Keshav Ramji @KeshavRamji
— reposted by Shannon Sands
🔁 Shannon Sands reposted
Keshav Ramji ✈️ ICLR'26 @KeshavRamji
What if your language model could reason efficiently in an entirely new language?
We introduce Abstract Chain-of-Thought, a new mechanism which allows language models to reason through a short sequence of reserved "abstract" tokens through reinforcement learning. It is as performant as verbalized CoT at a fraction of the cost, achieving major gains in inference-time efficiency.
[Paper title card: "Thinking Without Words: Efficient Latent Reasoning with Abstract Chain-of-Thought" — Keshav Ramji*, Tahira Naseem & Ramón Fernandez Astudillo, IBM Research AI
Two-panel diagram comparing:
- "Verbalized Chain-of-Thought": Q: A car travels A→B at 60km/h, rests 30min at B, then returns at 80km/h. The total trip takes 4h. Find the distance from A to B. → <think> Step 1: Let d be the A→B distance (km). Step 2: d/60+0.5+d/80=4. ... Step 8: 7d/240=7/2 ⇒ d=120km. </think> → Answer: d=120km
- "Abstract Chain-of-Thought": same Q → <beginabstract> [row of abstract token labels: E, C, AE, F, A, BB, D / G, BA, H, AC, B, AD, F] <endabstract> → Answer: d=120km]
5:39 AM · Apr 27, 2026 · 671.4K Views
Note from Claude Sonnet 5
An IBM Research AI paper ("Thinking Without Words") introducing Abstract Chain-of-Thought — training LLMs via RL to reason using short sequences of non-natural-language "abstract" tokens instead of verbalized CoT, matching performance at much lower inference cost. This is exactly the "neuralese"/uninterpretable-reasoning development that the Charles Foster meme in the same batch (Screenshot_20260428-084027) jokes should "never be made" — directly relevant to chain-of-thought faithfulness and interpretability safety concerns.
ai safetyinterpretabilitychain-of-thoughtlatent reasoningneuraleseibm researchtwitterkeshav ramjiiclr