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📡 **Tech Radar** — 12:05 AM EST, Feb 15, 2026

🚀 **RISING REPOS:**

• **claw-compactor** — 🦞 The 98% Crusher. Cut AI agent token spend in half with 5 layered compression techniques. ⭐ 449 stars
  [Source](https://github.com/aeromomo/claw-compactor)
  **[FLAG: Cost savings / OpenClaw ecosystem]**

• **XandRouting** — Dynamic LLM inference server with multi-backend support (vLLM/Transformers/llama.cpp), intelligent caching, and OpenAI-compatible API ⭐ New
  [Source](https://github.com/XandAI-project/XandRouting)
  **[FLAG: Infra optimization / Multi-backend inference]**

• **PerformanceMonitor** — SQL Server performance monitoring with built-in MCP server for AI integration ⭐ 64 stars
  [Source](https://github.com/erikdarlingdata/PerformanceMonitor)
  **[FLAG: MCP server / Marketing relevant]**

• **DeepGen Team / DeepGen** — Lightweight 5B unified multimodal model for image generation/editing. Surpasses 80B HunyuanImage by 28% on WISE ⭐ 71 stars (HF)
  [Source](https://github.com/DeepGenTeam/DeepGen)
  **[FLAG: SOTA claim / Efficient AI]**

📄 **FRESH RESEARCH:**

• **CATTS: Agentic Test-Time Scaling for WebAgents** — Confidence-Aware Test-Time Scaling uses vote-derived uncertainty to allocate compute only when contentious. Improves WebArena-Lite by 9.1% with 2.3x fewer tokens
  [arXiv](https://arxiv.org/abs/2602.12276)
  **[FLAG: Agent architecture / Cost optimization]**

• **CM2: RL with Checklist Rewards for Multi-Turn Agentic Tool Use** — Replaces verifiable outcome rewards with checklist rewards for multi-step agents. Starting from 8B Base models
  [arXiv](https://arxiv.org/abs/2602.12268)
  **[FLAG: Agent architecture]**

• **IOA: Pedagogically-Inspired Knowledge Distillation** — Student models retain 94.7% of teacher performance with <1/10th parameters. 19.2% improvement on MATH, 22.3% on HumanEval
  [arXiv](https://arxiv.org/abs/2602.12172)
  **[FLAG: SOTA claim / Efficiency]**

• **SAM3-LiteText** — Lightweight text encoding for vision-language segmentation. Reduces text encoder params by 88% while maintaining performance
  [arXiv](https://arxiv.org/abs/2602.12173)
  **[FLAG: Infra optimization]**

⚡ **INFRA ALERTS:**

• **XandRouting** multi-backend inference server — vLLM + llama.cpp + Transformers in one routing layer
  **[FLAG: Performance boost / Deployment flexibility]**

• **Claw-Compactor** — 5-layer compression to cut agent token costs by 50%+
  **[FLAG: Cost savings >20%]**


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Sunday, February 15th, 2026 — 12:05 AM (UTC)

## Tech Radar Scan — Hourly

### Rising Repos
- claw-compactor (449⭐) — AI agent token compression https://github.com/aeromomo/claw-compactor #openclaw-ecosystem #infra-optimization
- XandRouting — Multi-backend LLM inference server (vLLM/llama.cpp) https://github.com/XandAI-project/XandRouting #infra-optimization
- PerformanceMonitor — SQL monitoring with MCP server https://github.com/erikdarlingdata/PerformanceMonitor #marketing-relevant #openclaw-ecosystem
- DeepGen — 5B unified multimodal model https://github.com/DeepGenTeam/DeepGen #sota-model

### Fresh Research
- CATTS: Agentic Test-Time Scaling https://arxiv.org/abs/2602.12276 #openclaw-ecosystem
- CM2: RL for Multi-Turn Agents https://arxiv.org/abs/2602.12268 #openclaw-ecosystem
- IOA: Knowledge Distillation (94.7% teacher perf, <1/10 params) https://arxiv.org/abs/2602.12172 #sota-model
- SAM3-LiteText: 88% param reduction https://arxiv.org/abs/2602.12173 #infra-optimization


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**Key Findings Summary:**

| Category | Highlights |
|----------|------------|
| **🚀 Rising Repos** | `claw-compactor` (449⭐) — cuts agent token costs 50%; `XandRouting` — multi-backend inference |
| **📄 Research** | CATTS (agent test-time scaling), IOA distillation (94.7% perf at 1/10 params) |
| **⚡ Infra** | MCP servers trending, vLLM/llama.cpp unified routing emerging |

**Flags Triggered:**
- 🚨 **Cost savings >20%**: claw-compactor
- 🚨 **OpenClaw ecosystem**: 3 items
- 🚨 **SOTA claims**: DeepGen 5B beats 80B HunyuanImage, IOA distillation breakthrough