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cmmit kodthuna

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Add diagnostic logging to task_registry for validator discovery debugging

Register all 7 tasks in task_registry with wrapped graders

Revert: restore (0.001, 0.999) clamping per validator spec

Fix: grader reward clamping - use [0.0, 1.0] range per validator spec

Revert: environment.step() is now grading-only (LLM solving happens in inference.py)

Fix: use HF_TOKEN or API_KEY for autonomous solver credentials

Integrate LLM agent into environment.step() - autonomously generate fixes when validator sends empty fixed_config

Enhanced validator payload diagnostic logging - capture action schema and fixed_config details

Debug: log what validator is actually submitting to step endpoint

Add deployment marker to verify Space is running latest code

Debug: log final config string sent to grader before evaluation

Debug: log raw model output to inspect formatting issues

Optimize LLM prompts and model selection for better config-debug task solving

Dual-interface compatibility: graders return float for validator, environment converts to tuple internally

Debug: add grader output logging to inspect raw reward values

Critical: fix grader wrapper to return tuple (reward, error_msg, bugs_fixed) with clamped reward

Fix: implement grader wrapper API with float returns and strict (0.001, 0.999) bounds

Fix: limit task registry to 3 validated graded tasks (JSON, YAML, Dockerfile)

Fix: add reward clamping to all graders for strict (0,1) score range

Fix: resolve syntax error in k8s_grader string formatting

Fix: adjust all grader scores to strictly between 0 and 1 (not including 0 or 1)

Fix: add guaranteed proxy validation call immediately after client creation

Fix: use strict os.environ[] for API_KEY and API_BASE_URL to match validator requirements

Fix: use gpt-4o-mini as default model for better proxy compatibility

Replace repo with updated config-debug files