AI Research
You Can Ship
We scan thousands of AI papers so you don't have to. Only the ones with real implementation value make it through.
Curated from leading AI labs
The Problem
1,000+ AI papers are published every month. Most are theoretical, incremental, or impossibly academic. The handful that could actually help you build better products are buried in noise. Finding them is a full-time job.
How We Curate Research
Rigorous selection first. Clear explanations second.
Daily Scanning
We monitor arXiv, major lab announcements, and research communities daily. Nothing actionable slips through.
Actionability Filter
Does it have code? Can we infer implementation? If there's no path from paper to production, we skip it.
Practitioner Lens
Will this help ML engineers ship features? Will it change how PMs prioritize? Will executives make better bets? That's our bar.
Plain English
Selected papers get rewritten for practitioners. No jargon, no assumed PhD knowledge, no dense equations.
Visual Explanations
D3.js charts and architecture diagrams make complex methods concrete. See the data, not just read about it.
Implementation Blueprint
Every article includes what the paper doesn't: tech stack, code patterns, parameters, and production gotchas.
Implementation Blueprint
Research papers tell you what works. They rarely tell you how to build it.
Every Tekta.ai article includes an Implementation Blueprint - our unique addition that bridges the gap between academic research and production code. This is what sets us apart from paper summaries elsewhere.
- Tech stack recommendationsSpecific tools, not vague suggestions
- Code snippetsWorking examples you can adapt
- Key parametersThe numbers that actually matter
- Pitfalls & gotchasWhat will trip you up
# Recommended tech stack
stack = {
"base_model": "Phi-3 (7B)",
"fine_tuning": "LoRA via PEFT",
"serving": "vLLM",
}
# Key parameters
config = {
"batch_size": 1024,
"learning_rate": 1e-4,
"layers_to_update": "final 1/4",
}
# What will trip you up
gotchas = [
"Don't update all layers",
"Monitor for overfitting",
"Check licensing terms",
]What We Filter Out
Most papers don't make the cut. Here's why.
Theoretical-only: "We prove convergence bounds for stochastic gradient descent under non-convex objectives with Lipschitz-continuous gradients..."
Incremental: "Our method achieves 0.3% improvement on GLUE benchmark..."
No implementation path: Requires custom hardware, proprietary datasets, or infrastructure only Google has.
Has code or clear method: Paper includes GitHub repo, or architecture is detailed enough to implement with standard tools.
Meaningful improvement: 20%+ gains, new capability, or solves a real production problem (not just benchmark gaming).
Runs on your infrastructure: Works with cloud GPUs, open models, and APIs you actually have access to.
Who This Is For
We write for practitioners, not academics
Developers
Get implementation details, code patterns, and architecture insights without wading through proofs and equations.
- Working code examples
- Tech stack recommendations
- Performance benchmarks
Business Leaders
Understand the strategic implications of AI advances. Make informed decisions about technology adoption.
- Executive summaries
- Business implications
- ROI considerations
Product Managers
Evaluate which AI capabilities are ready for production. Understand trade-offs to make better build vs. buy decisions.
- Practical applicability notes
- Limitations clearly stated
- When to use what guidance
Recently Curated
Papers that passed our actionability filter this week
CORAL: Dynamic Multi-Agent Coordination Without Predefined Workflows
Most multi-agent systems require engineers to manually define workflows and routing rules. CORAL introduces an information-flow orchestrator that dynamically coordinates agents through natural language, eliminating predefined state machines. On the GAIA benchmark, this approach outperforms workflow-based systems by 8.5 percentage points when using heterogeneous models.
MAXS: The 'Measure Twice, Cut Once' Agent Architecture
Standard LLM agents rush to execute, like junior devs who code before thinking. MAXS looks 4 steps ahead before committing. The result: 63.5% accuracy vs 52.9% for CoT, 100x cheaper than MCTS. A senior-dev approach to agent reasoning.
ViDoRe V3: The Benchmark That Exposes What Your RAG Pipeline Cannot See
Most RAG benchmarks test text retrieval on text documents. But production documents contain charts, tables, and diagrams. ViDoRe V3 is a comprehensive multimodal RAG benchmark revealing that visual retrievers beat text-only by 8+ points, textual rerankers deliver 13x more improvement than visual ones, and current VLMs fail at visual grounding with 85% gap to human performance.
YaPO: Sparse Steering Vectors That Actually Work
Dense activation steering entangles behaviors. YaPO learns sparse steering vectors in SAE latent space, converging 10x faster than BiPO while achieving better cultural alignment across 15 cultural contexts. The sparse approach generalizes to hallucination reduction, jailbreak resistance, and power-seeking mitigation without degrading general knowledge.
HiMem: Hierarchical Memory That Actually Remembers What Matters
Current LLM memory systems treat all conversations equally, but human memory does not work that way. HiMem introduces a two-tier architecture inspired by cognitive science: abstract 'notes' for distilled knowledge and concrete 'episodes' for raw events. The result is 11.7 pp higher accuracy on LoCoMo with 53% fewer tokens than A-MEM.
LiveVectorLake: Real-Time Versioned Knowledge Base for RAG
Standard RAG systems re-embed entire documents on every update, wasting compute and losing version history. LiveVectorLake introduces a dual-tier architecture with chunk-level change detection: a hot tier (Milvus) for sub-100ms current queries and a cold tier (Delta Lake) for complete version history. Results: 10-15% content reprocessing vs 85-95% baseline, 65ms median query latency, and zero temporal leakage on historical queries.