Harsh Dubey

What an agent can do with the early-failure-detection-cot corpus

HarshDubey23/early-failure-detection-cot: Research on LLM reasoning choke points, failure onset & counterfactual commitment in process supervision (ProcessBench, Llama-3.1, Qwen2.5)

• Look up how early-failure-detection-cot implements chain-of-thought, llm, reasoning, with answers cited to file and line.
• Reuse a worked example: the commit “ChokeBench predictive validity (held-out halves and models); DRA test…” (16 files · Python).
• Answer from early-failure-detection-cot's real Python source instead of memory (AI, prompts and agents).

• How does early-failure-detection-cot implement chain-of-thought?
• What does results do in early-failure-detection-cot?

Search the GitHub repository HarshDubey23/early-failure-detection-cot with XERJ (https://hub.xerj.org/corpus/harshdubey23--early-failure-detection-cot).
Run these in a shell, in order, and check each result before the next:
1. xerj --help  (success: it prints usage). If xerj is missing: curl -fsSL https://xerj.org/get | sh
2. git clone https://github.com/HarshDubey23/early-failure-detection-cot early-failure-detection-cot && git -C early-failure-detection-cot checkout 7da2240a724fbacf5cc3388f8f6e5bd028cf2940
3. Start a local node and leave it running: xerj --insecure --data-dir ./xerj-data
4. xerj autoindex ./early-failure-detection-cot --url http://127.0.0.1:9200 --prefix early-failure-detection --progress plain
5. xerj autoindex map --url http://127.0.0.1:9200
Then answer my question from the indexed files and cite the ax_path of every hit. Query syntax: https://xerj.org/llms.txt
Licence: MIT. Keep the copyright notice and credit the authors.

corpus harshdubey23--early-failure-detection-cot · AI, prompts and agents
Related corpora: btrblocks, flexrag, StartLuxLabs/StartLux-Decision, Siddarthb07/Anima
AI search and retrieval with XERJ · Case study: reference coding for agents