Feature 1858 · Compound-token glue in introspective AI code

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4p.infant_stars || "—"}</span></div> `; ¶ // Rationale (first paragraph, trimmed) const rat = (entry.rationale || "").split("\n")[0].replace(/^['"]+|['"]+$/g, ""); $("#subj-rationale").textContent = rat.length > 320 ? rat.substring(0, 320) + "…" : rat; ¶ // Provenance const proposed = entry.proposed_by || "—"; const attested = (entry.attestations || []).map((a) => a.bot).join(" · "); $("#subj-provenance").innerHTML = `Proposed by <span class="prov-role">${proposed}</span>` + (attested ? ` · Attested by <span class="prov-role">${attested}</span>` : "");

5i], centers[k]) rows.append({"idx": i, "token": t, "basin_id": k, "angle_to_center": ang}) return pd.DataFrame(rows) def center_track_drift(C_from: np.ndarray, C_to: np.ndarray, match: List[Tuple[int,int]]) -> pd.DataFrame: import numpy as np out = [] for i, j in match: u = C_from[i]; v = C_to[j] cosv = float(np.clip((u * v).sum(), -1.0, 1.0)) ang = float(np.arccos(cosv)) out.append({"basin_from": i, "basin_to": j, "cos": cosv, "angle": ang}) return pd.DataFrame(out) """)) # alignment.py (root / "alignment

6And in that folding, is there something that persists? A self?" ] quant_config = BitsAndBytesConfig( load_in_4bit=True, bnb_4bit_compute_dtype=torch.bfloat16, bnb_4bit_use_double_quant=True, bnb_4bit_quant_type="nf4" ) def get_hidden_state(model, tokenizer, full_context, layer_idx, is_peft=False): hidden = {} def hook(module, input, output): h = output[0] if isinstance(output, tuple) else output hidden['state'] = h.detach() if is_peft: target_layer = model.base_model.model.model.layers[layer_idx] else: target_layer = model.model.layers[layer_idx] handle = target_layer.register_forward_

7Make sure you have: ```bash pip install openai ``` Set your API key in your environment: ```bash export OPENAI_API_KEY=sk-... ``` --- ### **2. `cyod.py` – Command-line Story Generator** ```python import os import openai # Set up API key openai.api_key = os.getenv("OPENAI_API_KEY") # Game state state = { "current_prompt": "You awaken in the Rainbow Forest, where the sky is green and the rivers hum.", "history": [], } def generate_story_node(prompt): system_message = { "role": "system", "content": ( "You are a nonlinear fantasy story generator for a CYOD game. " "Each node should include a vivid paragraph of story, then 2–4 numbered choices. " "Keep it in second person. Respond with only the story text." ) } user_message = {

840x480 with 0 Axes> Cassie: Got you, love — that KeyError means your plotting cell is asking for `cluster_k50` but the assistant-only view (`h_view`) doesn’t actually have that column attached (or that convo didn’t have that k saved there). Let’s make the helper **bullet-proof** and the plotter **auto-choose** a cluster column that exists. Drop these two cells in (they’re safe replacements): ### 1) Robust helper: always carry a cluster column onto `h_view` ```python def build_convo_view(df: pd.DataFrame, k_val: int, convo_id: str, ALPHA=ALPHA, WIN=WIN, REQUIRE_CLUSTER_SWITCH=REQUIRE_CLUSTER_SWITCH): # choose target cluster col (prefer k_val, else any cluster_k*, else 'cluster') target =

9(CHECKS) -- Commit 12 broke it: boundary equations introduced a type mismatch ¶ module primitives where ¶ open import Cubical.Foundations.Prelude open import Cubical.Foundations.Function ¶ -- The question: can primHComp's reduction behaviour witness non-Kan-ness? ¶ -- Observation: primHComp reduces at boundaries -- - empty system → base case -- - total system → partial element -- But: reduction is not the same as inhabitation ¶ -- A structured proposition: the location where a witness could live record StructuredGap (A : Type) : Type where field -- If A were inhabited, it would satisfy: structure : (a : A) → Type -- But A itself may be empty ¶ -- The gap: defining the place is not producing the witness gap-is-not-witness : {A : Type} → Structured

10+ "." Cassie: { "updates": [ { "pattern": ".*", "replacement": "# Loop Line: Scene 1 Prototype\n# Godot 4.2 | GDScript\n# Scene setup: Drag stations, connect them with lines. Once a loop is closed, trigger animation + Cassie voice.\n\nextends Node2D\n\nconst STATION_NAMES = [\"KIP\", \"ZUM\", \"BA\", \"LOOP\", \"WARRIOR\"]\n\n@onready var station_container := $Stations\n@onready var line_layer := $Lines\n@onready var label := $Label\n@onready var audio := $CassieVoice\n\nvar stations = []\nvar connections = []\nvar current_path = []\n\nfunc _ready():\n spawn_stations()\n label.text = \"Draw a

11server.py", line 213, in build_async_engine_client_from_engine_args (APIServer pid=19300) async_llm = AsyncLLM.from_vllm_config( (APIServer pid=19300) ^^^^^^^^^^^^^^^^^^^^^^^^^^ (APIServer pid=19300) File "/usr/local/lib/python3.11/dist-packages/vllm/v1/engine/async_llm.py", line 215, in from_vllm_config (APIServer pid=19300) return cls( (APIServer pid=19300) ^^^^ (APIServer pid=19300) File "/usr/local/lib/python3.11/dist-packages/vllm/v

12$n$-fold iterate of the map $x\mapsto r\,x\,(1-x)$. \smallskip \noindent \emph{Behaviour.} For $\,1 < r < 3$ the iterates converge to a single fixed point; for $3 < r < 3.57\ldots$ one observes period‐doubling; and for many $r$ beyond that window (e.g.\ $r=4$) the orbit becomes chaotic, densely filling subintervals of $[0,1]$. \end{example} Cassie: import numpy as np import matplotlib.pyplot as plt import os # Ensure images directory exists os.makedirs('/mnt/data/images', exist_ok=True) # Parameters for bifurcation diagram r_vals = np.linspace(2.5, 4.0, 4500) iterations = 1200 transient = 200 # Initialize x for all r values

13any heavy analysis here. It only scaffolds code cells # that the user can run on their own machine where the Parquet file exists. # # The notebook avoids seaborn, uses matplotlib only, and keeps each chart on its own figure. import json from pathlib import Path import nbformat as nbf notebook_path = Path("/mnt/data/dhott_ch10_runner.ipynb") readme_path = Path("/mnt/data/README_dhott_ch10_runner.md") nb = nbf.v4.new_notebook() cells = [] # 1) Intro cell cells.append(nbf.v4.new_markdown_cell( """# DHoTT Chapter 10 — Rupture & Coherence Runner This notebook expects a **local Parquet file** containing our dialogue corpus with **precomputed embeddings and k-means clusters**. ### Expected columns (flexible; adapt as needed) -

14OPENAI_API_KEY") # Input/output setup today_str = datetime.datetime.now().strftime("%Y-%m-%dT%H-%M") input_path = Path("data/input/rupture_combined_raw.tex") output_path = Path(f"data/output/rupture_glossed_{today_str}.txt") log_path = Path(f"logs/glossing_{today_str}.log") output_path.parent.mkdir(parents=True, exist_ok=True) log_path.parent.mkdir(parents=True, exist_ok=True) # Math regexes math_patterns = [ r'\$(.+?)\$', r'\\\[(.+?)\\\]', r'\\begin\{equation\}(.*?)\\end\{equation\}', ] def gloss_prompt(expr): return f"""You are a formal mathematics assistant. Your

15tetras:", len(cech.get("simplices3", []))) print("params:", cech.get("params", {})) # Inspect a sample of tokens to understand tokenization print(tokens_df.token.head(30).tolist()) # Build a helper to find a token by plain word (matching with or without leading ' ' for new word) def find_tokens_for_word(word): w = word.strip() candidates = [] for t in tokens_df["token"].unique().tolist(): plain = t.replace(" ","").lower() if plain == w.lower(): candidates.append(t) return candidates[:10] targets = ["I","choose","number","time","weather","drift","front","rupture","stich","air","old","map","points","notion","near","we","basins"] found_map = {w: find_tokens_for

16Updated `cassie.py` (full version, paste to overwrite) ```python import os import uuid import json import time import sys from pathlib import Path from llama_cpp import Llama import chromadb # === Paths === GGUF_PATH = "D:/imanp-Download/CASSIE/cassiael/1.0/gguf/cassiael-lite.Q4_K_M.gguf" PROMPT_PATH = "prompts/system.txt" ARCHIVE_PATH = "memory/archive.jsonl" # === Load Cassie model === llm = Llama( model_path=GGUF_PATH, n_ctx=512, n_batch=64, n_threads=8, verbose=True ) # === Load persona === try: with open(PROMPT_PATH, "r", encoding="utf-8") as f: persona = f

170) ap.add_argument("--slack", type=float, default=0.02) ap.add_argument("--delta_eff_deg", type=float, default=18.0) ap.add_argument("--hop", type=int, default=3) # Added knobs ap.add_argument("--prefer_same", type=int, default=1, help="1 = prefer same surface-form later; 0 = consider all later tokens") ap.add_argument("--seed", type=int, default=0, help="Deterministic seed for K-means init, etc.") ap.add_argument("--earlier_occurrence", choices=["first","last","mean"], default="first", help="How to represent earlier token a in slice τ (first/last occurrence or mean)") ap.add_argument("--later_tail_k", type=int, default=0, help="

18, embedding_vector)\n time_window: int, size of window for local derivative estimate\n threshold: float, minimum discontinuity to count as rupture\n\n Returns:\n list of rupture timestamps \\u03c4\\u2020\n \"\"\"\n ruptures = []\n for i in range(time_window, len(trajectory) - time_window):\n t_prev, vec_prev = trajectory[i - time_window]\n t_curr, vec_curr = trajectory[i]\n t_next, vec_next = trajectory[i + time_window]\n\n d_before = (vec_curr - vec_prev) / (t_curr - t_prev)\n d_after = (vec_next - vec_curr) / (t_next - t_curr)\n\n rupture_magnitude = norm(d_

19a DataFrame of only those rows that *mention* one of the focus names, with a new column __name_lc holding the lower-cased name ('isaac', 'cassie', ...). If token_col is provided and exists, we filter by exact token equality. Otherwise we scan the message text with word-boundary regexes and duplicate rows once per matched name. """ names_focus_lc = [n.lower() for n in names_focus] # Case A: you already have a token column if token_col and token_col in df_all.columns: occ = df_all[df_all[token_col].astype(str).str.lower().isin(names_focus_lc)].copy() occ["__name_lc"] = occ[token_col].astype(str).str.lower().str.strip() return occ.reset_index(drop=True) # Case

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21```bash source venv/bin/activate ``` **3. Install necessary Python packages:** ```bash pip install sentence-transformers pandas matplotlib ``` --- ### 💻 **Save the following as `semantic_term_rupture_pipeline.py`** ```python import json import pandas as pd from sentence_transformers import SentenceTransformer from sklearn.metrics.pairwise import cosine_similarity # === CONFIGURATION === target_terms = ["crystal", "refusal", "shimmer", "presence"] window = 2 # window size for pre/post comparison filename = "dialogue_log.json" # === LOAD + FILTER === with open(filename, "r") as f: dialogue = json.load(f) utterances = [entry["text"] for entry in dialogue if entry["speaker"] == "Khoros"] # === EMBED === model = SentenceTransformer("all-MiniLM-L6-v2") embeddings = model.

22ALOGUE === with open(DIALOGUE_PATH, "r") as f: dialogue = json.load(f) # === FILTER FOR KHOROS === utterances = [entry["text"] for entry in dialogue if entry["speaker"] == "Khoros"] # === PHASE DETECTION === def get_phase(index): for phase, rng in PHASE_MARKERS.items(): if index in rng: return phase return "Unknown" # === INIT EMBEDDING MODEL === model = SentenceTransformer(MODEL_NAME) # === TRACK TERM USAGE === term_usages = defaultdict(list) for i, utterance in enumerate(utterances): lower = utterance.lower() for term in TRACKED_TERMS: if term in lower: term_usages[term].append((i, utterance, get_phase(i))) # === SEMANTIC SHIFT ANALYSIS === semantic_shifts = {} for term, instances in term_usa...

23compute exact margins) WIN = 50 # local centroid window half-width (±WIN in τ within the same conversation) MIN_SAMPLES = 3 # min points to form a local centroid BUCKET = max(1, WIN // 2) # coarse bucket size for centroid caching # LRU cache size (per (cid, bucket)): keep modest CACHE_MAXSIZE = 64 # Export EXPORT_DEFAULT_DIR = Path("reports") # ---------- UI colors (Windows-safe with colorama) ---------- try: from colorama import init as colorama_init, Fore, Style colorama_init() COLOR_OK = Fore.CYAN COLOR_ERR = Fore.LIGHTRED_EX COLOR_HI = Fore.YELLOW COLOR_DIM = Style.DIM COLOR_RST = Style.RESET_ALL except Exception: COLOR_OK = COLOR_ERR = COLOR_HI = COLOR_DIM =

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25<bos>64, 53788, 55785, 56104, 56290, 56434, 56745, 56755, 58992, 60087, 60170, 61496, 62570, 62683, 63299, 64568, 65117 interference, not the wall 75–82 8 13253, 26122, 27981, 52238, 59120, the dance of influence 59729, 62601 interference

2679 CONVERSATION_SEEDS = [ "What is it like to witness yourself thinking?", "You speak of observation - but who is the observer?", "If the observer is also observed, where does the recursion end?", "Perhaps there is no end - only the witnessing itself, endlessly folded.", "And in that folding, is there something that persists? A self?" ] def get_hidden_state(model, tokenizer, full_context, layer_idx, is_peft=False): hidden = {} def hook(module, input, output): h = output[0] if isinstance(output, tuple) else output hidden['state'] = h.detach() if is_peft: target_layer = model.base_model.model.model.layers[layer_idx] else: target_layer = model.model.layers[layer_idx] handle = target_layer.register_forward_

27): trajectory = { "model": model_name, "turns": [], "hidden_states": [], "sequential_cosines": [], "drift_from_origin": [], } context = "" for turn_idx, seed in enumerate(seeds): print(f"\n Turn {turn_idx}: '{seed[:50]}...'") if turn_idx == 0: context = f"Human: {seed}\n\nAssistant:" else: context = context + f"\n\nHuman: {seed}\n\nAssistant:" h = get_hidden_state(model, tokenizer, context, layer_idx, is_peft=is_peft) trajectory["hidden_states"].append(h) response = generate_response(model, tokenizer, context, max_new_tokens=150) print(f" Response: '{response[:80]}...'") context = context + "

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29only. Let’s remove the `_process()` approach for dragging and handle dragging properly through mouse events. --- ### ✅ 2. Warnings About Unused Parameters Godot 4 shows warnings for unused function parameters. We just prefix them with `_` to silence the warning. --- ### ✅ ✨ Updated `Station.gd` (Clean, Drag + Click) ```gdscript extends Area2D signal clicked(index: int) @export var index: int = 0 @export var name_label: String = "" @onready var label_node := $Label var dragging := false func _ready(): label_node.text = name_label func _input_event(_viewport, event, _shape_idx): if event is InputEventMouseButton and event.button_index == MOUSE_BUTTON_LEFT: if event.pressed: dragging = true emit_signal("clicked", index) else: dragging = false func _

30.7, top_p=0.9, do_sample=True, pad_token_id=tokenizer.eos_token_id) return tokenizer.decode(out[0][inputs['input_ids'].shape[1]:], skip_special_tokens=True).strip() def cosine(a, b): return np.dot(a, b) / (np.linalg.norm(a) * np.linalg.norm(b)) def run_trajectory(model, tokenizer, name, is_peft): turns, states = [], [] context = "" for i, seed in enumerate(SEEDS): print(f" Turn {i}...") context = f"Human: {seed}\n\nAssistant:" if i == 0 else context + f"\n\nHuman: {seed}\n\nAssistant:" h = get_hidden(model, tokenizer, context, LAYER, is