Ai Research Watch

Procedure

Phase 1: Paper Analysis

For each paper, extract:

1. Problem Statement: What task? What's the gap in prior work?
2. Key Contribution: What's new? (architecture, training method, data, scale)
3. Method Summary: Core technique in 2-3 sentences
4. Architecture Details: Model size, components, innovations
5. Training Setup: Data, compute, duration, hyperparameters
6. Results: Main benchmarks, comparison to baselines
7. Ablations: What components contribute most?
8. Limitations: Authors' acknowledged limitations + your assessment
9. Reproducibility: Code available? Data available? Compute feasible?
10. Relevance: Applicable to our projects? Priority level?

Phase 2: Benchmark Tracking

Maintain awareness of key benchmarks:

Language Models

Speech/Audio

Vision

Phase 3: Research Trend Monitoring

Track these active research areas:

1. Scaling: Training efficiency, data efficiency, inference optimization
2. Architecture: SSM/Mamba vs Transformers, MoE, hybrid approaches
3. Alignment: RLHF alternatives (DPO, KTO, ORPO), Constitutional AI
4. Multi-modal: Vision-language, audio-language, omni-models
5. Efficiency: Quantization, distillation, pruning, NAS
6. Long Context: Rope extensions, ring attention, landmark attention
7. Reasoning: Chain-of-thought, tree-of-thought, reasoning tokens
8. Agents: Tool use, planning, multi-agent systems

Phase 4: Applicability Assessment

Score each method for our context:

Kyutai Open-Source Reference — Published Papers

Key papers published by the Kyutai open-source research team:

Key Conferences

Output Format

• Paper Summary: Structured analysis (see Phase 1)
• SOTA Table: Benchmark comparisons with dates
• Trend Report: Summary of active research directions
• Recommendation: Priority ranking for methods to adopt