Correct Antislop citation to ICLR 2026; add examples of banned patterns

Citation now points to the ICLR 2026 paper (OpenReview gLcyM1khyp, arXiv:2510.15061) with full BibTeX. Adds measured baseline-vs-FTPO counts for a sample of banned patterns.

README.md

@@ -38,17 +38,58 @@ The LoRA adapter that produced this checkpoint is released separately at
 
 ## What this is
 
-Language models overuse a characteristic vocabulary. *Shivers down the spine*, *testament
-to*, *barely above a whisper*, and a long tail of names like Elara and Kael. Antislop
-identifies those patterns by comparing model output against a human-written baseline, then
-uses a backtracking sampler to catch the model reaching for one and record what it should
-have reached for instead. Those records become preference pairs, and **FTPO (Final Token
-Preference Optimization)** trains the preference into the weights so it persists with the
-sampler switched off.
+Language models overuse a characteristic vocabulary: stock dialogue tags, a fixed set of
+atmospheric props, and a long tail of names like Elara and Kael. Antislop identifies those
+patterns *for this specific model* by comparing its output against a human-written
+baseline, then uses a backtracking sampler to catch the model reaching for one and record
+what it should have reached for instead. Those records become preference pairs, and **FTPO
+(Final Token Preference Optimization)** trains the preference into the weights so it
+persists with the sampler switched off.
 
 FTPO adjusts only the specific token choices that need adjusting, holding the rest of the
 vocabulary steady. That precision is what keeps capabilities intact.
 
+## What gets banned
+
+The banlist is not a hand-written style guide. It is 4,267 patterns measured as overused in
+this model's own output relative to human prose. A sample, with raw counts across the 400
+held-out prompts, **sampler off**, so these are weight-level changes:
+
+| Banned pattern | Baseline | FTPO |
+|---|---|---|
+| *said, his voice dropping* | 18 | **0** |
+| *panic, cold and sharp* | 15 | **0** |
+| *voice dropping an octave* | 17 | 4 |
+| *heart hammered against my ribs* | 12 | 1 |
+| *breath hitching in my throat* | 6 | **0** |
+| *dust motes dancing* | 11 | 2 |
+| *smelled of ozone and old paper* | 5 | **0** |
+| *sharp, metallic tang* | 5 | **0** |
+| *words hung in the air* | 5 | **0** |
+
+Every count above is spread across distinct outputs rather than concentrated in one, e.g.
+*said, his voice dropping* appears 18 times in 17 different stories before training and in
+none after.
+
+The name distribution shows the same effect. These are the default protagonists the base
+model reaches for unprompted:
+
+| Name | Baseline (uses / stories) | FTPO |
+|---|---|---|
+| Elias | 244 / 43 | 44 / 10 |
+| Kael | 204 / 39 | 47 / 15 |
+| Elara | 139 / 30 | 66 / 15 |
+| Kaelen | 78 / 16 | 15 / 3 |
+| Thorne | 58 / 25 | 16 / 12 |
+
+The banlist also catches assistant register bleeding into fiction, e.g. *happy to help
+craft* (4 → 0) and *let me know if you'd like* (5 → 0), where the model breaks frame to
+address the user mid-story.
+
+Note that suppression is a reduction, not a hard filter. Patterns still surface
+occasionally; the sampler is available at inference time if you want them driven closer to
+zero.
+
 ## Results
 
 Evaluated on 400 held-out Reddit writing prompts (indices 1000 to 1399, with the pipeline
@@ -162,11 +203,26 @@ MIT-licensed.
 
 ## Citation
 
-The method:
+The method was published at **ICLR 2026**:
+
+```bibtex
+@inproceedings{paech2026antislop,
+  title     = {Antislop: A Comprehensive Framework for Identifying and Eliminating
+               Repetitive Patterns in Language Models},
+  author    = {Paech, Samuel and Roush, Allen and Goldfeder, Judah and Shwartz-Ziv, Ravid},
+  booktitle = {The Fourteenth International Conference on Learning Representations},
+  year      = {2026},
+  url       = {https://openreview.net/forum?id=gLcyM1khyp},
+  eprint    = {2510.15061},
+  archivePrefix = {arXiv},
+  primaryClass = {cs.CL}
+}
+```
 
-> Paech, Roush, Goldfeder, and Shwartz-Ziv. *Antislop: A Comprehensive Framework for
-> Identifying and Eliminating Repetitive Patterns in Language Models.* October 2025.
-> Code: [github.com/sam-paech/auto-antislop](https://github.com/sam-paech/auto-antislop)
+- Paper: [ICLR 2026 poster](https://iclr.cc/virtual/2026/poster/10008156) ·
+  [OpenReview](https://openreview.net/forum?id=gLcyM1khyp) ·
+  [arXiv:2510.15061](https://arxiv.org/abs/2510.15061)
+- Code: [github.com/sam-paech/auto-antislop](https://github.com/sam-paech/auto-antislop) (MIT)
 
 ## Acknowledgements