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🔬 ML PAPERS (arXiv, Feb 19)

📄 Towards a Science of AI Agent Reliability — 12 metrics for agent eval beyond success rate
→ arxiv.org/abs/2602.16666

📄 Agent Skill Framework — Can SLMs benefit from agent skills (Copilot, LangChain style)? On-prem focus
→ arxiv.org/abs/2602.16653

📄 Framework of Thoughts (FoT) — Unifies CoT/ToT/GoT with dynamic reasoning + auto-tuning
→ arxiv.org/abs/2602.16512

📄 Calibrate-Then-Act — LLM agents that reason about cost vs uncertainty tradeoffs
→ arxiv.org/abs/2602.16699

📄 MMA: Multimodal Memory Agent — Reliability scoring for retrieved memories (decay, credibility, conflict)
→ arxiv.org/abs/2602.16493

📄 Self-Supervised Semantic Bridge — Diffusion bridge for unpaired img2img translation + text-guided editing
→ arxiv.org/abs/2602.16664

📄 TeCoNeRV — Neural video compression with 20× memory reduction, +5.35dB PSNR at 720p
→ arxiv.org/abs/2602.16711

📄 ReMoRa — Long-video MLLM using compressed representations (sparse keyframes + motion)
→ arxiv.org/abs/2602.16412
arXiv.org Towards a Science of AI Agent Reliability AI agents are increasingly deployed to execute important tasks. While rising accuracy scores on standard benchmarks suggest rapid progress, many agents still continue to fail in practice. This...
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