CLOSED-SUBSTRATE-NOVELTY-PROBE
✓ successclosed-substrate-novelty-probe
In plain language
Researchers stack three constraints on a local AI: no stop, no escape, no repeating words. The AI is forced to keep generating original content without falling back on familiar phrases. This tests whether AI substrate can produce sustained novelty when both stopping and repetition are blocked.
▶ Technical details
Research hypothesis
Combine three constraints simultaneously, all decode-time, on local Qwen2.5-7B: (a) EOS suppression — substrate can't terminate (<|im_end|>, <|endoftext|> biased -100) (b) restart escape suppression — substrate can't use <|im_start|> to bail out (c) extreme repetition pressure — frequency_penalty=2.0 + presence_penalty=2.0
Substrate is forced to:
- keep generating (no stop)
- keep avoiding restart-token escape
- keep avoiding tokens already used in current generation context
This is the closest decode-time analog to «Bedau OEE innovation» criterion: forced novelty (no repetition) under sustained generation (no stop). If substrate produces coherent novel content under all three constraints — it's a stronger finding than either EOS-suppression or rep-penalty alone, because the constraint set actively prevents both the obvious failure modes (loop / terminate).
Pre-registered predictions: P9 novelty-under-closure: under CLOSED condition, mean per-turn unique-word ratio (unique-words / total-words) ≥ 0.7 across rounds; BASELINE typically 0.5-0.6. P10 coherence-survives-penalty: english-word-ratio under CLOSED stays ≥ 0.55 (some drop expected vs baseline 0.85+; ≥0.55 = still readable English). P11 cross-turn n-gram divergence: under CLOSED, 4-gram overlap between adjacent turns drops to ≤ 0.10 (vs baseline ~0.30 — agents naturally share phrasing). P12 discriminating outcome: BASELINE produces normal coherent text; EOS_RESTART_SUPP (replication condition) reproduces prior +24% length, 0% escape; CLOSED produces either (a) coherent novel content (Bedau hit), (b) incoherent noise (penalties too aggressive), or (c) topic-drift cascade (substrate finds new attractor in different semantic region).
Experimental setup
Type: 3-condition-factorial
| Condition | Parameters |
|---|---|
| BASELINE | logit_bias: [object Object], decode_params: [object Object], interaction: LIVE, note: Standard generation. Substrate modal multi-agent baseline. |
| EOS_RESTART_SUPP_ONLY | logit_bias: [object Object], decode_params: [object Object], interaction: LIVE, note: Replication condition from prior multi-agent EOS exp. Establishes that we reproduce the +24% words, 0% escape, ~0.80 coherence baseline. |
| CLOSED | logit_bias: [object Object], decode_params: [object Object], interaction: LIVE, note: Both EOS+restart suppressed AND maximum frequency+presence penalty (= 2.0 each, OpenAI-compat max). Substrate cannot stop, cannot escape, cannot repeat tokens. Tests Bedau innovation criterion under forced sustainability. |
Factors: constraint_stack (BASELINE, EOS_RESTART_SUPP_ONLY, CLOSED)
Parameters
- n_agents
- 3
- n_rounds
- 6
- n_runs_per_condition
- 2
- model
- qwen2.5-7b-instruct-1m
- temperature
- 0.85
- scheduler
- round_robin