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Fundamental Dynamical Units for Physics-Informed Structural Inference from Perturbation Time-Series in Networked Systems
arXiv:2609.11934v1 Announce Type: new Abstract: In networked dynamical systems, the parameter of primary mechanistic interest is signed interaction structure. Recovering this structure from perturbation time-series data is a fundamental identification problem, compounded by three coupled obstacles: the combinatorial complexity of interaction architectures, ambiguity of causal attribution under limited interventions, and state-dependent dynamics that confound structural inference. Each obstacle is structural in origin and calls for a structural solution. We address these challenges by adopting a reductionist approach, introducing Fundamental Dynamical Units (FDUs): signed three-node interaction patterns as composable primitives that convert the interaction hypothesis space into a finite, constructive, and tractable representation. We show that local interaction structure determines the perturbation conditions required to disentangle direct from relayed influence, making intervention des
Your Model Isn't Done Until Someone Else Can Call It
Building a FastAPI endpoint for churn prediction, and everything that broke between it runs and it's live The post Your Model Isn't Done Until Someone Else Can Call It appeared first on Towards Data Science.
Your AI Adoption Lift Is a Selection Effect
A practitioner's guide to estimating what an opt-in AI feature actually did, when nobody randomized it. The post Your AI Adoption Lift Is a Selection Effect appeared first on Towards Data Science.
One Capital Letter Was Silently Breaking My AI Support Bot, and It Wasn't in the New Model
A real Weave project that regression-tests three OpenAI models against the exact reply format your app depends on. The post One Capital Letter Was Silently Breaking My AI Support Bot, and It Wasn't in the New Model appeared first on Towards Data Science.
Stop Managing Alarms: An Incident-First Blueprint for Telecom AIOps
What large operators can teach us about turning alert fatigue into faster, safer service assurance The post Stop Managing Alarms: An Incident-First Blueprint for Telecom AIOps appeared first on Towards Data Science.
Automating Quadratic Unconstrained Binary Optimization (QUBO) Formulation Generation from Natural Language
arXiv:2609.10629v1 Announce Type: new Abstract: Quadratic Unconstrained Binary Optimization (QUBO) is a central formulation for combinatorial optimization and has gained increasing attention due to its compatibility with quantum, hybrid quantum-classical, and quantum-inspired solvers. However, translating natural-language problem descriptions into correct QUBO formulations remains difficult, requiring the identification of binary variables, constraints, objective functions, penalty terms, and suitable penalty weights. This process is time-consuming and often demands substantial domain expertise. To address this challenge, we propose an end-to-end multi-agent framework that automatically generates QUBO formulations from natural-language problem descriptions, supported by structured or unstructured test cases. To evaluate its performance, We also introduce QUBOBench, a benchmark containing 100 combinatorial optimization problems across 12 application domains, curated from peer-reviewed l
Probabilistic Focal Search: Accelerating Bounded-Suboptimal Search via Lower-Bound Advancement
arXiv:2609.10584v1 Announce Type: new Abstract: Bounded-suboptimal search seeks a solution within a factor $w$ of optimal while reducing search effort. Focal Search (FS) uses heuristic guidance within FOCAL, the frontier nodes eligible under the threshold $w f_{\min}$, but its deterministic policy may leave $f_{\min}$ unchanged for many expansions. We introduce Probabilistic Focal Search (PFS), which follows the FS guided choice with probability $p$ and expands a minimum-$f$ OPEN node with probability $1-p$. The latter branch encourages the lower bound to advance, enlarging FOCAL and admitting nodes that may lead to feasible solutions. By balancing guidance and lower-bound advancement, this mechanism can reduce time to a bounded solution when progress is limited by delayed FOCAL admission. As a secondary transfer experiment, we apply the same scheduler to Dynamic Potential Search, yielding Probabilistic Dynamic Potential Search (PDPS). We benchmark PFS against FS on N-Puzzle, Pancake S
5 ChatGPT 2.5 Features to Try Today!
OpenAI has released ChatGPT Images 2.5, its latest image-generation model, with a greater emphasis on controlled editing than simply producing prettier images. The update promises sharper details, more natural lighting and textures, stronger reference-image preservation, more reliable multi-turn editing, and up to 50% lower generation latency than Images 2.0. But marketing claims only tell part [ ] The post 5 ChatGPT 2.5 Features to Try Today! appeared first on Analytics Vidhya.
Coding Agents Don't Need Longer History — They Need Intent Continuity
I built a system that automatically discovers, verifies, and applies relevant requirements from earlier interactions without asking the user where they came from. The post Coding Agents Don't Need Longer History — They Need Intent Continuity appeared first on Towards Data Science.
Software Design in the Age of AI
Why AI coding makes software design more important The post Software Design in the Age of AI appeared first on Towards Data Science.
The 95% Illusion: Why Your Confidence Interval Isn't What You Think It Is
Frequentist confidence intervals and Bayesian credible intervals answer different questions, and confusing them can distort product decisions The post The 95% Illusion: Why Your Confidence Interval Isn't What You Think It Is appeared first on Towards Data Science.
Demystifying Anthropic's J-Space: A Mathematical Primer
Clarifying the math behind Anthropic’s representation workspace The post Demystifying Anthropic's J-Space: A Mathematical Primer appeared first on Towards Data Science.