The Federal Aviation Administration (FAA) released 20 pages of training materials concerning the collection, processing, and analysis of biological evidence following fatal aviation accidents. The…
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Larry W. Bryant was a UFO researcher, writer, and government transparency advocate who pursued federal records through the Freedom of Information Act (FOIA). His work…
Read MoreMulti-agent coding systems don't necessarily fail because agents can't communicate. They can fail because important commitments made in conversation have nowhere to live afterward. The post Multi-Agent Coding Isn’t Enough — Agents Need a Commitment Layer appeared first on Towards Data Science.
How do you set yourself up for a career that will stand the test of time when things are changing so fast? The post Starting a Career in Data Science in the Age of AI appeared first on Towards Data Science.
The recurring cost of production AI is not inference. It is re-qualification: the eval reruns, prompt retuning, and regression testing you owe every time a model changes under you. Here is what that tax actually covers, and how to budget for it before it surprises you. The post We Pinned Our Model Version to Stay…
How to verify your app aligns with your intent without ever reading a line of generated code. The post Coding Agents Keep Shipping Silent Failures — Here Is How to Catch Them appeared first on Towards Data Science.
This article covers five prompt optimization strategies such as: prompt optimization, prompt engineering, LLM output quality, few-shot prompting, chain-of-thought, structured outputs.
In this second article in our short series on SLM optimization techniques we focus on the reuse of the prompt prefix with a key-value cache.
arXiv:2609.19212v1 Announce Type: new Abstract: Systematic generalization, the ability to solve novel problems by recombining known atomic elements, is central to human intelligence but difficult to study rigorously under controlled settings. Existing studies therefore rely on simplifications such as approximately linear action composition, productivity-based tests, and action-explicit goals, which make systematic generalization easier to study…
arXiv:2609.19203v1 Announce Type: new Abstract: AI applications have shifted from single, monolithic foundation models (FM) to compound agentic systems. Yet today's stacks remain fragmented: even as protocols (e.g., MCP, A2A) ease tool/agent connectivity, each framework embeds an implicit runtime for state, memory, budgets, and guardrails, making behavior non-portable and governance brittle. It mirrors computing before…
arXiv:2609.19182v1 Announce Type: new Abstract: Benchmarks are central to how progress in large language models (LLMs) is assessed and communicated. Yet model rankings alone reveal little about how evaluation requirements themselves are changing. The expanding variety of benchmarks offers another perspective: what researchers expect LLMs to do, and what they count as successful performance. We…
arXiv:2609.19180v1 Announce Type: new Abstract: Language models face unique challenges in analyzing interdisciplinary scientific research literature. In biophysics research, faithful answers require grounding observed data in source evidence, interpreting it through a quantitative physics model, and linking it to a biological mechanism. To address this challenge, we introduce BioPhys-Bridge, a novel benchmark dataset for evidence-grounded…
