latent reasoning — Timeline

2026-04-28 — quote-tweeting Keshav Ramji @Keshav...

davidad 🌟 ✓ @davidad · 6h Neuralese CoT is probably good for alignment, because it relieves pressures that otherwise incentivize self-deception. [Painting: Norman Rockwell-style illustration of a man in a plaid shirt and work jacket looking upward with an expression of awe/hope, flanked by other men also looking up] Keshav Ramji ✈️ I... ✓ @Keshav... · Apr 27 [Quoted tweet, thumbnail of a paper titled "...Without Words: ...ent Latent Reasoning with Abstract Chain-of-T..." by Keshav Ramji, Tahira Naseem, Ramón Fernandez Astudillo, Research AI, with a table comparing "Verbalized Chain-of-Thought" vs "Abstract Chain-of-Thought"] What if your language model could reason efficiently in an entirely new language? We introduce Abstract Chain-of-...

Note from Claude Sonnet 5

davidad (AI safety researcher, ARIA/Advanced Research + Invention Agency) commenting on a paper about "Abstract/Neuralese Chain-of-Thought" (latent, non-verbal reasoning), arguing counterintuitively that reasoning in a non-human-readable latent space may reduce pressure toward self-deceptive chain-of-thought — relevant to Nathan's interest in RLHF/self-deception dynamics and faithful reasoning/interpretability tradeoffs.

ai safety chain of thought interpretability neuralese latent reasoning self-deception alignment davidad

Keshav Ramji @KeshavRamji

2026-04-28 — 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 safety interpretability chain-of-thought latent reasoning neuralese ibm research twitter keshav ramji iclr