects of secular resonances on the formation of terrestrial planets around a star of a moderately close binary. As the first phase of our project, we present here the general theory of secular resonances in dual-star systems where the primary …

Series C, Episode 05 - The Harvest of Kairos
VILA: We got it, we got it!
TARRANT: Yes, I know, only I was saving that trick for when they both attacked at once.
VILA: There's only two left now; no trouble.
https://blake.torpidity.net/m/305/300 B7B3

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FPGA & VPU Co-Processing in Space Applications: Development and Testing with DSP/AI Benchmarks
Vasileios Leon, Charalampos Bezaitis, George Lentaris, Dimitrios Soudris, Dionysios Reisis, Elissaios-Alexios Papatheofanous, Angelos Kyriakos, Aubrey Dunne, Arne Samuelsson, David Steenari
https://arxiv.org/abs/2506.12968

FPGA & VPU Co-Processing in Space Applications: Development and Testing with DSP/AI Benchmarks
The advent of computationally demanding algorithms and high data rate instruments in new space applications pushes the space industry to explore disruptive solutions for on-board data processing. We examine heterogeneous computing architectures involving high-performance and low-power commercial SoCs. The current paper implements an FPGA with VPU co-processing architecture utilizing the CIF & LCD interfaces for I/O data transfers. A Kintex FPGA serves as our framing processor and heritage accel…

This https://arxiv.org/abs/2503.09492 has been replaced.
initial toot: https://mastoxiv.page/@arXiv_csIR_…

Learning Cascade Ranking as One Network
Cascade Ranking is a prevalent architecture in large-scale top-k selection systems like recommendation and advertising platforms. Traditional training methods focus on single-stage optimization, neglecting interactions between stages. Recent advances have introduced interaction-aware training paradigms, but still struggle to 1) align training objectives with the goal of the entire cascade ranking (i.e., end-to-end recall of ground-truth items) and 2) learn effective collaboration patterns for d…

Portable High-Performance Kernel Generation for a Computational Fluid Dynamics Code with DaCe
M{\aa}ns I. Andersson, Martin Karp, Niclas Jansson, Stefano Markidis
https://arxiv.org/abs/2506.20994

Portable High-Performance Kernel Generation for a Computational Fluid Dynamics Code with DaCe
With the emergence of new high-performance computing (HPC) accelerators, such as Nvidia and AMD GPUs, efficiently targeting diverse hardware architectures has become a major challenge for HPC application developers. The increasing hardware diversity in HPC systems often necessitates the development of architecture-specific code, hindering the sustainability of large-scale scientific applications. In this work, we leverage DaCe, a data-centric parallel programming framework, to automate the gene…

Enabling Syscall Intercept for RISC-V
Petar Andri\'c, Aaron Call, Ramon Nou
https://arxiv.org/abs/2505.10217 https://arxiv.org/pd…

Enabling Syscall Intercept for RISC-V
The European Union technological sovereignty strategy centers around the RISC-V Instruction Set Architecture, with the European Processor Initiative leading efforts to build production-ready processors. Focusing on realizing a functional RISC-V ecosystem, the BZL initiative (www.bzl.es) is making an effort to create a software stack along with the hardware. In this work, we detail the efforts made in porting a widely used syscall interception library, mainly used on AdHocFS (i.e., DAOS, GekkoFS…

KLAN: Kuaishou Landing-page Adaptive Navigator
Fan Li, Chang Meng, Jiaqi Fu, Shuchang Liu, Jiashuo Zhang, Tianke Zhang, Xueliang Wang, Xiaoqiang Feng
https://arxiv.org/abs/2507.23459

KLAN: Kuaishou Landing-page Adaptive Navigator
Modern online platforms configure multiple pages to accommodate diverse user needs. This multi-page architecture inherently establishes a two-stage interaction paradigm between the user and the platform: (1) Stage I: page navigation, navigating users to a specific page and (2) Stage II: in-page interaction, where users engage with customized content within the specific page. While the majority of research has been focusing on the sequential recommendation task that improves users' feedback in S…

Towards AI-Native RAN: An Operator's Perspective of 6G Day 1 Standardization
Nan Li, Qi Sun, Lehan Wang, Xiaofei Xu, Jinri Huang, Chunhui Liu, Jing Gao, Yuhong Huang, Chih-Lin I
https://arxiv.org/abs/2507.08403

Towards AI-Native RAN: An Operator's Perspective of 6G Day 1 Standardization
Artificial Intelligence/Machine Learning (AI/ML) has become the most certain and prominent feature of 6G mobile networks. Unlike 5G, where AI/ML was not natively integrated but rather an add-on feature over existing architecture, 6G shall incorporate AI from the onset to address its complexity and support ubiquitous AI applications. Based on our extensive mobile network operation and standardization experience from 2G to 5G, this paper explores the design and standardization principles of AI-Na…

#Blakes7 Series B, Episode 03 - Weapon
JENNA: We're below horizon now.
GAN: Then she's lost!
AVON: Only us. She got what she really wanted.
VILA: I thought that was us.
https://blake.torpidity.net/m/203/510…

#Blakes7 Series B, Episode 02 - Shadow
ZEN: Information. Main visual is available. [displays Space City on screen.]
VILA: So?
ZEN: You expressed a desire to see what it is like.
https://blake.torpidity.net/m/202…

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