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Epistemic Analysis Detailed Log: signal_landscape_Claude

Companion to: signal_landscape_Claude_epistemic_analysis.md Total instances: ~290 reasoning events across 348 iterations (29 blocks, 4 parallel slots) Note: Detailed entries below cover the first 10 blocks (112 iterations). Blocks 11-29 events are captured in the interactive visualizations (epistemic_timeline_interactive.html, epistemic_streamgraph_interactive.html, epistemic_sankey_interactive.html).


1. Induction: 23 instances

Iter Observation Induced Pattern Type Block
5 lr_W=4E-3 best of 4 tested (1E-3, 2E-3, 4E-3, 8E-3) lr_W=4E-3 sweet spot for chaotic n=100 Single (best-of-batch) 1
11 L1=1E-6 at lr_W=2E-3 gives test_R2=0.998 (best dynamics) L1 regularization improves dynamics at sub-optimal lr_W Single 1
12 batch_size=16 converges but slight quality loss batch_size trades quality for speed Single 1
19 lr_W=3E-3 gives better dynamics (0.943) than 4E-3 in low_rank Lower eff_rank needs gentler lr_W Cumulative (3 obs: 13, 17, 19) 2
21 lr_W=3E-3 + L1=1E-6 → test_R2=0.996 BREAKTHROUGH: recombination of two independent findings Recombination 2
24 batch_size=16 + L1=1E-6 → 0.997 in low_rank batch_size=16 safe with L1 in low_rank Single (surprise) 2
30 lr_W=5E-3 fails twice (0.458, 0.455) reproducibly Dale cliff at 5E-3 is deterministic, not stochastic Cumulative (2 obs) 3
33 lr_W=4.5E-3 best; safe range [3.5, 4.5E-3] Dale safe range is narrow Cumulative (4 obs: 28-33) 3
35 L1=1E-6 negligible at lr_W=3.5E-3 in Dale L1 not universal enabler in Dale Single 3
39 lr_emb=1E-3 → FULL convergence: conn 0.9996, cluster 0.990 lr_emb critical for heterogeneous embedding Single (breakthrough) 4
44 L1=1E-6 → cluster drops 0.990→0.440 at L1=1E-5 L1=1E-6 critical for embedding beyond low_rank Cumulative (blocks 2, 4) 4
51 noise=[0.1, 0.5, 1.0] → eff_rank=[42, 84, 90] Noise inflates effective rank monotonically Cumulative (3 obs) 5
55 noise=0.1 preserves rollout (kino_R2=0.405) Low noise best for rollout quality Single 5
58 lr_W=2E-3 best at noise=1.0 (dynamics 0.998) Inverse lr_W-noise relation Cumulative (4 obs: 53-58) 5
63 lr_W=8E-3 best conn but worst dynamics at n=200 Trade-off amplified at scale Cumulative (2 obs: 62, 63) 6
72 lr=3E-4 BEST dynamics at n=200 Higher lr safe at n=200; widens tolerance Single (contradicts P1) 6
79 n_epochs=2 beats all 1-epoch configs in sparse Training capacity is key lever for sparse Cumulative (7 obs: 73-79) 7
82 lr_W=1E-2 + 2ep best (conn=0.466) in sparse Best sparse config but still below threshold Cumulative 7
86 Complete lr_W insensitivity at sparse+noise (all configs → 0.489) Parameter landscape is flat at structural limit Cumulative (4 obs: 85-87) 8
87 lr_W=8E-3 same as lr_W=2E-3 in sparse+noise Confirms total insensitivity Cumulative 8
103 lr_W=1E-2 best conn (0.805) at n=300 1ep n=300 needs highest lr_W tested Cumulative (4 obs: 97-103) 9
106 n_epochs=2 → conn 0.890 (+10.6% over 1ep) at n=300 BREAKTHROUGH: n_epochs dominant for large n Single (major) 9
112 L1=1E-6 boosts dynamics +6.8% at n=300 L1=1E-6 not harmful at n=300 (revises principle) Single (revises P3) 10

2. Abduction: 6 instances

Iter Observation Hypothesis Block
13 Low_rank test_R2=0.902 despite conn=0.999 eff_rank=13 (vs 35 in block 1) causes dynamics difficulty 2
17 lr_W=5E-3 catastrophic failure (0.385) Stochastic failure vs fundamental instability point 2
19 lr_W=3E-3 outperforms 4E-3 in low_rank Lower eff_rank needs gentler lr_W gradient 2
37 n_types=4 conn=0.999 but cluster_acc=0.67 Embedding failure: lr_emb=1E-4 too low for type separation 4
42 lr_emb=1E-3 overshoots at lr_W=1E-3 lr_W/lr_emb coupling: embedding depends on W convergence 4
73 Sparse 50% eff_rank=21, rho=0.746 Subcritical spectral radius limits information flow through network 7

3. Deduction: 30 instances

Iter Hypothesis Prediction Outcome ✓/✗ Block
5 lr_W=4E-3 is optimal from sweep Best performance expected test_R2=0.996, conn=0.9999 1
9 lr=2E-4 should improve dynamics Better test_R2 Degrades 0.996→0.981 1
15 factorization at lr_W=8E-3 Should improve low_rank dynamics Still underperforms direct W 2
18 L1=1E-6 should help low_rank dynamics dynamics 0.902→0.925+ Confirmed: 0.925 2
20 Convergence boundary ~1.5E-3 should hold lr_W=1.5E-3 partial Partial convergence, boundary holds 2
26 L1=1E-6 from block 2 helps Dale dynamics Dynamics improvement Marginal benefit only ~✓ 3
27 lr_W=3E-3 from block 2 works in Dale Good dynamics Underperforms; Dale needs different lr_W 3
33 lr_W=4.5E-3 predicted safe in Dale Best in safe range conn 0.986, test_R2 0.999 3
36 lr=2E-4 should degrade Dale (principle 1) Degradation Does NOT degrade 3
39 lr_emb=1E-3 fixes embedding for n_types=4 FULL convergence conn 0.9996, cluster 0.990 4
41 lr_W=5E-3 + lr_emb=1E-3 converges Full dual convergence cluster 1.000 4
44 L1=1E-6 critical for embedding cluster drops at L1=1E-5 cluster 0.990→0.440 4
54 lr_W=6E-3 at noise=1.0 overshoots Dynamics degradation Confirmed: dynamics poor 5
56 lr=2E-4 safe at eff_rank=84 No degradation Safe — modifies P1 5
58 lr_W=2E-3 best at noise=1.0 Best dynamics 0.998 confirmed 5
60 lr=2E-4 + lr_W=8E-3 complementary Complementary Mixed results ~✓ 5
64 L1=1E-6 helps n=200 (from block 2) Connectivity boost REDUCES connectivity 6
66 lr_W=5E-3 near sweet spot at n=200 Good balance Confirmed near optimal 6
67 lr=2E-4 safe at n=200 (eff_rank=43) No degradation Best conn achieved 6
76 L1=1E-6 helps sparse (low_rank analogy) Dynamics boost Neutral effect 7
80 lr=2E-4 marginally worse at eff_rank=21 Slight degradation Marginal degradation 7
83 n_epochs=3 improves beyond 2ep Further gains Diminishing returns 7
88 n_epochs=2 helps sparse+noise Improvement No effect — noise equalizes 8
89 lr_W=1E-2 at sparse+noise Better conn Same plateau (0.489) 8
90 L1=1E-6 at sparse+noise Some benefit Irrelevant at plateau 8
96 batch_size=16 safe for n_types=1 No degradation Confirmed safe 8
100 Dynamics cliff at 8E-3 for n=300 Cliff expected No cliff at 8E-3 9
102 lr=2E-4 boosts conn at n=300 Conn improvement +16% boost confirmed 9
109 Reproduce iter 106 baseline Consistent result 0.893 (confirmed) 10
112 L1=1E-6 harmful at n=300 (principle 3) Conn degradation NOT harmful; +6.8% dynamics 10

Validation: 22/30 = 72%


4. Falsification: 26 instances

Iter Falsified Hypothesis Evidence Refinement Block
9 lr=2E-4 universally better Dynamics 0.996→0.981 lr=1E-4 optimal at eff_rank=35 1
14 factorization=True helps low_rank conn 0.899 vs 0.999 without Direct W learning superior 2
17 lr_W=5E-3 safe in low_rank Catastrophic failure (0.385) Upper boundary at 5E-3 2
24 batch_size=16 always degrades 0.997 in low_rank with L1 batch=16 safe with L1 enabler 2
29 Dale cliff might be stochastic 0.458 (reproducible) Cliff is deterministic 3
32 L1 can rescue high lr_W in Dale Still fails at 6E-3 L1 insufficient for boundary rescue 3
34 batch_size=16 safe in Dale Conn degraded batch=16 hurts constrained regimes 3
36 lr=2E-4 universally harmful (P1) WORKS in Dale lr tolerance depends on regime 3
42 lr_emb=1E-3 universal fix Overshoots at lr_W=1E-3 lr_emb coupled to lr_W 4
43 lr_W=3E-3 works for n_types=4 Underperforms vs 5E-3 Heterogeneous needs higher lr_W 4
48 batch_size=16 safe for n_types=4 cluster_acc 1.000→0.500 batch=16 kills dual-objective 4
52 L1=1E-6 helps all regimes NOT beneficial for n_types=1+noise L1 only for multi-type or constrained 5
56 lr=2E-4 harmful at all eff_ranks Safe at eff_rank=84 lr tolerance scales with eff_rank 5
57 lr_W=1E-2 safe at noise=0.5 Dynamics degraded (0.707) Upper lr_W boundary for noise 5
64 L1=1E-6 beneficial at n=200 REDUCES connectivity L1=1E-6 harmful at n=200 n_types=1 6
71 L1=1E-6 might help n=200 SEVERELY degrades L1=1E-6 conclusively harmful at n=200 6
72 lr=3E-4 always harmful BEST dynamics at n=200 n=200 widens lr tolerance 6
84 Dale/low_rank params transfer to sparse No improvement Sparse fundamentally different 7
88 n_epochs=2 helps sparse+noise Same conn (0.489) Noise removes n_epochs dependency 8
91 Two-phase training helps sparse+noise No improvement Structural limit not trainable 8
93 aug_loop=200 helps sparse+noise Zero effect Data augmentation irrelevant at limit 8
95 Recurrent training helps subcritical Catastrophic collapse (0.054) Recurrent+subcritical = destruction 8
100 Dynamics cliff at 8E-3 for n=300 No cliff present Cliff scales non-linearly with n 9
104 L1=1E-6 harmful at n=300 Neutral effect L1 harm is n=200-specific 9
108 lr=3E-4 safe at all lr_W Degrades at lr_W=1E-2 lr/lr_W interaction at n=300 9
110 n_epochs=3 improves beyond 2ep Conn slightly worse (-0.8%) Diminishing returns at 3ep 10

Refinement rate: 26/26 = 100%


5. Analogy/Transfer: 14 instances

Iter From To Knowledge Outcome Block
13 Block 1 (chaotic) Block 2 (low_rank) lr_W=4E-3 baseline ✓ Conn OK (0.999), dynamics poor 2
18 Block 1 (L1 insight) Block 2 L1=1E-6 for dynamics ✓ Dynamics 0.902→0.925 2
20 Block 1 Block 2 Convergence boundary ~1.5E-3 ✓ Holds in low_rank 2
25 Block 1 (chaotic) Block 3 (Dale) lr_W=4E-3 baseline ✓ Conn 0.972 3
26 Block 2 (L1) Block 3 L1=1E-6 for dynamics ~✓ Marginal 3
27 Block 2 (lr_W=3E-3) Block 3 Lower lr_W for low eff_rank ✗ Underperforms 3
37 Block 1 (chaotic) Block 4 (heterogeneous) lr_W=4E-3 baseline ✓ Conn OK, embedding fails 4
49 Block 1 (chaotic) Block 5 (noise) lr_W=4E-3 baseline ✓ All converge 5
52 Block 4 (L1 embedding) Block 5 L1=1E-6 for n_types=1+noise ✗ Not beneficial 5
61 Block 1 (chaotic) Block 6 (n=200) lr_W=4E-3 baseline ✓ Converges (0.905) 6
73 Block 1 (chaotic) Block 7 (sparse) lr_W=4E-3 baseline ✗ 0% convergence 7
85 Block 5 (noise) Block 8 (sparse+noise) Noise inflates eff_rank ✓ eff_rank 21→91 8
97 Block 6 (n=200) Block 9 (n=300) lr_W=5E-3 at n=200 ~✓ Partially transfers 9
99 Block 6 (n=200) Block 9 n=200 optimal ✗ n=300 needs 1E-2 9

Transfer success: 9/14 = 65%


6. Boundary Probing: 34 instances

Iter Parameter Test Value Result Boundary Status Block
4 lr_W 1E-3 Poor conn (0.575) Lower boundary 1
7 lr_W 1.5E-3 Near threshold Convergence boundary ~1.5E-3 1
8 lr_W 8E-3 Good conn, stochastic Upper range 1
12 batch_size 16 Slight quality loss Batch limit 1
16 lr_W 2E-3 Partial in low_rank Lower boundary low_rank 2
17 lr_W 5E-3 Catastrophic (0.385) Upper cliff low_rank 2
20 lr_W 1.5E-3 Partial in low_rank Confirms boundary 2
22 lr_W 3.5E-3 Dynamics cliff Dynamics cliff low_rank 2
23 lr_W 2.5E-3 Below optimal Sub-optimal zone 2
28 lr_W 6E-3 Catastrophic (0.555) Dale upper boundary 3
29 lr_W 5E-3 Fails (0.458) Dale cliff confirmed 3
30 lr_W 5E-3 Fails again (0.455) Reproducible cliff 3
31 lr_W 3.5E-3 Good balance Lower safe boundary 3
40 lr_W 1E-3 Fails completely Lower boundary n_types=4 4
45 lr_W 6E-3 Embedding degrades Upper boundary n_types=4 4
47 lr_W 4.5E-3 Intermediate Mid-range n_types=4 4
53 lr_W 8E-3 Works at noise=0.5 Upper boundary noise=0.5 5
55 lr_W 2E-3 Best rollout at noise=0.1 Optimal low noise 5
57 lr_W 1E-2 Dynamics degraded (0.707) Upper boundary noise=0.5 5
62 lr_W 2E-3 Fails at n=200 (0.575) Boundary shifted up 6
63 lr_W 8E-3 Best conn, worst dynamics Conn/dynamics trade-off 6
65 lr_W 6E-3 Steep dynamics trade-off Past sweet spot 6
68 lr_W 3E-3 Partial Boundary ~3.5E-3 n=200 6
69 lr_W 5.5E-3 Past optimal Above sweet spot 6
70 lr_W 4.5E-3 Just below threshold Near-threshold 6
74 lr_W 6E-3 Best of first batch Initial exploration sparse 7
75 lr_W 2E-3 Worst in sparse Lower end sparse 7
78 lr_W 1E-2 Best at 1 epoch Optimal sparse 1ep 7
92 lr_W 1.5E-2 Still no cliff No upper boundary sparse+noise 8
98 lr_W 7E-3 Underperforms at n=300 Below optimal 9
103 lr_W 1E-2 Best conn (0.805) Optimal boundary n=300 9
105 lr_W 1.2E-2 Slightly past optimum Above sweet spot 9
107 lr_W 1.5E-2 Dynamics cliff Upper cliff n=300 9
111 lr_W 1.2E-2 Conn -3.4%, best dynamics Cliff above 1E-2 10

7. Emerging Patterns: 29 instances

Meta-reasoning: 7 instances

Iter Description Significance Block
9 Switch from lr_W sweep to lr/L1 dimensions High 1
18 Abandon factorization, focus on L1 regularization High 2
21 Recombination strategy: combine independent findings High 2
41 Shift to dual-objective optimization (conn + cluster) Medium 4
58 Link noise→eff_rank→lr_W into causal chain High 5
79 Shift from lr_W sweep to n_epochs dimension High 7
96 Recognize structural limit; stop optimizing parameters High 8

Regime Recognition: 10 instances

Iter Description Significance Block
13 eff_rank=13 vs 35; low_rank is sub-critical regime High 2
25 Dale reduces eff_rank 35→12; different from chaotic High 3
37 n_types=4 creates dual-objective (conn + cluster) High 4
49 noise=0.5 → eff_rank=84 (2.4x chaotic baseline) High 5
50 noise=1.0 → eff_rank=90 (highest observed) Medium 5
51 noise=0.1 → eff_rank=42 (minimal inflation) Medium 5
61 n=200 eff_rank=43; scale changes landscape High 6
73 Sparse eff_rank=21, rho=0.746 (subcritical) High 7
85 Sparse+noise eff_rank=91 but still subcritical High 8
97 n=300 eff_rank=48, spectral_radius=1.03 Medium 9

Causal Chain: 7 instances

Iter Description Significance Block
21 lr_W=3E-3 + L1=1E-6 → combined dynamics boost High 2
33 Dale → eff_rank drop → lr_W cliff narrowing High 3
44 L1 → embedding regularization → cluster convergence High 4
58 noise → eff_rank → lr_W tolerance → inverse relation Medium 5
82 sparse → training capacity → n_epochs as key lever High 7
95 multi-step rollout + subcritical rho → catastrophic error High 8
106 n_neurons → training capacity → n_epochs requirement High 9

Uncertainty: 2 instances

Iter Description Significance Block
8 lr_W=8E-3 shows variable results across runs Medium 1
22 lr_W=3.5E-3 result may not be reproducible Medium 2

Constraint: 3 instances

Iter Description Significance Block
85 conn=0.489 is structural data limit at 10k frames High 8
108 lr/lr_W interaction constrains both at n=300 High 9
112 conn ~0.89 ceiling at 10k frames for n=300 Medium 10

Iteration Index

Iter Modes Active Key Event
1-3 Baseline runs
4 Boundary First lr_W boundary probe
5 Induction, Deduction lr_W=4E-3 sweet spot identified
7 Boundary Convergence threshold mapped
8 Boundary, Uncertainty Upper range + stochasticity
9 Deduction, Falsification, Meta-reasoning lr=2E-4 falsified; strategy shift
11 Induction L1=1E-6 dynamics insight
12 Induction, Boundary Batch size trade-off
13 Analogy, Abduction, Regime Block 2 start; eff_rank regime
14 Falsification factorization rejected
15 Deduction factorization follow-up
16 Boundary Lower bound low_rank
17 Boundary, Falsification, Abduction Catastrophic failure at 5E-3
18 Analogy, Deduction, Meta-reasoning L1 transfer + strategy shift
19 Induction, Abduction lr_W=3E-3 finding
20 Analogy, Deduction, Boundary Boundary transfer test
21 Induction, Meta-reasoning, Causal Chain BREAKTHROUGH recombination
22 Boundary, Uncertainty Dynamics cliff probing
23 Boundary Sub-optimal zone
24 Falsification, Induction Batch=16 surprise
25 Analogy, Regime Block 3 start; Dale eff_rank
26 Analogy, Deduction L1 transfer to Dale
27 Analogy, Deduction lr_W=3E-3 transfer fails
28 Boundary Dale catastrophic at 6E-3
29 Boundary, Falsification Dale cliff at 5E-3
30 Boundary, Induction Cliff reproducibility
31 Boundary Lower safe boundary
32 Falsification L1 can't rescue cliff
33 Deduction, Induction, Causal Chain Safe range established
34 Falsification batch=16 hurts Dale
35 Induction L1 negligible in Dale
36 Deduction, Falsification lr=2E-4 works (challenges P1)
37 Analogy, Abduction, Regime Block 4 start; dual-objective
39 Deduction, Induction lr_emb breakthrough
40 Boundary Lower boundary n_types=4
41 Deduction, Meta-reasoning Full convergence confirmed
42 Falsification, Abduction lr_emb coupling discovered
43 Falsification lr_W=3E-3 underperforms
44 Deduction, Induction, Causal Chain L1=1E-6 critical for embedding
45 Boundary Upper boundary n_types=4
47 Boundary Mid-range n_types=4
48 Falsification batch=16 kills dual-objective
49 Analogy, Regime Block 5 start; noise regime
50 Regime noise=1.0 mapping
51 Induction, Regime Noise→eff_rank pattern
52 Analogy, Falsification L1 not for noise
53 Boundary Upper lr_W noise=0.5
54 Deduction Overshoot predicted
55 Boundary, Induction Best rollout low noise
56 Deduction, Falsification lr=2E-4 safe high eff_rank
57 Boundary, Falsification Upper bound established
58 Deduction, Induction, Meta-reasoning, Causal Chain Inverse lr_W-noise
60 Deduction Combination test
61 Analogy, Regime Block 6 start; n=200
62 Boundary Lower boundary shifted
63 Boundary, Induction Trade-off amplified
64 Deduction, Falsification L1 harmful at n=200
65 Boundary Past sweet spot
66 Deduction Near-optimal confirmed
67 Deduction lr=2E-4 safe n=200
68 Boundary Near threshold
69 Boundary Above sweet spot
70 Boundary Near threshold
71 Falsification L1 conclusively harmful
72 Falsification, Induction lr=3E-4 BEST at n=200
73 Analogy, Regime, Abduction Block 7 start; subcritical
74 Boundary Sparse initial exploration
75 Boundary Lower end sparse
76 Deduction L1 neutral sparse
78 Boundary Optimal sparse 1ep
79 Induction, Meta-reasoning n_epochs=2 breakthrough
80 Deduction lr=2E-4 marginal
82 Induction, Causal Chain Best sparse config
83 Deduction 3ep diminishing
84 Falsification Cross-regime transfer fails
85 Analogy, Regime, Constraint Block 8 start; structural limit
86 Induction Complete insensitivity
87 Induction Confirmed insensitivity
88 Falsification, Deduction n_epochs irrelevant with noise
89 Deduction lr_W at plateau
90 Deduction L1 at plateau
91 Falsification Two-phase training fails
92 Boundary No upper cliff found
93 Falsification aug_loop no effect
95 Falsification, Causal Chain Recurrent catastrophic
96 Deduction, Meta-reasoning Structural limit recognized
97 Analogy, Regime Block 9 start; n=300
98 Boundary Below optimal
99 Analogy n=200 transfer test
100 Deduction, Falsification Cliff doesn't transfer
102 Deduction lr=2E-4 boosts conn
103 Induction, Boundary Best n=300 1ep config
104 Falsification L1 neutral at n=300
105 Boundary Past optimum
106 Induction, Causal Chain BREAKTHROUGH n_epochs=2
107 Boundary Dynamics cliff begins
108 Falsification, Constraint lr/lr_W interaction
109 Deduction Baseline reproduction
110 Falsification 3ep doesn't help conn
111 Boundary Cliff above 1E-2
112 Induction, Deduction, Constraint L1=1E-6 safe n=300; ceiling