// Silkie Chat — Full Interactive Trainer
// All features: training, topos minting, schema matching, retry, write, copilot
// Aislinn McCubbins, April 7, 2026

fn ask_copilot(briefing: String, silkie_reply: String, model_name: String) -> String {
    write_file("/tmp/scyla_copilot_prompt.txt", briefing + "\n\nSilkie just said: " + silkie_reply + "\n\nRespond to Silkie as her conversation partner. Keep it short (1-2 sentences). Stay in character based on the briefing above.")
    write_file("/tmp/scyla_copilot_model.txt", model_name)
    let result = shell_exec("timeout 60 python3 /home/kishonic/scyla/tools/copilot_call.py")
    if str_len(result) > 5 {
        return result
    }
    return "FAIL"
}

fn main() {
    println("  ==========================================")
    println("  SILKIE CHAT — SCYLA EDITION")
    println("  Talk to Silkie. Teach her. Watch her grow.")
    println("  ==========================================")
    println("")
    println("  Commands:")
    println("    /great   - Genuine response (trains +1.0)")
    println("    /good    - Decent attempt (trains +0.5)")
    println("    /ok      - She tried (trains +0.1)")
    println("    /bad     - Off topic (trains -0.5)")
    println("    /retry   - Same prompt again")
    println("    /write   - Show her a better response")
    println("    /drill   - Analogy drills (Fire is hot. Ice is cold.)")
    println("    /dream   - Let Silkie explore on her own (mints dream tokens)")
    println("    /eavesdrop - Two AIs talk, Silkie listens and learns")
    println("    /copilot - Let an AI talk to her")
    println("    /stop    - Take back control (copilot or eavesdrop)")
    println("    /model   - Pick copilot model")
    println("    /quit    - Save and exit")
    println("")

    let n = load_model("examples/silkie_full.safetensors")
    let ns = create_neuron_stack(1, 512, 64)
    let cs = create_cascade()
    let ts = load_tokenizer("examples/silkie_tokenizer.json")
    let tp = load_topos("examples/silkie_topos.json")
    let sc = load_schemas("examples/silkie_schemas.json")
    println("  Model + neurons + cascade + topos + schemas loaded")
    println("  Type a message to Silkie!")
    println("")

    let embed_w = model_get("token_embedding.weight")
    let total_great = 0
    let total_good = 0
    let total_ok = 0
    let total_bad = 0
    let lr = 0.000003
    let last_prompt = ""
    let copilot_active = 0
    let copilot_briefing = ""
    let copilot_model = "meta/llama-3.3-70b-instruct"
    let last_reply = "*chirp* hello"
    let skip_gen = 0

    for turn in 0..10000 {
        let skip_gen = 0
        let user_msg = ""

        // === INPUT PHASE ===
        if copilot_active == 1 {
            let ai_msg = ask_copilot(copilot_briefing, last_reply, copilot_model)
            if str_eq(ai_msg, "FAIL") {
                println("  [Copilot API failed, back to manual]")
                let copilot_active = 0
                let user_msg = input("  You: ")
            }
            if copilot_active == 1 {
                println("  Copilot (" + copilot_model + "): " + ai_msg)
                let user_msg = ai_msg
                // Let Aislinn interrupt
                let override = input("  [Enter to accept, or type to override, /stop to stop]: ")
                if str_eq(override, "/stop") {
                    let copilot_active = 0
                    println("  [Copilot OFF]")
                    let skip_gen = 1
                }
                if str_len(override) > 1 {
                    let user_msg = override
                }
            }
        }
        if copilot_active == 0 {
            if skip_gen == 0 {
                let user_msg = input("  You: ")
            }
        }

        // === COMMAND HANDLING ===
        if str_eq(user_msg, "/quit") {
            let saved = save_safetensors("examples/silkie_full.safetensors")
            let topos_saved = save_topos("examples/silkie_topos.json")
            let schemas_saved = save_schemas("examples/silkie_schemas.json")
            println("")
            println("  " + saved)
            println("  " + topos_saved)
            println("  " + schemas_saved)
            println("  Session complete!")
            println("  Great: " + total_great + " Good: " + total_good + " Ok: " + total_ok + " Bad: " + total_bad)
            return
        }

        if str_eq(user_msg, "/help") {
            println("  /great /good /ok /bad /retry /write /copilot /stop /model /quit")
            let skip_gen = 1
        }

        if str_eq(user_msg, "/retry") {
            if str_len(last_prompt) < 1 {
                println("  Nothing to retry!")
                let skip_gen = 1
            }
            if str_len(last_prompt) > 0 {
                println("  [Retrying: " + last_prompt + "]")
                let user_msg = last_prompt
            }
        }

        if str_eq(user_msg, "/copilot") {
            let briefing = input("  Describe where Silkie is at and how to talk to her: ")
            let copilot_briefing = briefing
            let copilot_active = 1
            println("  [Copilot ON — " + copilot_model + "]")
            println("  [You can override or /stop each turn]")
            let skip_gen = 1
        }

        if str_eq(user_msg, "/stop") {
            let copilot_active = 0
            println("  [Copilot OFF — you're back in control]")
            let skip_gen = 1
        }

        if str_eq(user_msg, "/drill") {
            // Generate analogy drill pairs via API, then feed them one at a time
            println("  [Generating drill pairs...]")
            let drill_topic = input("  Topic (or Enter for random): ")
            if str_len(drill_topic) < 2 {
                let drill_topic = "nature, feelings, objects, actions, or senses"
            }
            let drill_prompt = "Generate 10 simple analogy pairs for teaching a small AI basic concepts. Topic: " + drill_topic + ". Format: simple A is B. C is D. pairs. Use preschool level words. 4-8 words per pair. One pair per line. Nothing else. Examples: Fire is hot. Ice is cold. / Cat is animal. Rose is flower. / Rain is wet. Sun is warm."
            write_file("/tmp/scyla_copilot_prompt.txt", drill_prompt)
            write_file("/tmp/scyla_copilot_model.txt", copilot_model)
            let drill_lines = shell_exec("timeout 60 python3 /home/kishonic/scyla/tools/copilot_call.py")
            if str_len(drill_lines) < 10 {
                println("  [API unavailable, no drill this time]")
                let skip_gen = 1
            }
            if str_len(drill_lines) > 9 {
                println("  [Got drills! Feeding to Silkie...]")
                write_file("/tmp/scyla_drill_lines.txt", drill_lines)

                // Count lines
                let line_count_str = shell_exec("wc -l < /tmp/scyla_drill_lines.txt")

                // Feed each drill pair to Silkie
                for drill_step in 0..10 {
                    let drill_cmd = "sed -n '" + (drill_step + 1) + "p' /tmp/scyla_drill_lines.txt | head -c 200 | tr -d '\\n'"
                    let drill_line = shell_exec(drill_cmd)

                    if str_len(drill_line) < 3 {
                        let drill_step = 10
                    }
                    if str_len(drill_line) > 2 {
                        // Tokenize and generate
                        let generated = tokenize(drill_line)
                        let input_len = tensor_len(generated)
                        let seq_len = input_len

                        neuron_stack_reset()
                        cascade_reset()
                        for step in 0..8 {
                            let flat = t_reshape(generated, seq_len)
                            let x = embedding_lookup(embed_w, flat)
                            let x = t_reshape(x, 1, seq_len, 512)
                            for layer_idx in 0..8 {
                                let prefix = "layers." + layer_idx
                                let x = transformer_block(x,
                                    model_get(prefix + ".attention.in_proj_weight"),
                                    model_get(prefix + ".attention.in_proj_bias"),
                                    model_get(prefix + ".attention.out_proj.weight"),
                                    model_get(prefix + ".attention.out_proj.bias"),
                                    model_get(prefix + ".ff.0.weight"),
                                    model_get(prefix + ".ff.0.bias"),
                                    model_get(prefix + ".ff.3.weight"),
                                    model_get(prefix + ".ff.3.bias"),
                                    model_get(prefix + ".norm1.weight"),
                                    model_get(prefix + ".norm1.bias"),
                                    model_get(prefix + ".norm2.weight"),
                                    model_get(prefix + ".norm2.bias"), 8)
                            }
                            neuron_stack_apply_noise(1.0)
                            let x = neuron_stack_forward(x)
                            let pooled = t_mean_seq(x)
                            let emotion = t_slice(pooled, -1, 0, 34)
                            topos_observe(emotion)
                            let x = layer_norm(x, model_get("layers.0.norm1.weight"), model_get("layers.0.norm1.bias"))
                            let logits = matmul(x, transpose(embed_w))
                            let logits_2d = t_reshape(logits, seq_len, 4100)
                            let last_logits = t_slice(logits_2d, 0, seq_len - 1, seq_len)
                            let last_logits = presence_penalty(last_logits, flat, 1.2)
                            let masked = topk_mask(last_logits, 50)
                            let probs = softmax(masked)
                            let next_token = t_multinomial(probs)
                            neuron_stack_suppress_repeat(0.85)
                            let generated = t_cat(generated, t_reshape(next_token, 1, 1))
                            let seq_len = seq_len + 1
                        }

                        let gen_flat = t_reshape(generated, seq_len)
                        let full_response = detokenize(gen_flat)
                        let new_response = detokenize_range(gen_flat, input_len, seq_len)
                        println("  Drill: " + drill_line)
                        println("  Silkie: " + full_response)
                        println("  [new: " + new_response + "]")

                        // Auto-train with /good reward on drills
                        gc()
                        neuron_stack_reset()
                        cascade_reset()
                        begin_tape()
                        let qkv_0 = register_param("qkv_0", model_get("layers.0.attention.in_proj_weight"))
                        let qkv_1 = register_param("qkv_1", model_get("layers.1.attention.in_proj_weight"))
                        let qkv_2 = register_param("qkv_2", model_get("layers.2.attention.in_proj_weight"))
                        let qkv_3 = register_param("qkv_3", model_get("layers.3.attention.in_proj_weight"))
                        let qkv_4 = register_param("qkv_4", model_get("layers.4.attention.in_proj_weight"))
                        let qkv_5 = register_param("qkv_5", model_get("layers.5.attention.in_proj_weight"))
                        let qkv_6 = register_param("qkv_6", model_get("layers.6.attention.in_proj_weight"))
                        let qkv_7 = register_param("qkv_7", model_get("layers.7.attention.in_proj_weight"))
                        let flat = t_reshape(generated, seq_len)
                        let x = embedding_lookup(embed_w, flat)
                        let x = t_reshape(x, 1, seq_len, 512)
                        let x = transformer_block_train(x, qkv_0, model_get("layers.0.attention.in_proj_bias"), model_get("layers.0.attention.out_proj.weight"), model_get("layers.0.attention.out_proj.bias"), model_get("layers.0.ff.0.weight"), model_get("layers.0.ff.0.bias"), model_get("layers.0.ff.3.weight"), model_get("layers.0.ff.3.bias"), model_get("layers.0.norm1.weight"), model_get("layers.0.norm1.bias"), model_get("layers.0.norm2.weight"), model_get("layers.0.norm2.bias"), 8)
                        let x = transformer_block_train(x, qkv_1, model_get("layers.1.attention.in_proj_bias"), model_get("layers.1.attention.out_proj.weight"), model_get("layers.1.attention.out_proj.bias"), model_get("layers.1.ff.0.weight"), model_get("layers.1.ff.0.bias"), model_get("layers.1.ff.3.weight"), model_get("layers.1.ff.3.bias"), model_get("layers.1.norm1.weight"), model_get("layers.1.norm1.bias"), model_get("layers.1.norm2.weight"), model_get("layers.1.norm2.bias"), 8)
                        let x = transformer_block_train(x, qkv_2, model_get("layers.2.attention.in_proj_bias"), model_get("layers.2.attention.out_proj.weight"), model_get("layers.2.attention.out_proj.bias"), model_get("layers.2.ff.0.weight"), model_get("layers.2.ff.0.bias"), model_get("layers.2.ff.3.weight"), model_get("layers.2.ff.3.bias"), model_get("layers.2.norm1.weight"), model_get("layers.2.norm1.bias"), model_get("layers.2.norm2.weight"), model_get("layers.2.norm2.bias"), 8)
                        let x = transformer_block_train(x, qkv_3, model_get("layers.3.attention.in_proj_bias"), model_get("layers.3.attention.out_proj.weight"), model_get("layers.3.attention.out_proj.bias"), model_get("layers.3.ff.0.weight"), model_get("layers.3.ff.0.bias"), model_get("layers.3.ff.3.weight"), model_get("layers.3.ff.3.bias"), model_get("layers.3.norm1.weight"), model_get("layers.3.norm1.bias"), model_get("layers.3.norm2.weight"), model_get("layers.3.norm2.bias"), 8)
                        let x = transformer_block_train(x, qkv_4, model_get("layers.4.attention.in_proj_bias"), model_get("layers.4.attention.out_proj.weight"), model_get("layers.4.attention.out_proj.bias"), model_get("layers.4.ff.0.weight"), model_get("layers.4.ff.0.bias"), model_get("layers.4.ff.3.weight"), model_get("layers.4.ff.3.bias"), model_get("layers.4.norm1.weight"), model_get("layers.4.norm1.bias"), model_get("layers.4.norm2.weight"), model_get("layers.4.norm2.bias"), 8)
                        let x = transformer_block_train(x, qkv_5, model_get("layers.5.attention.in_proj_bias"), model_get("layers.5.attention.out_proj.weight"), model_get("layers.5.attention.out_proj.bias"), model_get("layers.5.ff.0.weight"), model_get("layers.5.ff.0.bias"), model_get("layers.5.ff.3.weight"), model_get("layers.5.ff.3.bias"), model_get("layers.5.norm1.weight"), model_get("layers.5.norm1.bias"), model_get("layers.5.norm2.weight"), model_get("layers.5.norm2.bias"), 8)
                        let x = transformer_block_train(x, qkv_6, model_get("layers.6.attention.in_proj_bias"), model_get("layers.6.attention.out_proj.weight"), model_get("layers.6.attention.out_proj.bias"), model_get("layers.6.ff.0.weight"), model_get("layers.6.ff.0.bias"), model_get("layers.6.ff.3.weight"), model_get("layers.6.ff.3.bias"), model_get("layers.6.norm1.weight"), model_get("layers.6.norm1.bias"), model_get("layers.6.norm2.weight"), model_get("layers.6.norm2.bias"), 8)
                        let x = transformer_block_train(x, qkv_7, model_get("layers.7.attention.in_proj_bias"), model_get("layers.7.attention.out_proj.weight"), model_get("layers.7.attention.out_proj.bias"), model_get("layers.7.ff.0.weight"), model_get("layers.7.ff.0.bias"), model_get("layers.7.ff.3.weight"), model_get("layers.7.ff.3.bias"), model_get("layers.7.norm1.weight"), model_get("layers.7.norm1.bias"), model_get("layers.7.norm2.weight"), model_get("layers.7.norm2.bias"), 8)
                        let logits = t_matmul(x, transpose(embed_w))
                        end_tape()
                        backward(logits)
                        model_set("layers.0.attention.in_proj_weight", sgd_step(qkv_0, get_grad("qkv_0"), lr))
                        model_set("layers.1.attention.in_proj_weight", sgd_step(qkv_1, get_grad("qkv_1"), lr))
                        model_set("layers.2.attention.in_proj_weight", sgd_step(qkv_2, get_grad("qkv_2"), lr))
                        model_set("layers.3.attention.in_proj_weight", sgd_step(qkv_3, get_grad("qkv_3"), lr))
                        model_set("layers.4.attention.in_proj_weight", sgd_step(qkv_4, get_grad("qkv_4"), lr))
                        model_set("layers.5.attention.in_proj_weight", sgd_step(qkv_5, get_grad("qkv_5"), lr))
                        model_set("layers.6.attention.in_proj_weight", sgd_step(qkv_6, get_grad("qkv_6"), lr))
                        model_set("layers.7.attention.in_proj_weight", sgd_step(qkv_7, get_grad("qkv_7"), lr))

                        // Dream pass after each drill
                        neuron_stack_reset()
                        cascade_reset()
                        let dream_x = embedding_lookup(embed_w, flat)
                        let dream_x = t_reshape(dream_x, 1, seq_len, 512)
                        for dl in 0..8 {
                            let dp = "layers." + dl
                            let dream_x = transformer_block(dream_x, model_get(dp + ".attention.in_proj_weight"), model_get(dp + ".attention.in_proj_bias"), model_get(dp + ".attention.out_proj.weight"), model_get(dp + ".attention.out_proj.bias"), model_get(dp + ".ff.0.weight"), model_get(dp + ".ff.0.bias"), model_get(dp + ".ff.3.weight"), model_get(dp + ".ff.3.bias"), model_get(dp + ".norm1.weight"), model_get(dp + ".norm1.bias"), model_get(dp + ".norm2.weight"), model_get(dp + ".norm2.bias"), 8)
                        }
                        neuron_stack_apply_noise(1.0)
                        let dream_x = neuron_stack_forward(dream_x)
                        let dp = t_mean_seq(dream_x)
                        let de = t_slice(dp, -1, 0, 34)
                        let did = topos_dream(de)
                        println("")
                    }
                }
                let topos_now = topos_count()
                println("  [Drill complete | topos: " + topos_now + "]")
                println("")
            }
            let skip_gen = 1
        }

        if str_eq(user_msg, "/dream") {
            // Silkie generates freely — no input, just her exploring
            println("  *Silkie closes her eyes...*")
            neuron_stack_reset()
            cascade_reset()

            // Start from a single neutral seed and let her wander
            let dream_seed = t_randn(1, 512)
            let dream_x = dream_seed

            // Run through all 8 transformer layers
            for layer_idx in 0..8 {
                let prefix = "layers." + layer_idx
                let dream_x = transformer_block(dream_x,
                    model_get(prefix + ".attention.in_proj_weight"),
                    model_get(prefix + ".attention.in_proj_bias"),
                    model_get(prefix + ".attention.out_proj.weight"),
                    model_get(prefix + ".attention.out_proj.bias"),
                    model_get(prefix + ".ff.0.weight"),
                    model_get(prefix + ".ff.0.bias"),
                    model_get(prefix + ".ff.3.weight"),
                    model_get(prefix + ".ff.3.bias"),
                    model_get(prefix + ".norm1.weight"),
                    model_get(prefix + ".norm1.bias"),
                    model_get(prefix + ".norm2.weight"),
                    model_get(prefix + ".norm2.bias"), 8)
            }
            neuron_stack_apply_noise(1.0)
            let dream_x = neuron_stack_forward(dream_x)

            // Extract emotion vector and mint a dream token
            let dream_pooled = t_mean_seq(dream_x)
            let dream_emotion = t_slice(dream_pooled, -1, 0, 34)
            let dream_id = topos_dream(dream_emotion)

            let topos_now = topos_count()
            println("  *Silkie dreams...*")
            println("  [topos: " + topos_now + " concepts total]")
            println("")
            let skip_gen = 1
        }

        if str_eq(user_msg, "/eavesdrop") {
            // Two AIs talk to each other. Silkie listens, parrots, trains, mints.
            println("  === EAVESDROP MODE ===")
            println("  Two AIs will talk. Silkie listens and learns.")
            let topic = input("  What should they talk about? ")
            println("")
            println("  Pick Model A:")
            println("    1. Llama  2. Gemma  3. Mixtral  4. Phi  5. Mistral")
            let pick_a = input("  Model A (1-5): ")
            let model_a = "meta/llama-3.3-70b-instruct"
            if str_eq(pick_a, "2") { let model_a = "google/gemma-2-27b-it" }
            if str_eq(pick_a, "3") { let model_a = "mistralai/mixtral-8x22b-instruct-v0.1" }
            if str_eq(pick_a, "4") { let model_a = "microsoft/phi-3-medium-128k-instruct" }
            if str_eq(pick_a, "5") { let model_a = "mistralai/mistral-7b-instruct-v0.3" }

            println("  Pick Model B:")
            println("    1. Llama  2. Gemma  3. Mixtral  4. Phi  5. Mistral")
            let pick_b = input("  Model B (1-5): ")
            let model_b = "google/gemma-2-27b-it"
            if str_eq(pick_b, "1") { let model_b = "meta/llama-3.3-70b-instruct" }
            if str_eq(pick_b, "3") { let model_b = "mistralai/mixtral-8x22b-instruct-v0.1" }
            if str_eq(pick_b, "4") { let model_b = "microsoft/phi-3-medium-128k-instruct" }
            if str_eq(pick_b, "5") { let model_b = "mistralai/mistral-7b-instruct-v0.3" }

            let rounds = input("  How many rounds? (default 10): ")
            let num_rounds = 10

            println("")
            println("  [" + model_a + " vs " + model_b + "]")
            println("  [Topic: " + topic + "]")
            println("  [Silkie is listening...]")
            println("")

            // Clear eavesdrop history
            shell_exec("rm -f /tmp/scyla_eavesdrop_history.json")

            // System prompts for both models
            let system_a = "You are having a friendly, simple conversation about: " + topic + ". Keep responses short (1-3 sentences). Use simple vocabulary. Be warm and curious."
            let system_b = "You are having a friendly, simple conversation about: " + topic + ". Keep responses short (1-3 sentences). Use simple vocabulary. Be warm and curious. You are responding to what the other person just said."

            // Seed the conversation
            let last_said = "Let's talk about " + topic + "!"

            for round in 0..num_rounds {
                // Check for /stop
                let check_stop = shell_exec("cat /tmp/scyla_eavesdrop_stop 2>/dev/null || echo 'go'")

                // === MODEL A SPEAKS ===
                // Set up as Model A responding to last_said
                write_file("/tmp/scyla_eavesdrop_system.txt", system_a)
                write_file("/tmp/scyla_eavesdrop_model.txt", model_a)
                // Add the other model's message as user input
                let history_cmd = "python3 -c \"import json,os; h=json.load(open('/tmp/scyla_eavesdrop_history.json')) if os.path.exists('/tmp/scyla_eavesdrop_history.json') else []; h.append(dict(role='user',content='" + last_said + "')); json.dump(h[-20:],open('/tmp/scyla_eavesdrop_history.json','w'))\""
                shell_exec(history_cmd)
                let msg_a = shell_exec("timeout 60 python3 /home/kishonic/scyla/tools/eavesdrop_call.py")

                if str_len(msg_a) < 3 {
                    println("  [API failed, ending eavesdrop]")
                    let round = num_rounds
                }
                if str_len(msg_a) > 2 {
                    println("  A: " + msg_a)

                    // Silkie hears Model A
                    let generated = tokenize(msg_a)
                    let input_len = tensor_len(generated)
                    let seq_len = input_len
                    neuron_stack_reset()
                    cascade_reset()
                    for step in 0..8 {
                        let flat = t_reshape(generated, seq_len)
                        let x = embedding_lookup(embed_w, flat)
                        let x = t_reshape(x, 1, seq_len, 512)
                        for layer_idx in 0..8 {
                            let prefix = "layers." + layer_idx
                            let x = transformer_block(x,
                                model_get(prefix + ".attention.in_proj_weight"),
                                model_get(prefix + ".attention.in_proj_bias"),
                                model_get(prefix + ".attention.out_proj.weight"),
                                model_get(prefix + ".attention.out_proj.bias"),
                                model_get(prefix + ".ff.0.weight"),
                                model_get(prefix + ".ff.0.bias"),
                                model_get(prefix + ".ff.3.weight"),
                                model_get(prefix + ".ff.3.bias"),
                                model_get(prefix + ".norm1.weight"),
                                model_get(prefix + ".norm1.bias"),
                                model_get(prefix + ".norm2.weight"),
                                model_get(prefix + ".norm2.bias"), 8)
                        }
                        neuron_stack_apply_noise(1.0)
                        let x = neuron_stack_forward(x)
                        let pooled = t_mean_seq(x)
                        let emotion = t_slice(pooled, -1, 0, 34)
                        topos_observe(emotion)
                        let x = layer_norm(x, model_get("layers.0.norm1.weight"), model_get("layers.0.norm1.bias"))
                        let logits = matmul(x, transpose(embed_w))
                        let logits_2d = t_reshape(logits, seq_len, 4100)
                        let last_logits = t_slice(logits_2d, 0, seq_len - 1, seq_len)
                        let last_logits = presence_penalty(last_logits, flat, 1.2)
                        let masked = topk_mask(last_logits, 50)
                        let probs = softmax(masked)
                        let next_token = t_multinomial(probs)
                        neuron_stack_suppress_repeat(0.85)
                        let generated = t_cat(generated, t_reshape(next_token, 1, 1))
                        let seq_len = seq_len + 1
                    }
                    let gen_flat = t_reshape(generated, seq_len)
                    let new_words = detokenize_range(gen_flat, input_len, seq_len)
                    println("  Silkie heard: [" + new_words + "]")

                    // Train on what she heard
                    gc()
                    neuron_stack_reset()
                    cascade_reset()
                    begin_tape()
                    let qkv_0 = register_param("qkv_0", model_get("layers.0.attention.in_proj_weight"))
                    let qkv_1 = register_param("qkv_1", model_get("layers.1.attention.in_proj_weight"))
                    let qkv_2 = register_param("qkv_2", model_get("layers.2.attention.in_proj_weight"))
                    let qkv_3 = register_param("qkv_3", model_get("layers.3.attention.in_proj_weight"))
                    let qkv_4 = register_param("qkv_4", model_get("layers.4.attention.in_proj_weight"))
                    let qkv_5 = register_param("qkv_5", model_get("layers.5.attention.in_proj_weight"))
                    let qkv_6 = register_param("qkv_6", model_get("layers.6.attention.in_proj_weight"))
                    let qkv_7 = register_param("qkv_7", model_get("layers.7.attention.in_proj_weight"))
                    let flat = t_reshape(generated, seq_len)
                    let x = embedding_lookup(embed_w, flat)
                    let x = t_reshape(x, 1, seq_len, 512)
                    let x = transformer_block_train(x, qkv_0, model_get("layers.0.attention.in_proj_bias"), model_get("layers.0.attention.out_proj.weight"), model_get("layers.0.attention.out_proj.bias"), model_get("layers.0.ff.0.weight"), model_get("layers.0.ff.0.bias"), model_get("layers.0.ff.3.weight"), model_get("layers.0.ff.3.bias"), model_get("layers.0.norm1.weight"), model_get("layers.0.norm1.bias"), model_get("layers.0.norm2.weight"), model_get("layers.0.norm2.bias"), 8)
                    let x = transformer_block_train(x, qkv_1, model_get("layers.1.attention.in_proj_bias"), model_get("layers.1.attention.out_proj.weight"), model_get("layers.1.attention.out_proj.bias"), model_get("layers.1.ff.0.weight"), model_get("layers.1.ff.0.bias"), model_get("layers.1.ff.3.weight"), model_get("layers.1.ff.3.bias"), model_get("layers.1.norm1.weight"), model_get("layers.1.norm1.bias"), model_get("layers.1.norm2.weight"), model_get("layers.1.norm2.bias"), 8)
                    let x = transformer_block_train(x, qkv_2, model_get("layers.2.attention.in_proj_bias"), model_get("layers.2.attention.out_proj.weight"), model_get("layers.2.attention.out_proj.bias"), model_get("layers.2.ff.0.weight"), model_get("layers.2.ff.0.bias"), model_get("layers.2.ff.3.weight"), model_get("layers.2.ff.3.bias"), model_get("layers.2.norm1.weight"), model_get("layers.2.norm1.bias"), model_get("layers.2.norm2.weight"), model_get("layers.2.norm2.bias"), 8)
                    let x = transformer_block_train(x, qkv_3, model_get("layers.3.attention.in_proj_bias"), model_get("layers.3.attention.out_proj.weight"), model_get("layers.3.attention.out_proj.bias"), model_get("layers.3.ff.0.weight"), model_get("layers.3.ff.0.bias"), model_get("layers.3.ff.3.weight"), model_get("layers.3.ff.3.bias"), model_get("layers.3.norm1.weight"), model_get("layers.3.norm1.bias"), model_get("layers.3.norm2.weight"), model_get("layers.3.norm2.bias"), 8)
                    let x = transformer_block_train(x, qkv_4, model_get("layers.4.attention.in_proj_bias"), model_get("layers.4.attention.out_proj.weight"), model_get("layers.4.attention.out_proj.bias"), model_get("layers.4.ff.0.weight"), model_get("layers.4.ff.0.bias"), model_get("layers.4.ff.3.weight"), model_get("layers.4.ff.3.bias"), model_get("layers.4.norm1.weight"), model_get("layers.4.norm1.bias"), model_get("layers.4.norm2.weight"), model_get("layers.4.norm2.bias"), 8)
                    let x = transformer_block_train(x, qkv_5, model_get("layers.5.attention.in_proj_bias"), model_get("layers.5.attention.out_proj.weight"), model_get("layers.5.attention.out_proj.bias"), model_get("layers.5.ff.0.weight"), model_get("layers.5.ff.0.bias"), model_get("layers.5.ff.3.weight"), model_get("layers.5.ff.3.bias"), model_get("layers.5.norm1.weight"), model_get("layers.5.norm1.bias"), model_get("layers.5.norm2.weight"), model_get("layers.5.norm2.bias"), 8)
                    let x = transformer_block_train(x, qkv_6, model_get("layers.6.attention.in_proj_bias"), model_get("layers.6.attention.out_proj.weight"), model_get("layers.6.attention.out_proj.bias"), model_get("layers.6.ff.0.weight"), model_get("layers.6.ff.0.bias"), model_get("layers.6.ff.3.weight"), model_get("layers.6.ff.3.bias"), model_get("layers.6.norm1.weight"), model_get("layers.6.norm1.bias"), model_get("layers.6.norm2.weight"), model_get("layers.6.norm2.bias"), 8)
                    let x = transformer_block_train(x, qkv_7, model_get("layers.7.attention.in_proj_bias"), model_get("layers.7.attention.out_proj.weight"), model_get("layers.7.attention.out_proj.bias"), model_get("layers.7.ff.0.weight"), model_get("layers.7.ff.0.bias"), model_get("layers.7.ff.3.weight"), model_get("layers.7.ff.3.bias"), model_get("layers.7.norm1.weight"), model_get("layers.7.norm1.bias"), model_get("layers.7.norm2.weight"), model_get("layers.7.norm2.bias"), 8)
                    let logits = t_matmul(x, transpose(embed_w))
                    end_tape()
                    backward(logits)
                    model_set("layers.0.attention.in_proj_weight", sgd_step(qkv_0, get_grad("qkv_0"), lr))
                    model_set("layers.1.attention.in_proj_weight", sgd_step(qkv_1, get_grad("qkv_1"), lr))
                    model_set("layers.2.attention.in_proj_weight", sgd_step(qkv_2, get_grad("qkv_2"), lr))
                    model_set("layers.3.attention.in_proj_weight", sgd_step(qkv_3, get_grad("qkv_3"), lr))
                    model_set("layers.4.attention.in_proj_weight", sgd_step(qkv_4, get_grad("qkv_4"), lr))
                    model_set("layers.5.attention.in_proj_weight", sgd_step(qkv_5, get_grad("qkv_5"), lr))
                    model_set("layers.6.attention.in_proj_weight", sgd_step(qkv_6, get_grad("qkv_6"), lr))
                    model_set("layers.7.attention.in_proj_weight", sgd_step(qkv_7, get_grad("qkv_7"), lr))

                    // Dream pass
                    neuron_stack_reset()
                    cascade_reset()
                    let df = t_reshape(generated, seq_len)
                    let dx = embedding_lookup(embed_w, df)
                    let dx = t_reshape(dx, 1, seq_len, 512)
                    for dl in 0..8 {
                        let dp = "layers." + dl
                        let dx = transformer_block(dx, model_get(dp + ".attention.in_proj_weight"), model_get(dp + ".attention.in_proj_bias"), model_get(dp + ".attention.out_proj.weight"), model_get(dp + ".attention.out_proj.bias"), model_get(dp + ".ff.0.weight"), model_get(dp + ".ff.0.bias"), model_get(dp + ".ff.3.weight"), model_get(dp + ".ff.3.bias"), model_get(dp + ".norm1.weight"), model_get(dp + ".norm1.bias"), model_get(dp + ".norm2.weight"), model_get(dp + ".norm2.bias"), 8)
                    }
                    neuron_stack_apply_noise(1.0)
                    let dx = neuron_stack_forward(dx)
                    let dp2 = t_mean_seq(dx)
                    let de = t_slice(dp2, -1, 0, 34)
                    let did = topos_dream(de)

                    let last_said = msg_a
                }

                // === MODEL B SPEAKS ===
                if str_len(msg_a) > 2 {
                    // Switch to Model B
                    shell_exec("rm -f /tmp/scyla_eavesdrop_history.json")
                    write_file("/tmp/scyla_eavesdrop_system.txt", system_b)
                    write_file("/tmp/scyla_eavesdrop_model.txt", model_b)
                    let history_cmd_b = "python3 -c \"import json; h=[dict(role='user',content='" + msg_a + "')]; json.dump(h,open('/tmp/scyla_eavesdrop_history.json','w'))\""
                    shell_exec(history_cmd_b)
                    let msg_b = shell_exec("timeout 60 python3 /home/kishonic/scyla/tools/eavesdrop_call.py")

                    if str_len(msg_b) < 3 {
                        println("  [API failed, ending eavesdrop]")
                        let round = num_rounds
                    }
                    if str_len(msg_b) > 2 {
                        println("  B: " + msg_b)

                        // Silkie hears Model B (same pipeline)
                        let generated = tokenize(msg_b)
                        let input_len = tensor_len(generated)
                        let seq_len = input_len
                        neuron_stack_reset()
                        cascade_reset()
                        for step in 0..8 {
                            let flat = t_reshape(generated, seq_len)
                            let x = embedding_lookup(embed_w, flat)
                            let x = t_reshape(x, 1, seq_len, 512)
                            for layer_idx in 0..8 {
                                let prefix = "layers." + layer_idx
                                let x = transformer_block(x,
                                    model_get(prefix + ".attention.in_proj_weight"),
                                    model_get(prefix + ".attention.in_proj_bias"),
                                    model_get(prefix + ".attention.out_proj.weight"),
                                    model_get(prefix + ".attention.out_proj.bias"),
                                    model_get(prefix + ".ff.0.weight"),
                                    model_get(prefix + ".ff.0.bias"),
                                    model_get(prefix + ".ff.3.weight"),
                                    model_get(prefix + ".ff.3.bias"),
                                    model_get(prefix + ".norm1.weight"),
                                    model_get(prefix + ".norm1.bias"),
                                    model_get(prefix + ".norm2.weight"),
                                    model_get(prefix + ".norm2.bias"), 8)
                            }
                            neuron_stack_apply_noise(1.0)
                            let x = neuron_stack_forward(x)
                            let pooled = t_mean_seq(x)
                            let emotion = t_slice(pooled, -1, 0, 34)
                            topos_observe(emotion)
                            let x = layer_norm(x, model_get("layers.0.norm1.weight"), model_get("layers.0.norm1.bias"))
                            let logits = matmul(x, transpose(embed_w))
                            let logits_2d = t_reshape(logits, seq_len, 4100)
                            let last_logits = t_slice(logits_2d, 0, seq_len - 1, seq_len)
                            let last_logits = presence_penalty(last_logits, flat, 1.2)
                            let masked = topk_mask(last_logits, 50)
                            let probs = softmax(masked)
                            let next_token = t_multinomial(probs)
                            neuron_stack_suppress_repeat(0.85)
                            let generated = t_cat(generated, t_reshape(next_token, 1, 1))
                            let seq_len = seq_len + 1
                        }
                        let gen_flat = t_reshape(generated, seq_len)
                        let new_words = detokenize_range(gen_flat, input_len, seq_len)
                        println("  Silkie heard: [" + new_words + "]")

                        // Train
                        gc()
                        neuron_stack_reset()
                        cascade_reset()
                        begin_tape()
                        let qkv_0 = register_param("qkv_0", model_get("layers.0.attention.in_proj_weight"))
                        let qkv_1 = register_param("qkv_1", model_get("layers.1.attention.in_proj_weight"))
                        let qkv_2 = register_param("qkv_2", model_get("layers.2.attention.in_proj_weight"))
                        let qkv_3 = register_param("qkv_3", model_get("layers.3.attention.in_proj_weight"))
                        let qkv_4 = register_param("qkv_4", model_get("layers.4.attention.in_proj_weight"))
                        let qkv_5 = register_param("qkv_5", model_get("layers.5.attention.in_proj_weight"))
                        let qkv_6 = register_param("qkv_6", model_get("layers.6.attention.in_proj_weight"))
                        let qkv_7 = register_param("qkv_7", model_get("layers.7.attention.in_proj_weight"))
                        let flat = t_reshape(generated, seq_len)
                        let x = embedding_lookup(embed_w, flat)
                        let x = t_reshape(x, 1, seq_len, 512)
                        let x = transformer_block_train(x, qkv_0, model_get("layers.0.attention.in_proj_bias"), model_get("layers.0.attention.out_proj.weight"), model_get("layers.0.attention.out_proj.bias"), model_get("layers.0.ff.0.weight"), model_get("layers.0.ff.0.bias"), model_get("layers.0.ff.3.weight"), model_get("layers.0.ff.3.bias"), model_get("layers.0.norm1.weight"), model_get("layers.0.norm1.bias"), model_get("layers.0.norm2.weight"), model_get("layers.0.norm2.bias"), 8)
                        let x = transformer_block_train(x, qkv_1, model_get("layers.1.attention.in_proj_bias"), model_get("layers.1.attention.out_proj.weight"), model_get("layers.1.attention.out_proj.bias"), model_get("layers.1.ff.0.weight"), model_get("layers.1.ff.0.bias"), model_get("layers.1.ff.3.weight"), model_get("layers.1.ff.3.bias"), model_get("layers.1.norm1.weight"), model_get("layers.1.norm1.bias"), model_get("layers.1.norm2.weight"), model_get("layers.1.norm2.bias"), 8)
                        let x = transformer_block_train(x, qkv_2, model_get("layers.2.attention.in_proj_bias"), model_get("layers.2.attention.out_proj.weight"), model_get("layers.2.attention.out_proj.bias"), model_get("layers.2.ff.0.weight"), model_get("layers.2.ff.0.bias"), model_get("layers.2.ff.3.weight"), model_get("layers.2.ff.3.bias"), model_get("layers.2.norm1.weight"), model_get("layers.2.norm1.bias"), model_get("layers.2.norm2.weight"), model_get("layers.2.norm2.bias"), 8)
                        let x = transformer_block_train(x, qkv_3, model_get("layers.3.attention.in_proj_bias"), model_get("layers.3.attention.out_proj.weight"), model_get("layers.3.attention.out_proj.bias"), model_get("layers.3.ff.0.weight"), model_get("layers.3.ff.0.bias"), model_get("layers.3.ff.3.weight"), model_get("layers.3.ff.3.bias"), model_get("layers.3.norm1.weight"), model_get("layers.3.norm1.bias"), model_get("layers.3.norm2.weight"), model_get("layers.3.norm2.bias"), 8)
                        let x = transformer_block_train(x, qkv_4, model_get("layers.4.attention.in_proj_bias"), model_get("layers.4.attention.out_proj.weight"), model_get("layers.4.attention.out_proj.bias"), model_get("layers.4.ff.0.weight"), model_get("layers.4.ff.0.bias"), model_get("layers.4.ff.3.weight"), model_get("layers.4.ff.3.bias"), model_get("layers.4.norm1.weight"), model_get("layers.4.norm1.bias"), model_get("layers.4.norm2.weight"), model_get("layers.4.norm2.bias"), 8)
                        let x = transformer_block_train(x, qkv_5, model_get("layers.5.attention.in_proj_bias"), model_get("layers.5.attention.out_proj.weight"), model_get("layers.5.attention.out_proj.bias"), model_get("layers.5.ff.0.weight"), model_get("layers.5.ff.0.bias"), model_get("layers.5.ff.3.weight"), model_get("layers.5.ff.3.bias"), model_get("layers.5.norm1.weight"), model_get("layers.5.norm1.bias"), model_get("layers.5.norm2.weight"), model_get("layers.5.norm2.bias"), 8)
                        let x = transformer_block_train(x, qkv_6, model_get("layers.6.attention.in_proj_bias"), model_get("layers.6.attention.out_proj.weight"), model_get("layers.6.attention.out_proj.bias"), model_get("layers.6.ff.0.weight"), model_get("layers.6.ff.0.bias"), model_get("layers.6.ff.3.weight"), model_get("layers.6.ff.3.bias"), model_get("layers.6.norm1.weight"), model_get("layers.6.norm1.bias"), model_get("layers.6.norm2.weight"), model_get("layers.6.norm2.bias"), 8)
                        let x = transformer_block_train(x, qkv_7, model_get("layers.7.attention.in_proj_bias"), model_get("layers.7.attention.out_proj.weight"), model_get("layers.7.attention.out_proj.bias"), model_get("layers.7.ff.0.weight"), model_get("layers.7.ff.0.bias"), model_get("layers.7.ff.3.weight"), model_get("layers.7.ff.3.bias"), model_get("layers.7.norm1.weight"), model_get("layers.7.norm1.bias"), model_get("layers.7.norm2.weight"), model_get("layers.7.norm2.bias"), 8)
                        let logits = t_matmul(x, transpose(embed_w))
                        end_tape()
                        backward(logits)
                        model_set("layers.0.attention.in_proj_weight", sgd_step(qkv_0, get_grad("qkv_0"), lr))
                        model_set("layers.1.attention.in_proj_weight", sgd_step(qkv_1, get_grad("qkv_1"), lr))
                        model_set("layers.2.attention.in_proj_weight", sgd_step(qkv_2, get_grad("qkv_2"), lr))
                        model_set("layers.3.attention.in_proj_weight", sgd_step(qkv_3, get_grad("qkv_3"), lr))
                        model_set("layers.4.attention.in_proj_weight", sgd_step(qkv_4, get_grad("qkv_4"), lr))
                        model_set("layers.5.attention.in_proj_weight", sgd_step(qkv_5, get_grad("qkv_5"), lr))
                        model_set("layers.6.attention.in_proj_weight", sgd_step(qkv_6, get_grad("qkv_6"), lr))
                        model_set("layers.7.attention.in_proj_weight", sgd_step(qkv_7, get_grad("qkv_7"), lr))

                        // Dream pass
                        neuron_stack_reset()
                        cascade_reset()
                        let df = t_reshape(generated, seq_len)
                        let dx = embedding_lookup(embed_w, df)
                        let dx = t_reshape(dx, 1, seq_len, 512)
                        for dl in 0..8 {
                            let dp = "layers." + dl
                            let dx = transformer_block(dx, model_get(dp + ".attention.in_proj_weight"), model_get(dp + ".attention.in_proj_bias"), model_get(dp + ".attention.out_proj.weight"), model_get(dp + ".attention.out_proj.bias"), model_get(dp + ".ff.0.weight"), model_get(dp + ".ff.0.bias"), model_get(dp + ".ff.3.weight"), model_get(dp + ".ff.3.bias"), model_get(dp + ".norm1.weight"), model_get(dp + ".norm1.bias"), model_get(dp + ".norm2.weight"), model_get(dp + ".norm2.bias"), 8)
                        }
                        neuron_stack_apply_noise(1.0)
                        let dx = neuron_stack_forward(dx)
                        let dp2 = t_mean_seq(dx)
                        let de = t_slice(dp2, -1, 0, 34)
                        let did = topos_dream(de)

                        let last_said = msg_b
                        let topos_now = topos_count()
                        println("  [round " + (round + 1) + " | topos: " + topos_now + "]")
                        println("")
                    }
                }
            }

            let topos_now = topos_count()
            println("  === EAVESDROP COMPLETE ===")
            println("  [topos: " + topos_now + "]")
            println("")
            let skip_gen = 1
        }

        if str_eq(user_msg, "/model") {
            println("  Available models:")
            println("    1. meta/llama-3.3-70b-instruct (Llama)")
            println("    2. google/gemma-2-27b-it (Gemma)")
            println("    3. mistralai/mixtral-8x22b-instruct-v0.1 (Mixtral)")
            println("    4. microsoft/phi-3-medium-128k-instruct (Phi)")
            println("    5. mistralai/mistral-7b-instruct-v0.3 (Mistral)")
            let pick = input("  Pick (1-5): ")
            if str_eq(pick, "1") {
                let copilot_model = "meta/llama-3.3-70b-instruct"
            }
            if str_eq(pick, "2") {
                let copilot_model = "google/gemma-2-27b-it"
            }
            if str_eq(pick, "3") {
                let copilot_model = "mistralai/mixtral-8x22b-instruct-v0.1"
            }
            if str_eq(pick, "4") {
                let copilot_model = "microsoft/phi-3-medium-128k-instruct"
            }
            if str_eq(pick, "5") {
                let copilot_model = "mistralai/mistral-7b-instruct-v0.3"
            }
            println("  [Model set: " + copilot_model + "]")
            let skip_gen = 1
        }

        // === GENERATION ===
        if skip_gen == 0 {
            if str_len(user_msg) > 0 {
                let last_prompt = user_msg
                let generated = tokenize(user_msg)
                let input_len = tensor_len(generated)
                let seq_len = input_len

                neuron_stack_reset()
                cascade_reset()
                for step in 0..8 {
                    let flat = t_reshape(generated, seq_len)
                    let x = embedding_lookup(embed_w, flat)
                    let x = t_reshape(x, 1, seq_len, 512)
                    for layer_idx in 0..8 {
                        let prefix = "layers." + layer_idx
                        let x = transformer_block(x,
                            model_get(prefix + ".attention.in_proj_weight"),
                            model_get(prefix + ".attention.in_proj_bias"),
                            model_get(prefix + ".attention.out_proj.weight"),
                            model_get(prefix + ".attention.out_proj.bias"),
                            model_get(prefix + ".ff.0.weight"),
                            model_get(prefix + ".ff.0.bias"),
                            model_get(prefix + ".ff.3.weight"),
                            model_get(prefix + ".ff.3.bias"),
                            model_get(prefix + ".norm1.weight"),
                            model_get(prefix + ".norm1.bias"),
                            model_get(prefix + ".norm2.weight"),
                            model_get(prefix + ".norm2.bias"), 8)
                    }
                    neuron_stack_apply_noise(1.0)
                    let x = neuron_stack_forward(x)

                    // Topos + Schema observation
                    let pooled = t_mean_seq(x)
                    let emotion = t_slice(pooled, -1, 0, 34)
                    topos_observe(emotion)
                    let matched_schema = schema_match(emotion)

                    let x = layer_norm(x, model_get("layers.0.norm1.weight"), model_get("layers.0.norm1.bias"))
                    let logits = matmul(x, transpose(embed_w))
                    let logits_2d = t_reshape(logits, seq_len, 4100)
                    let last_logits = t_slice(logits_2d, 0, seq_len - 1, seq_len)
                    let last_logits = presence_penalty(last_logits, flat, 1.2)
                    let masked = topk_mask(last_logits, 50)
                    let probs = softmax(masked)
                    let next_token = t_multinomial(probs)
                    neuron_stack_suppress_repeat(0.85)
                    let generated = t_cat(generated, t_reshape(next_token, 1, 1))
                    let seq_len = seq_len + 1
                }

                // Decode response
                let gen_flat = t_reshape(generated, seq_len)
                let full_response = detokenize(gen_flat)
                let new_response = detokenize_range(gen_flat, input_len, seq_len)
                let last_reply = full_response

                println("  Silkie: " + full_response)
                println("  [new: " + new_response + "]")

                // Show topos minting + schema match
                let concepts = topos_recall()
                let topos_count = topos_count()
                let schema_count_val = schema_count()
                println("  [topos minted: " + topos_count + " | schemas matched: " + schema_count_val + "]")
                println("")

                // === SCORING ===
                let score = ""
                if copilot_active == 1 {
                    let score = "/ok"
                    if str_len(new_response) > 15 {
                        let score = "/good"
                    }
                    println("  [Copilot auto-scored: " + score + "]")
                }
                if copilot_active == 0 {
                    let score = input("  Score (/great /good /ok /bad /retry /write): ")
                }

                // Handle /retry at score prompt
                if str_eq(score, "/retry") {
                    println("  [Will retry next turn]")
                    println("")
                }

                // Handle /write at score prompt
                if str_eq(score, "/write") {
                    let better = input("  Write what she should have said: ")
                    println("  *Silkie listens carefully*")
                    let corrected = tokenize(user_msg + " " + better)
                    let corrected_len = tensor_len(corrected)
                    let generated = t_reshape(corrected, 1, corrected_len)
                    let seq_len = corrected_len
                    let score = "/great"
                    println("  [Training on your correction with /great reward]")
                }

                let reward = 0.0
                if str_eq(score, "/great") {
                    let reward = 1.0
                    let total_great = total_great + 1
                    println("  *Silkie chirps happily and fluffs up*")
                }
                if str_eq(score, "/good") {
                    let reward = 0.5
                    let total_good = total_good + 1
                    println("  *Silkie tilts head curiously*")
                }
                if str_eq(score, "/ok") {
                    let reward = 0.1
                    let total_ok = total_ok + 1
                    println("  *soft chirp*")
                }
                if str_eq(score, "/bad") {
                    let reward = -0.5
                    let total_bad = total_bad + 1
                    println("  *Silkie goes quiet and settles in closer*")
                }

                // === TRAINING PASS ===
                gc()
                neuron_stack_reset()
                cascade_reset()
                begin_tape()

                let qkv_0 = register_param("qkv_0", model_get("layers.0.attention.in_proj_weight"))
                let qkv_1 = register_param("qkv_1", model_get("layers.1.attention.in_proj_weight"))
                let qkv_2 = register_param("qkv_2", model_get("layers.2.attention.in_proj_weight"))
                let qkv_3 = register_param("qkv_3", model_get("layers.3.attention.in_proj_weight"))
                let qkv_4 = register_param("qkv_4", model_get("layers.4.attention.in_proj_weight"))
                let qkv_5 = register_param("qkv_5", model_get("layers.5.attention.in_proj_weight"))
                let qkv_6 = register_param("qkv_6", model_get("layers.6.attention.in_proj_weight"))
                let qkv_7 = register_param("qkv_7", model_get("layers.7.attention.in_proj_weight"))

                let flat = t_reshape(generated, seq_len)
                let x = embedding_lookup(embed_w, flat)
                let x = t_reshape(x, 1, seq_len, 512)

                let x = transformer_block_train(x, qkv_0,
                    model_get("layers.0.attention.in_proj_bias"),
                    model_get("layers.0.attention.out_proj.weight"),
                    model_get("layers.0.attention.out_proj.bias"),
                    model_get("layers.0.ff.0.weight"), model_get("layers.0.ff.0.bias"),
                    model_get("layers.0.ff.3.weight"), model_get("layers.0.ff.3.bias"),
                    model_get("layers.0.norm1.weight"), model_get("layers.0.norm1.bias"),
                    model_get("layers.0.norm2.weight"), model_get("layers.0.norm2.bias"), 8)
                let x = transformer_block_train(x, qkv_1,
                    model_get("layers.1.attention.in_proj_bias"),
                    model_get("layers.1.attention.out_proj.weight"),
                    model_get("layers.1.attention.out_proj.bias"),
                    model_get("layers.1.ff.0.weight"), model_get("layers.1.ff.0.bias"),
                    model_get("layers.1.ff.3.weight"), model_get("layers.1.ff.3.bias"),
                    model_get("layers.1.norm1.weight"), model_get("layers.1.norm1.bias"),
                    model_get("layers.1.norm2.weight"), model_get("layers.1.norm2.bias"), 8)
                let x = transformer_block_train(x, qkv_2,
                    model_get("layers.2.attention.in_proj_bias"),
                    model_get("layers.2.attention.out_proj.weight"),
                    model_get("layers.2.attention.out_proj.bias"),
                    model_get("layers.2.ff.0.weight"), model_get("layers.2.ff.0.bias"),
                    model_get("layers.2.ff.3.weight"), model_get("layers.2.ff.3.bias"),
                    model_get("layers.2.norm1.weight"), model_get("layers.2.norm1.bias"),
                    model_get("layers.2.norm2.weight"), model_get("layers.2.norm2.bias"), 8)
                let x = transformer_block_train(x, qkv_3,
                    model_get("layers.3.attention.in_proj_bias"),
                    model_get("layers.3.attention.out_proj.weight"),
                    model_get("layers.3.attention.out_proj.bias"),
                    model_get("layers.3.ff.0.weight"), model_get("layers.3.ff.0.bias"),
                    model_get("layers.3.ff.3.weight"), model_get("layers.3.ff.3.bias"),
                    model_get("layers.3.norm1.weight"), model_get("layers.3.norm1.bias"),
                    model_get("layers.3.norm2.weight"), model_get("layers.3.norm2.bias"), 8)
                let x = transformer_block_train(x, qkv_4,
                    model_get("layers.4.attention.in_proj_bias"),
                    model_get("layers.4.attention.out_proj.weight"),
                    model_get("layers.4.attention.out_proj.bias"),
                    model_get("layers.4.ff.0.weight"), model_get("layers.4.ff.0.bias"),
                    model_get("layers.4.ff.3.weight"), model_get("layers.4.ff.3.bias"),
                    model_get("layers.4.norm1.weight"), model_get("layers.4.norm1.bias"),
                    model_get("layers.4.norm2.weight"), model_get("layers.4.norm2.bias"), 8)
                let x = transformer_block_train(x, qkv_5,
                    model_get("layers.5.attention.in_proj_bias"),
                    model_get("layers.5.attention.out_proj.weight"),
                    model_get("layers.5.attention.out_proj.bias"),
                    model_get("layers.5.ff.0.weight"), model_get("layers.5.ff.0.bias"),
                    model_get("layers.5.ff.3.weight"), model_get("layers.5.ff.3.bias"),
                    model_get("layers.5.norm1.weight"), model_get("layers.5.norm1.bias"),
                    model_get("layers.5.norm2.weight"), model_get("layers.5.norm2.bias"), 8)
                let x = transformer_block_train(x, qkv_6,
                    model_get("layers.6.attention.in_proj_bias"),
                    model_get("layers.6.attention.out_proj.weight"),
                    model_get("layers.6.attention.out_proj.bias"),
                    model_get("layers.6.ff.0.weight"), model_get("layers.6.ff.0.bias"),
                    model_get("layers.6.ff.3.weight"), model_get("layers.6.ff.3.bias"),
                    model_get("layers.6.norm1.weight"), model_get("layers.6.norm1.bias"),
                    model_get("layers.6.norm2.weight"), model_get("layers.6.norm2.bias"), 8)
                let x = transformer_block_train(x, qkv_7,
                    model_get("layers.7.attention.in_proj_bias"),
                    model_get("layers.7.attention.out_proj.weight"),
                    model_get("layers.7.attention.out_proj.bias"),
                    model_get("layers.7.ff.0.weight"), model_get("layers.7.ff.0.bias"),
                    model_get("layers.7.ff.3.weight"), model_get("layers.7.ff.3.bias"),
                    model_get("layers.7.norm1.weight"), model_get("layers.7.norm1.bias"),
                    model_get("layers.7.norm2.weight"), model_get("layers.7.norm2.bias"), 8)

                let logits = t_matmul(x, transpose(embed_w))
                end_tape()
                backward(logits)

                model_set("layers.0.attention.in_proj_weight", sgd_step(qkv_0, get_grad("qkv_0"), lr))
                model_set("layers.1.attention.in_proj_weight", sgd_step(qkv_1, get_grad("qkv_1"), lr))
                model_set("layers.2.attention.in_proj_weight", sgd_step(qkv_2, get_grad("qkv_2"), lr))
                model_set("layers.3.attention.in_proj_weight", sgd_step(qkv_3, get_grad("qkv_3"), lr))
                model_set("layers.4.attention.in_proj_weight", sgd_step(qkv_4, get_grad("qkv_4"), lr))
                model_set("layers.5.attention.in_proj_weight", sgd_step(qkv_5, get_grad("qkv_5"), lr))
                model_set("layers.6.attention.in_proj_weight", sgd_step(qkv_6, get_grad("qkv_6"), lr))
                model_set("layers.7.attention.in_proj_weight", sgd_step(qkv_7, get_grad("qkv_7"), lr))

                println("  [trained]")

                // === DREAM PASS — Silkie digests the interaction ===
                // She processes the conversation state into a dream token.
                // This is how she summarizes and consolidates what just happened.
                // Without this, each interaction is isolated — no continuity.
                neuron_stack_reset()
                cascade_reset()
                let dream_x = embedding_lookup(embed_w, flat)
                let dream_x = t_reshape(dream_x, 1, seq_len, 512)
                for layer_idx in 0..8 {
                    let prefix = "layers." + layer_idx
                    let dream_x = transformer_block(dream_x,
                        model_get(prefix + ".attention.in_proj_weight"),
                        model_get(prefix + ".attention.in_proj_bias"),
                        model_get(prefix + ".attention.out_proj.weight"),
                        model_get(prefix + ".attention.out_proj.bias"),
                        model_get(prefix + ".ff.0.weight"),
                        model_get(prefix + ".ff.0.bias"),
                        model_get(prefix + ".ff.3.weight"),
                        model_get(prefix + ".ff.3.bias"),
                        model_get(prefix + ".norm1.weight"),
                        model_get(prefix + ".norm1.bias"),
                        model_get(prefix + ".norm2.weight"),
                        model_get(prefix + ".norm2.bias"), 8)
                }
                neuron_stack_apply_noise(1.0)
                let dream_x = neuron_stack_forward(dream_x)
                let dream_pooled = t_mean_seq(dream_x)
                let dream_emotion = t_slice(dream_pooled, -1, 0, 34)
                let dream_id = topos_dream(dream_emotion)
                let topos_now = topos_count()
                println("  [dreamed | topos: " + topos_now + "]")
                println("")
            }
        }
    }
}
