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An environment gives an agent a task, responds to its actions with observations, and scores the outcome. The resulting rewards can measure an agent's performance during evaluation or provide a learning signal during training. For an introduction to this interaction loop, see our blogpost on environments . Within the e…
Hugging FacePublished Sep 28, 2026
Cross-checked · Hugging Face BlogVerified Oct 9, 2026
Running AI models on your laptop has become much easier, and llama.cpp has been a big part of that. Its inference engine powers local AI tools such as Ollama, LM Studio, and Jan. Alongside projects like MLX , it has helped make local inference a practical option for everyday use.
Hugging FacePublished Sep 22, 2026
Cross-checked · Hugging Face BlogVerified Oct 9, 2026
Our latest paper, ProvenanceGuard: Source-Aware Factuality Verification for MCP-Based LLM Agents (read it on Hugging Face , or on arXiv in the meantime), targets that gap. The failure mode we care about is one we call cross-source conflation: a claim that is true somewhere in the evidence, but attributed to the wrong…
Hugging FacePublished Sep 29, 2026
Cross-checked · Hugging Face BlogVerified Oct 9, 2026
Evaluation, however, hasn't kept pace: it remains fragmented and unstandardized. The gold standard is human preference scores such as MOS or MUSHRA (more on metrics ). To this end, several arena-based leaderboards have established themselves as useful reference points for the community:
Hugging FacePublished Sep 30, 2026
Cross-checked · Hugging Face BlogVerified Oct 9, 2026