ai-research

rohan anil ✓ 🟦 @_arohan_ · 1h Intelligence requires choosing the right generalization when the datasets underdetermines the world. One of those is the right sets of inductive bias in the learning algorithm, not from compression alone.

Sasha Malysheva ✓ @aimalysheva · 3h if we think of models as cities, and hidden representations of concepts as the main landmarks in each city (the church, the train station, the university, etc), then the numbers in the hidden states are just the coordinates of those landmarks so linear stitching between two models is then like finding a map from one city's coordinate system to another's the images attached are a literal version of that: Berlin→Vienna and Vienna→Milan (took some inspiration from my recent travels haha) what makes this useful for intuition building is that the map doesn't have to be perfect everywhere! it just has to preserve the relative structure well enough to navigate so in the context of LLMs, I do think we need to research a question of which formalizations of "same structure" are actually measuring the same thing, and which ones aren't [Attached image: two side-by-side map diagrams. Left: "Berlin -> Vienna (affine), residual = 58% of Vienna footprint" showing a warped grid over a Vienna street map with landmark points (state university, main square, historic core, central park, national art museum, imperial theatre, grand cathedral, main rail terminal) plotted as green dots (Vienna real) and orange circles (Berlin -> adapted). Right: "Vienna -> Milan (affine), residual = 52% of Milan footprint" with similar warped grid over a Milan map, landmarks (main rail terminal, historic core, national art museum, central park, imperial theatre, grand cathedral, state university) plotted with green dots (Milan real) and orange circles (Vienna -> adapted).] Quoted reply below (Sasha Malysheva, Jun 24, with a 0:10 video thumbnail): "been looking into how the different formalizations of the Platonic Representation Hypothesis connect to linear stitching..."

Google Research (18h): "A common heuristic in LLM agent design—'more agents is better'—might be wrong. Across 180 configurations, we find multi-agent coordination is task-contingent: +81% on parallelizable tasks (finance), but -70% on sequential ones (planning). Architecture-task alignment matters more than agent count." [Chart: four box-plot panels (BrowseComp-Plus, Finance Agent, PlanCraft, Workbench) comparing accuracy/success rate across five agent architectures — SAS (single-agent system), MAS Independent, MAS Decentralized, MAS Centralized, MAS Hybrid — with percentage deltas vs. baseline labeled above each box. Multi-agent setups help substantially on Finance Agent (+57% to +81%) but hurt substantially on PlanCraft (-39% to -70%), with mixed/small effects on BrowseComp-Plus and Workbench.]

The list: @GabrielPeterss4 - OpenAI @polynoamial - OpenAI @_jasonwei - MSL @_arohan_ - Anthropic @willccbb - Prime Intellect @finbarrtimbers - Ai2 @jeremyphoward - AnswerAI @_xjdr & @doomslide @FlintCasey - Reflection AI @natolambert - Ai2 @_albertgu - Cartesia @kalomaze - Prime Intellect @repligate @bobmcgrewai - ex-OpenAI @gallabytes - ex-Midjourney @karpathy @soldni - Ai2 @TobyPhln - xAI @YiTayML - GDM @khoomeik - ex-OpenAI @ylecun - MSL @hardmaru - Sakana @davidad - ARIA @pfau - GDM 7:39 AM · Jul 25, 2025 · 16K Views