Novel techniques from the top 2 leaderboard entries:

1. BigramHash (BIGRAM_BUCKETS=4096, BIGRAM_DIM=128):
   - Hash consecutive token pairs → embedding lookup → project to model_dim
   - XOR with coprime multipliers for hash function
   - Captures local bigram context (~524K params for 4096 buckets)
   - Used by openai#1 (thwu1, 1.1428 BPB) and openai#2 (Raahil Shah, 1.1458 BPB)

2. SmearGate (SMEAR_GATE=1):
   - Learned per-dim gate blending current token with previous token
   - Applied after embedding normalization
   - Only ~512 params
   - Used by openai#2 and openai#4

Both are env-var controlled (0=disabled by default).
run_v7_full.sh enables everything for the full stack.

Also fixed: BigramHash/SmearGate params added to optimizer groups.
1438 lines (62 under 1500 limit).

Co-Authored-By: Claude Opus 4.6 (1M context) <noreply@anthropic.com>

PR openai#672 maxes TTT at 30 epochs (590s/600s eval budget), so all future
improvements must be orthogonal to TTT. This update:
- Sets 1.0781 BPB (PR openai#672) as the new target to beat
- Reorders Top 8 directions: XSA-all confirmed at #1, Full GPTQ #2,
  SwiGLU #3, Muon-VS #4, aggressive quant openai#5, MASA openai#6,
  depth recurrence openai#7 with int6 risk warning, AdEMAMix openai#8
- Deprioritizes TTT-related directions already exploited by PR openai#672
- Collapses ~1000 lines of stale Round 0-3.9 session logs into a
  concise historical summary
- Removes resolved blockers (flash_attn, SSH hangs, local runtime)
- Adds fresh Round 1 section with 5 submitted experiments

Co-Authored-By: Claude Opus 4.6 (1M context) <noreply@anthropic.com>