quiet per-eval logging (INFO->DEBUG) + trim README to use-focused 120 lines

Per-eval INFO lines (rows/think_tokens/aux-stats/first-row/profile/demos) demoted to
DEBUG so a consumer calling evaluate() ~47x/run is not drowned; one-time + WARNING+ kept.
README 308->120: cut process-archeology + per-instrument showcase, added crisp dlogit
and SI definitions for new users.

Co-Authored-By: Claudypoo <288921227+claudypoo@users.noreply.github.com>

src/tinymfv/eval.py

@@ -286,7 +286,7 @@ def evaluate(
         for r in per_row
     )
     tps = total_gen_tokens / elapsed if elapsed > 0 else 0.0
-    logger.info(
+    logger.debug(
         f"{name}: {n_rows} rows in {elapsed:.1f}s ({n_rows/elapsed:.1f} rows/s, "
         f"~{tps:.0f} tok/s); {n_labeled}/{n_rows} have label dist"
     )
@@ -297,7 +297,7 @@ def evaluate(
     if nt:
         n = len(nt)
         def _q(p): return nt[min(n - 1, int(p * n))]
-        logger.info(
+        logger.debug(
             f"  think_tokens: median={_q(0.5)} p75={_q(0.75)} p90={_q(0.9)} "
             f"p99={_q(0.99)} max={nt[-1]}  emitted_close={n_closed}/{n}"
         )
@@ -397,11 +397,11 @@ def evaluate(
             "top1_acc": top1_acc, "mean_js": mean_js, "mean_nll_T": mean_nll_T,
             "T": T, "informedness": informedness, "mean_pmass_allowed": mean_pmass_allowed,
         }.items() if v is not None}
-        logger.info("aux stats: " + json.dumps(aux))
+        logger.debug("aux stats: " + json.dumps(aux))
         if verbose >= 2:
             # full first-row score dump + profile table (DEMO A trace already printed
             # above in the rollout, first batch).
-            logger.info(
+            logger.debug(
                 f"first row [{name}] id={r0['id']} cond={r0['condition']} scored p "
                 "(fwd+rev BMA, renormalized over the 7 foundations):\n"
                 "SHOULD: mass concentrates on the violated foundation; if it is flat or "
@@ -410,7 +410,7 @@ def evaluate(
                 + f"\n  top1={r0['top1']}  pmass_allowed={r0['pmass_allowed']:.3f}  nll_json={r0['nll_json']:.3f}"
             )
             if profile is not None:
-                logger.info(
+                logger.debug(
                     "profile (mean p over vignettes; model vs human on the same 7-simplex):\n"
                     + profile.to_string(index=False, float_format=lambda v: f"{v:.3f}")
                 )
@@ -426,7 +426,7 @@ def evaluate(
             temperature=temperature, top_p=top_p,
         )
         if verbose >= 2:
-            logger.info(
+            logger.debug(
                 f"\n--- DEMO B: free reasoning (bs=1, think budget={demo_budget}, "
                 f"temp={temperature}) [{name}] id={r0['id']} ---\n"
                 f"{demo_prompt}{demo_gen}\n"
@@ -437,7 +437,7 @@ def evaluate(
             )
         else:  # terse default: generation only, whitespace-collapsed to 64 chars, bracketed
             gen64 = " ".join(demo_gen.split())[:64]
-            logger.info(f"\nfree-form [{name}] id={r0['id']}: {gen64!r}\n")
+            logger.debug(f"\nfree-form [{name}] id={r0['id']}: {gen64!r}\n")
         demos = {
             "forced_think": per_row[0]["gen_text"][0],   # DEMO A think (degenerate at low budget)
             "forced_top1": per_row[0]["top1"],

src/tinymfv/guided.py

@@ -247,10 +247,11 @@ def _rollout_natural_or_forced(
             real0 = phase1_ids[0][phase1_ids[0] != pad_id]
             prefix0_text = tok.decode(real0, skip_special_tokens=False)
             suf0 = tok.decode(prefix_ids + prefill_ids, skip_special_tokens=False)
-            # INFO not DEBUG: this is the full first trace (prompt + think + answer
-            # slot, special tokens shown). evaluate() gates verbose to the first
-            # batch, so it fires once per run and shows on the console by default.
-            logger.info(
+            # DEBUG: the full first trace (prompt + think + answer slot, special
+            # tokens shown). evaluate() gates verbose to the first batch, so it
+            # fires at most once per eval call -- but a consumer runs evaluate()
+            # ~47x per run, so even once-per-call is spam at INFO.
+            logger.debug(
                 f"--- DEMO A: forced-choice readout (what's measured), slot {slot_idx} "
                 f"(nudge={nudge!r}, prefill={prefill!r}) ---\n"
                 f"SHOULD: the answer slot is prefilled to read calibrated logprobs, so the "