BlackArc11 papers
Applied & Industry
Agents in the real world: operations research, scientific discovery, document work, and the economics of adoption.
- Papers
- 11
- Read time
- 3.3h
- Avg length
- 18m
A Survey of Data Agents: Emerging Paradigm or Overstated Hype?
Borrowing the SAE self-driving levels, this survey defines a 6-level autonomy ladder (L0–L5) for "data agents," maps every published system onto it, and argues the honest answer to the hype question is "we're stuck at the L2→L3 jump — real autonomous orchestration over messy data doesn't exist yet."
- Read
- 22 min
- Source
- arXiv:2510.23587
America's AI Action Plan
The U.S. federal government's official roadmap to "win the AI race" by stripping regulation, building energy + data-center infrastructure at speed, and exporting the full American AI stack to allies while locking China out of compute.
- Read
- 18 min
Artificial Intelligence for Operations Research: Revolutionizing the OR Process
A field map of where machine learning plugs into the classic "predict → model → solve" optimization pipeline, so you can replace hand-tuned solver heuristics and human modeling with learned components that adapt to your data.
- Read
- 22 min
- Source
- arXiv:2401.03244
Clustering Market Regimes Using the Wasserstein Distance
Instead of describing each slice of market history by a handful of summary statistics, treat each slice as a full probability distribution and cluster those distributions directly using optimal-transport distance — a tiny, model-free tweak to k-means that detects bull/bear regimes more robustly than moment-matching or hidden Markov models.
- Read
- 16 min
- Source
- arXiv:2006.03487
Extract-0: A Specialized Language Model for Document Information Extraction
A 7B-parameter model, fine-tuned for $196 with synthetic data + LoRA + a semantic-similarity reward, beats GPT-4.1 and o3 at pulling structured JSON out of documents.
- Read
- 18 min
- Source
- arXiv:2509.22906
Future of Work with AI Agents: Auditing Automation and Augmentation Potential across the U.S. Workforce
Stanford asked 1,500 workers across 104 jobs which of their tasks they actually *want* AI agents to take over, scored those same tasks for technical feasibility with 52 AI experts, and built a public map (WORKBank) showing where agent builders are aiming at the wrong targets.
- Read
- 16 min
- Source
- arXiv:2506.06576
How People Use ChatGPT
OpenAI ran LLM classifiers over a privacy-scrubbed sample of ~1.1M real ChatGPT conversations to measure, for the first time from the inside, who uses the product and what they actually do with it — and the headline is that ~70% of usage is non-work, "writing" and "decision support" dominate, and coding/companionship are surprisingly tiny.
- Read
- 16 min
Kosmos: An AI Scientist for Autonomous Discovery
Kosmos is an AI scientist that runs for 12 hours straight — 200 agent rollouts, 42,000 lines of analysis code, 1,500 papers — staying coherent the whole time by routing everything through a shared, structured "world model" instead of one ballooning chat context.
- Read
- 22 min
- Source
- arXiv:2511.02824
Large Language Models and Operations Research: A Structured Survey
A map of how LLMs are eating operations research — turning plain-English problem descriptions into solvable optimization models, generating and evolving the heuristics that solve them, and in some cases solving the problem directly — organized into three clean pillars you can build a service line around.
- Read
- 22 min
- Source
- arXiv:2509.18180
LLM-Powered AI Agent Systems and Their Applications in Industry
A field-map paper that draws a clean line between "AI model" and "AI agent," sorts real-world agent deployments into three buckets by how they touch the world (software, physical, adaptive-hybrid), sketches a five-stage reference loop for how one of these agents actually runs, and catalogs the four problems (latency, hallucination, no benchmarks, security) you'll hit the moment you ship one.
- Read
- 9 min
- Source
- arXiv:2505.16120
Tabular Data Understanding with LLMs: A Survey of Recent Advances and Challenges
Feeding a 2D table into a 1D language model is a lossy, format-sensitive mess, and this survey maps out how people represent tables, what tasks they test on, and the three structural gaps (shallow reasoning, fragility on complex inputs, no format generalization) that anyone building table-aware AI keeps tripping over.
- Read
- 16 min
- Source
- arXiv:2508.00217