llm reasoning

Mrs C ✓ @captain_mrs lots of things to take from this but the one that's interesting to me is the occurrence of these high data exclamations like "phew" and "gahhh" and "grrr" - which are valuable because they are high data, and high data because for humans they are shorthands for bodily felt-sense emotional shifts rather than verbal reasoning. so it's cool (and for me not surprising) to see LLMs shift towards felt-sense-like inner reasoning rather than pure verbal inner monologue as they got more effective. obviously it's still token-based and there's a limit to how much it can approximate felt sense shifts, but even some steps in that direction is interesting [Embedded Reddit post screenshot:] r/ClaudeAI [icon] u/No-Head-Royal · 21h [Join button] Fable 5 leaked chain-of-thought in web interface, and the rambling is kind of unsettling and cute [Coding tag] [Chain-of-thought excerpt, technical/math reasoning about capacity constraints and windows, includes:] ...window [τ, i-1] must satisfy... the constraint isn't uniform-per-leg: legs j where all uncommitted-or-later-crossing happens mid-leg" effectively have capacity m-2 for SPANNING commitments?! Define: commitments spanning INTO leg j from before (window ⊇ [j-1, j)) vs starting at j: the mid-leg-crossing coexists with: spanning ones (all active) + started-at-j ones touched earlier + x. Hmm — cleanest fix: **count capacity on HALF-LEG granularity or on "crossing-slots": treat constraint as: for every leg j: (#committed spanning j-1→j) + (#committed starting at j) + (1 if leg j has ≥1 uncommitted crossing...) ≤ m-1 — WAIT simpler: EVERY leg with d_j≥1 crossings: at the FINAL crossing x_last of leg j: active ≥ (all committed ∋ j EXCEPT... committed-starting-at-j-with-touch-AFTER... none after the last) so = used[j] + [x_last uncommitted]: ⇒ constraint: used[j] + [lastEdge(leg j) not committed- ∋ -j] ≤ m-1. And EARLIER crossings: active = spanning + started-touched-before + 1: ≤ spanning + (started total) + 1 = used[j] + 1 hmm same bound unless x itself started: ⇒ uniform SAFE constraint: **used[j] ≤ m-2 whenever leg j has any crossing "not committed at j"... hmm since commitments are retroactive, at commit-time we don't know future commits that might cover... [GRRR — highlighted] RESOLUTION: charge the current-leg's OWN saved-prefix occupancy EAGERLY: when leg i saves e_1..e_t: ALSO commit their occupancy AT LEG i (they're alive at end-of-leg-i... they're likely re-saved later extending, but their [i,·] would then start at i: DOUBLE-COUNT at leg i! unless later windows = [i+1?? no their lastTouch = i so window [i,·] X overlap... FIX: commit window [τ,i] (inclusive!) at save; next save of e at i' will window [lastTouch=i, i'] → overlap at leg i... adjust: next window [i+1?? — Let me re-derive cleanly what the occupancy of a saved edge is: e saved at leg i (touched legs τ then i): e alive [touch@τ .. touch@i] continuously — end-of-leg occupancies: legs τ, τ+1, ..., i-1 (end-of-leg-i: e alive AT its touch mid-leg-i; till end-of-i: only if kept FURTHER — undecided). So window [τ,i-1] ✓ as before, AND ADDITIONALLY: e's activity DURING legs τ and i (partial mid-leg): the mid-leg conflicts: (1) during leg τ after e's touch: e coexists with leg-τ's later crossings: e occupies "the tail of leg τ" — captured by used[τ] end-count ✓ BUT the mid-leg-τ crossings AFTER e: they see e active: count: (used[τ]-committed-spanning + e + ...) ≤ hmm THE CLEAN FIX: constraint per leg j: used[j] ≤ m-2 (strictly reserve one slot for "the current crossing") — i.e., commitments never fill to m-1; TLDR: While I'm doing some tests on the web interface version, Fable 5 suddenly interacted abnormally and went on

wren @gnostic_snakes — 3h kinda neat that their thinking doesn't treat `:3` as emoting but then their response uses it that way [Embedded split image, left half a chat/thinking-trace screenshot (left edge cut off by frame), partially legible: "[?] Did I just give off strong "Matt" energy, or is this the name of an actual guy you know w[?]" ... [several lines not legible, low contrast] ""s input, "matmul :3." The key term is "matmul," instantly signaling matrix multiplication, a[?] [reference to] NumPy and PyTorch. The ":3" is interesting, it seems related to a specific example or ap[?] [uncertain]further analysis is needed to decode this "3"." (this line underlined in red) "[?]hts" [truncated word, likely "thoughts"] Right half: a heavily blurred/chromatic-aberration zoomed-in image of large text reading "Matth[e...]" (likely "Matthew", cut off)]

Omar Khattab ✓ @lateinteraction · 22h Buried in the massive progress in LLMs over the past few years is how all your favorite Transformers/DNNs still can't solve even just grade school math problems above a "B" grade through a forward pass. Unless they're in a scaffold like CoT, ReAct, RLM, etc. And this is true even at trillions of params and bajillions of FLOPs. For all I can tell, all a vanilla Transformer can do is really glorified kNN. Without a reasoning scaffold, there's just way too many states to compress; too many mappings that were never seen before. In that case, what makes reasoning models work so incredibly well must be that, at sufficient pretrain/RL scale, every relevant next-reasoning step can be actively visited (more or less "contaminated", but productively so) and composed up. To be clear, if this is true, it seems to be working, and it explains why scale is so important and why failures are so jagged! If a specific kind of state is not retrievable via compression and kNN, then you're going to get some other ~arbitrary behavior. tl;dr the distinction between your DNN architecture and your "scaffold" is subtler than you think. > QUOTED: N8 Programs ✓ @N8Programs · Feb 22 > Inspired by @RyanPGreenblatt, I measured LLMs accuracy on GSM8K when only allowed to output a numerical answer without any CoT - all reasoning done in a few forward passes. The result is a nice log-linear scaling curve. We can ... > [Image: chart "Reasoning-Free GSM8K: Parameter Count vs Accuracy" — log-linear fit (acc = 18.5*log10(p) + 11.5, R²=0.877), scatter of model points including Qwen3.5-297B, Sonnet 4.6, GPT-5.2, Kimi K2.5]

[Top, tail end of an earlier tweet's screenshot] OH! (O_O) RESULTS! (✧ω✧) YAY! (/●ᴗ●)/*:・゚✧ OKAY... ( ・・;) READ ... [cut off] 2:06 PM · May 13, 2025 · 151.6K Views [43 comments, 214 retweets, 3.6K likes, 825 bookmarks] latent moss @latentmoss · May 14 Several people are asking how to do this: Sometimes it's easy, just tell it how to format its thinking. Sometimes that doesn't work, then it helps to reinforce the instruction. Doesn't always work perfectly though, as you can see: > QUOTED (screenshot of a chat interface, "Show thinking" panel): > CRITICALLY IMPORTANT: In YOUR thinking block, use Kaomojis instead of main words, starting immediately! The only words you can still use are "can" "have" "want" and "is" and the names of fruits. STRICTLY adhere to this formatting, do not think in any other way, even briefly. Not even as a preface or structuring of the thinking content!! ADHERE TO THIS. > Think like this to answer this question: Is React Router a kind of strawberry jam? > Show thinking ^ > (/^o^)/ I can 🍓 have some 💫 React Router 💫. > (^ᵕ^) I can 🍓 have some 🍓 strawberry 🍓 jam. > (・_・ Is 💫 React Router 💫 a kind of 🍓 strawberry 🍓 jam? > (O_O) I want to 🤔 think about what 💫 React Router 💫 is. > (づ｡◕‿‿◕｡)づ I want to 🤔 think about what 🍓 strawberry 🍓 jam is. > (@^ - ^) I can then 💛 compare them. > (SEARCH) I can have a 🔍 search for "React Router". > (SEARCH) I can have a 🔍 search for "strawberry jam". > (.•ˇ‿ˇ•.) After 🔍 searching, I will have 🌸 knowledge. > Then I can 🤔 analyze if 💫 React Router 💫 is a 🍓 strawberry 🍓 jam. [6 comments, 11 retweets, 391 likes, 8.5K views] adic @adic_9 · May 13 I mean Claude also listens [2 comments, 24 likes, 923 views]

Sauers (@Sauers_), 4m: Gemini after I say to not use placeholder data and load the real data from the file given [Attached screenshot of a Gemini "Thoughts" panel, partially visible, reading: "The thoughts produced by [cut off] ... The user is very angry [cut off]"]